In the proposed paper are discussed results of an industry project concerning energy management in
building. Specifically the work analyses the improvement of electrical outlets controlled and activated by a
logic unit and a data mining engine. The engine executes a Long Short-Terms Memory (LSTM) neural
network algorithm able to control, to activate and to disable electrical loads connected to multiple outlets
placed into a building and having defined priorities. The priority rules are grouped into two level: the first
level is related to the outlet, the second one concerns the loads connected to a single outlet. This algorithm,
together with the prediction processing of the logic unit connected to all the outlets, is suitable for alerting
management for cases of threshold overcoming. In this direction is proposed a flow chart applied on three
for three outlets and able to control load matching with defined thresholds. The goal of the paper is to
provide the reading keys of the data mining outputs useful for the energy management and diagnostic of the
electrical network in a building. Finally in the paper are analyzed the correlation between global active
power, global reactive power and energy absorption of loads of the three intelligent outlet. The prediction
and the correlation analyses provide information about load balancing, possible electrical faults and energy
cost optimization.
Performance assessment of an optimization strategy proposed for power systemsTELKOMNIKA JOURNAL
In the present article, the selection process of the topology of an artificial neural network (ANN) as well as its configuration are exposed. The ANN was adapted to work with the Newton Raphson (NR) method for the calculation of power flow and voltage optimization in the PQ nodes of a 10-node power system represented by the IEEE 1250 standard system. The purpose is to assess and compare its results with the ones obtained by implementing ant colony and genetic algorithms in the optimization of the same system. As a result, it is stated that the voltages in all system nodes surpass 0,99 p.u., thus representing a 20% increase in the optimal scenario, where the algorithm took 30 seconds, of which 9 seconds were used in the training and validation processes of the ANN.
SYNCHROPHASOR DATA BASED INTELLIGENT ALGORITHM FOR REAL TIME EVENT DETECTION ...IAEME Publication
The wide area measurement system (WAMS) has been installed at several locations in power system. Phasor measurements units (PMU) are considered as the building blocks of WAMS are being installed at various locations of power system. PMU is sending very large volume of data to Power system control center with the sampling rate of 50 or 25 samples per second. However there are always several events per day occurring in the system but the rate at which data is received and the volume of data to be analyzed is a big challenge for power system engineer. There is a need for developing an intelligent system to handle large volume of Synchrophasor data and identify Power system event in the present context. This paper presents an intelligent algorithm to automatically detect such events using wide area measurements in real time. In this work, Synchrophasor measurements received from PMU are fed to KNN based pattern recognition algorithm which is used to identify the Power system events. The severity and the type of the event can be judged through the change in voltage magnitude and phase angle at various buses. The developed algorithm is tested for IEEE 14 bus system and results are verified.
The aim of this research is the speed tracking of the permanent magnet synchronous motor (PMSM) using an intelligent Neural-Network based adapative backstepping control. First, the model of PMSM in the Park synchronous frame is derived. Then, the PMSM speed regulation is investigated using the classical method utilizing the field oriented control theory. Thereafter, a robust nonlinear controller employing an adaptive backstepping strategy is investigated in order to achieve a good performance tracking objective under motor parameters changing and external load torque application. In the final step, a neural network estimator is integrated with the adaptive controller to estimate the motor parameters values and the load disturbance value for enhancing the effectiveness of the adaptive backstepping controller. The robsutness of the presented control algorithm is demonstrated using simulation tests. The obtained results clearly demonstrate that the presented NN-adaptive control algorithm can provide good trackingperformances for the speed trackingin the presence of motor parameter variation and load application.
Maximum power point tracking techniques for photovoltaic systems: a comparati...IJECEIAES
Photovoltaic (PV) systems are one of the most important renewable energy resources (RER). It has limited energy efficiency leading to increasing the number of PV units required for certain input power i.e. to higher initial cost. To overcome this problem, maximum power point tracking (MPPT) controllers are used. This work introduces a comparative study of seven MPPT classical, artificial intelligence (AI), and bio-inspired (BI) techniques: perturb and observe (P&O), modified perturb and observe (M-P&O), incremental conductance (INC), fuzzy logic controller (FLC), artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and cuckoo search (CS). Under the same climatic conditions, a comparison between these techniques in view of some criteria’s: efficiencies, tracking response, implementation cost, and others, will be performed. Simulation results, obtained using MATLAB/SIMULINK program, show that the MPPT techniques improve the lowest efficiency resulted without control. ANFIS is the highest efficiency, but it requires more sensors. CS and ANN produce the best performance, but CS provided significant advantages over others in view of low implementation cost, and fast computing time. P&O has the highest oscillation, but this drawback is eliminated using M-P&O. FLC has the longest computing time due to software complexity, but INC has the longest tracking time.
Among the most widespread renewable energy sources is solar energy; Solar panels offer a green, clean, and environmentally friendly source of energy. In the presence of several advantages of the use of photovoltaic systems, the random operation of the photovoltaic generator presents a great challenge, in the presence of a critical load. Among the most used solutions to overcome this problem is the combination of solar panels with generators or with the public grid or both. In this paper, an energy management strategy is proposed with a safety aspect by using artificial neural networks (ANNs), in order to ensure a continuous supply of electricity to consumers with a maximum solicitation of renewable energy.
Comparison of backstepping, sliding mode and PID regulators for a voltage inv...IJECEIAES
In the present paper, an efficient and performant nonlinear regulator is designed for the control of the pulse width modulation (PWM) voltage inverter that can be used in a standalone photovoltaic microgrid. The main objective of our control is to produce a sinusoidal voltage output signal with amplitude and frequency that are fixed by the reference signal for different loads including linear or nonlinear types. A comparative performance study of controllers based on linear and non-linear techniques such as backstepping, sliding mode, and proportional integral derivative (PID) is developed to ensure the best choice among these three types of controllers. The performance of the system is investigated and compared under various operating conditions by simulations in the MATLAB/Simulink environment to demonstrate the effectiveness of the control methods. Our investigation shows that the backstepping controller can give better performance than the sliding mode and PID controllers. The accuracy and efficiency of the proposed backstepping controller are verified experimentally in terms of tracking objectives.
Performance assessment of an optimization strategy proposed for power systemsTELKOMNIKA JOURNAL
In the present article, the selection process of the topology of an artificial neural network (ANN) as well as its configuration are exposed. The ANN was adapted to work with the Newton Raphson (NR) method for the calculation of power flow and voltage optimization in the PQ nodes of a 10-node power system represented by the IEEE 1250 standard system. The purpose is to assess and compare its results with the ones obtained by implementing ant colony and genetic algorithms in the optimization of the same system. As a result, it is stated that the voltages in all system nodes surpass 0,99 p.u., thus representing a 20% increase in the optimal scenario, where the algorithm took 30 seconds, of which 9 seconds were used in the training and validation processes of the ANN.
SYNCHROPHASOR DATA BASED INTELLIGENT ALGORITHM FOR REAL TIME EVENT DETECTION ...IAEME Publication
The wide area measurement system (WAMS) has been installed at several locations in power system. Phasor measurements units (PMU) are considered as the building blocks of WAMS are being installed at various locations of power system. PMU is sending very large volume of data to Power system control center with the sampling rate of 50 or 25 samples per second. However there are always several events per day occurring in the system but the rate at which data is received and the volume of data to be analyzed is a big challenge for power system engineer. There is a need for developing an intelligent system to handle large volume of Synchrophasor data and identify Power system event in the present context. This paper presents an intelligent algorithm to automatically detect such events using wide area measurements in real time. In this work, Synchrophasor measurements received from PMU are fed to KNN based pattern recognition algorithm which is used to identify the Power system events. The severity and the type of the event can be judged through the change in voltage magnitude and phase angle at various buses. The developed algorithm is tested for IEEE 14 bus system and results are verified.
The aim of this research is the speed tracking of the permanent magnet synchronous motor (PMSM) using an intelligent Neural-Network based adapative backstepping control. First, the model of PMSM in the Park synchronous frame is derived. Then, the PMSM speed regulation is investigated using the classical method utilizing the field oriented control theory. Thereafter, a robust nonlinear controller employing an adaptive backstepping strategy is investigated in order to achieve a good performance tracking objective under motor parameters changing and external load torque application. In the final step, a neural network estimator is integrated with the adaptive controller to estimate the motor parameters values and the load disturbance value for enhancing the effectiveness of the adaptive backstepping controller. The robsutness of the presented control algorithm is demonstrated using simulation tests. The obtained results clearly demonstrate that the presented NN-adaptive control algorithm can provide good trackingperformances for the speed trackingin the presence of motor parameter variation and load application.
Maximum power point tracking techniques for photovoltaic systems: a comparati...IJECEIAES
Photovoltaic (PV) systems are one of the most important renewable energy resources (RER). It has limited energy efficiency leading to increasing the number of PV units required for certain input power i.e. to higher initial cost. To overcome this problem, maximum power point tracking (MPPT) controllers are used. This work introduces a comparative study of seven MPPT classical, artificial intelligence (AI), and bio-inspired (BI) techniques: perturb and observe (P&O), modified perturb and observe (M-P&O), incremental conductance (INC), fuzzy logic controller (FLC), artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and cuckoo search (CS). Under the same climatic conditions, a comparison between these techniques in view of some criteria’s: efficiencies, tracking response, implementation cost, and others, will be performed. Simulation results, obtained using MATLAB/SIMULINK program, show that the MPPT techniques improve the lowest efficiency resulted without control. ANFIS is the highest efficiency, but it requires more sensors. CS and ANN produce the best performance, but CS provided significant advantages over others in view of low implementation cost, and fast computing time. P&O has the highest oscillation, but this drawback is eliminated using M-P&O. FLC has the longest computing time due to software complexity, but INC has the longest tracking time.
Among the most widespread renewable energy sources is solar energy; Solar panels offer a green, clean, and environmentally friendly source of energy. In the presence of several advantages of the use of photovoltaic systems, the random operation of the photovoltaic generator presents a great challenge, in the presence of a critical load. Among the most used solutions to overcome this problem is the combination of solar panels with generators or with the public grid or both. In this paper, an energy management strategy is proposed with a safety aspect by using artificial neural networks (ANNs), in order to ensure a continuous supply of electricity to consumers with a maximum solicitation of renewable energy.
Comparison of backstepping, sliding mode and PID regulators for a voltage inv...IJECEIAES
In the present paper, an efficient and performant nonlinear regulator is designed for the control of the pulse width modulation (PWM) voltage inverter that can be used in a standalone photovoltaic microgrid. The main objective of our control is to produce a sinusoidal voltage output signal with amplitude and frequency that are fixed by the reference signal for different loads including linear or nonlinear types. A comparative performance study of controllers based on linear and non-linear techniques such as backstepping, sliding mode, and proportional integral derivative (PID) is developed to ensure the best choice among these three types of controllers. The performance of the system is investigated and compared under various operating conditions by simulations in the MATLAB/Simulink environment to demonstrate the effectiveness of the control methods. Our investigation shows that the backstepping controller can give better performance than the sliding mode and PID controllers. The accuracy and efficiency of the proposed backstepping controller are verified experimentally in terms of tracking objectives.
Parametric estimation in photovoltaic modules using the crow search algorithmIJECEIAES
The problem of parametric estimation in photovoltaic (PV) modules considering man- ufacturer information is addressed in this research from the perspective of combinatorial optimization. With the data sheet provided by the PV manufacturer, a non-linear non-convex optimization problem is formulated that contains information regarding maximum power, open-circuit, and short-circuit points. To estimate the three parameters of the PV model (i.e., the ideality diode factor (a) and the parallel and series resistances (R p and R )), the crow search algorithm (CSA) is employed, which is a metaheuristic optimization technique inspired by the behavior of the crows searching food deposits. The CSA allows the exploration and exploitation of the solution space through a simple evolution rule derived from the classical PSO method. Numerical simulations reveal the effectiveness and robustness of the CSA to estimate these parameters with objective function values lower than 1 10 s 28 and processing times less than 2 s. All the numerical simulations were developed in MATLAB 2020a and compared with the sine-cosine and vortex search algorithms recently reported in the literature.
An optimum location of on-grid bifacial based photovoltaic system in Iraq IJECEIAES
Bifacial photovoltaic (PV) module can gain 30% more energy compared to monofacial if a suitable location were chosen. Iraq (a Middle East country) has a variable irradiation level according to its geographic coordinates, thus, the performance of PV systems differs. This paper an array (17 series, 13 parallel) was chosen to produce 100 kWp for an on-grid PV system. It investigates the PV system in three cities in Iraq (Mosul, Baghdad, and Basrah). Effect of albedo factor, high and pitch of the bifacial module on energy yield have been studied using PVsyst (software). It has been found that the effect is less for a pitch greater than 6 m. The energy gained from bifacial and monofacial PV system module in these cities shows that Mosul is the most suitable for installing both PV systems followed by Baghdad and lastly Basrah. However, in Basrah, the bifacial gain is 12% higher in the energy than monofacial as irradiation there is higher than the other locations, especially for elevation above 1.5 m. Moreover, the cost of bifacial array is 7.23% higher than monofacial, but this additional cost is acceptable since the bifacial gain is about 11.3% higher energy compared to the monofacial.
This paper focuses on the artificial bee colony (ABC) algorithm, which is a nonlinear optimization problem. is proposed to find the optimal power flow (OPF). To solve this problem, we will apply the ABC algorithm to a power system incorporating wind power. The proposed approach is applied on a standard IEEE-30 system with wind farms located on different buses and with different penetration levels to show the impact of wind farms on the system in order to obtain the optimal settings of control variables of the OPF problem. Based on technical results obtained, the ABC algorithm is shown to achieve a lower cost and losses than the other methods applied, while incorporating wind power into the system, high performance would be gained.
This paper presents a fast and accurate fault detection, classification and direction discrimination algorithm of transmission lines using one-dimensional convolutional neural networks (1D-CNNs) that have ingrained adaptive model to avoid the feature extraction difficulties and fault classification into one learning algorithm. A proposed algorithm is directly usable with raw data and this deletes the need of a discrete feature extraction method resulting in more effective protective system. The proposed approach based on the three-phase voltages and currents signals of one end at the relay location in the transmission line system are taken as input to the proposed 1D-CNN algorithm. A 132kV power transmission line is simulated by Matlab simulink to prepare the training and testing data for the proposed 1D- CNN algorithm. The testing accuracy of the proposed algorithm is compared with other two conventional methods which are neural network and fuzzy neural network. The results of test explain that the new proposed detection system is efficient and fast for classifying and direction discrimination of fault in transmission line with high accuracy as compared with other conventional methods under various conditions of faults.
This paper demonstrates a mathematical representation of Photovoltaic (PV) solar cells and hence panels performance. One-diode solar cell model is implemented to simulate the cell and extract the performance indications. The tested PV modules are BP Solar (60 Watt) and Synthesis Power (50 Watts), which are operating in a PV generation system in the University of Anbar - Iraq, College of Applied Sciences. The math model demonstrates Power versus Voltage (P-V) characteristic curves to depict and study various parameters with affecting variations in the PV array performance. The parameters include ambient and cell temperature degrees and solar irradiance (G) level which are the main elements to dictate the productivity of a solar system. G is represented by sun unit (1 sun=1 kW/m2). The outcomes of the simulation model characteristics curves have been compared with curves provided by the tested modules data sheets. MATLAB software has been used to simulate the model and extract the results. This paper also investigated photovoltaic simulation with maximum power point tracking (MPPT) converter to evaluate hence predict the behaviors of the whole photovoltaic DC current generation using PSIM Power Electronics program. The model focuses on the basic components in PV systems; The panel and the DC-DC converter. The modeling outcome data will be used as a reference verifying the performance of the tested modules during the year seasons under the dominating dusty hot weather in western Iraq.
The significance of the solar energy is to intensify the effectiveness of the Solar Panel with the use of a primordial solar tracking system. Here we propounded a solar positioning system with the use of the global positioning system (GPS) , artificial neural network (ANN) and image processing (IP) . The azimuth angle of the sun is evaluated using GPS which provide latitude, date, longitude and time. The image processing used to find sun image through which centroid of sun is calculated and finally by comparing the centroid of sun with GPS quadrate to achieve optimum tracking point. Weather conditions and situation observed through AI decision making with the help of IP algorithms. The presented advance adaptation is analyzed and established via experimental effects which might be made available on the memory of the cloud carrier for systematization. The proposed system improve power gain by 59.21% and 10.32% compare to stable system (SS) and two-axis solar following system (TASF) respectively. The reduced tracking error of IoT based Two-axis solar following system (IoT-TASF) reduces their azimuth angle error by 0.20 degree.
Design and Implementation of Maximum Power Point Tracking in Photovoltaic Sys...inventionjournals
ABSTRACT: This paper presents an algorithm for maximum power point tracking to optimize photovoltaic systems. Beta algorithm is a type of MPPT algorithm. It is having fast tracking ability. The algorithm has been verified on a photovoltaic system modeled in Lab VIEW environment. This algorithm significantly improves the efficiency during the tracking.
The operational performance of a three phase induction motor is impaired by unbalanced voltage supply due to the generation of negative sequence currents, and negative sequence torque which increase motor losses and also trigger torque pulsations. In this study, data mining approach was applied in developing a predictive model using the historical, simulated operational data of a motor for classifying sample motor data under the appropriate type of voltage supply i.e. balanced (BV) and unbalance voltage supply (UB = 1% to 5%). A dataset containing the values of a three phase induction motor’s performance parameter values was analysed using KNIME (Konstanz Information Miner) analytics platform. Three predictive models; the Naïve Bayes, Decision Tree and the Probabilistic Neural Network (PNN) Predictors were deployed for comparative analysis. The dataset was divided into two; 70% for model training and learning, and 30% for performance evaluation. The three predictors had accuracies of 98.649%, 100% and 98.649% respectively, and this confirms the suitability of data mining methods for predictive evaluation of a three phase induction motor’s performance using machine learning.
Design methodology of smart photovoltaic plant IJECEIAES
In this article, we present a new methodology to design an intelligent photovoltaic power plant connected to an electrical grid with storage to supply the laying hen rearing centers. This study requires a very competent design methodology in order to optimize the production and consumption of electrical energy. Our contribution consists in proposing a robust dimensioning synthesis elaborated according to a data flow chart. To achieve this objective, the photovoltaic system was first designed using a deterministic method, then the software "Homer" was used to check the feasibility of the design. Then, controllers (fuzzy logic) were used to optimize the energy produced and consumed. The power produced by the photovoltaic generator (GPV) is optimized by two fuzzy controllers: one to extract the maximum energy and another to control the batteries. The energy consumed by the load is optimized by a fuzzy controller that regulates the internal climate of the livestock buildings. The proposed control strategies are developed and implemented using MATLAB/Simulink.
This paper presents simulation and experimental results of anti-windup PI controller to improve induction machine speed control based on direct torque control (DTC) strategy. Problems like rollover can arise in conventional PI controller due to saturation effect. In order to avoid such problems anti-windup PI controller is presented. This controller is simple for implementation in practice. The proposed anti-windup PI controller demonstrates better dynamic step changes response in speed in terms of overshoots. All simulation work was done using Simulink in the MATLAB software. The experimental results were obtained by practical implementation on a dSPACE 1104 board for a 1.5 KW induction machine. Simulation and experimental results have proven a good performance and verified the validity of the presented control strategy.
Electricity is a major source of energy for fast growing population and the use of nonrenewable source is harmful for our environment. This reason belongs to devastating of environment, so it is required to take immediate action to solve these problems which result the solar energy development. Production of a solar energy can be maximizing if we use solar follower. The major part of solar panels is microcontroller with arrangement of LDR sensor is used to follow the sun, where the sensors is less efficient to track the sun because of the low sensitivity of LDR. We are proposing a method to track sun more effetely with the help of both LDR sensors and image processing. This type of mechanism can track sun with the help of image processing software which combines both result of sensors and processed sun image to control the solar panel. The combination of both software and hardware can control thousands of solar panels in solar power plants.
A hybrid algorithm for voltage stability enhancement of distribution systems IJECEIAES
This paper presents a hybrid algorithm by applying a hybrid firefly and particle swarm optimization algorithm (HFPSO) to determine the optimal sizing of distributed generation (DG) and distribution static compensator (D-STATCOM) device. A multi-objective function is employed to enhance the voltage stability, voltage profile, and minimize the total power loss of the radial distribution system (RDS). Firstly, the voltage stability index (VSI) is applied to locate the optimal location of DG and D-STATCOM respectively. Secondly, to overcome the sup-optimal operation of existing algorithms, the HFPSO algorithm is utilized to determine the optimal size of both DG and D-STATCOM. Verification of the proposed algorithm has achieved on the standard IEEE 33-bus and Iraqi 65-bus radial distribution systems through simulation using MATLAB. Comprehensive simulation results of four different cases show that the proposed HFPSO demonstrates significant improvements over other existing algorithms in supporting voltage stability and loss reduction in distribution networks. Furthermore, comparisons have achieved to demonstrate the superiority of HFPSO algorithms over other techniques due to its ability to determine the global optimum solution by easy way and speed converge feature.
Intelligent control of battery energy storage for microgrid energy management...IJECEIAES
In this paper, an intelligent control strategy for a microgrid system consisting of Photovoltaic panels, grid-connected, and li-ion battery energy storage systems proposed. The energy management based on the managing of battery charging and discharging by integration of a smart controller for DC/DC bidirectional converter. The main novelty of this solution are the integration of artificial neural network (ANN) for the estimation of the battery state of charge (SOC) and for the control of bidirectional converter. The simulation results obtained in the MATLAB/Simulink environment explain the performance and the robust of the proposed control technique.
Hybrid bypass technique to mitigate leakage current in the grid-tied inverterIJECEIAES
The extensive use of fossil fuel is destroying the balance of nature that could lead to many problems in the forthcoming era. Renewable energy resources are a ray of hope to avoid possible destruction. Smart grid and distributed power generation systems are now mainly built with the help of renewable energy resources. The integration of renewable energy production system with the smart grid and distributed power generation is facing many challenges that include addressing the issue of isolation and power quality. This paper presents a new approach to address the aforementioned issues by proposing a hybrid bypass technique concept to improve the overall performance of the grid-tied inverter in solar power generation. The topology with the proposed technique is presented using traditional H5, oH5 and H6 inverter. Comparison of topologies with literature is carried out to check the feasibility of the method proposed. It is found that the leakage current of all the proposed inverters is 9 mA and total harmonic distortion is almost about 2%. The proposed topology has good efficiency, common mode and differential mode characteristics.
Benchmarking study between capacitive and electronic load technic to track I-...IJECEIAES
To detect defects of solar panel and understand the effect of external parameters such as fluctuations in illumination, temperature, and the effect of a type of dust on a photovoltaic (PV) panel, it is essential to plot the Ipv=f(Vpv) characteristic of the PV panel, and the simplest way to plot this I-V characteristic is to use a variable resistor. This paper presents a study of comparison and combination between two methods: capacitive and electronic loading to track I-V characteristic. The comparison was performed in terms of accuracy, response time and instrumentation cost used in each circuit, under standard temperature and illumination conditions by using polycrystalline solar panel type SX330J and monocrystalline solar panels type ET-M53630. The whole system is based on simple components, less expensive and especially widely used in laboratories. The results will be between the datasheet of the manufacturer with the experimental data, refinements and improvements concerning the number of points and the trace time have been made by combining these two methods.
Optimizing of the installed capacity of hybrid renewable energy with a modifi...IJECEIAES
The lack of wind speed capacity and the emission of photons from sunlight are the problem in a hybrid system of photovoltaic (PV) panels and wind turbines. To overcome this shortcoming, the incremental conductance (IC) algorithm is applied that could control the converter work cycle and the switching of the buck boost therefore maximum efficiency of maximum power point tracking (MPPT) is reached. The operation of the PV-wind hybrid system, consisting of a 100 W PV array device and a 400 W wind subsystem, 12 V/100 Ah battery energy storage and LED, the PV-wind system requires a hybrid controller for battery charging and usage and load lamp and it’s conducted in experimental setup. The experimental has shown that an average increase in power generated was 38.8% compared to a single system of PV panels or a single wind turbine sub-system. Therefore, the potential opportunities for increasing power production in the tropics wheather could be carried out and applied with this model.
The gravitational search algorithm for incorporating TCSC devices into the sy...IJECEIAES
This paper proposes a gravitational search algorithm (GSA) to allocate the thyristor-controlled series compensator (TCSC) incorporation with the issue of reactive power management. The aim of using TCSC units in this study is to minimize active and reactive power losses. Reserve beyond the thermal border, enhance the voltage profile and increase transmission-lines flow while continuing the whole generation cost of the system a little increase compared with its single goal base case. The optimal power flow (OPF) described is a consideration for finding the best size and location of the TCSCs devices seeing techno-economic subjects for minimizing fuel cost of generation units and the costs of installing TCSCs devices. The GSA algorithm's high ability in solving the proposed multi-objective problem is tested on two 9 and 30 bus test systems. For each test system, four case studies are considered to represent both normal and emergency operating conditions. The proposed GSA method's simulation results show that GSA offers a practical and robust highquality solution for the problem and improves system performance.
Development of methods for managing energy consumption and energy efficiency...IJECEIAES
The work aims to analyze and examine renewable energy sources (RES) to develop interconnected energy efficiency and energy consumption management system by integrating the software-defined machine-tomachine (M2M) communication. The article’s objectives include analysis of using RES as alternative raw materials for electricity production, the study of intelligent technologies for integrating RES into monitoring and control systems, research of devices and methods for monitoring energy production and consumption, analysis of sensor application for automation of control systems in the energy sector, a study of data transmission and information processing rates. The study results showed that the data transfer rate was delayed by 6 seconds to process 1,000 MB of information. It has been proven that wind energy can be used most efficiently within a 12-hour daily cycle, in contrast to tidal energy and solar energy. It is shown that due to the cyclical nature of obtaining energy from renewable sources, they do not fully provide energy to a large city, on the basis of which it is necessary to additionally use other energy sources. Three different types of power generation facilities were examined and compared. Wind farms were found to have the highest potential for electricity generation, amounting to 1,600-1,700 kW.
Parametric estimation in photovoltaic modules using the crow search algorithmIJECEIAES
The problem of parametric estimation in photovoltaic (PV) modules considering man- ufacturer information is addressed in this research from the perspective of combinatorial optimization. With the data sheet provided by the PV manufacturer, a non-linear non-convex optimization problem is formulated that contains information regarding maximum power, open-circuit, and short-circuit points. To estimate the three parameters of the PV model (i.e., the ideality diode factor (a) and the parallel and series resistances (R p and R )), the crow search algorithm (CSA) is employed, which is a metaheuristic optimization technique inspired by the behavior of the crows searching food deposits. The CSA allows the exploration and exploitation of the solution space through a simple evolution rule derived from the classical PSO method. Numerical simulations reveal the effectiveness and robustness of the CSA to estimate these parameters with objective function values lower than 1 10 s 28 and processing times less than 2 s. All the numerical simulations were developed in MATLAB 2020a and compared with the sine-cosine and vortex search algorithms recently reported in the literature.
An optimum location of on-grid bifacial based photovoltaic system in Iraq IJECEIAES
Bifacial photovoltaic (PV) module can gain 30% more energy compared to monofacial if a suitable location were chosen. Iraq (a Middle East country) has a variable irradiation level according to its geographic coordinates, thus, the performance of PV systems differs. This paper an array (17 series, 13 parallel) was chosen to produce 100 kWp for an on-grid PV system. It investigates the PV system in three cities in Iraq (Mosul, Baghdad, and Basrah). Effect of albedo factor, high and pitch of the bifacial module on energy yield have been studied using PVsyst (software). It has been found that the effect is less for a pitch greater than 6 m. The energy gained from bifacial and monofacial PV system module in these cities shows that Mosul is the most suitable for installing both PV systems followed by Baghdad and lastly Basrah. However, in Basrah, the bifacial gain is 12% higher in the energy than monofacial as irradiation there is higher than the other locations, especially for elevation above 1.5 m. Moreover, the cost of bifacial array is 7.23% higher than monofacial, but this additional cost is acceptable since the bifacial gain is about 11.3% higher energy compared to the monofacial.
This paper focuses on the artificial bee colony (ABC) algorithm, which is a nonlinear optimization problem. is proposed to find the optimal power flow (OPF). To solve this problem, we will apply the ABC algorithm to a power system incorporating wind power. The proposed approach is applied on a standard IEEE-30 system with wind farms located on different buses and with different penetration levels to show the impact of wind farms on the system in order to obtain the optimal settings of control variables of the OPF problem. Based on technical results obtained, the ABC algorithm is shown to achieve a lower cost and losses than the other methods applied, while incorporating wind power into the system, high performance would be gained.
This paper presents a fast and accurate fault detection, classification and direction discrimination algorithm of transmission lines using one-dimensional convolutional neural networks (1D-CNNs) that have ingrained adaptive model to avoid the feature extraction difficulties and fault classification into one learning algorithm. A proposed algorithm is directly usable with raw data and this deletes the need of a discrete feature extraction method resulting in more effective protective system. The proposed approach based on the three-phase voltages and currents signals of one end at the relay location in the transmission line system are taken as input to the proposed 1D-CNN algorithm. A 132kV power transmission line is simulated by Matlab simulink to prepare the training and testing data for the proposed 1D- CNN algorithm. The testing accuracy of the proposed algorithm is compared with other two conventional methods which are neural network and fuzzy neural network. The results of test explain that the new proposed detection system is efficient and fast for classifying and direction discrimination of fault in transmission line with high accuracy as compared with other conventional methods under various conditions of faults.
This paper demonstrates a mathematical representation of Photovoltaic (PV) solar cells and hence panels performance. One-diode solar cell model is implemented to simulate the cell and extract the performance indications. The tested PV modules are BP Solar (60 Watt) and Synthesis Power (50 Watts), which are operating in a PV generation system in the University of Anbar - Iraq, College of Applied Sciences. The math model demonstrates Power versus Voltage (P-V) characteristic curves to depict and study various parameters with affecting variations in the PV array performance. The parameters include ambient and cell temperature degrees and solar irradiance (G) level which are the main elements to dictate the productivity of a solar system. G is represented by sun unit (1 sun=1 kW/m2). The outcomes of the simulation model characteristics curves have been compared with curves provided by the tested modules data sheets. MATLAB software has been used to simulate the model and extract the results. This paper also investigated photovoltaic simulation with maximum power point tracking (MPPT) converter to evaluate hence predict the behaviors of the whole photovoltaic DC current generation using PSIM Power Electronics program. The model focuses on the basic components in PV systems; The panel and the DC-DC converter. The modeling outcome data will be used as a reference verifying the performance of the tested modules during the year seasons under the dominating dusty hot weather in western Iraq.
The significance of the solar energy is to intensify the effectiveness of the Solar Panel with the use of a primordial solar tracking system. Here we propounded a solar positioning system with the use of the global positioning system (GPS) , artificial neural network (ANN) and image processing (IP) . The azimuth angle of the sun is evaluated using GPS which provide latitude, date, longitude and time. The image processing used to find sun image through which centroid of sun is calculated and finally by comparing the centroid of sun with GPS quadrate to achieve optimum tracking point. Weather conditions and situation observed through AI decision making with the help of IP algorithms. The presented advance adaptation is analyzed and established via experimental effects which might be made available on the memory of the cloud carrier for systematization. The proposed system improve power gain by 59.21% and 10.32% compare to stable system (SS) and two-axis solar following system (TASF) respectively. The reduced tracking error of IoT based Two-axis solar following system (IoT-TASF) reduces their azimuth angle error by 0.20 degree.
Design and Implementation of Maximum Power Point Tracking in Photovoltaic Sys...inventionjournals
ABSTRACT: This paper presents an algorithm for maximum power point tracking to optimize photovoltaic systems. Beta algorithm is a type of MPPT algorithm. It is having fast tracking ability. The algorithm has been verified on a photovoltaic system modeled in Lab VIEW environment. This algorithm significantly improves the efficiency during the tracking.
The operational performance of a three phase induction motor is impaired by unbalanced voltage supply due to the generation of negative sequence currents, and negative sequence torque which increase motor losses and also trigger torque pulsations. In this study, data mining approach was applied in developing a predictive model using the historical, simulated operational data of a motor for classifying sample motor data under the appropriate type of voltage supply i.e. balanced (BV) and unbalance voltage supply (UB = 1% to 5%). A dataset containing the values of a three phase induction motor’s performance parameter values was analysed using KNIME (Konstanz Information Miner) analytics platform. Three predictive models; the Naïve Bayes, Decision Tree and the Probabilistic Neural Network (PNN) Predictors were deployed for comparative analysis. The dataset was divided into two; 70% for model training and learning, and 30% for performance evaluation. The three predictors had accuracies of 98.649%, 100% and 98.649% respectively, and this confirms the suitability of data mining methods for predictive evaluation of a three phase induction motor’s performance using machine learning.
Design methodology of smart photovoltaic plant IJECEIAES
In this article, we present a new methodology to design an intelligent photovoltaic power plant connected to an electrical grid with storage to supply the laying hen rearing centers. This study requires a very competent design methodology in order to optimize the production and consumption of electrical energy. Our contribution consists in proposing a robust dimensioning synthesis elaborated according to a data flow chart. To achieve this objective, the photovoltaic system was first designed using a deterministic method, then the software "Homer" was used to check the feasibility of the design. Then, controllers (fuzzy logic) were used to optimize the energy produced and consumed. The power produced by the photovoltaic generator (GPV) is optimized by two fuzzy controllers: one to extract the maximum energy and another to control the batteries. The energy consumed by the load is optimized by a fuzzy controller that regulates the internal climate of the livestock buildings. The proposed control strategies are developed and implemented using MATLAB/Simulink.
This paper presents simulation and experimental results of anti-windup PI controller to improve induction machine speed control based on direct torque control (DTC) strategy. Problems like rollover can arise in conventional PI controller due to saturation effect. In order to avoid such problems anti-windup PI controller is presented. This controller is simple for implementation in practice. The proposed anti-windup PI controller demonstrates better dynamic step changes response in speed in terms of overshoots. All simulation work was done using Simulink in the MATLAB software. The experimental results were obtained by practical implementation on a dSPACE 1104 board for a 1.5 KW induction machine. Simulation and experimental results have proven a good performance and verified the validity of the presented control strategy.
Electricity is a major source of energy for fast growing population and the use of nonrenewable source is harmful for our environment. This reason belongs to devastating of environment, so it is required to take immediate action to solve these problems which result the solar energy development. Production of a solar energy can be maximizing if we use solar follower. The major part of solar panels is microcontroller with arrangement of LDR sensor is used to follow the sun, where the sensors is less efficient to track the sun because of the low sensitivity of LDR. We are proposing a method to track sun more effetely with the help of both LDR sensors and image processing. This type of mechanism can track sun with the help of image processing software which combines both result of sensors and processed sun image to control the solar panel. The combination of both software and hardware can control thousands of solar panels in solar power plants.
A hybrid algorithm for voltage stability enhancement of distribution systems IJECEIAES
This paper presents a hybrid algorithm by applying a hybrid firefly and particle swarm optimization algorithm (HFPSO) to determine the optimal sizing of distributed generation (DG) and distribution static compensator (D-STATCOM) device. A multi-objective function is employed to enhance the voltage stability, voltage profile, and minimize the total power loss of the radial distribution system (RDS). Firstly, the voltage stability index (VSI) is applied to locate the optimal location of DG and D-STATCOM respectively. Secondly, to overcome the sup-optimal operation of existing algorithms, the HFPSO algorithm is utilized to determine the optimal size of both DG and D-STATCOM. Verification of the proposed algorithm has achieved on the standard IEEE 33-bus and Iraqi 65-bus radial distribution systems through simulation using MATLAB. Comprehensive simulation results of four different cases show that the proposed HFPSO demonstrates significant improvements over other existing algorithms in supporting voltage stability and loss reduction in distribution networks. Furthermore, comparisons have achieved to demonstrate the superiority of HFPSO algorithms over other techniques due to its ability to determine the global optimum solution by easy way and speed converge feature.
Intelligent control of battery energy storage for microgrid energy management...IJECEIAES
In this paper, an intelligent control strategy for a microgrid system consisting of Photovoltaic panels, grid-connected, and li-ion battery energy storage systems proposed. The energy management based on the managing of battery charging and discharging by integration of a smart controller for DC/DC bidirectional converter. The main novelty of this solution are the integration of artificial neural network (ANN) for the estimation of the battery state of charge (SOC) and for the control of bidirectional converter. The simulation results obtained in the MATLAB/Simulink environment explain the performance and the robust of the proposed control technique.
Hybrid bypass technique to mitigate leakage current in the grid-tied inverterIJECEIAES
The extensive use of fossil fuel is destroying the balance of nature that could lead to many problems in the forthcoming era. Renewable energy resources are a ray of hope to avoid possible destruction. Smart grid and distributed power generation systems are now mainly built with the help of renewable energy resources. The integration of renewable energy production system with the smart grid and distributed power generation is facing many challenges that include addressing the issue of isolation and power quality. This paper presents a new approach to address the aforementioned issues by proposing a hybrid bypass technique concept to improve the overall performance of the grid-tied inverter in solar power generation. The topology with the proposed technique is presented using traditional H5, oH5 and H6 inverter. Comparison of topologies with literature is carried out to check the feasibility of the method proposed. It is found that the leakage current of all the proposed inverters is 9 mA and total harmonic distortion is almost about 2%. The proposed topology has good efficiency, common mode and differential mode characteristics.
Benchmarking study between capacitive and electronic load technic to track I-...IJECEIAES
To detect defects of solar panel and understand the effect of external parameters such as fluctuations in illumination, temperature, and the effect of a type of dust on a photovoltaic (PV) panel, it is essential to plot the Ipv=f(Vpv) characteristic of the PV panel, and the simplest way to plot this I-V characteristic is to use a variable resistor. This paper presents a study of comparison and combination between two methods: capacitive and electronic loading to track I-V characteristic. The comparison was performed in terms of accuracy, response time and instrumentation cost used in each circuit, under standard temperature and illumination conditions by using polycrystalline solar panel type SX330J and monocrystalline solar panels type ET-M53630. The whole system is based on simple components, less expensive and especially widely used in laboratories. The results will be between the datasheet of the manufacturer with the experimental data, refinements and improvements concerning the number of points and the trace time have been made by combining these two methods.
Optimizing of the installed capacity of hybrid renewable energy with a modifi...IJECEIAES
The lack of wind speed capacity and the emission of photons from sunlight are the problem in a hybrid system of photovoltaic (PV) panels and wind turbines. To overcome this shortcoming, the incremental conductance (IC) algorithm is applied that could control the converter work cycle and the switching of the buck boost therefore maximum efficiency of maximum power point tracking (MPPT) is reached. The operation of the PV-wind hybrid system, consisting of a 100 W PV array device and a 400 W wind subsystem, 12 V/100 Ah battery energy storage and LED, the PV-wind system requires a hybrid controller for battery charging and usage and load lamp and it’s conducted in experimental setup. The experimental has shown that an average increase in power generated was 38.8% compared to a single system of PV panels or a single wind turbine sub-system. Therefore, the potential opportunities for increasing power production in the tropics wheather could be carried out and applied with this model.
The gravitational search algorithm for incorporating TCSC devices into the sy...IJECEIAES
This paper proposes a gravitational search algorithm (GSA) to allocate the thyristor-controlled series compensator (TCSC) incorporation with the issue of reactive power management. The aim of using TCSC units in this study is to minimize active and reactive power losses. Reserve beyond the thermal border, enhance the voltage profile and increase transmission-lines flow while continuing the whole generation cost of the system a little increase compared with its single goal base case. The optimal power flow (OPF) described is a consideration for finding the best size and location of the TCSCs devices seeing techno-economic subjects for minimizing fuel cost of generation units and the costs of installing TCSCs devices. The GSA algorithm's high ability in solving the proposed multi-objective problem is tested on two 9 and 30 bus test systems. For each test system, four case studies are considered to represent both normal and emergency operating conditions. The proposed GSA method's simulation results show that GSA offers a practical and robust highquality solution for the problem and improves system performance.
Development of methods for managing energy consumption and energy efficiency...IJECEIAES
The work aims to analyze and examine renewable energy sources (RES) to develop interconnected energy efficiency and energy consumption management system by integrating the software-defined machine-tomachine (M2M) communication. The article’s objectives include analysis of using RES as alternative raw materials for electricity production, the study of intelligent technologies for integrating RES into monitoring and control systems, research of devices and methods for monitoring energy production and consumption, analysis of sensor application for automation of control systems in the energy sector, a study of data transmission and information processing rates. The study results showed that the data transfer rate was delayed by 6 seconds to process 1,000 MB of information. It has been proven that wind energy can be used most efficiently within a 12-hour daily cycle, in contrast to tidal energy and solar energy. It is shown that due to the cyclical nature of obtaining energy from renewable sources, they do not fully provide energy to a large city, on the basis of which it is necessary to additionally use other energy sources. Three different types of power generation facilities were examined and compared. Wind farms were found to have the highest potential for electricity generation, amounting to 1,600-1,700 kW.
Short Term Load Forecasting: One Week (With & Without Weekend) Using Artifici...IJLT EMAS
This paper present for analysis of short term load forecasting: one week (with & without weekend) using ANN techniques for SLDC of Gujarat. In this paper short term electric load forecasting using neural network; based on historical load demand, The Levenberg-Marquardt optimization technique which has one of the best learning rates was used as a back propagation algorithm for the Multilayer Feed Forward ANN model using MATLAB.12 ANN tool box. Design a model for one week (with & w/o weekend) load pattern for STLF using the neural network have been input variables are (Min., Avg., & Max. load demands for previous week, Min., Avg., & Max. temperature for previous week & Min., Avg., & Max. humidity for previous week). And Nov-12 to Apr-13 (6 Months) historical load data from the SLDC, Gujarat are used for training, testing and showing the good performance. Using this ANN model computing the mean absolute error between the exact and predicted values, we were able to obtain an absolute mean error within specified limit and regression value close to one. This represents a high degree of accuracy.
Defining Control Strategies for Micro Grids Islanded Operation with Maximum P...IAES-IJPEDS
This paper explains about an intelligent control method for the maximum
power point tracking (MPPT) of a photo voltaic system with different
temperature and insolation conditions. This method uses a fuzzy logic
controller applied to a DC-DC converter. The different steps of the design of
this controller are presented together with its simulation and the feasibility of
control methods to be adopted for the operation of a micro grid when it
becomes isolated. Normally, the micro grid operates in interconnected mode
with the medium voltage network; however, scheduled or forced isolation
can take place. In such conditions, the micro grid must have the ability to
operate stably and autonomously. An evaluation of the need of storage
devices and load to take off strategies is included in this paper. The MPPT of
a photovoltaic system for Micro Grid operaion using a Fuzzy logic control
scheme is successfully designed and simulated by using MATLAB/Simulink
Software.
Energy Splitting for SWIPT in QoS-constraint MTC Network: A Non-Cooperative G...IJCNCJournal
This paper studies the emerging wireless energy harvesting algorithm dedicated for machine type communication (MTC) in a typical cellular network where one transmitter (e.g. the base station, a hybrid access point) with constant power supply communicates with a set of users (e.g. wearable devices, sensors). In the downlink direction, the information transmission and power transfer are conducted simultaneously by the base station. Since MTC only transmits several bits control signal in the downlink direction, the received signal power can be split into two parts at the receiver side. One is used for information decoding and the other part is used for energy harvesting. Since we assume that the users are without power supply or battery, the uplink transmission power is totally from the energy harvesting. Then, the users are able to transmit their measured or collected data to the base station in the uplink direction. Game theory is used in this paper to exploit the optimal ratio for energy harvesting of each user since power splitting scheme is adopted. The results show that this proposed algorithm is capable of modifying dynamically to achieve the prescribed target downlink decoding signal-to-noise plus interference ratio (SINR) which ensures the high reliability of MTC while maximizing the uplink throughput.
Implementation of modular MPPT algorithm for energy harvesting embedded and I...IJECEIAES
The establishment of the latest IoT systems available today such as smart cities, smart buildings, and smart homes and wireless sensor networks (WSNs) are let the main design restriction on the inadequate supply of battery power. Hence proposing a solar-based photovoltaic (PV) system which is designed DC-DC buck-boost converter with an improved modular maximum power point tracking (MPPT) algorithm. The output voltage depends on the inductor, capacitor values, metal oxide semiconductor field effect transistor (MOSFET) switching frequency, and duty cycle. This paper focuses on the design and simulation of min ripple current/voltage and improved efficiency at PV array output, to store DC power. The stored DC power will be used for smart IoT systems. From the simulation results, the current ripples are observed to be minimized from 0.062 A to 0.02 A maintaining the duty cycle at 61.09 for switching frequencies ranges from 300 kHz to 10 MHz at the input voltage 48 V and the output voltage in buck mode 24 V, boost mode 100 V by maintaining constant 99.7 efficiencies. The improvised approach is compared to various existed techniques. It is noticed that the results are more useful for the self-powered Embedded & Internet of Things systems.
Implementation of a decentralized real-time management system for electrical ...journalBEEI
Intelligent management of the electrical network is the implementation of an integrated system based on a reliable and secure communication architecture for transmitting end-to-end information between the equipment and the management system. The main objective of this work is to develop an intelligent telecontrol solution for the electrical distribution network combining communication techniques and an intelligent reconfiguration strategy. The solution is based on a graphic model and a secure communication architecture using the internet of things to ensure flexibility in terms of management of the intelligent network. This intelligent multi-criteria solution uses a secure communication architecture and the MQTT protocol to ensure system interoperability and security. The tests were carried out on the IEEE 33 bus network and consequently, an optimization of the losses and a clear improvement in the nodal voltage were recorded despite the variation of the electric charge.
Renewable energy allocation based on maximum flow modelling within a microgridIJECEIAES
This paper designs a microgrid-wide energy allocation mechanism on top of a network flow model from distributed generators to consumer entities. Basically, the flow graph consists of a set of nodes representing consumers or generators as well as a set of weighted links representing the amount of energy generation, consumer-side demand, and transmission cable capacity. The main idea lies in that a special node is added to account for the interaction with the main grid and that two-pass allocation is executed. In the first pass, the maximum flow solver decides the amount of the insufficiency, which must be supplemented by the main power network, usually with predefined cost. The second pass runs the flow solver again to fill the energy lack and calculates the surplus of renewable energy generation. The experiment result observes the stability in energy distribution over the microgrid while the amount of the total energy production can be accommodated by the maximum link capacity.
Optimized design of an extreme low power datalogger for photovoltaic panels IJECEIAES
The paper focuses on the design and implementation of a low cost and compact data logger prototype using an extreme low power (XLP) and low pin count programmable interface controllers (PIC) microcontroller using its own flash memory for the periodic data acquisition storage, while many other works focus in the Arduino Eco-system. It is planned to pick four important analog measures from the photovoltaic system, and store them directly as 10-bit numerical counts, this yields to faster data acquisition and storage (no time consuming for mathematical computation to convert each numerical count of raw data to meaningful real-world data). Avoiding the use of any kind of display and keypad, and keeping the ratio run time over sleep time as low as possible, has a maximum impact on lowering the power consumption. This prototype can be serially linked to a personal computer (PC) to view the acquisition of measurements in real time, and to retrieve all collected data through a terminal application. The experimental results are stored in commaseparated values (CSV) files to ease post data analysis with any spread sheet software, for statistical calculations and graphs drawing, in order for instance, to find the faults of the photovoltaic system and optimize its management and its performance.
Towards automatic setup of non intrusive appliance load monitoring – feature ...IJECEIAES
Given climate change concerns and incessantly increasing energy demands of the present time, improving energy efficiency becomes of significant environmental and economic impact. Monitoring household electrical consumption through a non-intrusive appliance load monitoring (NIALM) system achieves significant efficiency improvement by providing appliance-level energy consumption and relaying this information back to the user. This paper focuses on feature extraction and clustering, which constitute two of the four modules of the proposed automatic-setup NIALM system, the other two being labeling and classification. The feature extraction module applies the Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT), a well-known parametric estimation technique, to the drawn electric current. The result is a compact representation of the signal in terms of complex numbers referred to as poles and residues. These complex numbers are then used to determine a feature vector consisting of the contribution of the fundamental, the third and the fifth harmonic currents to the maximum of the total load current. Once a signature is extracted, the clustering module applies distance-based rules inferred off-line from various databases and decides either to create a new class out of the new signature or to discard it and increase the count of an existing signature. As a result, the feature space is clustered without the a priori knowledge of the number of appliances into singleton clusters. Results obtained from a set of appliances indicate that these two modules succeed in creating an unlabeled database of signatures.
Optimal state estimation techniques for accurate measurements in internet of...IJECEIAES
The employment of microgrids in smart cities is not only changing the landscape of power generation, transmission, and distribution but it helps in green alleviation by converting passive consumers into active produces (using renewable energy sources). Real-time monitoring is a crucial factor in the successful adoption of microgrids. Real-time state estimation of a microgrid is possible through internet-of-things (IoT). State estimation can provide the necessary monitoring of grid for many system optimization applications. We will use raw and missing data before we learn from data, the processing must be done. This paper describes various Kalman variants use for preprocessing. In this paper a formulated approach along with algorithms are described for optimal state estimation and forecasting, with weights update using deep neural networks (DNN) is presented to enable accurate measurements at component and system level model analysis in an IoT enabled microgrid. The real load data experiments are carried out on the IEEE 118-bus benchmark system for the power system state estimation and forecasting. This research paves a way for developing a novel DNN based algorithms for a power system under dynamically varying conditions and corresponding time dependencies.
Study on the performance indicators for smart grids: a comprehensive reviewTELKOMNIKA JOURNAL
This paper presents a detailed review on performance indicators for smart grid (SG) such as voltage stability enhancement, reliability evaluation, vulnerability assessment, Supervisory Control and Data Acquisition (SCADA) and communication systems. Smart grids reliability assessment can be performed by analytically or by simulation. Analytical method utilizes the load point assessment techniques, whereas the simulation technique uses the Monte Carlo simulation (MCS) technique. The reliability index evaluations will consider the presence or absence of energy storage elements using the simulation technologies such as MCS, and the analytical methods such as systems average interruption frequency index (SAIFI), and other load point indices. This paper also presents the difference between SCADA and substation automation, and the fact that substation automation, though it uses the basic concepts of SCADA, is far more advanced in nature.
Similar to Intelligent Electrical Multi Outlets Controlled and Activated by a Data Mining Engine Oriented to Building Electrical Management (20)
Design and Implementation of Smart Cooking Based on Amazon EchoIJSCAI Journal
Smart cooking based on Amazon Echo uses the internet of things and cloud computing to assist in cooking
food. People may speak to Amazon Echo during the cooking in order to get the information and situation of
the cooking. Amazon Echo recognizes what people say, then transfers the information to the cloud services,
and speaks to people the results that cloud services make by querying the embedded cooking knowledge and
achieving the information of intelligent kitchen devices online. An intelligent food thermometer and its mobile
application are well-designed and implemented to monitor the temperature of cooking food
Forecasting Macroeconomical Indices with Machine Learning : Impartial Analysi...IJSCAI Journal
The importance of economic freedom has often been stressed by supporters of liberalism, but can its actual
effect be observed in a data driven, objective way? To analyze this relation the Economic Freedom of the
World (EFW) index and the Human Development Index (HDI) were examined with modern machine learning algorithms and a wide-ranging approach. Considering the EFW index’s preference of a liberalistic
oriented economic policy, an objective recommendation for creating an economic policy that improves
people’s everyday lives might be derived by the analysis results. It was found that these more advanced
algorithms achieve a considerably stronger correlation between both indices than pure statistical means
yet leave a small room for interpretation towards a counter-liberalistic implementation of demand-driven
economic policy.
Nov 2018 Table of contents; current issue -International Journal on Soft Comp...IJSCAI Journal
International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI) is an open access peer-reviewed journal that provides an excellent international forum for sharing knowledge and results in theory, methodology and applications of Artificial Intelligence, Soft Computing. The Journal looks for significant contributions to all major fields of the Artificial Intelligence, Soft Computing in theoretical and practical aspects. The aim of the Journal is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and share cutting-edge development in the field.
6th international conference on artificial intelligence and applications (aia...IJSCAI Journal
6th International Conference on Artificial Intelligence and Applications (AIAP-2019) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Artificial Intelligence and its applications. The Conference looks for significant contributions to all major fields of the Artificial Intelligence, Soft Computing in theoretical and practical aspects. The aim of the Conference is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and share cutting-edge development in the field.
Generating images from a text description is as challenging as it is interesting. The Adversarial network
performs in a competitive fashion where the networks are the rivalry of each other. With the introduction of
Generative Adversarial Network, lots of development is happening in the field of Computer Vision. With
generative adversarial networks as the baseline model, studied Stack GAN consisting of two-stage GANS
step-by-step in this paper that could be easily understood. This paper presents visual comparative study of
other models attempting to generate image conditioned on the text description. One sentence can be related
to many images. And to achieve this multi-modal characteristic, conditioning augmentation is also
performed. The performance of Stack-GAN is better in generating images from captions due to its unique
architecture. As it consists of two GANS instead of one, it first draws a rough sketch and then corrects the
defects yielding a high-resolution image.
Temporally Extended Actions For Reinforcement Learning Based Schedulers IJSCAI Journal
Temporally extended actions have been proved to enhance the performance of reinforcement learning
agents. The broader framework of ‘Options’ gives us a flexible way of representing such extended course of
action in Markov decision processes. In this work we try to adapt options framework to model an operating
system scheduler, which is expected not to allow processor stay idle if there is any process ready or waiting
for its execution. A process is allowed to utilize CPU resources for a fixed quantum of time (timeslice) and
subsequent context switch leads to considerable overhead. In this work we try to utilize the historical
performances of a scheduler and try to reduce the number of redundant context switches. We propose a
machine-learning module, based on temporally extended reinforcement-learning agent, to predict a better
performing timeslice. We measure the importance of states, in option framework, by evaluating the impact
of their absence and propose an algorithm to identify such checkpoint states. We present empirical
evaluation of our approach in a maze-world navigation and their implications on "adaptive timeslice
parameter" show efficient throughput time.
Knowledgebase Systems in Neuro Science - A StudyIJSCAI Journal
The improvement of health and nutritional status of the society has been one of the thrust areas for social
developments programmes of the country. The present states of healthcare facilities in India are inadequate
when compared to international standards. The average Indian spending on healthcare is much below the
global average spending. Indian healthcare Industry is growing at the rapid pace of more than 18%, the
fastest in the world. The prospects for Indian healthcare are to the tune of USD 40 billion, while global
market is USD 1660 trillion. India has all the prospects to become medical tourism destination of the
world, because it has a large pool of low-cost scientifically trained technical personal and is one of the
favoured counties for cost effective healthcare. As per the reports of Global Burden of Neurological
Disorders Estimations and Projections survey there is big shortage of neurologist in India and around the
world. So Authors would like to develop an innovative IT based solution to help doctors in rural areas to
gain expertise in Neuro Science and treat patients like expert neurologist. This paper aims to survey the
Soft Computing techniques in treating neural patient’s problems used throughout the world
An Iranian Cash Recognition Assistance System For Visually Impaireds IJSCAI Journal
In economical societies of today, using cash is an inseparable aspect of human’s life. People use cash for
marketing, services, entertainments, bank operations and so on. This huge amount of contact with cash and
the necessity of knowing the monetary value of it caused one of the most challenging problems for visually
impaired people. In this paper we propose a mobile phone based approach to identify monetary value of a
picture taken from a banknote using some image processing and machine vision techniques. While the
developed approach is very fast, it can recognize the value of the banknote by an average accuracy rate of
about 97% and can overcome different challenges like rotation, scaling, collision, illumination changes,
perspective, and some others.
An Experimental Study of Feature Extraction Techniques in Opinion MiningIJSCAI Journal
The feature selection or extraction is the most important task in Opinion mining and Sentimental Analysis
(OSMA) for calculating the polarity score. These scores are used to determine the positive, negative, and
neutral polarity about the product, user reviews, user comments, and etc., in social media for the purpose
of decision making and Business Intelligence to individuals or organizations. In this paper, we have
performed an experimental study for different feature extraction or selection techniques available for
opinion mining task. This experimental study is carried out in four stages. First, the data collection process
has been done from readily available sources. Second, the pre-processing techniques are applied
automatically using the tools to extract the terms, POS (Parts-of-Speech). Third, different feature selection
or extraction techniques are applied over the content. Finally, the empirical study is carried out for
analyzing the sentiment polarity with different features
Monte-Carlo Tree Search For The "Mr Jack" Board Game IJSCAI Journal
Recently the use of the Monte-Carlo Tree Search algorithm, and in particular its most famous
implementation, the Upper Confidence Tree can be seen has a key moment for artificial intelligence in
games. This family of algorithms provides huge improvements in numerous games, such as Go, Havannah,
Hex or Amazon. In this paper we study the use of this algorithm on the game of Mr Jack and in particular
how to deal with a specific decision-making process.Mr Jack is a 2-player game, from the family of board
games. We will present the difficulties of designing an artificial intelligence for this kind of games, and we
show that Monte-Carlo Tree Search is robust enough to be competitive in this game with a smart approach.
Unsupervised learning models of invariant features in images: Recent developm...IJSCAI Journal
Object detection and recognition are important problems in computer vision and pattern recognition
domain. Human beings are able to detect and classify objects effortlessly but replication of this ability on
computer based systems has proved to be a non-trivial task. In particular, despite significant research
efforts focused on meta-heuristic object detection and recognition, robust and reliable object recognition
systems in real time remain elusive. Here we present a survey of one particular approach that has proved
very promising for invariant feature recognition and which is a key initial stage of multi-stage network
architecture methods for the high level task of object recognition.
Ontologies are being used to organize information in many domains like artificial intelligence,
information science, semantic web, library science. Ontologies of an entity having different information
can be merged to create more knowledge of that particular entity. Ontologies today are powering more
accurate search and retrieval in websites like Wikipedia etc. As we move towards the future to Web 3.0,
also termed as the semantic web, ontologies will play a more important role.
Ontologies are represented in various forms like RDF, RDFS, XML, OWL etc. Querying ontologies can
yield basic information about an entity. This paper proposes an automated method for ontology creation,
using concepts from NLP (Natural Language Processing), Information Retrieval and Machine Learning.
Concepts drawn from these domains help in designing more accurate ontologies represented using the
XML format. This paper uses document classification using classification algorithms for assigning labels
to documents, document similarity to cluster similar documents to the input document, together, and
summarization to shorten the text and keep important terms essential in making the ontology. The module
is constructed using the Python programming language and NLTK (Natural Language Toolkit). The
ontologies created in XML will convey to a lay person the definition of the important term's and their
lexical relationships.
Big Data Analytics: Challenges And Applications For Text, Audio, Video, And S...IJSCAI Journal
All types of machine automated systems are generating large amount of data in different forms like
statistical, text, audio, video, sensor, and bio-metric data that emerges the term Big Data. In this paper we
are discussing issues, challenges, and application of these types of Big Data with the consideration of big
data dimensions. Here we are discussing social media data analytics, content based analytics, text data
analytics, audio, and video data analytics their issues and expected application areas. It will motivate
researchers to address these issues of storage, management, and retrieval of data known as Big Data. As
well as the usages of Big Data analytics in India is also highlighted
A Study on Graph Storage Database of NOSQLIJSCAI Journal
Big Data is used to store huge volume of both structured and unstructured data which is so large and is
hard to process using current / traditional database tools and software technologies. The goal of Big Data
Storage Management is to ensure a high level of data quality and availability for business intellect and big
data analytics applications. Graph database which is not most popular NoSQL database compare to
relational database yet but it is a most powerful NoSQL database which can handle large volume of data in
very efficient way. It is very difficult to manage large volume of data using traditional technology. Data
retrieval time may be more as per database size gets increase. As solution of that NoSQL databases are
available. This paper describe what is big data storage management, dimensions of big data, types of data,
what is structured and unstructured data, what is NoSQL database, types of NoSQL database, basic
structure of graph database, advantages, disadvantages and application area and comparison of various
graph database.
A Study on Graph Storage Database of NOSQLIJSCAI Journal
Big Data is used to store huge volume of both structured and unstructured data which is so large and is
hard to process using current / traditional database tools and software technologies. The goal of Big Data
Storage Management is to ensure a high level of data quality and availability for business intellect and big
data analytics applications. Graph database which is not most popular NoSQL database compare to
relational database yet but it is a most powerful NoSQL database which can handle large volume of data in
very efficient way. It is very difficult to manage large volume of data using traditional technology. Data
retrieval time may be more as per database size gets increase. As solution of that NoSQL databases are
available. This paper describe what is big data storage management, dimensions of big data, types of data,
what is structured and unstructured data, what is NoSQL database, types of NoSQL database, basic
structure of graph database, advantages, disadvantages and application area and comparison of various
graph database.
Estimation Of The Parameters Of Solar Cells From Current-Voltage Characterist...IJSCAI Journal
This paper presents a method for calculating the light generated current, the series resistance, shun
resistance and the two components of the reverse saturation current usually encountered in the double
diode representation of
the solar cell from the experimental values of the current
-
voltage characteristics
of the cell using genetic algorithm. The theory is able to regenerate the above mentioned parameters to
very good accuracy when applied to cell data that was generated from
pre
-
defined parameters. The
method is applied to various types of space quality solar cells and sub cells. All parameters except the
light generated current are seen to be nearly the same in the case of a cell whose characteristics under
illumination and i
n dark were analyzed. The light generated current is nearly equal to the short
-
circuit
current in all cases. The parameters obtained by this method and another method are nearly equal
wherever applicable. The parameters are also shown to represent the cur
rent
-
voltage characteristics
well
Implementation of Folksonomy Based Tag Cloud Model for Information Retrieval ...IJSCAI Journal
In the magnitude of internet one need to devote extra time to investigate an
ticipated resource, especially
when one need to search information from documents. For the higher range internet there is serious need
to demand the essentiality to discover the reserved resources. One of the solutions for information retrieval
from docume
nt repository is to attach tags to documents. Numerous online social bookmarking services
permit users to attach tags with resources which are eventually meta
-
data, frequently stated as folksonomy.
In current paper, authors implemented this model for infor
mation retrieval by utilizing these tags, after
retrieving by using delicious API and synthesize tag cloud in an Indian University to search and retrieve
information from document repository
Study of Distance Measurement Techniques in Context to Prediction Model of We...IJSCAI Journal
Internet is the boon in modern era as every organization uses it for dissemination of information and ecommerce
related applications. Sometimes people of organization feel delay while accessing internet in
spite of proper bandwidth. Prediction model of web caching and prefetching is an ideal solution of this
delay problem. Prediction model analysing history of internet user from server raw log files and determine
future sequence of web objects and placed all web objects to nearer to the user so access latency could be
reduced to some extent and problem of delay is to be solved. To determine sequence of future web objects,
it is necessary to determine proximity of one web object with other by identifying proper distance metric
technique related to web caching and prefetching. This paper studies different distance metric techniques
and concludes that bio informatics based distance metric techniques are ideal in context to Web Caching
and Web Prefetching
A BINARY BAT INSPIRED ALGORITHM FOR THE CLASSIFICATION OF BREAST CANCER DATAIJSCAI Journal
Advancement in information and technology has made a major impact on medical science where the
researchers come up with new ideas for improving the classification rate of various diseases. Breast cancer
is one such disease killing large number of people around the world. Diagnosing the disease at its earliest
instance makes a huge impact on its treatment. The authors propose a Binary Bat Algorithm (BBA) based
Feedforward Neural Network (FNN) hybrid model, where the advantages of BBA and efficiency of FNN is
exploited for the classification of three benchmark breast cancer datasets into malignant and benign cases.
Here BBA is used to generate a V-shaped hyperbolic tangent function for training the network and a fitness
function is used for error minimization. FNNBBA based classification produces 92.61% accuracy for
training data and 89.95% for testing data.
Design of Dual Axis Solar Tracker System Based on Fuzzy Inference SystemsIJSCAI Journal
Electric power is a basic need in today’s life. Due to the extensive usage of power, there is a need to look
for an alternate clean energy source. Recently many researchers have focused on the solar energy as a
reliable alternative power source. Photovoltaic panels are used to collect sun radiation and convert it into
electrical energy. Most of the photovoltaic panels are deployed in a fixed position, they are inefficient as
they are fixed only at a specific angle. The efficiency of photovoltaic systems can be considerably increased
with an ability to change the panels angel according to the sun position. The main goal of such systems is
to make the sun radiation perpendicular to the photovoltaic panels as much as possible all the day times.
This paper presents a dual axis design for a fuzzy inference approach-based solar tracking system. The
system is modeled using Mamdani fuzzy logic model and the different combinations of ANFIS modeling.
Models are compared in terms of the correlation between the actual testing data output and their
corresponding forecasted output. The Mean Absolute Percent Error and Mean Percentage Error are used
to measure the models error size. In order to measure the effectiveness of the proposed models, we
compare the output power produced by a fixed photovoltaic panels with the output which would be
produced if the dual-axis panels are used. Results show that dual-axis solar tracker system will produce
22% more power than a fixed panels system.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Gen AI Study Jams _ For the GDSC Leads in India.pdf
Intelligent Electrical Multi Outlets Controlled and Activated by a Data Mining Engine Oriented to Building Electrical Management
1. International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), Vol.7, No.4, November 2018
DOI :10.5121/ijscai.2018.7401 1
INTELLIGENT ELECTRICAL MULTI OUTLETS
CONTROLLED AND ACTIVATED BY A DATA MINING
ENGINE ORIENTED TO BUILDING ELECTRICAL
MANAGEMENT
Alessandro Massaro, Giacomo Meuli, Angelo Galiano
Dyrecta Lab, IT Research Laboratory, Via Vescovo Simplicio, 45, 70014 Conversano
(BA), Italy
ABSTRACT
In the proposed paper are discussed results of an industry project concerning energy management in
building. Specifically the work analyses the improvement of electrical outlets controlled and activated by a
logic unit and a data mining engine. The engine executes a Long Short-Terms Memory (LSTM) neural
network algorithm able to control, to activate and to disable electrical loads connected to multiple outlets
placed into a building and having defined priorities. The priority rules are grouped into two level: the first
level is related to the outlet, the second one concerns the loads connected to a single outlet. This algorithm,
together with the prediction processing of the logic unit connected to all the outlets, is suitable for alerting
management for cases of threshold overcoming. In this direction is proposed a flow chart applied on three
for three outlets and able to control load matching with defined thresholds. The goal of the paper is to
provide the reading keys of the data mining outputs useful for the energy management and diagnostic of the
electrical network in a building. Finally in the paper are analyzed the correlation between global active
power, global reactive power and energy absorption of loads of the three intelligent outlet. The prediction
and the correlation analyses provide information about load balancing, possible electrical faults and energy
cost optimization.
KEYWORDS
Intelligent Electrical Outlets, Energy Management, Load Monitoring and Piloting, Smart Grids, Energy
Routing, Data Mining, KNIME, Long Short-Term Memory (LSTM), Neural Network, Correlation Matrix.
1.INTRODUCTION: STATE OF THE ART AND MAIN PROJECT SPECIFICATIONS
In [1], some authors highlighted the importance of Machine Learning (ML) algorithms for the
planning of the activation of electric utilities starting from the distribution of electrical energy and
by analyzing predictive data [1]. Other researchers studied accurately the topic of electric energy
management in the smart home, by analyzing the combined architecture of the electric power
transmission together with the communication network using smart meters [2]. The use of the
smart meters is useful for the real time reading of the electrical power providing, though an
external control unit, information about the "status" of the power consumption and reconstruction
of periodic load curve. Other scientific studies focused the attention on peak load prediction [3],
and on price prediction [4]. A complete home energy management model has been presented in
[5]: in this model are defined the optimal consumption strategies, planning at the same time load
control activities. Important topics for the energy consumption optimization strategies are the
2. International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), Vol.7, No.4, November 2018
2
daily and annual energy losses simulations [6]: in this direction important inputs for the
calculations are load profiles (load curves), the number of daily use of electric loads (number of
users / day), the probability of utilization, and the percentage of loads per home. Important tools
suitable for residential building energy management system are data mining tools adopted in
literature for non-intrusive applications [7], for the time forecasting prediction of the electricity
consumption [8], and for the loads control [9]. It is important to observe that for domestic
applications the total consumer load is defined as a combination of small loads to be controlled
by piloted switches [10], where groups of small loads can be controlled by a multi-outlet device.
Load management must be performed using a load controller [11], consisting of a PC-
programmable microcontroller. Another applications involving multi-outlets are the management
of electricity produced by photovoltaic panels [12], and energy management improved by
sensors, MySQL databases and graphical interfaces [13]. The concept of intelligent electrical
outlets has been developed in [14], in which a system server center administrates and controls by
web services the electrical loads. Intelligent systems combined with sensors and smart meters can
be integrated into more complex systems such as those reported in [15]. Smart sockets (electrical
outlets) can also be applied to multi-floor buildings by configuring wireless nodes for control
[16]. Energy building management by data mining improving decision intelligence has been
analyzed in [17]. In particular data mining clustering approach has been adopted for energy usage
profiles of domestic residences [18]. The grouping of electrical users by data mining dendograms
has been applied to associate electrical utilities used during the week [19]. Other researchers
analyzed how intelligent electrical outlets can also be used to prevent fires (overheating alerting)
or electrical malfunctions [20]. In [21] Artificial Neural Networks (ANNs) have been adopted to
predict energy consumption, besides in [22] the Long-Short-Term-Memory (LSTM) based
Recurrent Neural Network (RNN) provided a good accurate method for complex electric load
time series forecasting also for long forecasting horizon. Concerning the electronic circuits able
to process and to transmit data, some researchers studied the possibility to implement Arduino
[23] or Raspberry [24] technologies, thus suggesting to improve a logic unit of electrical outlets
by microcontrollers. Following the state of the art has been formulated the idea of the project
sketched in the architecture of Fig. 1decribed below:
- controlled electrical outlets (Outlet 1, Outlet 2, etc.) transmit data in cloud by a personal
computer (PC) having an USB interface, an intelligent unit able to detect electrical current
and electrical power (P(t)=V(t)·I(t)) and to control the load activations, and an ethernet
interface connecting outlet to internet or intranet network;
- a data mining engine implementing data mining algorithms.
The application of the data mining algorithms will therefore assist to:
- optimize the activation and the deactivation of electrical loads basing on consumption
prediction (alert thresholds are enabled by the data mining engine which will deactivate
loads in threshold overcoming cases);
- optimize costs and reduce energy waste, taking into account energy tariff conditions;
- define dynamically the thresholds by considering tariffs and predicted consumptions;
- predict electrical faults by an advanced analysis of the anomalies found in the network
connected to the multi-outlet system.
The paper is structured as follows:
- design principles of the LSTM neural network prototype systems implemented to
optimize an electrical building network, describing logics to activate and to deactivate
loads connected to different electrical outlets;
3. International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), Vol.7, No.4, November 2018
3
- the testing of the LSTM algorithm by considering a dataset of measured values of a
network of three electrical outlets providing measured data;
- a complete scenario to understand prediction of global active power of the building;
- a correlation analysis between different attributes including active and reactive power,
explaining the status of the analyzed building electrical network.
The highlights of the proposed paper are the following ones:
1- application of the load prediction theory discussed in [25], together with LSTM electrical
power prediction in order to increase the reliability of the prediction;
2- definition of the flowchart able to enable and disable loads characterized by two priority
level (priority of the electrical outlet and load priority);
3- testing of the LSTM model showing good performance;
4- explanation of the correlation of reactive power with active one by identifying inefficient
outlets.
Below is summarized the features and the requirements of the whole proposed mode :
Features and requirements Description
Electrical load prediction (prediction 1) Slope behavior prediction of the load
(processing of the intelligent unit)
LSTM model (prediction 2) Training and testing of the LSTM model
based on real dataset found online
Prototype model Testing based on 3 electrical outlets
having different priority
Implementation of the priority roles and
enabling /disabling procedures
Flowchart describing two priority levels
of electrical loads and criteria to
deactivate/activate loads by processing
prediction results (prediction 1 and
prediction 2)
Post processing Analysis of correlations between all the
attribute analyzed in the model in order
to check possible load malfunctions
Table 1. Table of the work requirements and features.
Data Mining
Engine
Alert threshold
Actual consumption (without monitoring)
Consumption (monitoring/prediction)
Time (day)
Energy[kW]
prediction of electrical
faults
Cost optimization
Analytics/Dashboards
A A A
Outlet 1 Outlet 2 Outlet N
Power supply line
Ethernet
Interface
Intelligent
Unit
USB
Interface
PC
A Ammeters Piloted
Switch
Intranet/Internet
Figure 1. Schematic system architecture of intelligent electrical outlets integrated into internet/intranet
network.
4. International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), Vol.7, No.4, November 2018
4
2. LOGICS AND DESIGN OF THE DATA MINING ENGINE MANAGING
INTELLIGENT ELECTRICAL OUTLETS
In this section are discussed the logics of electrical load management and the design procedures of
the data mining engine reported in Fig. 1.
2.1 DESIGN OF THE PROTOTYPE SYSTEM
The main design concept is illustrated in Fig. 2 where are identified three main design stages such
as:
1. Measurement. A time dependent total power is measured by the total current detection;
2. Evaluation. An algorithm, enabled by the intelligent unit, assesses when the energy
consumption is too high, when it is within normal limits or when it is significantly lower.
Depending on the following three cases, it checks:
- when the energy consumed is at the limit (the algorithm intervenes on the load management by
deactivating the outlet to which the least priority device is connected);
- when the energy consumed is too high (the algorithm intervenes by interrupting the current of
outlets having devices with lower priority by restoring the maximum permissible power range);
- when the energy consumed is significantly lower (the algorithm intervenes by rehabilitating the
outlets previously disabled having higher priority);
3. management of electrical outlets. According with the evaluation, the system decides which
outlets to activate or deactivate, according to the following rules:
-priority: (the outlet which has a lower priority value will typically be the one of minor importance
for the application; if an electrical outlet is to be reactivated, the algorithm reorders the priority
values in descending order and follows the order of the list);
-absorption entity (indicates the energy absorbed by the single outlet calculated through the
evaluation of the current in the last minute);
-availability: indicates the on/off status of the outlet.
Measurement
Evaluation
Electrical
outlets
management
Figure 2. Design concept of the intelligent electrical outlet system.
In Fig. 3 (a) is indicated a typical current trend versus minutes: the main value to process is the
total current of the electrical outlet providing the global active power of the whole electrical
network. This last parameter is utilised in order to check the overcoming of the threshold limit. In
Fig. 3 (b) is plotted an example related to an alerting condition due to the threshold overcoming.
The intelligent logic unit executes the algorithm which evaluates the slope of the curve at each
5. International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), Vol.7, No.4, November 2018
5
minute by comparing it with the slope of the line representing the maximum absorption (see Fig. 4)
[25]. The blue line indicates the measured electrical power of an electrical outlet connected with
different loads, and the red line indicates the threshold line having a defined slope according with
energy management risk. Each power variation during the time is characterized by a slope. If the
intelligent unit predicts that the total power overcomes the threshold line, and if also the LSTM
network provides the same prediction, will be deactivated no priority load or groups of no priority
loads. The slope prediction is estimated by assuming the same slope behaviour of the last minute
(measurement of the last minute). By considering a system composed by different electrical outlet
of a building it is necessary to predict also the behaviour of the whole energy managed by all the
electrical outlets. In this case the concept of priority is extended also to outlet priority by
introducing a second level of priority: the first priority level is related to the outlet and the second
one concerns the loads of a single outlets. A good design takes into account the following
considerations:
- the priority loads are grouped into outlets having the same or similar priority;
- the amperometers and the power meters are placed not only for the measurements of the
absorbed energy of an outlet but also for the measurements of main loads of the same
outlet;
- the typical load curves of the building in different periods of the years (important for the
initial load distribution evaluation).
The predictions of the energy absorbed of each outlet at the time step t-1 (indicated in Fig. 5 by the
attribute sub-metering) are the input of the Long Short-Term neural network predicting the global
active power at the time t. The global active power represent the main attribute of the electrical
building energy which will be considered for electricity management.
Time
Current 1
Current 2
Current 3
Total outlet current
Current threshold
Electricalcurrent[A]
Minute
Electricalcurrent[A]
Current 1
Current 2
Total outlet
current
(a)
(b)
Figure 3. Example of current measurement and evaluation processes of an electrical outlet: (a) outlet 1; (b)
outlet 2.
6. International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), Vol.7, No.4, November 2018
6
Energy[Wh]
Minute
Measured Threshold
Figure 4. Evaluation of load slope and comparison with the threshold [25]: global energy threshold.
Figure 5. Model of the LSTM predicting global power.
In Fig. 6 and Fig. 7 are illustrated the flowcharts describing the control logic assuming the load
priority configuration of table 2 where are reported the priority levels of three electrical outlets and
7 loads. The alerting condition management procedure is explained by the flow chart of Fig. 6: the
logic unit predict the slope of the global energy, if the prediction generates an alerting condition
will be analysed the prediction of the LSTM algorithm, if also the LSTM output provides a further
alerting condition will be activated the logic of load disabling shown in the flow chart of Fig. 7.
According to the priority level of table 1 will be disabled before the loads of the outlet 1 having
lower priority and successively the loads of the other outlets until the estimated value does not
exceed the threshold. This procedure is valid for a precise time t. For each time steps will be
repeated the disabling procedure. When will be predicted no risk condition will be enable gradually
the loads initially disabled by the automatic procedure. If only the prediction of slope provides an
alerting condition because the LSTM indicates no risk, for a better security, will be disabled only
the first one or two loads having lower priority. the LSTM network is “reinforcing”, and is used to
estimate with greater accuracy that there is a certain probability of exceeding the threshold. The
proposed procedure can be applied for different time units (minutes, days, weeks, etc.).
7. International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), Vol.7, No.4, November 2018
7
Outlet Priority level Outlet Load
3 1 Load
priority
Load
7 1
6 2
2 2 Load
priority
Load
5 3
4 4
3 5
1 3 Load
priority
Load
2 6
1 7
Table 2. Example of priority configuration for three electrical outlets and seven loads.
START
exceeding
threshold?
Prediction of Slope
exceeding
threshold?
LSTM
Prediction exceeding
threshold
YES
YES
NO
END
NO
Deactivation load N.1
of outlet N.1
Figure 6. Sequential model for threshold alerting condition.
8. International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), Vol.7, No.4, November 2018
8
LSTM
Prediction exceeding
threshold
exceeding
threshold?
Load N.1 deactivation
exceeding
threshold?
Load N.2 deactivation
Load N.3 deactivation
exceeding
threshold?
Load N.4 deactivation
YES
YES
YES
NO
NO
NO
OUTLETN.1OUTLETN.2
END
END
END
Figure 7. Flowchart of the load disabling procedure.
2.2 EXPERIMENTAL DATASET
In order to check the implemented LSTM algorithm has been adopted the dataset proposed in [26]
and [27]. This dataset contains 2075259 measurements related to period between December 2006
and November 2010 (47 months). We provide below more information about the dataset attributes.
- "Global_active_power": indicates the global active power absorbed by the family unit
(household global minute-averaged active power), expressed in kW (kilowatt);
9. International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), Vol.7, No.4, November 2018
9
- "Global_reactive_power": indicates the global reactive power absorbed by the family unit
(household global minute-averaged reactive power), expressed in kW (kilowatt);
- "Voltage": indicates the minute-averaged voltage, expressed in V (volts);
- "Global_intensity": indicates the global current absorbed by the family unit (household
global minute-averaged current intensity), expressed in A (ampere);
- "Sub_metering_1": corresponds to the energy absorption of the loads that usually are in
"kitchen", therefore they are mainly considered a dishwasher, an oven and a microwave
oven, and is expressed in Wh (watt hour of active energy);
- "Sub_metering_2": corresponds to the energy absorption of the loads that usually are in a
"laundry" and are mainly part of a washing machine, a dryer, a refrigerator and a light, and
is expressed in Wh (watt hour of active energy);
- "Sub_metering_3": corresponds to the energy absorption of an electric water heater and an
air conditioner; it is expressed in Wh (watt hour of active energy);
- The formula global_active_power*1000/60- sub_metering_1 -sub_metering_2 -
sub_metering_3 represents the active energy consumed every minute (Wh) in the
household by electrical equipment not measured in sub-meterings 1, 2 and 3;
- The dataset contains some missing values in the measurements (nearly 1,25% of the rows);
- date: Date in format dd/mm/yyyy;
- time: time in format hh:mm:ss.
The experimental dataset is plotted in Fig. 8 where all the attributes will train the LSTM model of
Fig.5. In order to analyse better the dispersion of the measurement for a check of the measurements
reliability, the scattering multiple graphical library of the Rapid Miner tool has been adopted. In
Fig. 9 are plotted all the dataset attributes by observing a good voltage trend, a typical global power
distribution and a clear differentiation of load distributions (the outlet associated to sub_metering_3
it consumes on average more energy than the other outlets). This first check is important in order to
verify if the if the priority rule is respected, and if there are some unbalanced cases or malfunctions.
Year 1 Year 2 Year 3 Year 4
Figure 8. Experimental dataset (daily samples).
10. International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), Vol.7, No.4, November 2018
10
(a) (b)
(c) (d)
Figure 9. Experimental dataset: scattering multiple library of the Rapid Miner tool.
3. LSTM IMPLEMENTATION AND RESULTS
In order to implement the LSTM time series forecasting algorithm it is advisable to choose an
appropriate programming language. A suitable language is "python" which offers a series of
libraries optimized for Machine Learning and Data Mining. The main libraries used for the LSTM
implementation are:
• "Numpy": it is a library that adds support for the management of vectors and for large
multidimensional arrays with high level mathematical functions;
• "Matplotlib": it is a library for creating graphs;
• "Seaborn": it is a graphical display library based on matplotlib. It presents a high-level interface
for drawing statistical graphs;
• "Pandas": it is a library that provides structures and tools for data analysis. The heart of the library
are DataFrame objects, ie 2D data structures indexed both on the columns and on the rows. An
object of the DataFrame class can be seen as a SQL table;
• "Scipy": it is an open-source library of mathematical algorithms and tools. It contains modules for
optimization, linear algebra, integration, special functions, Fast Fourier Transform, signal and
image processing and other tools for science and engineering purposes;
• "Scikit-learn": it is an open-source library for machine learning. It contains classification
algorithms, regression, clustering, Support Vector Machine, logistic regression, Bayesian classifier,
k-mean and DBSCAN;
• "Keras": it is a high level API for neural networks and for Deep-Learning. It was developed with
the aim of allowing a rapid experimentation, ie, being able to move from idea to result in the
shortest possible time. Therefore the use of "Keras" allows an easy and fast prototyping, supports
convolutional networks and recurring networks. It also has additional features for parallelizing
processes using GPUs (strictly CUDA).
11. International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), Vol.7, No.4, November 2018
11
Below is reported the python script enabling the importing of the libraries:
import sys
import numpy as np
from scipy.stats import randint
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.cross_validation import KFold
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import MinMaxScaler
from sklearn.pipeline import Pipeline
from sklearn.model_selection import cross_val_score
from sklearn.feature_selection import SelectFromModel
from sklearn import metrics
from sklearn.metrics import mean_squared_error,r2_score
import keras
from keras.layers import Dense
from keras.models import Sequential
from keras.utils import to_categorical
from keras.optimizers import SGD
from keras.callbacks import EarlyStopping
from keras.utils import np_utils
import itertools
from keras.layers import LSTM
from keras.layers.convolutional import Conv1D
from keras.layers.convolutional import MaxPooling1D
from keras.layers import Dropout
the dataset is read by the following script:
df = pd.read_csv('../input/household_power_consumption.txt', sep=';', parse_dates={'dt' : ['Date',
'Time']},
infer_datetime_format=True, low_memory=False, na_values=['nan','?'], index_col='dt')
This operation allows the reading of the dataset and performs a parsing on the "Date" and "Time"
columns: in particular it is set as "datetime" format to allow easier management of the dataset
tuples representing them according to a "time" -series". Another operation carried out is the
conversion of the "missing value" represented as "nan" or "?" And their conversion into "nan" of
the numpy type. However, in order to perform a correct LSTM execution it is important to attribute
a value to them: generally, the "missing value" is generally placed at their corresponding average
value. The following code carries out this assumption and verifies that there are no more "nan"
values.
droping_list_all=[]
for j in range(0,7):
if not df.iloc[:, j].notnull().all():
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droping_list_all.append(j)
droping_list_all
The next code performs the replacement of the value and verifies the absence of "nan":
for j in range(0,7):
df.iloc[:,j]=df.iloc[:,j].fillna(df.iloc[:,j].mean())
df.isnull().sum()
The Kaggle platform [28] has been adopted for the execution of the LSTM algorithm.
3.1 LSTM NEURAL NETWORK PREDICTIVE RESULTS
Before to execute the LSTM algorithm all attributes have been normalized by the following python
script:
scaler = MinMaxScaler(feature_range=(0, 1))
scaled = scaler.fit_transform(values)
Once the features are selected, the normalized dataset is divided into the following two groups:
• "Train dataset" corresponding to the set of patterns used to train the system;
• "Test dataset" corresponding to the set of patterns adopted to evaluate the final
performance of the system.
We chose to train the model on three years of data, and we will test it on the remaining year. The
following code "splits" the normalized data in the two groups of "train" and "test":
# split dati in train e test
values = reframed.values
n_train_time = 365*24*3
train = values[:n_train_time, :]
test = values[n_train_time:, :]
# the train and test data are divided into algorithm inputs and outputs
train_X, train_y = train[:, :-1], train[:, -1]
test_X, test_y = test[:, :-1], test[:, -1]
# the input reshape is done [samples, timestep, features]
train_X = train_X.reshape((train_X.shape[0], 1, train_X.shape[1]))
test_X = test_X.reshape((test_X.shape[0], 1, test_X.shape[1]))
print(train_X.shape, train_y.shape, test_X.shape, test_y.shape)
The LSTM network is characterized as follows:
• 100 neurons number;
• 20% dropout (a random cut of 20% of the input values is made to prevent overfitting);
• the model has been trained according to 20 training periods with a dataset size of 60
samples
• for the evaluation of the error is performed by the "Mean Squarred Error" and the gradient
with the "Adam" optimizer
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Below is listed the code executing LSTM algorithm:
model = Sequential()
model.add(LSTM(100, input_shape=(train_X.shape[1], train_X.shape[2])))
model.add(Dropout(0.2))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')
# fit network
history = model.fit(train_X, train_y, epochs=20, batch_size=60, validation_data=(test_X, test_y),
verbose=2, shuffle=False)
# model training
model = Sequential()
model.add(LSTM(100, input_shape=(train_X.shape[1], train_X.shape[2])))
model.add(Dropout(0.2))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')
# addestramento rete
history = model.fit(train_X, train_y, epochs=20, batch_size=50, validation_data=(test_X, test_y),
verbose=2, shuffle=False)
# prediction
yhat = model.predict(test_X)
test_X = test_X.reshape((test_X.shape[0], 7))
# inversion scale for the forecast
inv_yhat = np.concatenate((yhat, test_X[:, -6:]), axis=1)
inv_yhat = scaler.inverse_transform(inv_yhat)
inv_yhat = inv_yhat[:,0]
# inversion scale current values
test_y = test_y.reshape((len(test_y), 1))
inv_y = np.concatenate((test_y, test_X[:, -6:]), axis=1)
inv_y = scaler.inverse_transform(inv_y)
inv_y = inv_y[:,0]
# Root Mean Square Error evaluation
rmse = np.sqrt(mean_squared_error(inv_y, inv_yhat))
plt.plot(inv_y, label="True Value")
plt.plot(inv_yhat, label="Predicted Value")
plt.legend(['Valore vero', Predicted Value], loc='upper right')
plt.show()
print('Test RMSE: %.3f' % rmse)
In Fig. 10 (a), Fig. 10 (b), Fig. 10 (c), and Fig. 10 (d) are illustrated the output results concerning
the comparison between actual measured value and predicted ones of global active power, for 100,
200, 400 and 1000 samples, respectively. A good matching between actual and predicted results is
observed. In Fig.10 is explained in details how predicted results can be analysed: by focusing the
attention on the first 10 samples and by assuming that are available the first five measurements, the
predicted value will refer to the next predicted day (sample 6 of Fig. 11). By waiting the
measurement of the sixth day will be possible to estimate the prediction error as the difference
between measured value and predicted one. The plot of Fig. 11 is fundamental in order to check the
reliability of the predicted slope behaviour of the global energy. LSTM could be adopted also for
medium and long period prediction.
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(a)
(b)
(c)
(d)
Figure 10. Outputs of the LSTM algorithm: comparison between measured value (actual) and predicted ones
plotted for different number of samples.
The error estimation has been performed by the model loss of Fig. 12 representing loss (the training
loss is the average of the losses over each batch of training data) versus epoch (an epoch is an
iteration on all the data of train; one epoch is when the entire dataset is passed forward and
backward through the neural network only once): from the plot of loss, we can see that the model
has comparable performance on both train and validation datasets (labeled test). In Fig. 12 a good
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fit is observed, where the train and validation loss decrease and stabilize around the same value
after few epochs (the performance of the model is good on both the train and validation sets). The
model loss evaluation has been performed in 21 seconds (train on 8760 samples, validated on
25828 samples for 20 epochs).
Unknow value
Predicted value
Error
Figure 11. Explanation of the predicted global active power and estimation of the prediction error (one
sample refers to a day).
Figure 12. Model loss.
The recurrent neural network LSTM is then indicated for the prediction of electric loads: using a
suitable dataset with high samples it is possible to obtain time forecasts and trends that matching
with real surveys. The use of this algorithm allows to optimize and manage various scenarios such
as:
Activation and deactivation of electrical loads following the predictive assessment of energy
consumption;
Reduction of costs and energy wastage according to the fundamental time slots if a multi-hour
rate is usually divided as follows:
o peak band: this is the band with the highest price, from Monday to Friday from
8:00 to 19:00 and corresponds to the moment of maximum energy demand;
o Mid-range: prices below the peak range and goes from Monday to Friday, from
7:00 to 8:00 and from 19:00 to 23:00, and Saturday, from 7:00 to 23:00: 00;
o Off-peak: it's the lowest price range and goes from Monday to Saturday, from
midnight to 7.00am and from 11.00pm to 12.00pm, including all Sundays and public
holidays. The algorithm is organized on the basis of timestamp so the prediction can be
made also based on the range of hours and days, corresponding with the time slots, instead
of the evaluation based on days, months, quarters and years. All this can be done using the
library function for managing the date pandas "pandas" date_range.
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Prediction of hypothetical electrical faults by evaluating the excessive absorptions by
comparing them with the maximum specifications of the system (for example the system can have
a maximum power of 3000W and a maximum current ~ 13.63A). Therefore considering the
selected dataset, particular attention will be paid to the features "Global_active_power" ie to the
active power of the system and "Global_intensity" which indicates the total current absorbed.
Another strategy is to adopt LSTM by focusing the attention on defined periods: for example if it is
important to predict the energy in the next Monday, you could train the model by only the energy
measurement of all the past Monday. Other scenarios and data interpretation are obtained by the
correlation analysis of attributes.
3.2 CORRELATION MATRIX RESULTS AND FINAL COMMENTS ABOUT WORK RESULTS
By correlation we mean a relationship between two statistical variables such that each value of the
first variable corresponds with a certain regularity a value of the second, that is the tendency of a
variable to vary according to another. The degree of correlation between two variables is expressed
through the so-called "correlation indices". These indices take values between -1 and 1:
• "-1": the variables are considered inversely correlated;
• "1": the variables are considered absolutely correlated ie when the variation of a
variable corresponds to a variation strictly dependent on the other;
• "0": indicates a lack of correlation.
The correlation coefficients are derived from the correlation indexes taking into account the
magnitudes of the deviations from the mean. In particular, the Pearson-Bravais correlation
coefficient is calculated as the ratio between the covariance of the two variables and the product of
their standard deviations:
(1)
The correlation relationships between the main attribute of the experimental dataset have been
estimated by the Konstanz Information Miner (KNIME) “Rank Correlation” algorithm. Calculates
for each pair of selected columns a correlation coefficient, i.e. a measure of the correlation of the
two variables. Below is described the adopted algorithm. All measures are based on the rank of the
cells. The rank of a cell value refers to its position in a sorted list of all entries. All correlation can
be calculated on the attributes load as DataColumn in the local repository. The algorithm estimates
the Spearman's rank correlation coefficient which is a statistical measure of the strength of a
monotonic relationship between paired data. The Spearman correlation coefficient is defined as the
Pearson correlation coefficient between the ranked variables [29], where the monotonic
relationship is characterised by a relationship between ordered sets that preserves the given order,
i.e., either never increases or never decreases as its independent variable increases. The value of
this measure ranges from -1 (strong negative correlation) to 1 (strong positive correlation). A
perfect Spearman correlation of +1 or −1 occurs when each of the variables is a perfect monotone
function of the other. A value of zero indicates the absence of association. In Fig. 13 (a) is
illustrated the KNIME workflow implementing correlation algorithm. Figure 13 (b) and Fig. 13 (c)
show the correlation matrix results. From the matrix is observed a strong correlation between the
outlet 3 (Sub_metering_3) and the outlet 1 (Sub_metering_3) and between the outlet 3 and the
active power, moreover the reactive power is correlated with the outlet 2 (Sub_metering_2):
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according to Fig. 9 (a) the outlet 3 abosrbs the main active energy, besides the loads connected to
outled 2 are unbalanced (this will induce to control the electrical network of outlet 2 in order to
optimize energy costs and to define the best thresholds).
(a) (b)
(c)
Figure 13. KNIME: correlation matrix results.
In table 3 are summarized the advantages and the disadvantages of the whole model proposed in
the paper:
Advantages Disadvantages
Great reliability level of the electrical power
prediction
Priority rules defined on the loads connected
on two electrical outlets (for more complex
load distribution is required a more complex
flowchart implementation)
LSTM model applied on real electrical
consumption and exhibiting high
performance
//
Possibility to predict load consumption on
different windows time (minute, hour, day,
week , month, etc.)
//
Possibility to understand in post processing
modality the failure/malfunction causes (by
means of correlation matrix results)
//
Table 3. Advantages and disadvantages of the features and results of the proposed work.
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In the following table are listed all the main parameters and variables adopted in the proposed
worked:
Parameters/variables Description
Sub_metering_1 Energy absorption of the loads expressed in
Wh (watt hour of active energy) related to
the electrical outlet 1
Sub_metering_2 Energy absorption of the loads expressed in
Wh (watt hour of active energy) related to
the electrical outlet 2
Sub_metering_3 Energy absorption of the loads expressed in
Wh (watt hour of active energy) related to
the electrical outlet 3
Global_active_power Global active power absorbed by all the
electrical outlets, expressed in kW (kilowatt)
Global_reactive_power Global reactive power absorbed by all the
electrical outlets, expressed in kW (kilowatt)
Prediction time window Minute
Table 4. List of parameters and variables used in the model.
The results obtained in the proposed work derive from the comparison and from the application of
the model analyzed in [25]. The proposed work is an extension of the work [25].
4. CONCLUSION
The proposed work shows results of an LSTM neural network addressed on logics for enabling and
disabling loads of a building. The logics are based on the estimation of loads thresholds, on the
global active power prediction and on the comparison with load curve slope prediction. By
applying the LSTM algorithm has been observed a good performance of the model predicting
global active power. Finally correlation analysis provided important information about the real
status of an examined electrical network having loads connected to three intelligent electrical
outlets. The intelligence of the prototype system is the controlling of the total electric power by
means of consumption prediction, by defining priority rules of loads and of electrical outlets, and
by analysing possible unbalanced loads or electrical malfunctions.
ACKNOWLEDGEMENTS
The work has been developed in the frameworks of the industry project: “Sistema MultiPresa/Data
Mining Intelligente orientato al Building Electrical Management ‘Intelligent Electrical Outlets’
[Intelligent Electrical Multi Outlets/Data Mining System oriented to Building Electrical
Management ‘Intelligent Electrical Outlets’]. The authors would like to thank the following
researchers and collaborators: G. Birardi, V. Calati, G. Lonigro, V. Maritati, D. D. Romagno, G.
Ronchi, and G. Sicolo.
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CorrespondingAuthor
Alessandro Massaro: Research & Development Chief of Dyrecta Lab s.r.l.