Daily Peak Load Forecast Using Artificial Neural NetworkIJECEIAES
The paper presents an Artificial Neural Network (ANN) model for short-term load forecasting of daily peak load. A multi-layered feed forward neural network with Levenberg-Marquardt learning algorithm is used because of its good generalizing property and robustness in prediction. The input to the network is in terms of historical daily peak load data and corresponding daily peak temperature data. The network is trained to predict the load requirement ahead. The effectiveness of the proposed ANN approach to the short-term load forecasting problems is demonstrated by practical data from the Bangalore Electricity Supply Company Limited (BESCOM). The comparison between the proposed and the conventional methods is made in terms of percentage error and it is found that the proposed ANN model gives more accurate predictions with optimal number of neurons in the hidden layer.
This paper analyses the optimal power system planning with DGs used as real and reactive power compensator. Recently planning of DG placement reactive power compensation are the major problems in distribution system. As the requirement in the power is more the DG placement becomes important. When planned to make the DG placement, cost analysis becomes as a major concern. And if the DGs operate as reactive power compensator it is most helpful in power quality maintenance. So, this paper deals with the optimal power system planning with renewable DGs which can be used as a reactive power compensators. The problem is formulated and solved using popular meta-heuristic techniques called cuckoo search algorithm (CSA) and particle swarm optimization (PSO). the comparative results are presented.
This research aim to forecast solar radiation,how much of electricity can be produced in next four months in two cities of India and performance evaluation of forecasting models. These models have been used for long-term forecasting of solar radiation using time series data.Forecasting models like ARIMA,TBATS have been used for this research.Forecasted solar radiation is further used for forecasting solar electricity generation.Performance evaluation of forecasting models has also been done.
Performed analysis on Temperature, Wind Speed, Humidity and Pressure data-sets and implemented decision tree & clustering to predict possibility of rain
Created graphs and plots using algorithms such as k-nearest neighbors, naïve bayes, decision tree and k means clustering
Solar Photovoltaic Power Forecasting in Jordan using Artificial Neural NetworksIJECEIAES
In this paper, Artificial Neural Networks (ANNs) are used to study the correlations between solar irradiance and solar photovoltaic (PV) output power which can be used for the development of a real-time prediction model to predict the next day produced power. Solar irradiance records were measured by ASU weather station located on the campus of Applied Science Private University (ASU), Amman, Jordan and the solar PV power outputs were extracted from the installed 264KWp power plant at the university. Intensive training experiments were carried out on 19249 records of data to find the optimum NN configurations and the testing results show excellent overall performance in the prediction of next 24 hours output power in KW reaching a Root Mean Square Error (RMSE) value of 0.0721. This research shows that machine learning algorithms hold some promise for the prediction of power production based on various weather conditions and measures which help in the management of energy flows and the optimisation of integrating PV plants into power systems.
Cuckoo Search Algorithm for Congestion Alleviation with Incorporation of Wind...IJECEIAES
The issue to alleviate congestion in the power system framework has emerged as an alluring field for the power system researchers. The research conducted in this article proposes a cuckoo search algorithm-based congestion alleviation strategy with the incorporation of wind farm. The bus sensitivity factor data are computed and utilized to sort out the sutiable position for the installation of the wind farm. The generators contributing in the real power rescheduleing process are selected as per the generator sensitivity values. The cuckoo search algorithm is implemented to minimize the congestion cost with the embodiment of the wind farm. The proposed method is tested on 39 bus New England framework and the results obtained with the cuckoo search-based congestion management approach outperforms the results opted with other heuristic optimization techniques in the past research literatures.
Daily Peak Load Forecast Using Artificial Neural NetworkIJECEIAES
The paper presents an Artificial Neural Network (ANN) model for short-term load forecasting of daily peak load. A multi-layered feed forward neural network with Levenberg-Marquardt learning algorithm is used because of its good generalizing property and robustness in prediction. The input to the network is in terms of historical daily peak load data and corresponding daily peak temperature data. The network is trained to predict the load requirement ahead. The effectiveness of the proposed ANN approach to the short-term load forecasting problems is demonstrated by practical data from the Bangalore Electricity Supply Company Limited (BESCOM). The comparison between the proposed and the conventional methods is made in terms of percentage error and it is found that the proposed ANN model gives more accurate predictions with optimal number of neurons in the hidden layer.
This paper analyses the optimal power system planning with DGs used as real and reactive power compensator. Recently planning of DG placement reactive power compensation are the major problems in distribution system. As the requirement in the power is more the DG placement becomes important. When planned to make the DG placement, cost analysis becomes as a major concern. And if the DGs operate as reactive power compensator it is most helpful in power quality maintenance. So, this paper deals with the optimal power system planning with renewable DGs which can be used as a reactive power compensators. The problem is formulated and solved using popular meta-heuristic techniques called cuckoo search algorithm (CSA) and particle swarm optimization (PSO). the comparative results are presented.
This research aim to forecast solar radiation,how much of electricity can be produced in next four months in two cities of India and performance evaluation of forecasting models. These models have been used for long-term forecasting of solar radiation using time series data.Forecasting models like ARIMA,TBATS have been used for this research.Forecasted solar radiation is further used for forecasting solar electricity generation.Performance evaluation of forecasting models has also been done.
Performed analysis on Temperature, Wind Speed, Humidity and Pressure data-sets and implemented decision tree & clustering to predict possibility of rain
Created graphs and plots using algorithms such as k-nearest neighbors, naïve bayes, decision tree and k means clustering
Solar Photovoltaic Power Forecasting in Jordan using Artificial Neural NetworksIJECEIAES
In this paper, Artificial Neural Networks (ANNs) are used to study the correlations between solar irradiance and solar photovoltaic (PV) output power which can be used for the development of a real-time prediction model to predict the next day produced power. Solar irradiance records were measured by ASU weather station located on the campus of Applied Science Private University (ASU), Amman, Jordan and the solar PV power outputs were extracted from the installed 264KWp power plant at the university. Intensive training experiments were carried out on 19249 records of data to find the optimum NN configurations and the testing results show excellent overall performance in the prediction of next 24 hours output power in KW reaching a Root Mean Square Error (RMSE) value of 0.0721. This research shows that machine learning algorithms hold some promise for the prediction of power production based on various weather conditions and measures which help in the management of energy flows and the optimisation of integrating PV plants into power systems.
Cuckoo Search Algorithm for Congestion Alleviation with Incorporation of Wind...IJECEIAES
The issue to alleviate congestion in the power system framework has emerged as an alluring field for the power system researchers. The research conducted in this article proposes a cuckoo search algorithm-based congestion alleviation strategy with the incorporation of wind farm. The bus sensitivity factor data are computed and utilized to sort out the sutiable position for the installation of the wind farm. The generators contributing in the real power rescheduleing process are selected as per the generator sensitivity values. The cuckoo search algorithm is implemented to minimize the congestion cost with the embodiment of the wind farm. The proposed method is tested on 39 bus New England framework and the results obtained with the cuckoo search-based congestion management approach outperforms the results opted with other heuristic optimization techniques in the past research literatures.
Electric distribution network reconfiguration for power loss reduction based ...IJECEIAES
This paper proposes a method for solving the distribution network reconfiguration (NR) problem based on runner root algorithm (RRA) for reducing active power loss. The RRA is a recent developed metaheuristic algorithm inspired from runners and roots of plants to search water and minerals. RRA is equipped with four tools for searching the optimal solution. In which, the random jumps and the restart of population are used for exploring and the elite selection and random jumps around the current best solution are used for exploiting. The effectiveness of the RRA is evaluated on the 16 and 69-node system. The obtained results are compared with particle swarm optimization and other methods. The numerical results show that the RRA is the potential method for the NR problem.
FORECASTING OF RENEWABLE ENERGY PRODUCTION BY USING GENETIC ALGORITHM (GA) FO...u772020
My topic of thesis is about constructing prediction model of wind energy and solar energy by artificial neural network with genetic algorithm.
Because the electricity price in Nordic area is determined in advance, it is beneficial to construct prediction model for renewable energy. With the mentioned prediction model, we can construct hybrid system (wind energy and solar energy) to produce hydrogen. Then the fuel cell can produce electricity and connect to the network by using the constructed prediction model. In our prediction model, the critical parameters like 10 minutes average wind speed and deviation for wind energy could be used as historical data to train to artificial neural network (ANN). We also hope to utilize genetic algorithm (GA) to speed up the ANN training and hybrid system scale design.
Implementing Workload Postponing In Cloudsim to Maximize Renewable Energy Uti...IJERA Editor
Green datacenters has become a major research area among researchers in academy and industry. One of the
recent approaches getting higher attention is supplying datacenters with renewable sources of energy, leading to
cleaner and more sustainable datacenters. However, this path poses new challenges. The main problem with
existing renewable energy technologies is high variability, which means high fluctuation of available energy
during different time periods on a day, month or year. In our paper, we address the issue of better managing
datacenter workload in order to achieve higher utilization of available renewable energy. We implement an
algorithm in CloudSim simulator which decides to postpone or urgently run a specific job asking for datacenter
resources, based on job’s deadline and available solar energy. The aim of this algorithm is to make workload
energy consumption through 24 hours match as much as possible the solar energy availability in 24 hours. Two
typical, clear and cloudy days, are taken in consideration for simulation. The results from our experiments show
that, for the chosen workload model, jobs are better managed by postponing or urgently running them, in terms
of leveraging available solar energy. This yields up to 17% higher utilization of daily solar energy
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.
Optimized placement of multiple FACTS devices using PSO and CSA algorithms IJECEIAES
This paper is an attempt to develop a multi-facts device placementin deregulated power system using optimization algorithms. The deregulated power system is the recent need in the power distribution as it has many independent sellers and buyers of electricity. The problem of deregulation is the quality of the power distribution as many sellers are involved. The placement of FACTS devices provides the solution for the above problem. There are researches available for multiple FACTS devices. The optimization algorithms like Particle Swarm Optimization (PSO) and Cuckoo Search Algorithm (CSA) are implemented to place the multiple FACTS devices in a power system. MATLAB based implementation is carried out for applying Optimal Power Flow (OPF) with variation in the bus power and the line reactance parameters. The cost function is used as the objective function. The cost reduction of FACTS as well as generation by placement of different compensators like, Static Var Compensator (SVC), Thyristor Controlled Series Compensator (TCSC) and Unified Power Flow Controller (UPFC). The cost calculation is done on the 3-seller scenario. The IEEE 14 bus is taken here as 3-seller system.
Optimum designing of a transformer considering lay out constraints by penalty...INFOGAIN PUBLICATION
Optimum designing of power electrical equipment and devices play a leading role in attaining optimal performance and price of equipments in electric power industry. Optimum transformer design considering multiple constraints is acquired using optimal determination of geometric parameters of transformer with respect to its magnetic and electric properties. As it is well known, every optimization problem requires an objective function to be minimized. In this paper optimum transformer design problem comprises minimization of transformers mean core mass and its windings by satisfying multiple constraints according to transformers ratings and international standards using a penalty-based method. Hybrid big bang-big crunch algorithm is applied to solve the optimization problem and results are compared to other methods. Proposed method has provided a reliable optimization solution and has guaranteed access to a global optimum. Simulation result indicates that using the proposed algorithm, transformer parameters such as core mass, efficiency and dimensions are remarkably improved. Moreover simulation time using this algorithm is quit less in comparison to other approaches.
A Comparative study on Different ANN Techniques in Wind Speed Forecasting for...IOSRJEEE
There are several available renewable sources of energy, among which Wind Power is the one which is most uncertain in nature. This is because wind speed changes continuously with time leading to uncertainty in availability of amount of wind power generated. Hence, a short-term forecasting of wind speed will help in prior estimation of wind power generation availability for the grid and economic load dispatch.This paper present a comparative study of a Wind speed forecasting model using Artificial Neural Networks (ANN) with three different learning algorithms. ANN is used because it is a non-linear data driven, adaptive and very powerful tool for forecasting purposes. Here an attempt is made to forecast Wind Speed using ANN with Levenberg-Marquard (LM) algorithm, Scaled Conjugate Gradient (SCG) algorithm and Bayesian Regularization (BR) algorithm and their results are compared based on their convergence speed in training period and their performance in testing period on the basis of Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Mean Square Error (MSE).A 48 hour ahead wind speed is forecasted in this work and it is compared with the measured values using all three algorithms and the best out of the three is selected based on minimum error.
Energy Grid Theorem.
«For a stable electrical network without sources and energy storage the value of its streams coincide with the solution of optimization task of energy streams across the network with minimal losses».
This paper proposes a Wavelet based Adaptive Neuro-Fuzzy Inference System (WANFIS) applied to forecast the wind power and enhance the accuracy of one step ahead with a 10 minutes resolution of real time data collected from a wind farm in North India. The proposed method consists two cases. In the first case all the inputs of wind series and output of wind power decomposition coefficients are carried out to predict the wind power. In the second case all the inputs of wind series decomposition coefficients are carried out to get wind power prediction. The performance of proposed WANFIS is compared to Wavelet Neural Network (WNN) and the results of the proposed model are shown superior to compared methods.
Cost Aware Expansion Planning with Renewable DGs using Particle Swarm Optimiz...IJERA Editor
This Paper is an attempt to develop the expansion-planning algorithm using meta heuristics algorithms. Expansion Planning is always needed as the power demand is increasing every now and then. Thus for a better expansion planning the meta heuristic methods are needed. The cost efficient Expansion planning is desired in the proposed work. Recently distributed generation is widely researched to implement in future energy needs as it is pollution free and capability of installing it in rural places. In this paper, optimal distributed generation expansion planning with Particle Swarm Optimization (PSO) and Cuckoo Search Algorithm (CSA) for identifying the location, size and type of distributed generator for future demand is predicted with lowest cost as the constraints. Here the objective function is to minimize the total cost including installation and operating cost of the renewable DGs. MATLAB based `simulation using M-file program is used for the implementation and Indian distribution system is used for testing the results.
Applying of Double Seasonal ARIMA Model for Electrical Power Demand Forecasti...IJECEIAES
The prediction of the use of electric power is very important to maintain a balance between the supply and demand of electric power in the power generation system. Due to a fluctuating of electrical power demand in the electricity load center, an accurate forecasting method is required to maintain the efficiency and reliability of power generation system continuously. Such conditions greatly affect the dynamic stability of power generation systems. The objective of this research is to propose Double Seasonal Autoregressive Integrated Moving Average (DSARIMA) to predict electricity load. Half hourly load data for of three years period at PT. PLN Gresik Indonesia power plant unit are used as case study. The parameters of DSARIMA model are estimated by using least squares method. The result shows that the best model to predict these data is subset DSARIMA with order ( [ 1,2,7,16,18,35,46 ] , 1, [ 1,3,13,21,27,46 ] )( 1,1,1 ) 48 ( 0,0,1 ) 336 with MAPE about 2.06%. Thus, future research could be done by using these predictive results as models of optimal control parameters on the power system side.
Multi-objective based economic environmental dispatch with stochastic solar-w...IJECEIAES
This paper presents an evolutionary based technique for solving the multi-objective based economic environmental dispatch by considering the stochastic behavior of renewable energy resources (RERs). The power system considered in this paper consists of wind and solar photovoltaic (PV) generators along with conventional thermal energy generators. The RERs are environmentally friendlier, but their intermittent nature affects the system operation. Therefore, the system operator should be aware of these operating conditions and schedule the power output from these resources accordingly. In this paper, the proposed EED problem is solved by considering the nonlinear characteristics of thermal generators, such as ramp rate, valve point loading (VPL), and prohibited operating zones (POZs) effects. The stochastic nature of RERs is handled by the probability distribution analysis. The aim of proposed optimization problem is to minimize operating cost and emission levels by satisfying various operational constraints. In this paper, the single objective optimization problems are solved by using particle swarm optimization (PSO) algorithm, and the multi-objective optimization problem is solved by using the multi-objective PSO algorithm. The feasibility of proposed approach is demonstrated on six generator power system.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
IMPROVED NEURAL NETWORK PREDICTION PERFORMANCES OF ELECTRICITY DEMAND: MODIFY...csandit
Accurate prediction of electricity demand can bring extensive benefits to any country as the
forecast values help the relevant authorities to take decisions regarding electricity generation,
transmission and distribution much appropriately. The literature reveals that, when compared
to conventional time series techniques, the improved artificial intelligent approaches provide
better prediction accuracies. However, the accuracy of predictions using intelligent approaches
like neural networks are strongly influenced by the correct selection of inputs and the number of
neuro-forecasters used for prediction. This research shows how a cluster analysis performed to
group similar day types, could contribute towards selecting a better set of neuro-forecasters in
neural networks. Daily total electricity demands for five years were considered for the analysis
and each date was assigned to one of the thirteen day-types, in a Sri Lankan context. As a
stochastic trend could be seen over the years, prior to performing the k-means clustering, the
trend was removed by taking the first difference of the series. Three different clusters were
found using Silhouette plots, and thus three neuro-forecasters were used for predictions. This
paper illustrates the proposed modified neural network procedure using electricity demand
data.
Firefly Algorithm to Opmimal Distribution of Reactive Power Compensation Units IJECEIAES
The issue of electric power grid mode of optimization is one of the basic directions in power engineering research. Currently, methods other than classical optimization methods based on various bio-heuristic algorithms are applied. The problems of reactive power optimization in a power grid using bio-heuristic algorithms are considered. These algorithms allow obtaining more efficient solutions as well as taking into account several criteria. The Firefly algorithm is adapted to optimize the placement of reactive power sources as well as to select their values. A key feature of the proposed modification of the Firefly algorithm is the solution for the multi-objective optimization problem. Algorithms based on a bio-heuristic process can find a neighborhood of global extreme, so a local gradient descent in the neighborhood is applied for a more accurate solution of the problem. Comparison of gradient descent, Firefly algorithm and Firefly algorithm with gradient descent is carried out.
Predict the Average Temperatures of Baghdad City by Used Artificial Neural Ne...IJERA Editor
This paper utilizes artificial neural networks (ANN) technique to improve temperature forecast performance of
Baghdad city. Our study based on Feed Forward Backpropagation Artificial Neural Networks (BPANN)
algorithm of which trained and tested by used a real world daily average temperatures of Bagdad city for ten
years past for months of January and July. Aimed at providing forecasts in a schedule, for all Days of the month
to help the meteorologist to foresee future weather temperature accurately and easily. Forecasts by ANN model
has been compared with the actual results and the realistic output (with IMOS). The results has been Compared
to the practical temperature prediction results, and shows that the BPANN forecasts have accuracy that gave
reasonably very good result and can be considered as a good method for temperature predicting..
Economic/Emission Load Dispatch Using Artificial Bee Colony AlgorithmIDES Editor
This paper presents an application of the
artificial bee colony (ABC) algorithm to multi-objective
optimization problems in power system. A new multiobjective
artificial bee colony (MOABC) algorithm to
solve the economic/ emission dispatch (EED) problem is
proposed in this paper. Non-dominated sorting is
employed to obtain a Pareto optimal set. Moreover, fuzzy
decision theory is employed to extract the best
compromise solution. A numerical result for IEEE 30-bus
test system is presented to demonstrate the capability of
the proposed approach to generate well-distributed
Pareto-optimal solutions of EED problem in one single
run. In addition, the EED problem is also solved using the
weighted sum method using ABC. Results obtained with
the proposed approach are compared with other
techniques available in the literature. Results obtained
show that the proposed MOABC has a great potential in
handling multi-objective optimization problem.
Extraction of photovoltaic generator parameters through combination of an an...IJECEIAES
In the present work, we propose an improved method based on a combination of an analytical and iterative approach to extract the photovoltaic (PV) module parameters using the measured current-voltage characteristics and the simple diode model. First, we calculate the series resistance using a set of analytical formulas for the base values of the three current-voltage curves. Then, the three other parameters are analytically expressed as functions of serial resistance and ideality factor based on the linear least-squares method. Finally, the ideality factor is calculated applying an iterative algorithm to minimize the normalized root mean square error (NRMSE) value. The proposed method was validated with a real experimental set of two PV generators, which showed the best fit to the I-V curve. Moreover, the proposed method needs only the initial value of the ideality factor.
Electric distribution network reconfiguration for power loss reduction based ...IJECEIAES
This paper proposes a method for solving the distribution network reconfiguration (NR) problem based on runner root algorithm (RRA) for reducing active power loss. The RRA is a recent developed metaheuristic algorithm inspired from runners and roots of plants to search water and minerals. RRA is equipped with four tools for searching the optimal solution. In which, the random jumps and the restart of population are used for exploring and the elite selection and random jumps around the current best solution are used for exploiting. The effectiveness of the RRA is evaluated on the 16 and 69-node system. The obtained results are compared with particle swarm optimization and other methods. The numerical results show that the RRA is the potential method for the NR problem.
FORECASTING OF RENEWABLE ENERGY PRODUCTION BY USING GENETIC ALGORITHM (GA) FO...u772020
My topic of thesis is about constructing prediction model of wind energy and solar energy by artificial neural network with genetic algorithm.
Because the electricity price in Nordic area is determined in advance, it is beneficial to construct prediction model for renewable energy. With the mentioned prediction model, we can construct hybrid system (wind energy and solar energy) to produce hydrogen. Then the fuel cell can produce electricity and connect to the network by using the constructed prediction model. In our prediction model, the critical parameters like 10 minutes average wind speed and deviation for wind energy could be used as historical data to train to artificial neural network (ANN). We also hope to utilize genetic algorithm (GA) to speed up the ANN training and hybrid system scale design.
Implementing Workload Postponing In Cloudsim to Maximize Renewable Energy Uti...IJERA Editor
Green datacenters has become a major research area among researchers in academy and industry. One of the
recent approaches getting higher attention is supplying datacenters with renewable sources of energy, leading to
cleaner and more sustainable datacenters. However, this path poses new challenges. The main problem with
existing renewable energy technologies is high variability, which means high fluctuation of available energy
during different time periods on a day, month or year. In our paper, we address the issue of better managing
datacenter workload in order to achieve higher utilization of available renewable energy. We implement an
algorithm in CloudSim simulator which decides to postpone or urgently run a specific job asking for datacenter
resources, based on job’s deadline and available solar energy. The aim of this algorithm is to make workload
energy consumption through 24 hours match as much as possible the solar energy availability in 24 hours. Two
typical, clear and cloudy days, are taken in consideration for simulation. The results from our experiments show
that, for the chosen workload model, jobs are better managed by postponing or urgently running them, in terms
of leveraging available solar energy. This yields up to 17% higher utilization of daily solar energy
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.
Optimized placement of multiple FACTS devices using PSO and CSA algorithms IJECEIAES
This paper is an attempt to develop a multi-facts device placementin deregulated power system using optimization algorithms. The deregulated power system is the recent need in the power distribution as it has many independent sellers and buyers of electricity. The problem of deregulation is the quality of the power distribution as many sellers are involved. The placement of FACTS devices provides the solution for the above problem. There are researches available for multiple FACTS devices. The optimization algorithms like Particle Swarm Optimization (PSO) and Cuckoo Search Algorithm (CSA) are implemented to place the multiple FACTS devices in a power system. MATLAB based implementation is carried out for applying Optimal Power Flow (OPF) with variation in the bus power and the line reactance parameters. The cost function is used as the objective function. The cost reduction of FACTS as well as generation by placement of different compensators like, Static Var Compensator (SVC), Thyristor Controlled Series Compensator (TCSC) and Unified Power Flow Controller (UPFC). The cost calculation is done on the 3-seller scenario. The IEEE 14 bus is taken here as 3-seller system.
Optimum designing of a transformer considering lay out constraints by penalty...INFOGAIN PUBLICATION
Optimum designing of power electrical equipment and devices play a leading role in attaining optimal performance and price of equipments in electric power industry. Optimum transformer design considering multiple constraints is acquired using optimal determination of geometric parameters of transformer with respect to its magnetic and electric properties. As it is well known, every optimization problem requires an objective function to be minimized. In this paper optimum transformer design problem comprises minimization of transformers mean core mass and its windings by satisfying multiple constraints according to transformers ratings and international standards using a penalty-based method. Hybrid big bang-big crunch algorithm is applied to solve the optimization problem and results are compared to other methods. Proposed method has provided a reliable optimization solution and has guaranteed access to a global optimum. Simulation result indicates that using the proposed algorithm, transformer parameters such as core mass, efficiency and dimensions are remarkably improved. Moreover simulation time using this algorithm is quit less in comparison to other approaches.
A Comparative study on Different ANN Techniques in Wind Speed Forecasting for...IOSRJEEE
There are several available renewable sources of energy, among which Wind Power is the one which is most uncertain in nature. This is because wind speed changes continuously with time leading to uncertainty in availability of amount of wind power generated. Hence, a short-term forecasting of wind speed will help in prior estimation of wind power generation availability for the grid and economic load dispatch.This paper present a comparative study of a Wind speed forecasting model using Artificial Neural Networks (ANN) with three different learning algorithms. ANN is used because it is a non-linear data driven, adaptive and very powerful tool for forecasting purposes. Here an attempt is made to forecast Wind Speed using ANN with Levenberg-Marquard (LM) algorithm, Scaled Conjugate Gradient (SCG) algorithm and Bayesian Regularization (BR) algorithm and their results are compared based on their convergence speed in training period and their performance in testing period on the basis of Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Mean Square Error (MSE).A 48 hour ahead wind speed is forecasted in this work and it is compared with the measured values using all three algorithms and the best out of the three is selected based on minimum error.
Energy Grid Theorem.
«For a stable electrical network without sources and energy storage the value of its streams coincide with the solution of optimization task of energy streams across the network with minimal losses».
This paper proposes a Wavelet based Adaptive Neuro-Fuzzy Inference System (WANFIS) applied to forecast the wind power and enhance the accuracy of one step ahead with a 10 minutes resolution of real time data collected from a wind farm in North India. The proposed method consists two cases. In the first case all the inputs of wind series and output of wind power decomposition coefficients are carried out to predict the wind power. In the second case all the inputs of wind series decomposition coefficients are carried out to get wind power prediction. The performance of proposed WANFIS is compared to Wavelet Neural Network (WNN) and the results of the proposed model are shown superior to compared methods.
Cost Aware Expansion Planning with Renewable DGs using Particle Swarm Optimiz...IJERA Editor
This Paper is an attempt to develop the expansion-planning algorithm using meta heuristics algorithms. Expansion Planning is always needed as the power demand is increasing every now and then. Thus for a better expansion planning the meta heuristic methods are needed. The cost efficient Expansion planning is desired in the proposed work. Recently distributed generation is widely researched to implement in future energy needs as it is pollution free and capability of installing it in rural places. In this paper, optimal distributed generation expansion planning with Particle Swarm Optimization (PSO) and Cuckoo Search Algorithm (CSA) for identifying the location, size and type of distributed generator for future demand is predicted with lowest cost as the constraints. Here the objective function is to minimize the total cost including installation and operating cost of the renewable DGs. MATLAB based `simulation using M-file program is used for the implementation and Indian distribution system is used for testing the results.
Applying of Double Seasonal ARIMA Model for Electrical Power Demand Forecasti...IJECEIAES
The prediction of the use of electric power is very important to maintain a balance between the supply and demand of electric power in the power generation system. Due to a fluctuating of electrical power demand in the electricity load center, an accurate forecasting method is required to maintain the efficiency and reliability of power generation system continuously. Such conditions greatly affect the dynamic stability of power generation systems. The objective of this research is to propose Double Seasonal Autoregressive Integrated Moving Average (DSARIMA) to predict electricity load. Half hourly load data for of three years period at PT. PLN Gresik Indonesia power plant unit are used as case study. The parameters of DSARIMA model are estimated by using least squares method. The result shows that the best model to predict these data is subset DSARIMA with order ( [ 1,2,7,16,18,35,46 ] , 1, [ 1,3,13,21,27,46 ] )( 1,1,1 ) 48 ( 0,0,1 ) 336 with MAPE about 2.06%. Thus, future research could be done by using these predictive results as models of optimal control parameters on the power system side.
Multi-objective based economic environmental dispatch with stochastic solar-w...IJECEIAES
This paper presents an evolutionary based technique for solving the multi-objective based economic environmental dispatch by considering the stochastic behavior of renewable energy resources (RERs). The power system considered in this paper consists of wind and solar photovoltaic (PV) generators along with conventional thermal energy generators. The RERs are environmentally friendlier, but their intermittent nature affects the system operation. Therefore, the system operator should be aware of these operating conditions and schedule the power output from these resources accordingly. In this paper, the proposed EED problem is solved by considering the nonlinear characteristics of thermal generators, such as ramp rate, valve point loading (VPL), and prohibited operating zones (POZs) effects. The stochastic nature of RERs is handled by the probability distribution analysis. The aim of proposed optimization problem is to minimize operating cost and emission levels by satisfying various operational constraints. In this paper, the single objective optimization problems are solved by using particle swarm optimization (PSO) algorithm, and the multi-objective optimization problem is solved by using the multi-objective PSO algorithm. The feasibility of proposed approach is demonstrated on six generator power system.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
IMPROVED NEURAL NETWORK PREDICTION PERFORMANCES OF ELECTRICITY DEMAND: MODIFY...csandit
Accurate prediction of electricity demand can bring extensive benefits to any country as the
forecast values help the relevant authorities to take decisions regarding electricity generation,
transmission and distribution much appropriately. The literature reveals that, when compared
to conventional time series techniques, the improved artificial intelligent approaches provide
better prediction accuracies. However, the accuracy of predictions using intelligent approaches
like neural networks are strongly influenced by the correct selection of inputs and the number of
neuro-forecasters used for prediction. This research shows how a cluster analysis performed to
group similar day types, could contribute towards selecting a better set of neuro-forecasters in
neural networks. Daily total electricity demands for five years were considered for the analysis
and each date was assigned to one of the thirteen day-types, in a Sri Lankan context. As a
stochastic trend could be seen over the years, prior to performing the k-means clustering, the
trend was removed by taking the first difference of the series. Three different clusters were
found using Silhouette plots, and thus three neuro-forecasters were used for predictions. This
paper illustrates the proposed modified neural network procedure using electricity demand
data.
Firefly Algorithm to Opmimal Distribution of Reactive Power Compensation Units IJECEIAES
The issue of electric power grid mode of optimization is one of the basic directions in power engineering research. Currently, methods other than classical optimization methods based on various bio-heuristic algorithms are applied. The problems of reactive power optimization in a power grid using bio-heuristic algorithms are considered. These algorithms allow obtaining more efficient solutions as well as taking into account several criteria. The Firefly algorithm is adapted to optimize the placement of reactive power sources as well as to select their values. A key feature of the proposed modification of the Firefly algorithm is the solution for the multi-objective optimization problem. Algorithms based on a bio-heuristic process can find a neighborhood of global extreme, so a local gradient descent in the neighborhood is applied for a more accurate solution of the problem. Comparison of gradient descent, Firefly algorithm and Firefly algorithm with gradient descent is carried out.
Predict the Average Temperatures of Baghdad City by Used Artificial Neural Ne...IJERA Editor
This paper utilizes artificial neural networks (ANN) technique to improve temperature forecast performance of
Baghdad city. Our study based on Feed Forward Backpropagation Artificial Neural Networks (BPANN)
algorithm of which trained and tested by used a real world daily average temperatures of Bagdad city for ten
years past for months of January and July. Aimed at providing forecasts in a schedule, for all Days of the month
to help the meteorologist to foresee future weather temperature accurately and easily. Forecasts by ANN model
has been compared with the actual results and the realistic output (with IMOS). The results has been Compared
to the practical temperature prediction results, and shows that the BPANN forecasts have accuracy that gave
reasonably very good result and can be considered as a good method for temperature predicting..
Economic/Emission Load Dispatch Using Artificial Bee Colony AlgorithmIDES Editor
This paper presents an application of the
artificial bee colony (ABC) algorithm to multi-objective
optimization problems in power system. A new multiobjective
artificial bee colony (MOABC) algorithm to
solve the economic/ emission dispatch (EED) problem is
proposed in this paper. Non-dominated sorting is
employed to obtain a Pareto optimal set. Moreover, fuzzy
decision theory is employed to extract the best
compromise solution. A numerical result for IEEE 30-bus
test system is presented to demonstrate the capability of
the proposed approach to generate well-distributed
Pareto-optimal solutions of EED problem in one single
run. In addition, the EED problem is also solved using the
weighted sum method using ABC. Results obtained with
the proposed approach are compared with other
techniques available in the literature. Results obtained
show that the proposed MOABC has a great potential in
handling multi-objective optimization problem.
Extraction of photovoltaic generator parameters through combination of an an...IJECEIAES
In the present work, we propose an improved method based on a combination of an analytical and iterative approach to extract the photovoltaic (PV) module parameters using the measured current-voltage characteristics and the simple diode model. First, we calculate the series resistance using a set of analytical formulas for the base values of the three current-voltage curves. Then, the three other parameters are analytically expressed as functions of serial resistance and ideality factor based on the linear least-squares method. Finally, the ideality factor is calculated applying an iterative algorithm to minimize the normalized root mean square error (NRMSE) value. The proposed method was validated with a real experimental set of two PV generators, which showed the best fit to the I-V curve. Moreover, the proposed method needs only the initial value of the ideality factor.
Advance Data Mining - Analysis and forecasting of power factor for optimum el...Shrikant Samarth
Task: Execute a research project using data mining techniques
Approach: The topic chosen was ‘Analysis and Forecasting of Power Factor for Optimum Electric Consumption in a Household.’ Research question – What can be the best short term range of forecast for power factor patterns so that optimum energy consumption can be achieved for a household?
To answer the question, CRISM- DM method was used. The ARIMA machine learning model was developed using R.
Findings: The best short term range of forecasts for the power factor was achieved for 6 months and 12 months duration using the ARIMA model. The MAPE value for the ARIMA model was around 1.83.
Tools: Rstudio
ESTIMATION OF THE PARAMETERS OF SOLAR CELLS FROM CURRENT-VOLTAGE CHARACTERIST...ijscai
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 in 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 current-voltage characteristics
well.
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
ESTIMATION OF THE PARAMETERS OF SOLAR CELLS FROM CURRENT-VOLTAGE CHARACTERIST...ijscai
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 in 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 current-voltage characteristics
well.
Dynamic Economic Dispatch Assessment Using Particle Swarm Optimization TechniquejournalBEEI
This paper presents the application of particle swarm optimization (PSO) technique for solving the dynamic economic dispatch (DED) problem. The DED is one of the main functions in power system planning in order to obtain optimum power system operation and control. It determines the optimal operation of generating units at every predicted load demands over a certain period of time. The optimum operation of generating units is obtained by referring to the minimum total generation cost while the system is operating within its limits. The DED based PSO technique is tested on a 9-bus system containing of three generator bus, six load bus and twelve transmission lines.
Valuation and Determination of Seven and Five Parameters of Photovoltaic Gene...Ah Roueiha
The mathematical modeling of solar cells is essential for any optimization operation of the efficiency or the diagnosis of photovoltaic generator. The photovoltaic module is generally represented by an equivalent circuit whose parameters are experimentally calculated by using the characteristic current-tension, I-V. The precise determination of these parameters stays a challenge for the researchers, making to a big difference in the models and the digital methods dedicated to their characterizations. In the present paper, We are interested to characterize the parameters of single diode and two diodes models, in order to plan the behavior of the photovoltaic generator under real functioning conditions. We developed an identification method of the parameters using Newton Raphson method by using the software Matlab/Simulink. This method is the faster technique which allows the identification of several parameters and can be used in real time applications. The results of the proposed method show an accordance with the experimental and simulated characteristics of photovoltaic generator.
Short-term load forecasting with using multiple linear regression IJECEIAES
In this paper short term load forecasting (STLF) is done with using multiple linear regression (MLR). A day ahead load forecasting is obtained in this paper. Regression coefficients were found out with the help of method of least square estimation. Load in electrical power system is dependent on temperature, due point and seasons and also load has correlation to the previous load consumption (Historical data). So the input variables are temperature, due point, load of prior day, hours, and load of prior week. To validate the model or check the accuracy of the model mean absolute percentage error is used and R squared is checked which is shown in result section. Using day ahead forecasted data weekly forecast is also obtained.
Advanced SOM & K Mean Method for Load Curve Clustering IJECEIAES
From the load curve classification for one customer, the main features such as the seasonal factors, the weekday factors influencing on the electricity consumption may be extracted. By this way some utilities can make decision on the tariff by seasons or by day in week. The popular clustering techniques are the SOM & K-mean or Fuzzy K-mean. SOM &Kmean is a prominent approach for clustering with a two-level approach: first, the data set will be clustered using the SOM and in the second level, the SOM will be clustered by K-mean. In the first level, two training algorithms were examined: sequential and batch training. For the second level, the K-mean has the results that are strongly depended on the initial values of the centers. To overcome this, this paper used the subtractive clustering approach proposed by Chiu in 1994 to determine the centers. Because the effective radius in Chiu’s method has some influence on the number of centers, the paper applied the PSO technique to find the optimum radius. To valid the proposed approach, the test on well-known data samples is carried out. The applications for daily load curves of one Southern utility are presented.
Optimal artificial neural network configurations for hourly solar irradiation...IJECEIAES
Solar energy is widely used in order to generate clean electric energy. However, due to its intermittent nature, this resource is only inserted in a limited way within the electrical networks. To increase the share of solar energy in the energy balance and allow better management of its production, it is necessary to know precisely the available solar potential at a fine time step to take into account all these stochastic variations. In this paper, a comparison between different artificial neural network (ANN) configurations is elaborated to estimate the hourly solar irradiation. An investigation of the optimal neurons and layers is investigated. To this end, feedforward neural network, cascade forward neural network and fitting neural network have been applied for this purpose. In this context, we have used different meteorological parameters to estimate the hourly global solar irirradiation in the region of Laghouat, Algeria. The validation process shows that choosing the cascade forward neural network two inputs gives an R2 value equal to 97.24% and an normalized root mean square error (NRMSE) equals to 0.1678 compared to the results of three inputs, which gives an R2 value equaled to 95.54% and an NRMSE equals to 0.2252. The comparison between different existing methods in literature show the goodness of the proposed models.
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Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
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• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
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Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
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Khaled eskaf presentation predicting power consumption using genetic algorithm
1. Predicting the Short Term
Electrical Energy Consumption
using
Dynamic Model
and
Genetic Algorithm
Prof. Dr. Ibrahim El-Mohr
Dr. Khaled Eskaf
Arab Academy for Science, Technology and Maritime
Transport
2. Presentation Outline:
2- Electrical Energy Consumption as a Dynamic System.
3- Database of Electrical Energy consumption for AAST-Syria.
4- Prediction the short term Electrical Energy Consumption using
Genetic Algorithm Technique.
5- Online Learning Procedure.
6- Comparison with other researchers results.
1- Introduction and define the problem.
9. 2
[1] M. Chan, D. Estève, C. Escriba, E. Campo, "A review of smart homes present state and
future challenges", Computer Methods and Programs in Biomedicine 9 (1) (2008).
[2] C.D. Nugent, D.D. Finlay, P. Fiorini, Y. Tsumaki, E. Prassler, "Editorial home automation as a
means of independent living", IEEE Transactions on Automation Science and Engineering 5 (1)
(2008).
[3] R. Priyadarsini, W. Xuchao, L. SiewEang, "A study on energy performance of hotel
buildings in Singapore", Energy and Buildings ,41 (12) (2009).
[4] K. Kawamoto, Y. Shimoda, M. Mizuno, "Energy saving potential of office equipment
power management", Energy and Buildings .36 (9) (2004).
[5] M.S. Hatamipour, H. Mahiyar, M. Taheri, "Evaluation of existing cooling systems for
reducing cooling power consumption", Energy and Buildings 39 (1) (2007).
[6] J.A. Clarke, J. Cockroft, S. Conner, J.W. Hand, N.J. Kelly, R. Moore, T. O’Brien, P. Strachan,
"Simulation-assisted control in building energy management systems", Energy and Buildings
, 34 (9) (2002).
10. [8] A.-P. Wang, P.-L.Hs, "The network-based energy management system for convenience
stores", Energy and Buildings ,40 (8) (2008).
[9] Domínguez, M., Reguera, P., Fuertes, J. J., Díaz, I. and Cuadrado, A. A., “Internet-based
remote monitoring of industrial processes using Self-organizing maps”, Engineering
Applications of Artificial Intelligence, (2007).
[11] Ming Meng ,DongxiaoNiu and Wei Sun, “Forecasting Monthly Electric Energy
Consumption Using Feature Extraction”, Energies journal (2011).
11. Number of air conditions
Number of heater
Number of working hours
answers
(Input Vector)
Asking some questions
PredictionAlgorithm
Number ……………
13. Feature extraction procedures were implemented
(Dynamic Model) on Electrical Energy Consumption time series,
in order to extract a knowledge (how Electrical Energy Consumption will change)
Genetic Algorithm was then used these features in order
to predict the future value of Electrical Energy Consumption
with an accepted accuracy.
So our project is not involved in asking questions
15. Objective:
2- Expect the future value of Electrical energy consumption
after 6 months or more from the current value.
1- Try to extract a knowledge from the time series of monthly
electrical energy consumption.
19. With the homogeneous equation :
the traditional approach is to assume a solution of the form :
where s is a constant. Upon substitution into the differential equation, we obtain :
which is satisfied for all values of t when
The previous equation which is known as the characteristic equation, has two roots :
Hence, the general solution is given by the equation:
where A and B are constants to be evaluated from the initial conditions.
Mathematical Model
21. F ( increase Electrical Energy
Consumption )
B ( decrease Electrical
Energy Consumption )
22. Impulse force (External force)
Electrical Energy
Consumption per-monthNatural frequencyDamping factor
Impulse force takes the form of the dirac delta function.
Mathematical Equation
23. Solution of the equation:
where
Is the frequency of the system.
24. From the previous equation (2),
I can evaluate the value of impulse force F(t), if the electric consumption Values
are given.
In other word I can evaluate F(t) without asking any questions
(external force)
I can extract a knowledge from the electrical energy consumption
values time series
26. Sampling Frequency 1
month
month Kwh per-
month
1
2
3
4
5
We will record the electricity power consumption for 12 months,
(sampling frequency 1 month) .
1
32. KWhKWhKWhKWhKWhKWhKWhKWhKWhKWhKWhKWh
F
Duration=12 month, sampling frequency= 1 month
KWhe
KWha
After 6 month
Twelve samples
B
We will determine the Fc (the changing factor of KWh),
that affect KWh at the end of period (according to the value of F and B)
100*
KWhe
KWheKWha
Fc
5
Determine Changing Factor (Fc)
36. Structure of Genetic Algorithm:
Population Representations:
Chromosomes are represented as follows:
F, B and Slopes of KWh Values. As shown in the following Figure.
Slope11Slope10Slope9Slope8Slope7Slope6Slope5Slope4Slope3Slope2Slope1BF
1.61.41.41.20.80.60.40.20-0.4-1.20.003260.85999
Population Size remains constant from generation to generation
(but it will increase as a result of new cases will appear).
37. Reproduction (survival of the fittest):
Parents are SELECTED for REPRODUCTION biased on the fitness function.
Consider the fitness function:
X=Xs – Xd .
X-ERROR < X<X+ERROR.
Where:
Xs= selected vector of Feature (F, B and Slopes of KWh).
Xd= desired vector of Feature (F, B and Slopes of KWh).
ERROR =Accepted error of values.
38. Crossover:
Using a heuristic crossover: Heuristic creates
children that lie on the line containing the two
parents, a small distance away from the parent with
the better fitness value in the direction away from
the parent with the worse fitness value.
Mutation:
Using a Gaussian function: Gaussian adds a
random number to each vector entry of an
individual. This random number is taken from a
Gaussian distribution centered on zero. The
variance of this distribution can be controlled
with two parameters.
42. Furthermore in the training phase the system is capable to learn any new cases,
which that mean, the system will keep learning any new features (new F ,B, ,slopes and Fc).
On Line Learning Procedure
43. Comparing performance
The Root-Mean-Square-Error (RMSE) is given by
Where Xp(t+6) is the output of the Genetic Algorithm,
X(t+6) the target output (the measured value) and
N the number of samples.
The leave-one-out cross-validation technique was used.
The range of root-mean-square error from 5X10-5 to 1X10-4 Kilowatt hour (kWh).