This document describes using particle swarm optimization (PSO) to estimate Turkey's future energy demand based on economic indicators like GDP, population, imports, and exports. PSO is applied to both linear and quadratic models to estimate energy demand. The linear and quadratic models are trained on 1979-2005 Turkish data and validated on 1996-2005 data, showing maximum relative errors of 3.37% and -2.38%, respectively. The quadratic model provided a better fit to the economic indicator fluctuations. Overall, the PSO approach proved robust and successful for energy demand estimation in Turkey.
Particle Swarm Optimization Approach for Estimation of Energy Demand of TurkeySSA KPI
This document discusses using a particle swarm optimization (PSO) approach to estimate energy demand in Turkey. It provides background on Turkey's growing energy needs and reliance on imports to meet demand. The literature review summarizes past studies that have used various statistical and meta-heuristic models like genetic algorithms and artificial neural networks to forecast Turkey's energy consumption. The goal of this study is to develop a more accurate PSO model for estimating Turkey's energy demand.
Comparison of Solar Radiation Intensity Forecasting Using ANFIS and Multiple ...journalBEEI
This document compares the performance of two methods for forecasting solar radiation intensity: Adaptive Neuro Fuzzy Inference System (ANFIS) and Multiple Linear Regression (MLR). It uses weather data from Basel, Switzerland to test the methods. The ANFIS method uses a fuzzy inference system combined with neural networks, while MLR uses a mathematical approach. The performance of both methods is evaluated using root mean square error (RMSE) and mean absolute error (MAE) across different training/testing data compositions and time periods. The results show that ANFIS consistently provides lower error values than MLR, indicating it provides more accurate solar radiation forecasts.
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.
This article represents results of an unbiased, factual, and scientifically valid analysis
of all available data on ecological, economic, and social indicators of energy
technologies and of how they influence sustainable development indicators. It marks out
indicators characterizing the impact of energy technologies on the environment providing
specific values to all energy sources considered (coal, gas, hydro, wind, solar, and
nuclear). The article demonstrates that renewable energy sources and nuclear power are
characterized by the best ecological indicators. The article also reveals that the most
efficient energy technologies for promoting sustainable development are natural gas and
nuclear power.
Prediction of the Power Output of Solar Cells Using Neural Networks: Solar Ce...CSCJournals
The prediction of the output power of solar cells in a given place has always been an important factor in planning the installation of solar cell panels, and guiding electrical companies to control, manage and distribute the energy into their electricity networks properly. The production of the electricity sector in Palestine using solar cells is a promising sector; this paper proposes a model which is used to predict future output power values of solar cells, which provides individuals and companies with future information, so they can organize their activities. We aim to create a model that able to connect time, place, and the relations between randomly distributed solar energy units. The system analyzes collected data from units through solar cells distributed in different places in Palestine. Multilayer Feed-Forward with Backpropagation Neural Networks (MFFNNBP) is used to predict the power output of the solar cells in different places in Palestine. The model depends on predicting the future produce of the power output of solar cell depending on the real power output of the previous values. The data used in this paper depends on data collection of one day, month, and year. Finally, this proposed model conduct a systematic process with the aim of determining the most suitable places for an installation solar cell panel in different places in Palestine.
Reviewing the factors of the Renewable Energy systems for Improving the Energ...IJERA Editor
Electricity demand around the globe has increased alarmingly and is increasing at high rates. Therefore,
electricity supply by the conventional resources is not sufficient right now and the generation of electricity by
these resources is causing pollution worldwide. As the recent world is moving towards the alternative and
renewable resources of energy that include sun, wind, water, and air. This paper focuses on reviewing the
renewable energy sources used to improve the energy efficiency. This paper presents how the maximum power
generation capacity can be achieved using these sources. Main focus of this paper is on solar and wind power
that is freely available all around the globe. This paper concludes that there are certain factors that should be
considered while generating power from these sources. The factors include the calculation of radiation data,
storage size and capacity calculation, and geographic dispersion of the plants.
Unit Commitment Problem in Electrical Power System: A Literature Review IJECEIAES
Unit commitment (UC) is a popular problem in electric power system that aims at minimizing the total cost of power generation in a specific period, by defining an adequate scheduling of the generating units. The UC solution must respect many operational constraints. In the past half century, there was several researches treated the UC problem. Many works have proposed new formulations to the UC problem, others have offered several methodologies and techniques to solve the problem. This paper gives a literature review of UC problem, its mathematical formulation, methods for solving it and Different approaches developed for addressing renewable energy effects and uncertainties.
A Review on the State of the Energy Sector of Turkey from the Persective of O...SSA KPI
AACIMP 2010 Summer School lecture by Gerhard Wilhelm Weber. "Applied Mathematics" stream. "Modern Operational Research and Its Mathematical Methods with a Focus on Financial Mathematics" course. Part 9.
More info at http://summerschool.ssa.org.ua
Particle Swarm Optimization Approach for Estimation of Energy Demand of TurkeySSA KPI
This document discusses using a particle swarm optimization (PSO) approach to estimate energy demand in Turkey. It provides background on Turkey's growing energy needs and reliance on imports to meet demand. The literature review summarizes past studies that have used various statistical and meta-heuristic models like genetic algorithms and artificial neural networks to forecast Turkey's energy consumption. The goal of this study is to develop a more accurate PSO model for estimating Turkey's energy demand.
Comparison of Solar Radiation Intensity Forecasting Using ANFIS and Multiple ...journalBEEI
This document compares the performance of two methods for forecasting solar radiation intensity: Adaptive Neuro Fuzzy Inference System (ANFIS) and Multiple Linear Regression (MLR). It uses weather data from Basel, Switzerland to test the methods. The ANFIS method uses a fuzzy inference system combined with neural networks, while MLR uses a mathematical approach. The performance of both methods is evaluated using root mean square error (RMSE) and mean absolute error (MAE) across different training/testing data compositions and time periods. The results show that ANFIS consistently provides lower error values than MLR, indicating it provides more accurate solar radiation forecasts.
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.
This article represents results of an unbiased, factual, and scientifically valid analysis
of all available data on ecological, economic, and social indicators of energy
technologies and of how they influence sustainable development indicators. It marks out
indicators characterizing the impact of energy technologies on the environment providing
specific values to all energy sources considered (coal, gas, hydro, wind, solar, and
nuclear). The article demonstrates that renewable energy sources and nuclear power are
characterized by the best ecological indicators. The article also reveals that the most
efficient energy technologies for promoting sustainable development are natural gas and
nuclear power.
Prediction of the Power Output of Solar Cells Using Neural Networks: Solar Ce...CSCJournals
The prediction of the output power of solar cells in a given place has always been an important factor in planning the installation of solar cell panels, and guiding electrical companies to control, manage and distribute the energy into their electricity networks properly. The production of the electricity sector in Palestine using solar cells is a promising sector; this paper proposes a model which is used to predict future output power values of solar cells, which provides individuals and companies with future information, so they can organize their activities. We aim to create a model that able to connect time, place, and the relations between randomly distributed solar energy units. The system analyzes collected data from units through solar cells distributed in different places in Palestine. Multilayer Feed-Forward with Backpropagation Neural Networks (MFFNNBP) is used to predict the power output of the solar cells in different places in Palestine. The model depends on predicting the future produce of the power output of solar cell depending on the real power output of the previous values. The data used in this paper depends on data collection of one day, month, and year. Finally, this proposed model conduct a systematic process with the aim of determining the most suitable places for an installation solar cell panel in different places in Palestine.
Reviewing the factors of the Renewable Energy systems for Improving the Energ...IJERA Editor
Electricity demand around the globe has increased alarmingly and is increasing at high rates. Therefore,
electricity supply by the conventional resources is not sufficient right now and the generation of electricity by
these resources is causing pollution worldwide. As the recent world is moving towards the alternative and
renewable resources of energy that include sun, wind, water, and air. This paper focuses on reviewing the
renewable energy sources used to improve the energy efficiency. This paper presents how the maximum power
generation capacity can be achieved using these sources. Main focus of this paper is on solar and wind power
that is freely available all around the globe. This paper concludes that there are certain factors that should be
considered while generating power from these sources. The factors include the calculation of radiation data,
storage size and capacity calculation, and geographic dispersion of the plants.
Unit Commitment Problem in Electrical Power System: A Literature Review IJECEIAES
Unit commitment (UC) is a popular problem in electric power system that aims at minimizing the total cost of power generation in a specific period, by defining an adequate scheduling of the generating units. The UC solution must respect many operational constraints. In the past half century, there was several researches treated the UC problem. Many works have proposed new formulations to the UC problem, others have offered several methodologies and techniques to solve the problem. This paper gives a literature review of UC problem, its mathematical formulation, methods for solving it and Different approaches developed for addressing renewable energy effects and uncertainties.
A Review on the State of the Energy Sector of Turkey from the Persective of O...SSA KPI
AACIMP 2010 Summer School lecture by Gerhard Wilhelm Weber. "Applied Mathematics" stream. "Modern Operational Research and Its Mathematical Methods with a Focus on Financial Mathematics" course. Part 9.
More info at http://summerschool.ssa.org.ua
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology
International Journal of Engineering Research and DevelopmentIJERD Editor
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
Optimal design of adaptive power scheduling using modified ant colony optimi...IJECEIAES
For generating and distributing an economic load scheduling approach, artificial neural network (ANN) has been introduced, because power generation and power consumption are economically non-identical. An efficient load scheduling method is suggested in this paper. Normally the power generation system fails due to its instability at peak load time. Traditionally, load shedding process is used in which low priority loads are disconnected from sources. The proposed method handles this problem by scheduling the load based on the power requirements. In many countries the power systems are facing limitations of energy. An efficient optimization algorithm is used to periodically schedule the load demand and the generation. Ant colony optimization (ACO) based ANN is used for this optimal load scheduling process. The present work analyse the technical economical and time-dependent limitations. Also the works meets the demanded load with minimum cost of energy. Inorder to train ANN back propagation (BP) technics is used. A hybrid training process is described in this work. Global optimization algorithms are used to provide back propagation with good initial connection weights.
Economic viability and profitability assessments of WECS IJECEIAES
The document discusses economic viability and profitability assessments of different types of wind energy conversion systems (WECS). It proposes simulating four WECS models using HOMER software to determine the optimal configuration based on costs. The models vary the wind turbine size and presence of a rectifier. The software calculates technical parameters and costs including net present cost, cost of energy, and cash flows to assess which system maximizes profitability based on metrics like net present value, internal rate of return, and payback period.
A Post-processing Approach for Solar Power Combined Forecasts of Ramp EventsMohamed Abuella
This dissertation applies a post-processing approach to improve combined forecasts of solar power and solar power ramp events. The approach combines different forecasting models, then adjusts the combined forecasts to better predict ramp events. It develops a classification system for ramp event thresholds and evaluates performance using customized metrics. The approach provides probabilistic forecasts of solar power ramp events with uncertainty analysis.
The study of reducing the cost of investment in wind energy based on the cat ...TELKOMNIKA JOURNAL
Wind and solar are the most important source of renewable energy for power supply in remote locations involves serious consideration of the reliability of these unconventional energy sources. We apply the cat swarm meta-heuristic optimization method to solve the problem of wind power system design optimization. The electrical power components of the system are characterized by their cost, capacity and reliability. This study seeks to optimize the design of parallel power systems in which multiple choices of generators wind, transformers and lines. Our plan has the advantage of allowing electrical components with different parameters to be customized in electrical power systems. The UMGF method is applied to allow rapid reliability estimation. A computer program is developed for the UMGF application and CS algorithm. An example is provided to explain.
Hourly probabilistic solar power forecasts 3vMohamed Abuella
This document summarizes a presentation on hourly probabilistic solar power forecasting. It discusses:
1) The need for solar power forecasting to address the variability of solar generation. Various forecasting models and horizons are used.
2) The combined physical and statistical approach used to generate solar power forecasts, which includes solar plant modeling, numerical weather prediction, and statistical corrections.
3) The models evaluated include multiple linear regression, artificial neural networks, and support vector regression. Ensemble learning with random forests is used to combine the models' outputs.
4) Probabilistic forecasting methods evaluated include ensemble-based, analog ensemble, and persistence approaches. Different probability distributions are generated.
5)
Stochastic control for optimal power flow in islanded microgridIJECEIAES
The problem of optimal power flow (OPF) in an islanded mircrogrid (MG) for hybrid power system is described. Clearly, it deals with a formulation of an analytical control model for OPF. The MG consists of wind turbine generator, photovoltaic generator, and diesel engine generator (DEG), and is in stochastic environment such as load change, wind power fluctuation, and sun irradiation power disturbance. In fact, the DEG fails and is repaired at random times so that the MG can significantly influence the power flow, and the power flow control faces the main difficulty that how to maintain the balance of power flow? The solution is that a DEG needs to be scheduled. The objective of the control problem is to find the DEG output power by minimizing the total cost of energy. Adopting the Rishel’s famework and using the Bellman principle, the optimality conditions obtained satisfy the Hamilton-Jacobi-Bellman equation. Finally, numerical examples and sensitivity analyses are included to illustrate the importance and effectiveness of the proposed model.
This document summarizes a study that examines the relationship between oil consumption and economic growth in Turkey from 1961 to 2016. It begins by reviewing previous literature on the relationship between energy use and economic growth, which has found varying results. It then describes the data and methodology used in the study, which employs autoregressive distributed lag (ARDL) modeling and Toda-Yamamoto causality testing to analyze the cointegration and causality between oil consumption and GDP in Turkey while accounting for structural breaks. The results of these analyses reveal that economic growth Granger causes oil consumption in Turkey but not vice versa, indicating that energy conservation policies would not harm economic growth.
Short term load forecasting system based on support vector kernel methodsijcsit
Load Forecasting is powerful tool to make important decisions such as to purchase and generate the
electric power, load switching, development plans and energy supply according to the demand. The
important factors for forecasting involve short, medium and long term forecasting. Factors in short term
forecasting comprises of whether data, customer classes, working, non-working days and special event
data, while long term forecasting involves historical data, population growth, economic development and
different categories of customers.In this paper we have analyzed the load forecasting data collected from
one grid that contain the load demands for day and night, special events, working and non-working days
and different hours in day. We have analyzed the results using Machine Learning techniques, 10 fold cross
validation and stratified CV. The Machines Learning techniques used are LDA, QDA, SVM Polynomial,
Gaussian, HRBF, MQ kernels as well as LDA and QDA. The errors methods employed against the
techniques are RSE, MSE, RE and MAPE as presented in the table 2 below. The result calculated using the
SVM kernel shows that SVM MQ gives the highest performance of 99.53 %.
This document summarizes an article that compares two optimization methods, NSGA-II and MOPSO, for sizing hybrid renewable energy systems. The article models a hybrid system with photovoltaic panels, wind turbines, and battery storage. It formulates the sizing of these components as a multi-objective optimization problem to minimize loss of power supply probability and cost of energy. The article simulates the system using two optimization methods and compares their accuracy and computation time in finding the optimal system configuration.
Design and implementation of smart electronic solar tracker based on ArduinoTELKOMNIKA JOURNAL
Demand of energy increases in the global and exponential exhaustion is favored of resources by
fossil fuel for electricity production with the new systems development. Compared with all other remainder
energies, the specialist sun energy is the most bountiful energy and it's typically easy to be changed into
electrical energy. The main thing of using solar panel is to produce electrical energy from sun's energy but
the optimum energy can be generated by tracking solar panel due to the sun movement from east to west.
The problem can be solved by proposed systems where the sun tracking by solar panel that based on high
intensity of sun ray. This paper concentrates on tracking the sun by using servo motor coupled with solar
panel. So that, the largest quantity of sun light at the incident panel along the day at any time is better than
that for method of fixed panel array which is less efficient. The microcontroller Arduino (mode UNO) was
programmed by using C++ language while the track of sun light processing was implemented by using
light depending resistor (LDR), Chip IC H-bridge and microcontroller Arduino (UNO) circuits have been
designed by using Proteus software. By circuit design and sun tracking control process, the cost reduction
has been improved and high amount of energy was saved when implemented this system.
Impact of compressed air energy storage system into diesel power plant with w...IJECEIAES
The wind energy plays an important role in power system because of its renewable, clean and free energy. However, the penetration of wind power (WP) into the power grid system (PGS) requires an efficient energy storage systems (ESS). compressed air energy storage (CAES) system is one of the most ESS technologies which can alleviate the intermittent nature of the renewable energy sources (RES). Nyala city power plant in Sudan has been chosen as a case study because the power supply by the existing power plant is expensive due to high costs for fuel transport and the reliability of power supply is low due to uncertain fuel provision. This paper presents a formulation of security-constrained unit commitment (SCUC) of diesel power plant (DPP) with the integration of CAES and PW. The optimization problem is modeled and coded in MATLAB which solved with solver GORUBI 8.0. The results show that the proposed model is suitable for integration of renewable energy sources (RES) into PGS with ESS and helpful in power system operation management.
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.
Estimation of wind power density with artificial neural networkmehmet şahin
Industry and technology are rapidly developing with each passing day. They need energy to sustain this evolution. The demand of energy is mainly provided from fossil fuels. Unfortunately, this kind of energy reserves are consumed away day by day. Therefore, there is a need to use alternative energy sources to supply energy needs. Alternative energy sources can be listed as; solar, wind, wave, biomass, geothermal and hydro-electric power. Our country has significant potential for wind energy. Wind power density estimation is required to determine the wind potential. In this study, the wind power density was estimated by using artificial neural network (ANN) method. Forty meteorological stations were used for ANN training, while eighteen meteorological stations were used to test the trained network. Network has trained according to, respectively; trainlm, trainbfg, trainscg, traincgp traincgb, traincgf ve trainoss learning algorithms. The correlation coefficient (R) and Mean bias error (MBE) of the best developed model were calculated as 0,9767 and -0,3124 W/m2 respectively. Root Mean Square Error (RMSE) was calculated as 1,4786 W/m2. In conclusion, the obtained results demonstrate that the developed model can be used to estimate the wind power density.
Using statistical and machine learning techniques to forecast the PV solar power, which can be implemented for: • Managing the economic dispatch, unit commitment, and trading of PV solar power generations with other conventional generations; • Using with situational awareness tools to manage the ramp limitation; Optimal energy management of energy storage systems; • Voltage regulator settings on feeders with PV distributed generation.
The quality of data and the accuracy of energy generation forecast by artific...IJECEIAES
The paper presents the issues related to predicting the amount of energy generation, in a particular wind power plant comprising five generators located in south-eastern Poland. Thelocation of wind power plant, the distribution and type of applied generators, and topographical conditions were given and the correlation between selected weather parameters and the volume of energy generation was discussed. The primary objective of the paper was to select learning data and perform forecasts using artificial neural networks. For comparison, conservative forecasts were also presented. Forecasts results obtained shaw that Artificial Neural Networks are more universal than conservative method. However their forecast accuracy of forecasts strongly depends on the selection of explanatory data.
Optimal Configuration of Wind Farms in Radial Distribution System Using Parti...journalBEEI
Recently, a wide range of wind farm based distributed generations (DGs) are being integrated into distribution systems to fulfill energy demands and to reduce the burden on transmission corridors. The non-optimal configuration of DGs could severely affect the distribution system operations and control. Hence, the aim of this paper is to analyze the wind data in order to build a mathematical model for power output and pinpoint the optimal location. The overall objective is minimization of power loss reduction in distribution system. The five years of wind data was taken from 24o 44’ 29” North, 67o 35’ 9” East coordinates in Pakistan. The optimal location for these wind farms were pinpointed via particle swarm optimization (PSO) algorithm using standard IEEE 33 radial distribution system. The result reveals that the proposed method helps in improving renewable energy near to load centers, reduce power losses and improve voltage profile of the system. Moreover, the validity and performance of the proposed model were also compared with other optimization algorithms.
Electric Load Forecasting Using Genetic Algorithm – A Review IJMER
Many real-world problems from operations research and management science are very
complex in nature and quite hard to solve by conventional optimization techniques. So, intelligent
solutions based on genetic algorithm (GA), to solve these complicated practical problems in various
sectors are becoming more and more widespread nowadays. GAs are being developed and deployed
worldwide in myriad applications, mainly because of their symbolic reasoning, flexibility and
explanation capabilities.
This paper provides an overview of GAs, as well as their current use in the field of electric load
forecasting. The types of GA are outlined, leading to a discussion of the various types and parameters of
load forecasting. The paper concludes by sharing thoughts and estimations on GA for load forecasting
for future prospects in this area. This review reveals that although still regarded as a novel
methodology, GA technologies are shown to have matured to the point of offering real practical benefits
in many of their applications.
This document discusses maximum power point tracking (MPPT) techniques to improve the efficiency of wind-solar hybrid systems. It begins with an introduction to MPPT and its importance for optimizing power output from solar panels. Different MPPT methods are described, including perturb and observe, incremental conductance, and current sweep. The document then focuses on implementing the perturb and observe MPPT algorithm using simulation software PSIM. Graphs of the simulation results are presented and analyzed. Finally, simulation software options for graphical user interfaces like VEE Pro and LabVIEW are discussed.
Optimal Economic Load Dispatch of the Nigerian Thermal Power Stations Using P...theijes
This document summarizes the application of particle swarm optimization (PSO) to solve the economic load dispatch (ELD) problem for Nigeria's thermal power stations. PSO is used to determine the optimal allocation of total power demand among generating units to minimize total fuel costs while satisfying constraints. The PSO algorithm is applied to a 1999 model of Nigeria's power network and results are compared to other heuristic methods. PSO efficiently distributes load to minimize costs and overcomes limitations of traditional optimization techniques for non-linear power system problems.
An Application of Energy and Exergy Analysis of Transport Sector of IndiaIJMER
The present article is dedicated for evaluating the transportation sector of India in terms of
energetic and exergetic aspects. In this regard, energy and exergy utilization efficiencies during the
period 2005-2011 are assessed based on real data obtained from Energy statistics of India. Sectoral
energy and exergy analyses are conducted to study the variations of energy and exergy efficiencies,
overall energy and exergy efficiencies for the entire sub-sector are found to be in the range of 21.30 to
30.03%. When compared with other neighbouring countries, such as Saudi Arabia, Malaysia and Turkey,
the Indian transport sector is the least efficient. Such difference is inevitable due to dissimilar transport
structure in these countries. It is expected that that the results of this study will be helpful in developing
highly useful and productive planning for future energy policies, especially for the transportation sector.
This, in turn, will help achieve the ‘energy-security’ goal of the country
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology
International Journal of Engineering Research and DevelopmentIJERD Editor
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
Optimal design of adaptive power scheduling using modified ant colony optimi...IJECEIAES
For generating and distributing an economic load scheduling approach, artificial neural network (ANN) has been introduced, because power generation and power consumption are economically non-identical. An efficient load scheduling method is suggested in this paper. Normally the power generation system fails due to its instability at peak load time. Traditionally, load shedding process is used in which low priority loads are disconnected from sources. The proposed method handles this problem by scheduling the load based on the power requirements. In many countries the power systems are facing limitations of energy. An efficient optimization algorithm is used to periodically schedule the load demand and the generation. Ant colony optimization (ACO) based ANN is used for this optimal load scheduling process. The present work analyse the technical economical and time-dependent limitations. Also the works meets the demanded load with minimum cost of energy. Inorder to train ANN back propagation (BP) technics is used. A hybrid training process is described in this work. Global optimization algorithms are used to provide back propagation with good initial connection weights.
Economic viability and profitability assessments of WECS IJECEIAES
The document discusses economic viability and profitability assessments of different types of wind energy conversion systems (WECS). It proposes simulating four WECS models using HOMER software to determine the optimal configuration based on costs. The models vary the wind turbine size and presence of a rectifier. The software calculates technical parameters and costs including net present cost, cost of energy, and cash flows to assess which system maximizes profitability based on metrics like net present value, internal rate of return, and payback period.
A Post-processing Approach for Solar Power Combined Forecasts of Ramp EventsMohamed Abuella
This dissertation applies a post-processing approach to improve combined forecasts of solar power and solar power ramp events. The approach combines different forecasting models, then adjusts the combined forecasts to better predict ramp events. It develops a classification system for ramp event thresholds and evaluates performance using customized metrics. The approach provides probabilistic forecasts of solar power ramp events with uncertainty analysis.
The study of reducing the cost of investment in wind energy based on the cat ...TELKOMNIKA JOURNAL
Wind and solar are the most important source of renewable energy for power supply in remote locations involves serious consideration of the reliability of these unconventional energy sources. We apply the cat swarm meta-heuristic optimization method to solve the problem of wind power system design optimization. The electrical power components of the system are characterized by their cost, capacity and reliability. This study seeks to optimize the design of parallel power systems in which multiple choices of generators wind, transformers and lines. Our plan has the advantage of allowing electrical components with different parameters to be customized in electrical power systems. The UMGF method is applied to allow rapid reliability estimation. A computer program is developed for the UMGF application and CS algorithm. An example is provided to explain.
Hourly probabilistic solar power forecasts 3vMohamed Abuella
This document summarizes a presentation on hourly probabilistic solar power forecasting. It discusses:
1) The need for solar power forecasting to address the variability of solar generation. Various forecasting models and horizons are used.
2) The combined physical and statistical approach used to generate solar power forecasts, which includes solar plant modeling, numerical weather prediction, and statistical corrections.
3) The models evaluated include multiple linear regression, artificial neural networks, and support vector regression. Ensemble learning with random forests is used to combine the models' outputs.
4) Probabilistic forecasting methods evaluated include ensemble-based, analog ensemble, and persistence approaches. Different probability distributions are generated.
5)
Stochastic control for optimal power flow in islanded microgridIJECEIAES
The problem of optimal power flow (OPF) in an islanded mircrogrid (MG) for hybrid power system is described. Clearly, it deals with a formulation of an analytical control model for OPF. The MG consists of wind turbine generator, photovoltaic generator, and diesel engine generator (DEG), and is in stochastic environment such as load change, wind power fluctuation, and sun irradiation power disturbance. In fact, the DEG fails and is repaired at random times so that the MG can significantly influence the power flow, and the power flow control faces the main difficulty that how to maintain the balance of power flow? The solution is that a DEG needs to be scheduled. The objective of the control problem is to find the DEG output power by minimizing the total cost of energy. Adopting the Rishel’s famework and using the Bellman principle, the optimality conditions obtained satisfy the Hamilton-Jacobi-Bellman equation. Finally, numerical examples and sensitivity analyses are included to illustrate the importance and effectiveness of the proposed model.
This document summarizes a study that examines the relationship between oil consumption and economic growth in Turkey from 1961 to 2016. It begins by reviewing previous literature on the relationship between energy use and economic growth, which has found varying results. It then describes the data and methodology used in the study, which employs autoregressive distributed lag (ARDL) modeling and Toda-Yamamoto causality testing to analyze the cointegration and causality between oil consumption and GDP in Turkey while accounting for structural breaks. The results of these analyses reveal that economic growth Granger causes oil consumption in Turkey but not vice versa, indicating that energy conservation policies would not harm economic growth.
Short term load forecasting system based on support vector kernel methodsijcsit
Load Forecasting is powerful tool to make important decisions such as to purchase and generate the
electric power, load switching, development plans and energy supply according to the demand. The
important factors for forecasting involve short, medium and long term forecasting. Factors in short term
forecasting comprises of whether data, customer classes, working, non-working days and special event
data, while long term forecasting involves historical data, population growth, economic development and
different categories of customers.In this paper we have analyzed the load forecasting data collected from
one grid that contain the load demands for day and night, special events, working and non-working days
and different hours in day. We have analyzed the results using Machine Learning techniques, 10 fold cross
validation and stratified CV. The Machines Learning techniques used are LDA, QDA, SVM Polynomial,
Gaussian, HRBF, MQ kernels as well as LDA and QDA. The errors methods employed against the
techniques are RSE, MSE, RE and MAPE as presented in the table 2 below. The result calculated using the
SVM kernel shows that SVM MQ gives the highest performance of 99.53 %.
This document summarizes an article that compares two optimization methods, NSGA-II and MOPSO, for sizing hybrid renewable energy systems. The article models a hybrid system with photovoltaic panels, wind turbines, and battery storage. It formulates the sizing of these components as a multi-objective optimization problem to minimize loss of power supply probability and cost of energy. The article simulates the system using two optimization methods and compares their accuracy and computation time in finding the optimal system configuration.
Design and implementation of smart electronic solar tracker based on ArduinoTELKOMNIKA JOURNAL
Demand of energy increases in the global and exponential exhaustion is favored of resources by
fossil fuel for electricity production with the new systems development. Compared with all other remainder
energies, the specialist sun energy is the most bountiful energy and it's typically easy to be changed into
electrical energy. The main thing of using solar panel is to produce electrical energy from sun's energy but
the optimum energy can be generated by tracking solar panel due to the sun movement from east to west.
The problem can be solved by proposed systems where the sun tracking by solar panel that based on high
intensity of sun ray. This paper concentrates on tracking the sun by using servo motor coupled with solar
panel. So that, the largest quantity of sun light at the incident panel along the day at any time is better than
that for method of fixed panel array which is less efficient. The microcontroller Arduino (mode UNO) was
programmed by using C++ language while the track of sun light processing was implemented by using
light depending resistor (LDR), Chip IC H-bridge and microcontroller Arduino (UNO) circuits have been
designed by using Proteus software. By circuit design and sun tracking control process, the cost reduction
has been improved and high amount of energy was saved when implemented this system.
Impact of compressed air energy storage system into diesel power plant with w...IJECEIAES
The wind energy plays an important role in power system because of its renewable, clean and free energy. However, the penetration of wind power (WP) into the power grid system (PGS) requires an efficient energy storage systems (ESS). compressed air energy storage (CAES) system is one of the most ESS technologies which can alleviate the intermittent nature of the renewable energy sources (RES). Nyala city power plant in Sudan has been chosen as a case study because the power supply by the existing power plant is expensive due to high costs for fuel transport and the reliability of power supply is low due to uncertain fuel provision. This paper presents a formulation of security-constrained unit commitment (SCUC) of diesel power plant (DPP) with the integration of CAES and PW. The optimization problem is modeled and coded in MATLAB which solved with solver GORUBI 8.0. The results show that the proposed model is suitable for integration of renewable energy sources (RES) into PGS with ESS and helpful in power system operation management.
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.
Estimation of wind power density with artificial neural networkmehmet şahin
Industry and technology are rapidly developing with each passing day. They need energy to sustain this evolution. The demand of energy is mainly provided from fossil fuels. Unfortunately, this kind of energy reserves are consumed away day by day. Therefore, there is a need to use alternative energy sources to supply energy needs. Alternative energy sources can be listed as; solar, wind, wave, biomass, geothermal and hydro-electric power. Our country has significant potential for wind energy. Wind power density estimation is required to determine the wind potential. In this study, the wind power density was estimated by using artificial neural network (ANN) method. Forty meteorological stations were used for ANN training, while eighteen meteorological stations were used to test the trained network. Network has trained according to, respectively; trainlm, trainbfg, trainscg, traincgp traincgb, traincgf ve trainoss learning algorithms. The correlation coefficient (R) and Mean bias error (MBE) of the best developed model were calculated as 0,9767 and -0,3124 W/m2 respectively. Root Mean Square Error (RMSE) was calculated as 1,4786 W/m2. In conclusion, the obtained results demonstrate that the developed model can be used to estimate the wind power density.
Using statistical and machine learning techniques to forecast the PV solar power, which can be implemented for: • Managing the economic dispatch, unit commitment, and trading of PV solar power generations with other conventional generations; • Using with situational awareness tools to manage the ramp limitation; Optimal energy management of energy storage systems; • Voltage regulator settings on feeders with PV distributed generation.
The quality of data and the accuracy of energy generation forecast by artific...IJECEIAES
The paper presents the issues related to predicting the amount of energy generation, in a particular wind power plant comprising five generators located in south-eastern Poland. Thelocation of wind power plant, the distribution and type of applied generators, and topographical conditions were given and the correlation between selected weather parameters and the volume of energy generation was discussed. The primary objective of the paper was to select learning data and perform forecasts using artificial neural networks. For comparison, conservative forecasts were also presented. Forecasts results obtained shaw that Artificial Neural Networks are more universal than conservative method. However their forecast accuracy of forecasts strongly depends on the selection of explanatory data.
Optimal Configuration of Wind Farms in Radial Distribution System Using Parti...journalBEEI
Recently, a wide range of wind farm based distributed generations (DGs) are being integrated into distribution systems to fulfill energy demands and to reduce the burden on transmission corridors. The non-optimal configuration of DGs could severely affect the distribution system operations and control. Hence, the aim of this paper is to analyze the wind data in order to build a mathematical model for power output and pinpoint the optimal location. The overall objective is minimization of power loss reduction in distribution system. The five years of wind data was taken from 24o 44’ 29” North, 67o 35’ 9” East coordinates in Pakistan. The optimal location for these wind farms were pinpointed via particle swarm optimization (PSO) algorithm using standard IEEE 33 radial distribution system. The result reveals that the proposed method helps in improving renewable energy near to load centers, reduce power losses and improve voltage profile of the system. Moreover, the validity and performance of the proposed model were also compared with other optimization algorithms.
Electric Load Forecasting Using Genetic Algorithm – A Review IJMER
Many real-world problems from operations research and management science are very
complex in nature and quite hard to solve by conventional optimization techniques. So, intelligent
solutions based on genetic algorithm (GA), to solve these complicated practical problems in various
sectors are becoming more and more widespread nowadays. GAs are being developed and deployed
worldwide in myriad applications, mainly because of their symbolic reasoning, flexibility and
explanation capabilities.
This paper provides an overview of GAs, as well as their current use in the field of electric load
forecasting. The types of GA are outlined, leading to a discussion of the various types and parameters of
load forecasting. The paper concludes by sharing thoughts and estimations on GA for load forecasting
for future prospects in this area. This review reveals that although still regarded as a novel
methodology, GA technologies are shown to have matured to the point of offering real practical benefits
in many of their applications.
This document discusses maximum power point tracking (MPPT) techniques to improve the efficiency of wind-solar hybrid systems. It begins with an introduction to MPPT and its importance for optimizing power output from solar panels. Different MPPT methods are described, including perturb and observe, incremental conductance, and current sweep. The document then focuses on implementing the perturb and observe MPPT algorithm using simulation software PSIM. Graphs of the simulation results are presented and analyzed. Finally, simulation software options for graphical user interfaces like VEE Pro and LabVIEW are discussed.
Optimal Economic Load Dispatch of the Nigerian Thermal Power Stations Using P...theijes
This document summarizes the application of particle swarm optimization (PSO) to solve the economic load dispatch (ELD) problem for Nigeria's thermal power stations. PSO is used to determine the optimal allocation of total power demand among generating units to minimize total fuel costs while satisfying constraints. The PSO algorithm is applied to a 1999 model of Nigeria's power network and results are compared to other heuristic methods. PSO efficiently distributes load to minimize costs and overcomes limitations of traditional optimization techniques for non-linear power system problems.
An Application of Energy and Exergy Analysis of Transport Sector of IndiaIJMER
The present article is dedicated for evaluating the transportation sector of India in terms of
energetic and exergetic aspects. In this regard, energy and exergy utilization efficiencies during the
period 2005-2011 are assessed based on real data obtained from Energy statistics of India. Sectoral
energy and exergy analyses are conducted to study the variations of energy and exergy efficiencies,
overall energy and exergy efficiencies for the entire sub-sector are found to be in the range of 21.30 to
30.03%. When compared with other neighbouring countries, such as Saudi Arabia, Malaysia and Turkey,
the Indian transport sector is the least efficient. Such difference is inevitable due to dissimilar transport
structure in these countries. It is expected that that the results of this study will be helpful in developing
highly useful and productive planning for future energy policies, especially for the transportation sector.
This, in turn, will help achieve the ‘energy-security’ goal of the country
This document summarizes an analysis of energy and exergy utilization in India's transportation sector from 2005-2011. It finds that the overall energy efficiency of the sector is between 21.3-30.03% when considering various modes of transport including aviation, pipelines, roadways and railways. When compared to other countries, the transportation sector in India is the least efficient. The results are intended to help develop energy policies to improve efficiency and achieve energy security goals.
Analysis of Households’ Electricity Consumption with Ordered Logit Models: Ex...inventionjournals
Percentage of households’ electricity demand in total energy demand of households is increasing day by day. However, households’ electricity consumption fails to provide the added value to Gross National Product unlike industry sector. Therefore, the factors that increase the energy consumption of households should be analyzed and in this respect, required energy saving policies should be generated. In this paper, the ordered logit models examined the variables affecting the electricity consumption of households in Turkey. According to goodness of fit indicators, Partial Proportional Odds Model was determined as the best model that fits into our dataset. The results obtained from model show that electrically powered items and their quantities, household size, income, housing type and properties are important factors that increase households’ electricity consumption.
Free Download Link (Copy URL):
https://sites.google.com/view/varunpratapsingh/teaching-engagements
Unit-I
Part-1 Introduction
Power and energy, sources of energy, review of thermodynamic cycles related to power plants,
fuels and combustion, calculations.
Part-2 Variable Load Problem
Industrial production and power generation compared ideal and realized load curves, terms, and factors. Effect of variable load on power plan operation, methods of meeting the variable load problem.
Part-3 Power plant economics and selection Effect of plant type on costs, rates, fixed elements, energy elements, customer elements, and investor’s profit; depreciation and replacement, theory of rates. Economics of plant selection, other considerations in plant selection.
The document analyzes wind energy potential and economics of small wind pumps in northern Nigeria. Eleven years of wind data from Jos, Kano, and Sokoto were used to estimate available wind energy for pumping water. At a 9m installation height, estimated energy was 190 kWh/m2/yr for Jos, 225 kWh/m2/yr for Kano, and 348 kWh/m2/yr for Sokoto. The monetary value of this energy as fuel cost savings from diesel or petrol pumps was calculated. At current fuel prices and interest/inflation rates, wind pumps were not economically competitive without subsidies. Subsidies of 16-24% for Sokoto, 48-51%
Optimal Planning of an Off-grid Electricity Generation with Renewable Energy ...IAES-IJPEDS
In recent years, several factors such as environmental pollution which is caused by fossil fuels and various diseases caused by them from one hand and concerns about the dwindling fossil fuels and price fluctuation of the products and resulting effects of these fluctuations in the economy from other hand has led most countries to seek alternative energy sources for fossil fuel supplies. Such a way that in 2006, about 18% of the consumed energy of the world is obtained through renewable energies. Iran is among the countries that are geographically located in hot and dry areas and has the most sun exposure in different months of the year. Except in the coasts of Caspian Sea, the percentage of sunny days throughout the year is between 63 to 98 percent in Iran. On the other hand, there are dispersed and remote areas and loads far from national grid which is impossible to provide electrical energy for them through transmission from national grid, therefore, for such cases the renewable energy technologies could be used to solve the problem and provide the energy. In this paper, technical and economic feasibility for the use of renewable energies for independent systems of the grid for a dispersed load in the area on the outskirts of Isfahan (Sepahan) with the maximum energy consumption of 3Kwh in a day is studied and presented. In addition, the HOMER simulation software is used as the optimization tool.
This document summarizes India's energy policy and the role of renewable energy. It discusses how India has a vast supply of renewable resources and is one of the largest countries deploying renewables. The document outlines various support schemes and policies India has adopted to promote renewable energy sources in its restructured power sector to meet future energy demand in a sustainable way. It also analyzes different energy planning tools used for energy policy planning and explains why the ENPEP-BALANCE tool was chosen to carry out energy system planning for India.
This document summarizes an article about India's energy policy and the need to promote renewable energy sources. It discusses how India has vast renewable energy resources and the government has implemented various policies and incentives to promote greater renewable energy deployment. The key challenges are India's limited fossil fuel reserves, high fuel transportation costs, aging conventional power plants, need to rationalize power tariffs, and reduce transmission and distribution losses in the power sector. The government is aiming to source 10% of additional grid power from renewable sources by 2012 to help address these challenges in a sustainable manner.
This document summarizes an article about India's energy policy and the need to promote renewable energy sources. It discusses how India has vast renewable energy resources and the government has implemented various policies and incentives to promote greater renewable energy deployment. The key challenges are India's limited fossil fuel reserves, high fuel transportation costs, aging conventional power plants, need to rationalize power tariffs, and reduce transmission and distribution losses in the power sector. The government is aiming to source 10% of additional grid power capacity from renewable sources by 2012 to help address these issues through its renewable energy policies.
Exergy analysis and igcc plant technology to improve the efficiency and to re...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Turkey faces increasing electricity demand and energy insecurity due to lack of domestic fossil fuel reserves. Renewable energy production has grown significantly in recent years through wind, solar and hydropower projects. The government aims to reach 30% renewable energy by 2023 through feed-in tariffs of up to $110/MWh for wind and $96/MWh for hydropower, as well as targets of 20,000 MW of wind and 3,000 MW of solar installed capacity. Renewable projects up to 500 kW are exempt from certificate requirements, while other permits like from water authorities may still be needed.
The document analyzes the energy consumption for cucumber greenhouse production in Iran using data envelopment analysis. Data was collected from 20 greenhouses and energy inputs (like diesel, fertilizer, labor) and outputs (cucumber yield) were calculated. Total energy input was 163,994 MJ/ha with diesel fuel as the highest at 45.15%. Output was 62,496 MJ/ha. Technical, pure technical and scale efficiencies were then calculated using DEA to evaluate energy efficiency and identify areas for improvement. The study found DEA to be useful for benchmarking energy use and determining how to reduce waste.
A Systematic Review of Renewable Energy Trend.pdfssuser793b4e
This paper systematically and successfully reviewed the renewable energy trend from 2010 to 2023. This review
detailed the difference renewable energy and conclusion was drawn that solar photovoltaic (PV) energy has the
leading trend in power generation growth and innovation. This research work explained in detail the most recent
solar photovoltaic optimization techniques and it was observed from the review that hybridization of intelligent and
non-intelligent maximum power point tracking technique has the best tracking power conversion efficiency. The
advantages and disadvantage of solar PV together with the solar optimization and innovational growth trends were
examined. This research showed that clean and renewable energy sources will continue to grow and the solar energy
industry is expected to experience significant growth and rapid innovation in the next 10 years. From the observed
rapid growth and innovation trend in solar energy, the world will have a very cheap, abundant and clean energy
before 2050.
Energy and exergy analysis for biomass co firing coal fuel based thermal powe...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Leveling the playing field - the economics of electricity generation in EuropeDavid Owain Clubb
This document provides an economic assessment of electricity generation costs in Europe. It calculates an 'Extended Levelised Cost of Electricity' (eLCOE) that includes externalities for various technologies. Onshore wind has the lowest eLCOE cost. The analysis finds external costs are significant for conventional generation. It performs a sensitivity analysis considering 'renewable friendly' and 'conventional friendly' scenarios. The document reviews methodology, results from existing studies, and estimates balancing costs, CO2 impacts, and electricity costs by technology in 2020. It aims to provide more complete information on external costs to help inform energy policy decisions.
This paper is related with the importance of the energy policy and renewable energy which play a important role in the development of the environmental benefits. India has a vast supply of renewable energy resources and it is one of the largest countries in the world for deploying renewable energy. This paper attempts to review the policies and planning measures undertaken by the Indian government for promotion of renewable energy. Low impact renewable energy (LIRE) technologies offer important benefits compared to conventional energy sources, such as fossil fuels or nuclear power. However due to their uncertainty different kinds of renewable-energy resources need to be operated in an integrated way, which complement each other. Global electricity demand is expected to increase considerably during the next decade and at the same time environmental pollution is also increasing with the development of conventional energy source. To meet the challenges for global energy demand various support schemes, policies and planning to promote use of renewable energy sources are discussed in this paper.
Viability study of on-grid PV/Wind integrated SystemIRJET Journal
This document summarizes a study on the viability of an on-grid PV/wind integrated system for rural electrification in India. The study uses HOMER software to model a system with solar PV arrays, a small wind turbine, and an existing grid connection. Results show the integrated system is technically and economically feasible, with a cost of energy of INR 5.992/kWh and 90% renewable energy penetration. Solar PV produces most energy, with peak output in September and November. The small wind turbine provides some additional power. The grid helps maintain supply reliability by allowing excess renewable energy to be sold back and purchasing power when renewable output is insufficient.
Does price shock in electricity sector correct the consumptionAlexander Decker
This document summarizes a study that examines whether price shocks in Iran's electricity sector can correct consumption patterns. The authors estimate an econometric model using time series data from 1973-2007. They find that electricity demand has a price elasticity of less than 1, indicating it is a necessary good. As such, shocking prices is not an efficient way to rectify consumption patterns, since demand does not respond much to price changes for necessary goods. They also find little substitution between electricity and gas. The authors conclude that abrupt price changes alone will not effectively change consumption patterns in Iran's electricity sector due to the inelastic nature of demand and lack of substitutes.
This document discusses electric vehicle battery swapping stations as a solution to barriers around EV adoption. It proposes a multi-objective optimization model to determine the optimal strategy for operating a battery swapping station. The model considers minimizing costs from battery utilization, damage from different charging methods, and dynamic electricity costs, while satisfying demand. The solution provides the optimal number of batteries to use from stock and charging decisions for incoming discharged batteries. The results from two optimization tools, Solver in MS Excel and Lingo software, were compared.
Similar to Estimation of Energy Demand of Turkey Particle Swarm Optimization Approach (20)
This document discusses student organizations and the university system in Germany. It provides an overview of the different types of higher education institutions in Germany, including universities, universities of applied sciences, and arts universities. It describes the degree system including bachelor's, master's, and Ph.D. programs. It also outlines the systems of student participation at universities, using the examples of Leipzig and Hanover. Student councils, departments, and faculty student organizations are discussed.
The document discusses grand challenges in energy and perspectives on moving towards more sustainable systems. It notes that while global energy demand and CO2 emissions rebounded in 2010 after the economic downturn, urgent changes are still needed. It explores perspectives on changing direction, including overcoming barriers like technologies, economies, management, and mindsets. The document advocates a systems approach and backcasting from desirable futures to identify pathways for transitioning between states.
Engineering can play an important role in sustainable development by focusing on meeting human needs over wants and prioritizing projects that serve the most vulnerable populations. Engineers should consider how their work impacts sustainability, affordability, and accessibility. A socially sustainable product is manufactured sustainably and also improves people's lives. Engineers are not neutral and should strive to serve societal needs rather than just generate profits. They can help redefine commerce and an engineering culture focused on meeting needs sustainably through services rather than creating unnecessary products and infrastructure.
Consensus and interaction on a long term strategy for sustainable developmentSSA KPI
The document discusses the need for a long-term vision for sustainable development to address major challenges like climate change, resource depletion, and inequity. A long-term perspective is required because these problems will take consistent action over many years to solve. However, short-term solutions may counteract long-term goals if not guided by an overall strategic vision. Developing a widely accepted long-term sustainable development vision requires input from many stakeholders to find balanced solutions and avoid dead ends. Strategic decisions with long-lasting technological and social consequences need a vision that can adapt to changing conditions over time.
Competences in sustainability in engineering educationSSA KPI
The document discusses competencies in sustainability for engineering education. It defines competencies and lists taxonomies that classify competencies into categories like knowledge, skills, attitudes, and ethics. Engineering graduates are expected to have competencies like critical thinking, systemic thinking, and interdisciplinarity. Analysis of competency frameworks from different universities found that competencies are introduced at varying levels, from basic knowledge to complex problem solving and valuing sustainability challenges. The document also outlines the University of Polytechnic Catalonia's framework for its generic sustainability competency.
The document discusses concepts related to sustainability including carrying capacity, ecological footprint, and the IPAT equation. It provides data on historical and projected world population growth. Examples are given showing the ecological footprint of different countries and how it is calculated based on factors like energy use, agriculture, transportation, housing, goods and services. The human development index is also introduced as a broader measure than GDP for assessing well-being. Graphs illustrate the relationship between increasing HDI, ecological footprint, and the goal of transitioning to sustainable development.
From Huygens odd sympathy to the energy Huygens' extraction from the sea wavesSSA KPI
Huygens observed that two pendulum clocks suspended near each other would synchronize their swings to be 180 degrees out of phase. He conducted experiments that showed the synchronization was caused by small movements transmitted through their common frame. While this discovery did not help solve the longitude problem as intended, it sparked further investigations into coupled oscillators and synchronization phenomena.
1) The document discusses whether dice rolls and other mechanical randomizers can truly produce random outcomes from a dynamics perspective.
2) It analyzes the equations of motion for different dice shapes and coin tossing, showing that outcomes are theoretically predictable if initial conditions can be reproduced precisely.
3) However, in reality small uncertainties in initial conditions mean mechanical randomizers can approximate random processes, even if they are deterministic based on their underlying dynamics.
This document discusses the concept of energy security costs. It defines energy security costs as externalities associated with short-term macroeconomic adjustments to changes in energy prices and long-term impacts of monopoly or monopsony power in energy markets. The document provides references on calculating health and environmental impacts of electricity generation and assessing costs and benefits of oil imports. It also outlines a proposed 4-hour course on basic concepts, examples, and a case study analyzing energy security costs for Ukraine based on impacts of increasing natural gas import prices.
Naturally Occurring Radioactivity (NOR) in natural and anthropic environmentsSSA KPI
This document provides an overview of naturally occurring radioactivity (NOR) and naturally occurring radioactive materials (NORM) with a focus on their relevance to the oil and gas industry. It discusses the main radionuclides of interest, including radium-226, radium-228, uranium, radon-222, and lead-210. It also summarizes the origins of NORM in the oil and gas industry and the types of radiation emitted by NORM.
Advanced energy technology for sustainable development. Part 5SSA KPI
All energy technologies involve risks that must be carefully evaluated and minimized to ensure sustainable development. No technology is perfectly safe, so ongoing analysis of benefits, risks and impacts is needed. Public understanding and acceptance of risks is also important.
Advanced energy technology for sustainable development. Part 4SSA KPI
The document discusses the impacts and benefits of energy technology research, using fusion research as a case study. It outlines four pathways through which energy research can impact economies and societies: 1) direct economic effects, 2) impacts on local communities, 3) impacts on industrial technology capabilities, and 4) long-term impacts on energy markets and technologies. It then analyzes the direct and indirect economic impacts of fusion research investments and the technical spin-offs that fusion research has produced. Finally, it evaluates the potential future role of fusion electricity in global energy markets under environmental constraints.
Advanced energy technology for sustainable development. Part 3SSA KPI
This document discusses using fusion energy for sustainable development through biomass conversion. It proposes a system where fusion energy is used to provide heat for gasifying biomass into synthetic fuels like methane and diesel. Experiments show biomass can be over 95% converted to hydrogen, carbon monoxide and methane gases using nickel catalysts at temperatures of 600-1000 degrees Celsius. A conceptual biomass reactor is presented that could process 6 million tons of biomass per year, consisting of 70% cellulose and 30% lignin, into synthetic fuels to serve as carbon-neutral transportation fuels. Fusion energy could provide the high heat needed for the gasification and synthesis processes.
Advanced energy technology for sustainable development. Part 2SSA KPI
The document summarizes fusion energy technology and its potential for sustainable development. Fusion occurs at extremely high temperatures and is the process that powers the Sun and stars. Researchers are working to develop fusion energy on Earth using hydrogen isotopes as fuel. Key challenges include confining the hot plasma long enough at high density for fusion reactions to produce net energy gain. Progress is being made towards achieving the conditions needed for a sustainable fusion reaction as defined by Lawson's criteria.
Advanced energy technology for sustainable development. Part 1SSA KPI
1. The document discusses the concept of sustainability and sustainable systems. It provides an example of a closed ecosystem with algae, water fleas, and fish, where energy and material balances must be maintained for long-term stability.
2. Key requirements for a sustainable system include energy balance between inputs and outputs, recycling of materials or wastes, and mechanisms to control population relationships and prevent overconsumption of resources.
3. Historically, the environment was seen as external and unchanging, but it is now recognized that the environment co-evolves interactively with the living creatures within it.
This document discusses the use of fluorescent proteins in current biological research. It begins with an overview of the development of optical microscopy and fluorescence techniques. It then focuses on the green fluorescent protein (GFP) and how it has been used as a molecular tag to study protein expression and interactions in living cells through techniques like gene delivery, transfection, viral infection, FRET, and optogenetics. The document concludes that fluorescent proteins have revolutionized cell biology by enabling the real-time visualization and control of molecular pathways and signaling processes in living systems.
Neurotransmitter systems of the brain and their functionsSSA KPI
1. Neurotransmitters are chemical substances released at synapses that transmit signals between neurons. The main neurotransmitters in the brain are acetylcholine, serotonin, dopamine, norepinephrine, glutamate, GABA, and endorphins.
2. Each neurotransmitter system is involved in regulating key brain functions and behaviors such as movement, mood, sleep, cognition, and pain perception.
3. Neurotransmitters act via membrane receptors on target neurons, including ionotropic receptors that are ligand-gated ion channels and metabotropic G-protein coupled receptors.
Your One-Stop Shop for Python Success: Top 10 US Python Development Providersakankshawande
Simplify your search for a reliable Python development partner! This list presents the top 10 trusted US providers offering comprehensive Python development services, ensuring your project's success from conception to completion.
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
AppSec PNW: Android and iOS Application Security with MobSFAjin Abraham
Mobile Security Framework - MobSF is a free and open source automated mobile application security testing environment designed to help security engineers, researchers, developers, and penetration testers to identify security vulnerabilities, malicious behaviours and privacy concerns in mobile applications using static and dynamic analysis. It supports all the popular mobile application binaries and source code formats built for Android and iOS devices. In addition to automated security assessment, it also offers an interactive testing environment to build and execute scenario based test/fuzz cases against the application.
This talk covers:
Using MobSF for static analysis of mobile applications.
Interactive dynamic security assessment of Android and iOS applications.
Solving Mobile app CTF challenges.
Reverse engineering and runtime analysis of Mobile malware.
How to shift left and integrate MobSF/mobsfscan SAST and DAST in your build pipeline.
Main news related to the CCS TSI 2023 (2023/1695)Jakub Marek
An English 🇬🇧 translation of a presentation to the speech I gave about the main changes brought by CCS TSI 2023 at the biggest Czech conference on Communications and signalling systems on Railways, which was held in Clarion Hotel Olomouc from 7th to 9th November 2023 (konferenceszt.cz). Attended by around 500 participants and 200 on-line followers.
The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
Generating privacy-protected synthetic data using Secludy and MilvusZilliz
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5th LF Energy Power Grid Model Meet-up SlidesDanBrown980551
5th Power Grid Model Meet-up
It is with great pleasure that we extend to you an invitation to the 5th Power Grid Model Meet-up, scheduled for 6th June 2024. This event will adopt a hybrid format, allowing participants to join us either through an online Mircosoft Teams session or in person at TU/e located at Den Dolech 2, Eindhoven, Netherlands. The meet-up will be hosted by Eindhoven University of Technology (TU/e), a research university specializing in engineering science & technology.
Power Grid Model
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For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
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[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...Jason Yip
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zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...Alex Pruden
Folding is a recent technique for building efficient recursive SNARKs. Several elegant folding protocols have been proposed, such as Nova, Supernova, Hypernova, Protostar, and others. However, all of them rely on an additively homomorphic commitment scheme based on discrete log, and are therefore not post-quantum secure. In this work we present LatticeFold, the first lattice-based folding protocol based on the Module SIS problem. This folding protocol naturally leads to an efficient recursive lattice-based SNARK and an efficient PCD scheme. LatticeFold supports folding low-degree relations, such as R1CS, as well as high-degree relations, such as CCS. The key challenge is to construct a secure folding protocol that works with the Ajtai commitment scheme. The difficulty, is ensuring that extracted witnesses are low norm through many rounds of folding. We present a novel technique using the sumcheck protocol to ensure that extracted witnesses are always low norm no matter how many rounds of folding are used. Our evaluation of the final proof system suggests that it is as performant as Hypernova, while providing post-quantum security.
Paper Link: https://eprint.iacr.org/2024/257
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Ever-changing customer expectations demand more modern digital experiences, and the bank needed to find a solution that could provide real-time data to its customer channels with low latency and operating costs. Join this session to learn how Citizens is leveraging Precisely to replicate mainframe data to its customer channels and deliver on their “modern digital bank” experiences.
How information systems are built or acquired puts information, which is what they should be about, in a secondary place. Our language adapted accordingly, and we no longer talk about information systems but applications. Applications evolved in a way to break data into diverse fragments, tightly coupled with applications and expensive to integrate. The result is technical debt, which is re-paid by taking even bigger "loans", resulting in an ever-increasing technical debt. Software engineering and procurement practices work in sync with market forces to maintain this trend. This talk demonstrates how natural this situation is. The question is: can something be done to reverse the trend?
Astute Business Solutions | Oracle Cloud Partner |
Estimation of Energy Demand of Turkey Particle Swarm Optimization Approach
1. 6th International Summer School National University of Technology of the Ukraine Kiev, Ukraine, August 8-20, 2011 Estimation of EnergyDemandof Turkey Particle SwarmOptimizationApproach Turan Paksoy a, Eren Özceylana, Nimet Y. Pehlivan b, Gerhard-Wilhelm Weberc a SelcukUniversity, Department of Industrial Engineering, Campus, 42031, Konya, Turkey b SelcukUniversity, Department of Statistics, Campus, 42031, Konya, Turkey CMiddle East Technical University, Institute of Applied Mathematics, Campus, 06531, Ankara, Turkey tpaksoy@yahoo.com, nimet@selcuk.edu.tr, eozceylan@selcuk.edu.tr
2. Outline Introduction Literature Review Particle Swarm Optimization (PSO) PSO Energy Demand Estimation (EEPSO) Estimation of Turkey Energy Demand ComparisonsandScenarioAnalyzes Conclusion and Future Search 2 Particle SwarmOptimizationApproachforEstimation of EnergyDemand of Turkey
3. It is widely known that energy consumption and demand level is directly related to the level of development of a country like Turkey. Hence, carrying an idea about energy demand and policy is a matter of serious importance. Introduction *Turkey, which is a Eurasian country thatstretches across the Anatolian peninsula in western Asia and Thrace in the Balkan region of South-Eastern Europe, has been one of the fastest growing power markets in the world with its young and growing population, rapid urbanization, strong economic growth and low per-capita electricity consumption for two decades. 3 Particle SwarmOptimizationApproachforEstimation of EnergyDemand of Turkey
4. Figure 1 shows energy demand growth rates of ENTSO-E (European Network of Transmission System Operators for Electricity) members and Turkey. High growth potential of Turkey could be seen clearly besides other European countries. EnergySituation of Turkey 4 Particle SwarmOptimizationApproachforEstimation of EnergyDemand of Turkey
5. Turkey’s energy demand has grown rapidly almost every year and will continue to grow along with its economy. The primary energy need of Turkey has been growing by some 6% per annum for decades. Turkey’s primary energy sources are hard coal, lignite, hydropower, oil, natural gas, geothermal and solar energy, wood, as well as animal and plant wastes. However, the level of energy production in Turkey is very low (Figure). At present, around 26% of the total energy demand is being met by domestic energy sources, while the rest originates from a diversified import-portfolio. EnergySituation of Turkey MTOE: million tons of oil equivalents 5 Particle SwarmOptimizationApproachforEstimation of EnergyDemand of Turkey
6. Coal, natural gas and oil consumptions are very close and have 91% in total primary energy consumption, while their production is 63.6% in total primary energy production. In other words, only a small percentage of total primary consumption was provided by domestic production. It is expected that by the year 2020, domestic energy consumption will reach 222 MTOE, while domestic production will be at 70 MTOE, or 30% of national demand. These indicators show that Turkey is forced to increase its dependence on foreign energy supplies. Thus, the accurate estimating of energy demand is a very critical factor in the Turkey's energy policy making. The goal of this study is to provide that accurate estimating model of energy demand using PSO. Energy of Turkey 6 Particle SwarmOptimizationApproachforEstimation of EnergyDemand of Turkey
7. Energy estimation modeling is a subject of widespread current interest among practitioners and academicians concerned with problems of energy production and consumption. First applications on energy demand forecasting in Turkey have been implemented by State Planning Organization (SPO) via using of simple regression techniques. Modern econometric techniques have been applied for energy planning and estimation of future energy demands of Turkey in 1984 first. One of the modern econometric techniques, model for analysis of energy demand (MAED) which is a kind of simulation model and developed by International Atomic Energy Agency (IAEA) was started to be used by Ministry of Energy and Natural Resources of Turkey (MENR). MAED is used to estimate the medium and long term energy demand, considering the relationships between several factors that affect the social, economic and technologic system of the country. Literature Review 7 Particle SwarmOptimizationApproachforEstimation of EnergyDemand of Turkey
8. The MAED was applied six times over the period, in the years 1986, 1990, 1994 1997, 2000 and 2005. In the overall assessment of Turkish energy demand forecasts, these studies always foresaw energy demand as being greater than it actually is. These policies lead Turkey to be import dependent and much more vulnerable to external shocks and prevent energy markets from liberalizing. Literature Review 8 Particle SwarmOptimizationApproachforEstimation of EnergyDemand of Turkey
9. Many models have been developed from many researches using various forms of mathematical formulations, which are directly or indirectly related to energy development models to find a relation between energy consumption and income. For energy forecasting, statistical models are also considered by Ediger and Tatlıdil (2002), Sarak and Satman (2003), Yumurtacı and Asmaz (2004), Görücü and Gümrah (2004), Aras and Aras (2004), Ediger and Akar (2007), Erdoğdu (2007), Mucuk and Uysal (2009), Akkurt et al., (2010) and Dilaver and Hunt (2011). In the energy estimation literature, meta-heuristic methods, which are used to solve combinatorial optimization problem, have been rarely applied to estimate energy consumption. A summary of techniques, used for Turkey’s energy demand forecasting is given in Table. Literature Review 9 Particle SwarmOptimizationApproachforEstimation of EnergyDemand of Turkey
10. Literature Review 10 Particle SwarmOptimizationApproachforEstimation of EnergyDemand of Turkey
11. The Particle Swarm Optimization is one of the recent meta-heuristic techniques proposed by Kennedy and Eberhart(1995)based on natural flocking and swarming behavior of birds and insects. It isinitialized with a population of random solutions and searches for optima by updating generations. In PSO, the potentialsolutions, or particles, move through the problem space by following the current optimum particles. The concept of PSO gained in popularity due to its simplicity. Like other swarm-based techniques, PSO consists of a number of individualrefining their knowledge of the given search space. However, unlike GA, the PSO algorithm has no evolutionary operators, such as crossover and mutation. Particle Swarm Optimization (PSO) 11 Particle SwarmOptimizationApproachforEstimation of EnergyDemand of Turkey
12. Particle Swarm Optimization (PSO) The individuals in a PSO have a position and a velocity and are denoted as particles. The PSO algorithm works by attracting the particles to search space positions of high fitness. Each particle has a memory function, and adjusts its trajectory according to two pieces of information, the best position that it has so far visited, and the global best position attained by the whole swarm. The system is initialized with a population of random solutions (particles) and searches iteratively through the d-dimensional problem space for optima by updating generations. Each particle keeps a memory of its previous best position, pbest, and a velocity along each dimension, represented as Vi= (νi1, νi1,…., νid). When a particle takes all the population as its topological neighbors, the best value is a global best and is called gbest. 12 Particle SwarmOptimizationApproachforEstimation of EnergyDemand of Turkey
13. Particle Swarm Optimization (PSO) The PSO concept consists of, at each time step, changing the velocity (V) of (accelerating) each particle toward its pbest location according to Eq. (1). 𝑣𝑖𝑑𝑡+1=𝑤.𝑣𝑖𝑑𝑡+𝑐1.𝑟𝑎𝑛𝑑1.𝑝𝑏𝑒𝑠𝑡𝑖𝑑𝑡−𝑥𝑖𝑑𝑡+𝑐2.𝑟𝑎𝑛𝑑2.𝑔𝑏𝑒𝑠𝑡𝑖𝑑𝑡−𝑥𝑖𝑑𝑡(1) The new position of the particle is determined by the sum of previous position and the new velocity which is given in Eq. (2): 𝑥𝑖𝑑𝑡+1= 𝑥𝑖𝑑𝑡+𝑣𝑖𝑑(𝑡+1)(2) Where 𝑐1 and 𝑐2 determine the relative influence of the social and cognitive components (learning factors), while 𝑟𝑎𝑛𝑑1 and 𝑟𝑎𝑛𝑑2 denote two random numbers uniformly distributed in the interval [0, 1]. wis a parameter called inertia weight used to control the impact of the previous velocities on the current one. In proposed PSO, inertia value of the equation changes on the each iteration. Inertia function is obtained as follow: 𝑤=𝑤𝑚𝑎𝑥−𝑤𝑚𝑎𝑥−𝑤𝑚𝑖𝑛𝑖𝑡𝑒𝑟𝑚𝑎𝑥∗𝑖𝑡𝑒𝑟(3) Where wmax is the first and maximum inertia force, wmin is minimum inertia force and itermax is maximum iteration number. 13 Particle SwarmOptimizationApproachforEstimation of EnergyDemand of Turkey
14. Flowchart of the PSO algorithm 14 Particle SwarmOptimizationApproachforEstimation of EnergyDemand of Turkey
15. Four indicators (population, GDP, import and export) were used in energy demand estimating models which are proposed based on PSO. These indicators are commonly used in literature and believed that energy demand of a country is mostly affected by them. Table shows four indicators and energy demand of Turkey between 1970 and 2005. The data are collected from Turkish Statistical Institute (TSI) and the MENR. Data until 2005 is used to make a comparison other models which are developed for the same problem. PSO Energy Demand Estimation (EEPSO) 15 Particle SwarmOptimizationApproachforEstimation of EnergyDemand of Turkey
16. Energy demand, GDP, population, import and export data of Turkey 16 Particle SwarmOptimizationApproachforEstimation of EnergyDemand of Turkey
17. PSO Energy Demand Estimation (EEPSO) As it seen in Table, it is clear that there is a linear relationship between four indicators and energy demand. For example, while GDP, population, import and export of Turkey increased 3.4; 0.63; 22 and 31.5 times respectively, energy consumption of Turkey has increased 1.98 times between 1979-2005 years. In this study, the estimation of energy demand based on economic indicators was modeled by using various forms, e.g. linear (Eq. (4)) and quadratic (Eq. (5)). Linear form (EEPSOL) can be expressed as, Elinear= w1.X1 + w2.X2 + w3.X3 + w4.X4 + w5(4) and quadratic form (EEPSOQ) can be expressed as, Equadratic= w1.X1 + w2.X2 + w3.X3 + w4.X4 + w5.X1.X2 + w6.X1.X3 + w7.X1.X4 + w8.X2.X3 + w9.X2.X4 + w10.X3.X4 + w11.X12 + w12.X22 + w13.X32 + w14.X42 + w15(5) 17 Particle SwarmOptimizationApproachforEstimation of EnergyDemand of Turkey
18. PSO Energy Demand Estimation (EEPSO) EEPSO model optimizes coefficients (wi) of the design parameters (Xi), which are included by models, concurrently. In energy demand estimating, the aim is to find the fittest model to the data. The fitness function of the model is given by, Min𝑓𝑣=𝑖=1𝑛(𝐸𝑖𝑜𝑏𝑠𝑒𝑟𝑣𝑒𝑑−𝐸𝑖𝑝𝑟𝑒𝑑𝑖𝑐𝑡𝑒𝑑)2(6) where Eobserved and Epredictedare the actual and predicted energy demand, respectively, n is the number of observations. 18 Particle SwarmOptimizationApproachforEstimation of EnergyDemand of Turkey
19. PSO Energy Demand Estimation (EEPSO) The EEPSO algorithm is composed of 4 main steps: Step1.Initialize a defined population of particles with random positions (Xi), velocities (Vi) and set iteration number, c1, c2 and wmax-min values. Step2.Compute the objective values (forecasting errors) of all particles. Define own best position of each particle and its objective value pbest equal to its initial position and objective value, and define global best position and its objective value gbest equal to the best initial particle position and its objective value. Step3.Change velocities and positions by using Eqs. (1) and (2). Step4.Repeat step 2 and step 3 until the predefined number of iterations is completed. 19 Particle SwarmOptimizationApproachforEstimation of EnergyDemand of Turkey
20. EEPSO models (linear (EEPSOL) and quadratic (EEPSOQ)) are developed to estimate the future energy demand values based on population, GDP (gross domestic product), import and export figures. The EEPSO model was coded with MATLAB 2009 and run on a Pentium IV, 1.66 GHz, 2 GB RAM notebook computer. One of the important problems is setting the best parameters of PSO. Four important factors, particle size, inertia weight (w), maximum iteration number (iter) and c1,2are considered. According to Shi and Eberhart(1998)c1 and c2 have a fixed value as 2. The other parameters except inertia weight (w) is considered with the same of Ünler(2008); as particle size: 20 and as maximum iteration number: 1000. A few statistical experiments are performed in order to find the best value of wmax and wmin. As a result of the statistical analysis, wmax and wmin are determined as 0.7 and 0.5. Estimation of Turkey Energy Demand 20 Particle SwarmOptimizationApproachforEstimation of EnergyDemand of Turkey
21. Estimation of Turkey Energy Demand Twenty-seven data (1979–2005) were used to determine the weighting parameters of EEPSO models. EEPSOL and EEPSOQ models with aforementioned parameters and data were tested 20 times and best results were considered. In the linear form, coefficients obtained are given below: Elinear= 0,003806X1 + 1,912274X2 + 0,373543X3 – 0,483516X4 – 55,899070 (7) In the quadratic form of the proposed EEPSO model, coefficients obtained are given below: Equadratic= -0,005446X1+ 0,044550X2 – 0,431963X3 + 1,039665X4 + 0,004848X1*X2 + 0,008802X1*X3 – 0,006318X1*X4 – 0,006640X2*X3 – 0,002213X2*X4 + 0,002804X3*X4 – 0,001327X12 + 0,009923X22 - 0,006355X32 – 0,003039X42 + 1,254002 (8) where X1 is GDP, X2 is population, X3 is import, X4 is export and f(v) is sum of squared errors. 21 Particle SwarmOptimizationApproachforEstimation of EnergyDemand of Turkey
22. Estimation of Turkey Energy Demand Ten data (1996–2005) were used to validate the models. Table shows relative errors between estimated and observed data. 22 Particle SwarmOptimizationApproachforEstimation of EnergyDemand of Turkey
23. Estimation of Turkey Energy Demand According to Table, proposed EEPSO approach for energy demand estimation are very robust and successful. Although the largest deviation is 3.37% for linear form and -2.38% for quadratic form, they are quite acceptable levels. The largest deviations are obtained in 1999 because of the decreasing in GDP, import and export in that year. Results show that quadratic form provided better fit estimation than the linear form due to the fluctuations of the economic indicators. 23 Particle SwarmOptimizationApproachforEstimation of EnergyDemand of Turkey
24. Estimation of Turkey Energy Demand It is also observed that while proposed EEPSOL approach is providing better fit estimation than Toksarı (2007) and Ünler(2008) in linear form, EEPSOQ remains between Toksarı(2007)and Ünler(2008)in quadratic form. Comparisons of energy demand in linear form 24 Particle SwarmOptimizationApproachforEstimation of EnergyDemand of Turkey
25. Estimation of Turkey Energy Demand Comparisons of energy demand in quadratic form 25 Particle SwarmOptimizationApproachforEstimation of EnergyDemand of Turkey
26. Estimation of Turkey Energy Demand When twenty seven data is considered (1979-2005), proposed approach finds less relative error than the other studies in both of linear and quadratic forms. Tables give coefficients and forecasting relative errors of each study in linear and quadratic forms. Comparisons of coefficients and relative errors in linear form Comparisons of coefficients and relative errors in quadratic form 26 Particle SwarmOptimizationApproachforEstimation of EnergyDemand of Turkey
27. In order to show the accuracy of proposed models, three scenarios are used for forecasting Turkey’s energy demand in the years 2006–2025 and they are compared with Toksari’s(2007)ACO, Ünler’s(2008)PSO models and MENR projections. Each scenario is explained below [40]; Scenario 1:It is assumed that the average growth rate of GDP is 6%, population growth rate is 0.17%, import growth rate is 4.5%, and export growth rate is 2% during the period of 2006–2025. Scenario 2:It is assumed that the average growth rate of GDP is 5%, population growth rate is 0.15%, %, import growth rate is 5%, and proportion of import covered by export is 45% during the period of 2006–2025. Scenario 3:It is assumed that the average growth rate of GDP is 4%, population growth rate is 0.18%, import growth rate is 4.5%, and export growth rate 3.5% during the period of 2006–2025. Scenarios 27 Particle SwarmOptimizationApproachforEstimation of EnergyDemand of Turkey
28. Tableshowsthe estimated values for two forms of proposed approach for the Scenario 1. Proposed EEPSOQ form gives lower forecasts of the energy demand than the Toksarı(2007), Ünler(2008)and MENR projections. The proposed EEPSOL form also gives lower estimates of the energy demand than the Toksarı(2007)and MENR projections. It gives a bit higher estimation values than Ünler’s(2008)linear model. Scenario 1 28 Particle SwarmOptimizationApproachforEstimation of EnergyDemand of Turkey
29. Scenario 1 Future projections of total energy demand in MTOE according to Scenario 1 (linear form) Future projections of total energy demand in MTOE according to Scenario 1 (quadratic form) 29 Particle SwarmOptimizationApproachforEstimation of EnergyDemand of Turkey
30. Table showsthe estimated values for two forms of proposed approach for the Scenario 2. As can be seen, three linear studies (Toksarı2007; Ünler2008; EEPSOL)give nearly the same estimation that proposed EEPSOL method is lower than Toksarı(2007)higher than Ünler(2008). Proposed EEPSOQ form gives lower forecasts of the energy demand than Toksarı(2007)and Ünler(2008). Scenario 2 30 Particle SwarmOptimizationApproachforEstimation of EnergyDemand of Turkey
31. Scenario 2 Future projections of total energy demand in MTOE according to Scenario 2(linear form) Future projections of total energy demand in MTOE according to Scenario 2 (quadratic form) 31 Particle SwarmOptimizationApproachforEstimation of EnergyDemand of Turkey
32. Estimated values for two forms of proposed approach for the Scenario 3 could be seen in Table. EEPSOL gives lower estimates of energy demand than Toksarı’s(2007)linear model and MENR projections. It is also lower than Ünler’s(2008)linear model until 2011 then they give nearly the same estimation. In quadratic form, as it can be seen fromtable, proposed EEPSOQ model gives the lowest forecasts of the energy demand. Scenario 3 32 Particle SwarmOptimizationApproachforEstimation of EnergyDemand of Turkey
33. Scenario 3 Future projections of total energy demand in MTOE according to Scenario 3 (quadratic form) Future projections of total energy demand in MTOE according to Scenario 3(linear form) 33 Particle SwarmOptimizationApproachforEstimation of EnergyDemand of Turkey
34. Conclusion Planning and estimating of energy is quite important to make sustainable energy policy for countries. The relation between energy demand and socio-economic development of a country shows the importance of the need for systematic optimization of the energy demand estimation in Turkey. That’s why, in this study, estimation of Turkey’s energy demand based on PSO is suggested via considering GDP, population, import and export indicators. Two forms (linear and quadratic) of the EEPSO model are developed because of fluctuations of the economic indicators. 27 years data (1979-2005) is used to show the availability and advantages of proposed approach than the previous studies. Three scenarios are proposed to forecast Turkey’s energy demand in the years 2006–2025 using the two forms of the EEPSO. They are compared with the MENR, Toksarı’sACOand Ünler’sPSOprojections. 34 Particle SwarmOptimizationApproachforEstimation of EnergyDemand of Turkey
35. Conclusion In this study, the following main conclusions may be drawn: While the largest deviation is 3.37% for linear form (EEPSOL), the largest deviation is 2.38% for quadratic form (EEPSOQ) in modeling with 27 years data (1979-2005). Then, it is observed that quadratic EEPSO provided better fit solution than linear form due to the fluctuations of the economic indicators. According to results of modeling and scenario analysis, it is clear that particle swarm optimization technique gives better forecasts than ant colony optimization technique. While EEPSOL gives lower relative error than Toksarı’s(2007)linear model with 8.77% and Ünler’s(2008)linear model with 2.12%, EEPSOQ gives lower relative error than Toksarı’s(2007)quadratic model with 22.95% and Ünler’s(2008)quadratic model with 22.16%. The estimation of energy demand of Turkey using EEPSOQ form is underestimated and EEPSOL form has close estimations when the results are compared with Toksarı’s(2007), Ünler’s(2008)and MENR projections (2006-2025). So, it can be say that EEPSO forms, especially EEPSOQ is more realistic and acceptable. 35 Particle SwarmOptimizationApproachforEstimation of EnergyDemand of Turkey
36. It is concluded that the suggested models are satisfactory tools for successful energy demand forecasting. The results presented here provide helpful insight into energy system modeling. They could be also instrumental to scholars and policy makers as a potential tool for developing energy plans. Future works should be focused on comparing the methods presented here with other available tools. Forecasting of energy demand can also be investigated with bee colony optimization, artificial bee colony, bacterial foraging optimization, fuzzy logic, artificial neural networks or other meta-heuristic such as tabu search, simulated annealing, etc. The results of the different methods can be compared with the PSO methods. Future Research 36 Particle SwarmOptimizationApproachforEstimation of EnergyDemand of Turkey
37. Merci Teşekkürler Grazie ευχαριστία Salamat Thank You благодарность Shoukran Danke Gracias 37 Particle SwarmOptimizationApproachforEstimation of EnergyDemand of Turkey