Global Electricity Consumption Estimation Using Particle Swarm Optimization (PSO)
An integrated Artificial Neural Network- Particle Swarm Optimization (PSO) is presented for analyzing global electricity consumption. To aim this purpose, following steps are done: STEP 1: in the first step, PSO is applied in order to determine world-s oil, natural gas, coal and primary energy demand equations based on socio-economic indicators. World-s population, Gross domestic product (GDP), oil trade movement and natural gas trade movement are used as socio-economic indicators in this study. For each socio-economic indicator, a feed-forward back propagation artificial neural network is trained and projected for future time domain. STEP 2: in the second step, global electricity consumption is projected based on the oil, natural gas, coal and primary energy consumption using PSO. global electricity consumption is forecasted up to year 2040.
[1] E. Assareh, M.A. Behrang., M.R. Assari., A. Ghanbarzadeh. Application
of particle swarm optimization (PSO) and genetic algorithm (GA)
techniques on demand estimation of oil in Iran. Energy 2010; 35: 5223-
5229.
[2] A. Azadeh, S.F. Ghaderi, S. Sohrabkhani. A simulated-based neural
network algorithm for forecasting electrical energy consumption in Iran.
Energy Policy 2008 ; 36: 2637- 2644
[3] E. Assareh, M.A. Behrang., A. Ghanbarzadeh. Forecasting energy
demand in Iran using genetic algorithm (GA) and particle swarm
optimization (PSO) methods. Energy Sources, Part B: Economics,
Planning, and Policy: 2009 Accepted manuscript.
[4] M.A. Behrang E. Assareh, A. Ghanbarzadeh, A. Total energy demand
estimation in Iran using Bees Algorithm. Energy Sources, Part B:
Economics, Planning, and Policy: 2009Accepted manuscript.
[5] M.A. Behrang., E. Assareh, M.R. Assari, M.R., and A. Ghanbarzadeh.
Assessment of electricity demand in Iran's industrial sector using
different intelligent optimization techniques. Applied Artificial
Intelligence 2011; 25: 292-304. doi:10.1080/08839514.2011.559572
[6] A. Unler. Improvement of energy demand forecasts using swarm
intelligence: The case of Turkey with projections to 2025. Energy Policy
2008; 36: 1937-194
[7] O.E. Canyurt, H.K. Ozturk.. Application of genetic algorithm (GA)
technique on demand estimation of fossil fuels in Turkey. Energy
Policy2008;36:2562-2569.
[8] Ermis, K., Midilli, A., Dincer, I., Rosen, M.A., 2007. Artificial neural
network analysis of world green energy use. Energy Policy 35: 1731-
1743.
[9] Kennedy J, Eberhart R. Particle swarm optimization. Proc Neural
Networks. Proceedings,vol.1944. IEEE International Conference on,
1995. p.1942-1948 .
[10] Engelbrecht A P. Fundamentals of computational swarm intelligence.
Hoboken, N.J.: Wiley, 2005.
[11] Brits R, Engelbrecht AP, van den Bergh F. Locating multiple optima
using particle swarm optimization. Applied Mathematics and
Computation 2007; 189(2): 1859-1883.
[12] Liu X, Liu H, Duan H. Particle swarm optimization based on dynamic
niche technology with applications to conceptual design. Advances in
Engineering Software 2007; 38(10): 668-676.
[13] Pan H, Wang L, Liu B. Particle swarm optimization for function
optimization in noisy environment. Applied Mathematics and
Computation 2006; 181(2): 908-919.
[14] Yang IT.Performing complex project crashing analysis with aid of
particle swarm optimization algorithm. International Journal of Project
Management 2007; 25(6): 637-646.
[15] Shi Y, Eberhart R. A modified particle swarm optimizer. Proceedings of
the IEEE International Conference on Evolutionary Computation.
Anchorage, Alaska, 1998a. p.69-73.
[16] Shi Y, Eberhart R. Parameter selection in particle swarm optimization.
Proceedings of the Seventh Annual Conference on Evolutionary
Programming. New York, 1998b. p.591-600.
[17] M.A. Behrang, E. Assareh, A.R. Noghrehabadi, and A. Ghanbarzadeh.
New sunshine-based models for predicting global solar radiation using
PSO (particle swarm optimization) technique. Energy 2011; 36: 3036-
3049. doi:10.1016/j.energy.2011.02.048.
[18] Workbook, Statistical Review of World Energy. Available online at
http://www.bp.com/statisticalreview. 2008.
[19] IEA. Keyword energy statistic. International Energy Agency
(IEA).U.S.A. 2008.
[20] Pham DT, Koç E, Ghanbarzadeh A, Otri S. Optimisation of the Weights
of Multi-Layered Perceptrons Using the Bees Algorithm. in:
Proceedings of 5th International Symposium on Intelligent
Manufacturing Systems, Sakarya University, Department of Industrial
Engineering, , 2006. May 29-31, pp. 38-46.
[21] Pham DT, Liu X. Neural Networks for identification, prediction and
control, Springer verlag, london. , 1995.
[22] Yilmaz AS, Ozer Z. Pitch angle control in wind turbines above the
rated wind speed by multi-layer percepteron and redial basis function
neural networks. Expert Systems with Applications 36: 9767-9775,
2009.
[23] M.A. Behrang, E. Assareh, A. Ghanbarzadeh, A.R. Noghrehabadi. The
potential of different artificial neural network (ANN) techniques in daily
global solar radiation modeling based on meteorological data. Solar
Energy 2010; 84: 1468-1480.
[1] E. Assareh, M.A. Behrang., M.R. Assari., A. Ghanbarzadeh. Application
of particle swarm optimization (PSO) and genetic algorithm (GA)
techniques on demand estimation of oil in Iran. Energy 2010; 35: 5223-
5229.
[2] A. Azadeh, S.F. Ghaderi, S. Sohrabkhani. A simulated-based neural
network algorithm for forecasting electrical energy consumption in Iran.
Energy Policy 2008 ; 36: 2637- 2644
[3] E. Assareh, M.A. Behrang., A. Ghanbarzadeh. Forecasting energy
demand in Iran using genetic algorithm (GA) and particle swarm
optimization (PSO) methods. Energy Sources, Part B: Economics,
Planning, and Policy: 2009 Accepted manuscript.
[4] M.A. Behrang E. Assareh, A. Ghanbarzadeh, A. Total energy demand
estimation in Iran using Bees Algorithm. Energy Sources, Part B:
Economics, Planning, and Policy: 2009Accepted manuscript.
[5] M.A. Behrang., E. Assareh, M.R. Assari, M.R., and A. Ghanbarzadeh.
Assessment of electricity demand in Iran's industrial sector using
different intelligent optimization techniques. Applied Artificial
Intelligence 2011; 25: 292-304. doi:10.1080/08839514.2011.559572
[6] A. Unler. Improvement of energy demand forecasts using swarm
intelligence: The case of Turkey with projections to 2025. Energy Policy
2008; 36: 1937-194
[7] O.E. Canyurt, H.K. Ozturk.. Application of genetic algorithm (GA)
technique on demand estimation of fossil fuels in Turkey. Energy
Policy2008;36:2562-2569.
[8] Ermis, K., Midilli, A., Dincer, I., Rosen, M.A., 2007. Artificial neural
network analysis of world green energy use. Energy Policy 35: 1731-
1743.
[9] Kennedy J, Eberhart R. Particle swarm optimization. Proc Neural
Networks. Proceedings,vol.1944. IEEE International Conference on,
1995. p.1942-1948 .
[10] Engelbrecht A P. Fundamentals of computational swarm intelligence.
Hoboken, N.J.: Wiley, 2005.
[11] Brits R, Engelbrecht AP, van den Bergh F. Locating multiple optima
using particle swarm optimization. Applied Mathematics and
Computation 2007; 189(2): 1859-1883.
[12] Liu X, Liu H, Duan H. Particle swarm optimization based on dynamic
niche technology with applications to conceptual design. Advances in
Engineering Software 2007; 38(10): 668-676.
[13] Pan H, Wang L, Liu B. Particle swarm optimization for function
optimization in noisy environment. Applied Mathematics and
Computation 2006; 181(2): 908-919.
[14] Yang IT.Performing complex project crashing analysis with aid of
particle swarm optimization algorithm. International Journal of Project
Management 2007; 25(6): 637-646.
[15] Shi Y, Eberhart R. A modified particle swarm optimizer. Proceedings of
the IEEE International Conference on Evolutionary Computation.
Anchorage, Alaska, 1998a. p.69-73.
[16] Shi Y, Eberhart R. Parameter selection in particle swarm optimization.
Proceedings of the Seventh Annual Conference on Evolutionary
Programming. New York, 1998b. p.591-600.
[17] M.A. Behrang, E. Assareh, A.R. Noghrehabadi, and A. Ghanbarzadeh.
New sunshine-based models for predicting global solar radiation using
PSO (particle swarm optimization) technique. Energy 2011; 36: 3036-
3049. doi:10.1016/j.energy.2011.02.048.
[18] Workbook, Statistical Review of World Energy. Available online at
http://www.bp.com/statisticalreview. 2008.
[19] IEA. Keyword energy statistic. International Energy Agency
(IEA).U.S.A. 2008.
[20] Pham DT, Koç E, Ghanbarzadeh A, Otri S. Optimisation of the Weights
of Multi-Layered Perceptrons Using the Bees Algorithm. in:
Proceedings of 5th International Symposium on Intelligent
Manufacturing Systems, Sakarya University, Department of Industrial
Engineering, , 2006. May 29-31, pp. 38-46.
[21] Pham DT, Liu X. Neural Networks for identification, prediction and
control, Springer verlag, london. , 1995.
[22] Yilmaz AS, Ozer Z. Pitch angle control in wind turbines above the
rated wind speed by multi-layer percepteron and redial basis function
neural networks. Expert Systems with Applications 36: 9767-9775,
2009.
[23] M.A. Behrang, E. Assareh, A. Ghanbarzadeh, A.R. Noghrehabadi. The
potential of different artificial neural network (ANN) techniques in daily
global solar radiation modeling based on meteorological data. Solar
Energy 2010; 84: 1468-1480.
@article{"International Journal of Mechanical, Industrial and Aerospace Sciences:50735", author = "E.Assareh and M.A. Behrang and R. Assareh and N. Hedayat", title = "Global Electricity Consumption Estimation Using Particle Swarm Optimization (PSO)", abstract = "An integrated Artificial Neural Network- Particle Swarm Optimization (PSO) is presented for analyzing global electricity consumption. To aim this purpose, following steps are done: STEP 1: in the first step, PSO is applied in order to determine world-s oil, natural gas, coal and primary energy demand equations based on socio-economic indicators. World-s population, Gross domestic product (GDP), oil trade movement and natural gas trade movement are used as socio-economic indicators in this study. For each socio-economic indicator, a feed-forward back propagation artificial neural network is trained and projected for future time domain. STEP 2: in the second step, global electricity consumption is projected based on the oil, natural gas, coal and primary energy consumption using PSO. global electricity consumption is forecasted up to year 2040.
", keywords = "Particle Swarm Optimization, Artificial NeuralNetworks, Fossil Fuels, Electricity, Forecasting.", volume = "5", number = "7", pages = "1190-5", }
