How to cite this paper
Nazari, H., Kazemi, A., Hashemi, M & Nazari, M. (2014). The application of particle swarm optimization algorithm in forecasting energy demand of residential - commercial sector with the use of economic indicators.Management Science Letters , 4(11), 2415-2422.
Refrences
Amjadi, M., Nezamabadi-Pour, H., & Farsangi, M. (2010). Estimation of electricity demand of Iran using two heuristic algorithms. Energy Conversion and Management, 51(3), 493-497.
Ardakani, F., & Ardehali, M. (2014). Long-term electrical energy consumption forecasting for developing and developed economies based on different optimized models and historical data types. Energy, 65, 452-461.
Assareh, E., Behrang, M., Assari, M., & Ghanbarzadeh, A. (2010). Application of PSO (particle swarm optimization) and GA (genetic algorithm) techniques on demand estimation of oil in Iran. Energy, 35(12), 5223-5229.
Assareh, E., Behrang, M., & Ghanbarzadeh, A. (2012). The integration of artificial neural networks and particle swarm optimization to forecast world green energy consumption. Energy Sources, Part B: Economics, Planning, and Policy, 7(4), 398-410.
Assareh, E., Behrang, M., & Ghanbarzdeh, A. (2012). Forecasting energy demand in Iran using genetic algorithm (GA) and particle swarm optimization (PSO) methods. Energy Sources, Part B: Economics, Planning, and Policy, 7(4), 411-422.
Avami, A., & Boroushaki, M. (2011). Energy consumption forecasting of Iran using recurrent neural networks. Energy Sources, Part B: Economics, Planning, and Policy, 6(4), 339-347.
Azadeh, A., & Tarverdian, S. (2007). Integration of genetic algorithm, computer simulation and design of experiments for forecasting electrical energy consumption. Energy Policy, 35(10), 5229-5241.
Bahrami, S., Hooshmand, R.A., & Parastegari, M. (2014). Short term electric load forecasting by wavelet transform and grey model improved by PSO (particle swarm optimization) algorithm. Energy, 72, 434-442.
Behrang, M., Assareh, E., Assari, M., & Ghanbarzadeh, A. (2011). Total energy demand estimation in Iran using bees algorithm. Energy Sources, Part B: Economics, Planning, and Policy, 6(3), 294-303.
Behrang, M., Assareh, E., Ghalambaz, M., Assari, M., & Noghrehabadi, A. (2011). Forecasting future oil demand in Iran using GSA (Gravitational Search Algorithm). Energy, 36(9), 5649-5654.
Forouzanfar, M., Doustmohammadi, A., Menhaj, M. B., & Hasanzadeh, S. (2010). Modeling and estimation of the natural gas consumption for residential and commercial sectors in Iran. Applied Energy, 87(1), 268-274.
Kaveh, A., Shamsapour, N., Sheikholeslami, R., & Mashhadian, M. (2012). Forecasting transport energy demand in Iran using meta-heuristic algorithms. Int. J. Optim. Civil Eng, 2(4), 533-544.
Mikki, S. M., & Kishk, A. A. (2008). Particle swarm optimization: A physics-based approach. Synthesis Lectures on Computational Electromagnetics, 3(1), 1-103.
Piltan, M., Shiri, H., & Ghaderi, S. (2012). Energy demand forecasting in Iranian metal industry using linear and nonlinear models based on evolutionary algorithms. Energy Conversion and Management, 58, 1-9.
Shakouri, G. H. K., A. (2011). Energy demand forecast of residential and commercial sectors: Iran case study. proceedings of the 41st international conference on computers & industrial engineering 23-25 October, LosAngeles, California, USA.
Ardakani, F., & Ardehali, M. (2014). Long-term electrical energy consumption forecasting for developing and developed economies based on different optimized models and historical data types. Energy, 65, 452-461.
Assareh, E., Behrang, M., Assari, M., & Ghanbarzadeh, A. (2010). Application of PSO (particle swarm optimization) and GA (genetic algorithm) techniques on demand estimation of oil in Iran. Energy, 35(12), 5223-5229.
Assareh, E., Behrang, M., & Ghanbarzadeh, A. (2012). The integration of artificial neural networks and particle swarm optimization to forecast world green energy consumption. Energy Sources, Part B: Economics, Planning, and Policy, 7(4), 398-410.
Assareh, E., Behrang, M., & Ghanbarzdeh, A. (2012). Forecasting energy demand in Iran using genetic algorithm (GA) and particle swarm optimization (PSO) methods. Energy Sources, Part B: Economics, Planning, and Policy, 7(4), 411-422.
Avami, A., & Boroushaki, M. (2011). Energy consumption forecasting of Iran using recurrent neural networks. Energy Sources, Part B: Economics, Planning, and Policy, 6(4), 339-347.
Azadeh, A., & Tarverdian, S. (2007). Integration of genetic algorithm, computer simulation and design of experiments for forecasting electrical energy consumption. Energy Policy, 35(10), 5229-5241.
Bahrami, S., Hooshmand, R.A., & Parastegari, M. (2014). Short term electric load forecasting by wavelet transform and grey model improved by PSO (particle swarm optimization) algorithm. Energy, 72, 434-442.
Behrang, M., Assareh, E., Assari, M., & Ghanbarzadeh, A. (2011). Total energy demand estimation in Iran using bees algorithm. Energy Sources, Part B: Economics, Planning, and Policy, 6(3), 294-303.
Behrang, M., Assareh, E., Ghalambaz, M., Assari, M., & Noghrehabadi, A. (2011). Forecasting future oil demand in Iran using GSA (Gravitational Search Algorithm). Energy, 36(9), 5649-5654.
Forouzanfar, M., Doustmohammadi, A., Menhaj, M. B., & Hasanzadeh, S. (2010). Modeling and estimation of the natural gas consumption for residential and commercial sectors in Iran. Applied Energy, 87(1), 268-274.
Kaveh, A., Shamsapour, N., Sheikholeslami, R., & Mashhadian, M. (2012). Forecasting transport energy demand in Iran using meta-heuristic algorithms. Int. J. Optim. Civil Eng, 2(4), 533-544.
Mikki, S. M., & Kishk, A. A. (2008). Particle swarm optimization: A physics-based approach. Synthesis Lectures on Computational Electromagnetics, 3(1), 1-103.
Piltan, M., Shiri, H., & Ghaderi, S. (2012). Energy demand forecasting in Iranian metal industry using linear and nonlinear models based on evolutionary algorithms. Energy Conversion and Management, 58, 1-9.
Shakouri, G. H. K., A. (2011). Energy demand forecast of residential and commercial sectors: Iran case study. proceedings of the 41st international conference on computers & industrial engineering 23-25 October, LosAngeles, California, USA.