Selecting the appropriate scenario for forecasting energy demands of residential and commercial sectors in Iran using two metaheuristic algorithms

Document Type : Research Paper


1 Faculty of Management, University of Tehran, Tehran, Iran

2 Faculty of Management, University of Tehran

3 Faculty of Power and Water (Shahid Abbaspour), Shahid Beheshti University


This study focuses on the forecasting of energy demands of residential and commercial sectors using linear and exponential functions. The coefficients were obtained from genetic and particle swarm optimization (PSO) algorithms. Totally, 72 different scenarios with various inputs were investigated. Consumption data in respect of residential and commercial sectors in Iran were collected from the annual reports of the central bank, Ministry of Energy and the Petroleum Ministry of Iran (2010). The data from 1967 to 2010 were considered for the case of this study. The available data were used partly to obtain the optimal, or near optimal values of the coefficient parameters (1967–2006) and for testing the models (2007–2010). Results show that the PSO energy demand estimation exponential model with inputs, including value addition of all economic sectors, value of constructed buildings, population, and price indices of electrical and fuel appliances using the mean absolute percentage error on tests data were 1.97%, was considered the most suitable model. Finally, basing on the best scenario, the energy demand of residential and commercial sectors is estimated at 1718 mega barrels of oil equivalent up to the year 2032.


AlRashidi, M., & El-Naggar, K. (2010).“Long term electric load forecasting based on particle swarm optimization”, Applied Energy,vol.87, NO. 1, 320-326.
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, VOL. 35, NO. 12, 5223-5229.
Azadeh, A., & Tarverdian, S. (2007). “Integration of genetic algorithm, computer simulation and design of experiments for forecasting electrical energy consumption”. Energy Policy, VOL. 35, NO. 10, 5229-5241.
Canyurt, O. E., & Ozturk, H. K. (2008). “Application of genetic algorithm (GA) technique on demand estimation of fossil fuels in Turkey”. Energy Policy, VOL. 36, NO. 7, 2562-2569.
Haupt, R. L., & Haupt, S. E. (2004). Practical genetic algorithms: John Wiley & Sons.
Karbassi, A., Abduli, M., & Mahin Abdollahzadeh, E. (2007). “Sustainability of energy production and use in Iran”. Energy Policy, VOL. 35, NO. 8, 5171-5180.
Kıran, M. S., Özceylan, E., Gündüz, M., & Paksoy, T. (2012). “A novel hybrid approach based on particle swarm optimization and ant colony algorithm to forecast energy demand of Turkey”. Energy Conversion and Management, VOL. 53, NO. 1, 75-83.
Labandeira, X., Labeaga, J. M., & López-Otero, X. (2011). “Energy demand for heating in Spain: An empirical analysis with policy purposes”. WP, 6, 2011.
Lee, Y.-S., & Tong, L.-I. (2011). “Forecasting energy consumption using a grey model improved by incorporating genetic programming”. Energy Conversion and Management, VOL. 52, NO. 1, 147-152.
Leticia, B., Boogen, N., & Filippini, M. (2012). “Residential electricity demand for Spain: new empirical evidence using aggregated data”: CEPE Center for Energy Policy and Economics, ETH Zurich.
Madlener, R., & Alt, R. (1996). “Residential energy demand analysis: an empirical application of the closure test principle”. Empirical Economics, VOL. 21, NO. 2, 203-220.
 Ministry of Energy (MOE). Energy balance annual report. Tehran, Iran. (2012).
Ozturk, H. K., & Ceylan, H. (2005). “Forecasting total and industrial sector electricity demand based on genetic algorithm approach: Turkey case study”. International journal of energy research, VOL. 29, NO. 9, 829-840.
Poyer, D. A., &Williams, M. (1993). “Residential energy demand: additional empirical evidence by minority household type”. Energy Economics, VOL. 15, NO. 2, 93-100.
Shakouri.G. H & Kazemi, 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, Los Angeles, California, USA.
Sözen, A., Gülseven, Z., & Arcaklioğlu, E. (2007). “Forecasting based on sectoral energy consumption of GHGs in Turkey and mitigation policies”. Energy Policy, VOL. 35, NO. 12, 6491-6505.
Ünler, A. (2008). “Improvement of energy demand forecasts using swarm intelligence: The case of Turkey with projections to 2025”. Energy Policy, VOL. 36, NO. 6, 1937-1944.