COMPARISON OF LEVENBERG-MARQUARDT BASED LEAST SQUARES METHOD AND A HEURISTIC TECHNIQUE FOR ELECTRICITY DEMAND ESTIMATION

Abstract

This paper focuses on demand estimation of electricity in Iran using artificial bee swarm optimization (ABSO) algorithm which is a recently invented metaheuristic technique. For this aim, two types of exponential and quadratic models are investigated to estimate the Iran’s electricity demand. These models are defined based on socio-economic indicators of population, gross domestic product (GDP), import and export figures. Owing to the fluctuations of the economic indicators and nonlinearity of the electricity demand, an efficient technique must be employed to find optimal or near optimal values of the models’ weighting factors. This paper proposes ABSO as an efficient approach for solving this problem. The available data of electricity demand in Iran from 1981 to 1999 is used for finding the optimal weighing factors and the data from 2000 to 2005 is used for testing the models. In order to evaluate the performance of the proposed methodology, the results are compared with the result obtained by the traditional nonlinear least-squares optimization method of and Levenberg–Marquardt (LM)

Keywords

• Electricity demand estimation, Socio-economic indicators, Levenberg-Marquardt, Artificial bee swarm optimization

References

1. 1] Central bank of islamic republic of Iran, Report and statistics; 2007.
2. [2] M.H. Amjadi, H. Nezamabadi-pour, M.M. Farsangi, Estimation of electricity demand of Iran using two heuristic algorithms, Energy Conver Manage 51 (2010), 493-497.
3. [3] M.D. Toksari, Ant colony optimization approach to estimate energy demand in Turkey. Energy Policy 35 (2007) 3984-3990.
4. [4] E. Assareh, M.A. Behrang, M.R. Assari, A. Ghanbarzadeh, Application of PSO (particle swarm optimization) and GA (genetic algorithm) techniques on demand estimation of oil in Iran. Energy 35 (2010) 5223-5229.
5. [5] H.K. Ozturk, H. Ceylan, O.E. Canyurt, A. Hepbasli, Electricity estimation using genetic algorithm approach: a case study of Turkey. Energy 30 (2005) 1003-1012.
6. [6] H. Ceylan, H.K. Ozturk, Estimating energy demand of Turkey based on economic indicators using genetic algorithm approach. Energy Conver Manage 45 (2004) 2525-2537.
7. [7] O.E. Canyurt, H.K. Ozturk, Application of genetic algorithm (GA) technique on demand estimation of fossil fuels in Turkey. Energy Policy 36 (2008) 2562-2569.
8. [8] A. Azadeh, S.F. Ghaderi, S. Sohrabkhani, A simulated-based neural network algorithm for forecasting electrical energy consumption in Iran. Energy Policy 36 (2008) 2637-2644.

9. [9] A. Azadeh, M. Saberi, O. Seraj. An integrated fuzzy regression algorithm for energy consumption estimation with non-stationary data: a case study of Iran. Energy 35 (2010) 2351-2366.
10. [10] M. Zhang, H. Mu, G. Li, Y. Ning, Forecasting the transport energy demand based on PLSR method in China. Energy 34 (2009) 1396-1400.
11. [11] M. Nava, J. Gasca, U. Gonzalez, The energy demand and the impact by fossil fuels use in the Mexico City Metropolitan Area, from 1988 to 2000. Energy 31 (2006) 3381-3390.
12. [12] A. Askarzadeh, A. Rezazadeh, A new artificial bee swarm algorithm for optimization of proton exchange membrane fuel cell model parameters, Journal of Zhejiang University Science C 12 (2011) 638-646.
13. [13] A. Askarzadeh, A. Rezazadeh, Artificial bee swarm optimization algorithm for parameters identification of solar cell models, Applied Energy 102 (2013) 943-949.
14. [14] Energy Balance Annual Report. Tehran: Ministry of Energy; 2005.