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

Authors : Alireza Askarzadeh, Ali Heydari

Pages : 59-67

Doi:10.24107/ijeas.251234

View : 16 | Download : 5

Publication Date : 2015-03-01

Article Type : Research Paper

Abstract :This paper focuses on demand estimation of electricity in Iran using artificial bee swarm optimization insert ignore into journalissuearticles values(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 insert ignore into journalissuearticles values(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 insert ignore into journalissuearticles values(LM);
Keywords : Electricity demand estimation, Socio economic indicators, Levenberg Marquardt, Artificial bee swarm optimization