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

Year 2015, , 59 - 67, 01.03.2015

Alireza Askarzadeh Ali Heydari

https://doi.org/10.24107/ijeas.251234

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

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