Electricity Load Forecasting Using Support Vector Regression with Memetic Algorithms

Abstract

Electricity load forecasting is an important issue that is widely explored and examined in power systems operation literature and commercial transactions in electricity markets literature as well. Among the existing forecasting models, support vector regression (SVR) has gained much attention. Considering the performance of SVR highly depends on its parameters; this study proposed a firefly algorithm (FA) based memetic algorithm (FA‐MA) to appropriately determine the parameters of SVR forecasting model. In the proposed FA‐MA algorithm, the FA algorithm is applied to explore the solution space, and the pattern search is used to conduct individual learning and thus enhance the exploitation of FA. Experimental results confirm that the proposed FA‐MA based SVR model can not only yield more accurate forecasting results than the other four evolutionary algorithms based SVR models and three well‐known forecasting models but also outperform the hybrid algorithms in the related existing literature.