Day-ahead electricity price forecasting using WPT, GMI and modified LSSVM-based S-OLABC algorithm

Abstract

Electricity price forecasting has nowadays become a significant task to all market players in deregulated electricity market. The information obtained from future electricity helps market participants to develop costeffective bidding strategies to maximize their profit. Accurate price forecasting involves all market participants such as customer or producer in competitive electricity markets. This paper presents a novel hybrid algorithm to forecast day-ahead prices in the electricity market. This hybrid algorithm consists of (a) generalized mutual information (GMI), wavelet packet transform (WPT) as pre-processing methods, (b) least squares support vector machine based on Bayesian model (LSSVM-B) as forecaster engine, (c) and a modified artificial bee colony (ABC) algorithm used for optimization. Moreover, the orthogonal learning (OL) is used as a global search tool to enhance the exploitation of the ABC algorithm. Hereafter, call the proposed hybrid algorithm as S-OLABC. The numerical simulation results performed in this paper for different cases in comparison to previously known classical and intelligent methods. In addition, it will be shown that GMI based on WPT has better performance in extracting input features compared to classical mutual information (MI).

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