Hybrid Empirical Mode Decomposition with Support Vector Regression Model for Short Term Load Forecasting

Wei-Chiang Hong and Guo-Feng Fan
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Wei-Chiang Hong: School of Computer Science and Technology, Jiangsu Normal University, Xuzhou 221116, Jiangsu, China
Guo-Feng Fan: School of Mathematics and Statistics, Ping Ding Shan University, Ping Ding Shan 467000, Henan, China

Energies, 2019, vol. 12, issue 6, 1-16

Abstract: For operational management of power plants, it is desirable to possess more precise short-term load forecasting results to guarantee the power supply and load dispatch. The empirical mode decomposition (EMD) method and the particle swarm optimization (PSO) algorithm have been successfully hybridized with the support vector regression (SVR) to produce satisfactory forecasting performance in previous studies. Decomposed intrinsic mode functions (IMFs), could be further defined as three items: item A contains the random term and the middle term; item B contains the middle term and the trend (residual) term, and item C contains the middle terms only, where the random term represents the high-frequency part of the electric load data, the middle term represents the multiple-frequency part, and the trend term represents the low-frequency part. These three items would be modeled separately by the SVR-PSO model, and the final forecasting results could be calculated as A+B-C (the defined item D). Consequently, this paper proposes a novel electric load forecasting model, namely H-EMD-SVR-PSO model, by hybridizing these three defined items to improve the forecasting accuracy. Based on electric load data from the Australian electricity market, the experimental results demonstrate that the proposed H-EMD-SVR-PSO model receives more satisfied forecasting performance than other compared models.

Keywords: empirical mode decomposition (EMD); particle swarm optimization (PSO) algorithm; intrinsic mode function (IMF); support vector regression (SVR); short term load forecasting (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (12)

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