Applications of Hybrid EMD with PSO and GA for an SVR-Based Load Forecasting Model

Guo-Feng Fan, Li-Ling Peng, Xiangjun Zhao and Wei-Chiang Hong ()
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Guo-Feng Fan: College of Mathematics & Information Science, Ping Ding Shan University, Pingdingshan 467000, China
Li-Ling Peng: College of Mathematics & Information Science, Ping Ding Shan University, Pingdingshan 467000, China
Xiangjun Zhao: School of Education Intelligent Technology, Jiangsu Normal University, 101 Shanghai Rd., Tongshan District, Xuzhou 221116, China

Energies, 2017, vol. 10, issue 11, 1-22

Abstract: Providing accurate load forecasting plays an important role for effective management operations of a power utility. When considering the superiority of support vector regression (SVR) in terms of non-linear optimization, this paper proposes a novel SVR-based load forecasting model, namely EMD-PSO-GA-SVR, by hybridizing the empirical mode decomposition (EMD) with two evolutionary algorithms, i.e., particle swarm optimization (PSO) and the genetic algorithm (GA). The EMD approach is applied to decompose the load data pattern into sequent elements, with higher and lower frequencies. The PSO, with global optimizing ability, is employed to determine the three parameters of a SVR model with higher frequencies. On the contrary, for lower frequencies, the GA, which is based on evolutionary rules of selection and crossover, is used to select suitable values of the three parameters. Finally, the load data collected from the New York Independent System Operator (NYISO) in the United States of America (USA) and the New South Wales (NSW) in the Australian electricity market are used to construct the proposed model and to compare the performances among different competitive forecasting models. The experimental results demonstrate the superiority of the proposed model that it can provide more accurate forecasting results and the interpretability than others.

Keywords: support vector regression; empirical mode decomposition (EMD); particle swarm optimization (PSO); genetic algorithm (GA); 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: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (14)

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