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Open AccessArticle

Electricity Consumption Forecasting using Support Vector Regression with the Mixture Maximum Correntropy Criterion

1
School of Automation and Information Engineering, Xi’an University of Technology, Xi’an 710048, China
2
State Key Laboratory of Electrical Insulation and Power Equipment, Xi’an Jiaotong University, Xi’an 710048, China
3
School of Statistics, Xi’an University of Finance and Economics, Xi’an 710100, China
*
Author to whom correspondence should be addressed.
Entropy 2019, 21(7), 707; https://doi.org/10.3390/e21070707
Received: 28 May 2019 / Revised: 5 July 2019 / Accepted: 15 July 2019 / Published: 19 July 2019
(This article belongs to the Special Issue Information Theoretic Learning and Kernel Methods)
The electricity consumption forecasting (ECF) technology plays a crucial role in the electricity market. The support vector regression (SVR) is a nonlinear prediction model that can be used for ECF. The electricity consumption (EC) data are usually nonlinear and non-Gaussian and present outliers. The traditional SVR with the mean-square error (MSE), however, is insensitive to outliers and cannot correctly represent the statistical information of errors in non-Gaussian situations. To address this problem, a novel robust forecasting method is developed in this work by using the mixture maximum correntropy criterion (MMCC). The MMCC, as a novel cost function of information theoretic, can be used to solve non-Gaussian signal processing; therefore, in the original SVR, the MSE is replaced by the MMCC to develop a novel robust SVR method (called MMCCSVR) for ECF. Besides, the factors influencing users’ EC are investigated by a data statistical analysis method. We find that the historical temperature and historical EC are the main factors affecting future EC, and thus these two factors are used as the input in the proposed model. Finally, real EC data from a shopping mall in Guangzhou, China, are utilized to test the proposed ECF method. The forecasting results show that the proposed ECF method can effectively improve the accuracy of ECF compared with the traditional SVR and other forecasting algorithms. View Full-Text
Keywords: electricity consumption forecasting; support vector regression; mixture maximum correntropy criterion; parameter optimization electricity consumption forecasting; support vector regression; mixture maximum correntropy criterion; parameter optimization
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Duan, J.; Tian, X.; Ma, W.; Qiu, X.; Wang, P.; An, L. Electricity Consumption Forecasting using Support Vector Regression with the Mixture Maximum Correntropy Criterion. Entropy 2019, 21, 707.

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