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Article

Electricity Consumption Forecasting Scheme via Improved LSSVM with Maximum Correntropy Criterion

School of Automation and Information Engineering, Xi’an University of Technology, Xi’an 710048, China
*
Author to whom correspondence should be addressed.
Current address: Department of Electrical Engineering, Xi’an University of Technology, Xi’an 710048, China.
These authors contributed equally to this work.
Entropy 2018, 20(2), 112; https://doi.org/10.3390/e20020112
Received: 15 November 2017 / Revised: 11 January 2018 / Accepted: 5 February 2018 / Published: 8 February 2018
(This article belongs to the Special Issue Entropy in Signal Analysis)
In recent years, with the deepening of China’s electricity sales side reform and electricity market opening up gradually, the forecasting of electricity consumption (FoEC) becomes an extremely important technique for the electricity market. At present, how to forecast the electricity accurately and make an evaluation of results scientifically are still key research topics. In this paper, we propose a novel prediction scheme based on the least-square support vector machine (LSSVM) model with a maximum correntropy criterion (MCC) to forecast the electricity consumption (EC). Firstly, the electricity characteristics of various industries are analyzed to determine the factors that mainly affect the changes in electricity, such as the gross domestic product (GDP), temperature, and so on. Secondly, according to the statistics of the status quo of the small sample data, the LSSVM model is employed as the prediction model. In order to optimize the parameters of the LSSVM model, we further use the local similarity function MCC as the evaluation criterion. Thirdly, we employ the K-fold cross-validation and grid searching methods to improve the learning ability. In the experiments, we have used the EC data of Shaanxi Province in China to evaluate the proposed prediction scheme, and the results show that the proposed prediction scheme outperforms the method based on the traditional LSSVM model. View Full-Text
Keywords: electricity consumption forecasting; least-square support vector machine; maximum correntropy criterion; K-fold cross-validation electricity consumption forecasting; least-square support vector machine; maximum correntropy criterion; K-fold cross-validation
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MDPI and ACS Style

Duan, J.; Qiu, X.; Ma, W.; Tian, X.; Shang, D. Electricity Consumption Forecasting Scheme via Improved LSSVM with Maximum Correntropy Criterion. Entropy 2018, 20, 112. https://doi.org/10.3390/e20020112

AMA Style

Duan J, Qiu X, Ma W, Tian X, Shang D. Electricity Consumption Forecasting Scheme via Improved LSSVM with Maximum Correntropy Criterion. Entropy. 2018; 20(2):112. https://doi.org/10.3390/e20020112

Chicago/Turabian Style

Duan, Jiandong, Xinyu Qiu, Wentao Ma, Xuan Tian, and Di Shang. 2018. "Electricity Consumption Forecasting Scheme via Improved LSSVM with Maximum Correntropy Criterion" Entropy 20, no. 2: 112. https://doi.org/10.3390/e20020112

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