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Hong-Juan Li
Engineering Research Center of Metallurgical Energy Conservation and Emission Reduction, Ministry of Education, Kunming University of Science and Technology, Kunming 650093, China

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Journal article
Published: 02 April 2013 in Energies
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Electric load forecasting is an important issue for a power utility, associated with the management of daily operations such as energy transfer scheduling, unit commitment, and load dispatch. Inspired by strong non-linear learning capability of support vector regression (SVR), this paper presents a SVR model hybridized with the empirical mode decomposition (EMD) method and auto regression (AR) for electric load forecasting. The electric load data of the New South Wales (Australia) market are employed for comparing the forecasting performances of different forecasting models. The results confirm the validity of the idea that the proposed model can simultaneously provide forecasting with good accuracy and interpretability.

ACS Style

Guo-Feng Fan; Shan Qing; Hua Wang; Wei-Chiang Hong; Hong-Juan Li. Support Vector Regression Model Based on Empirical Mode Decomposition and Auto Regression for Electric Load Forecasting. Energies 2013, 6, 1887 -1901.

AMA Style

Guo-Feng Fan, Shan Qing, Hua Wang, Wei-Chiang Hong, Hong-Juan Li. Support Vector Regression Model Based on Empirical Mode Decomposition and Auto Regression for Electric Load Forecasting. Energies. 2013; 6 (4):1887-1901.

Chicago/Turabian Style

Guo-Feng Fan; Shan Qing; Hua Wang; Wei-Chiang Hong; Hong-Juan Li. 2013. "Support Vector Regression Model Based on Empirical Mode Decomposition and Auto Regression for Electric Load Forecasting." Energies 6, no. 4: 1887-1901.