Abstract: 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.
Keywords: electric load prediction; support vector regression; empirical mode decomposition auto regression
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Fan, G.-F.; Qing, S.; Wang, H.; Hong, W.-C.; Li, H.-J. Support Vector Regression Model Based on Empirical Mode Decomposition and Auto Regression for Electric Load Forecasting. Energies 2013, 6, 1887-1901.
Fan G-F, Qing S, Wang H, Hong W-C, Li H-J. Support Vector Regression Model Based on Empirical Mode Decomposition and Auto Regression for Electric Load Forecasting. Energies. 2013; 6(4):1887-1901.
Fan, Guo-Feng; Qing, Shan; Wang, Hua; Hong, Wei-Chiang; Li, Hong-Juan. 2013. "Support Vector Regression Model Based on Empirical Mode Decomposition and Auto Regression for Electric Load Forecasting." Energies 6, no. 4: 1887-1901.