期刊
ENERGIES
卷 6, 期 4, 页码 1887-1901出版社
MDPI
DOI: 10.3390/en6041887
关键词
electric load prediction; support vector regression; empirical mode decomposition auto regression
资金
- National Natural Science Foundation of China [51064015]
- National Science Council, Taiwan [NSC 100-2628-H-161-001-MY4, NSC 101-2410-H- 161-001]
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.
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