期刊
ENERGY
卷 49, 期 -, 页码 279-288出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.energy.2012.10.035
关键词
EMD-based signal filtering; Seasonal adjustment; Feedforward neural network; Electricity demand forecasting; Multi-output forecasting
资金
- National Natural Science Foundation of China [71171102, 61073193, 90924025]
- National High-Tech Research & Development Program of China (863 Program) [2012AA011103]
For accurate electricity demand forecasting, this paper proposes a novel approach, MFES, that combines a multi-output FFNN (feedforward neural network) with EMD (empirical mode decomposition)-based signal filtering and seasonal adjustment. In electricity demand forecasting, noise signals, caused by various unstable factors, often corrupt demand series. To reduce these noise signals, MFES first uses an EMD-based signal filtering method which is fully data-driven. Secondly, MFES removes the seasonal component from the denoised demand series and models the resultant series using FFNN model with a multi-output strategy. This multi-output strategy can overcome the limitations of common multi-step-ahead forecasting approaches, including error amplification and the neglect of dependency between inputs and outputs. At last, MFES obtains the final prediction by restoring the season indexes back to the FFNN forecasts. Using the half-hour electricity demand series of New South Wales in Australia, this paper demonstrates that the proposed MFES model improves the forecasting accuracy noticeably comparing with existing models. (c) 2012 Elsevier Ltd. All rights reserved.
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