A novel two-stage forecasting model based on error factor and ensemble method for multi-step wind power forecasting

Yan Hao and Chengshi Tian

Applied Energy, 2019, vol. 238, issue C, 368-383

Abstract: With the fast growth of wind power penetration into the electric grid, wind power forecasting plays an increasingly significant role in the secure and economic operation of power systems. Although there have been numerous studies concerning wind power forecasting, most of them have failed to make the best of the information implied in the error value, focused only on simple error correction, adopted a simple ensemble method to aggregate the predictions of each component, and considered improving only forecasting accuracy. Recognizing these issues, a novel two-stage forecasting model based on the error factor, a nonlinear ensemble method and the multi-objective grey wolf optimizer algorithm is proposed for wind power forecasting. More specially, in stage I, the extreme learning machine optimized by the multi-objective grey wolf optimizer is used to forecast the components decomposed by variational mode decomposition, and an error prediction model based on the extreme learning machine optimized by the multi-objective grey wolf optimizer is utilized to predict forecast errors; also, a novel nonlinear ensemble method based on the extreme learning machine optimized by the multi-objective grey wolf optimizer is utilized to integrate all the components and forecast error values in stage II. Three real-world wind power datasets collected from Canada and Spain are introduced to demonstrate the forecasting performance of the developed model. The forecasting results reveal that the proposed model is superior to all the other considered models in terms of both accuracy and stability and thus can be a useful tool for wind power forecasting.

Keywords: Wind power forecasting; Two-stage forecasting model; Multi-objective optimization algorithm; Extreme learning machine (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (51)

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DOI: 10.1016/j.apenergy.2019.01.063

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