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

•A novel two-stage forecasting architecture is proposed for wind power forecasting.•Considering error factor in wind power forecasting to improve model’s performance.•A novel ensemble method is proposed in the proposed forecasting model.•The developed model can also perform better for wind power int...

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Vydáno v:Applied energy Ročník 238; s. 368 - 383
Hlavní autoři: Hao, Yan, Tian, Chengshi
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 15.03.2019
Témata:
algorithms
Canada
data collection
Extreme learning machine
Multi-objective optimization algorithm
prediction
Spain
Two-stage forecasting model
wind power
Wind power forecasting
Canada
Spain
Two-stage forecasting model
Multi-objective optimization algorithm
Extreme learning machine
Wind power forecasting
ISSN:0306-2619, 1872-9118
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Shrnutí:•A novel two-stage forecasting architecture is proposed for wind power forecasting.•Considering error factor in wind power forecasting to improve model’s performance.•A novel ensemble method is proposed in the proposed forecasting model.•The developed model can also perform better for wind power interval prediction. 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.
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ISSN:0306-2619
1872-9118
DOI:10.1016/j.apenergy.2019.01.063