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• W2016752668 abstract "Abstract As the energy crisis becomes a greater concern, wind energy, as one of the most promising renewable energy resources, becomes more widely used. Thus, wind energy forecasting plays an important role in wind energy utilization, especially wind speed forecasting, which is a vital component of wind energy management. In view of its importance, numerous wind speed forecasts have been proposed, each with advantages and disadvantages. Searching for more effective wind speed forecasts in wind energy management is a challenging task. As proposed, combined models have desirable forecasting abilities for wind speed. This paper reviewed the combined models for wind speed predictions and classified the combined wind speed forecasting approaches. To further study the combined models, two combination models, the no negative constraint theory (NNCT) combination model and the artificial intelligence algorithm combination model, are proposed. The hourly average wind speed data of three wind turbines in the Chengde region of China are used to illustrate the effectiveness of the proposed combination models, and the results show that the proposed combination models can always provide desirable forecasting results compared to the existing traditional combination models." @default.
• W2016752668 created "2016-06-24" @default.
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• W2016752668 date "2015-04-01" @default.
• W2016752668 modified "2023-10-06" @default.
• W2016752668 title "Combined forecasting models for wind energy forecasting: A case study in China" @default.
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• W2016752668 doi "https://doi.org/10.1016/j.rser.2014.12.012" @default.
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