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• W2892004704 abstract "Owing to the complexity and uncertainty of wind speed, accurate wind speed prediction has become a highly anticipated and challenging problem in recent years. Researchers have conducted numerous studies on wind speed prediction theory and practice; however, research on multi-step wind speed prediction remains scarce, which hinders further development in this area. To improve upon the accuracy and stability of multi-step wind speed prediction, this paper proposes a combination model based on a data preprocessing strategy, an improved optimization model, a no negative constraint theory, and several single prediction models. To improve upon forecasting performance, an improved water cycle algorithm based on a quasi-Newton algorithm is proposed to optimize the weight coefficients of the single models. In the empirical research, 10-min and 30-min wind speed data from Shandong Province in China, collected for case studies, were used to assess the comprehensive performance of the proposed combination model. Finally, we used 10-fold cross-validation and multiple error criteria to evaluate the comprehensive performance of the proposed combination model. The simulation results indicate that (a) the quasi-Newton algorithm can effectively increase the diversity of the water cycle algorithm particles, resulting in improved water cycle algorithm optimization performance; (b) the combination model exhibits superior predictive performance to a single model by taking advantage of each single model; and (c) the proposed combination model can effectively improve multi-step wind speed prediction results." @default.
• W2892004704 created "2018-09-27" @default.
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• W2892004704 date "2018-11-01" @default.
• W2892004704 modified "2023-10-17" @default.
• W2892004704 title "A combination forecasting approach applied in multistep wind speed forecasting based on a data processing strategy and an optimized artificial intelligence algorithm" @default.
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• W2892004704 doi "https://doi.org/10.1016/j.apenergy.2018.09.037" @default.
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