A combination forecasting approach applied in multistep wind speed forecasting based on a data processing strategy and an optimized artificial intelligence algorithm

• Published in: Applied energy Vol. 230; pp. 1108 - 1125
• Main Authors: Yang, Zhongshan, Wang, Jian
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 15.11.2018
• Subjects: Broyden family; Combination model; Complementary ensemble empirical model; Modified water cycle algorithm; Wind speed forecasting; ANN; DE; Combination model; HRM; ENN; Complementary ensemble empirical model; Broyden family; FNN; DM; WRF; SVM; BP; Wind speed forecasting; MM5; WNN; RBF; EEMD; KF; COSMO; WT; EMD; BP_2; S-T Model; SSA; NWP; CEEMD; PSO; FIS; IMFs; WCA; ARIMA; LRA; FE; Modified water cycle algorithm
• ISSN: 0306-2619; 1872-9118
• DOI: 10.1016/j.apenergy.2018.09.037

•A combined model is proposed for multi-step ahead wind speed forecasting.•Data preprocessing technology is introduced to improve the forecasting performance.•Quasi-Newton algorithm is used to increase the particle diversity of water cycle algorithm.•The accuracy and stability of wind speed forecasting are improved simultaneously.•The simulation results are validated well in China. 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.