A comprehensive wind speed prediction system based on Monte Carlo and artificial intelligence algorithms. (1st January 2022)

• Record Type: Journal Article
• Authors: Zhang, Yagang; Zhao, Yunpeng; Shen, Xiaoyu; Zhang, Jinghui
• Is Part Of: Applied energy. Volume 305(2022)
• Publication Date: 2022-01-01
• Subjects: Combined wind speed prediction system -- Data processing -- Parameter optimization -- Interval prediction -- Neural network; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2021.117815
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: A new hybrid prediction system is proposed to predict wind speed. Simplex and chaotic mapping optimization cuckoo algorithm are proposed. An uncertainty prediction model based on Monte Carlo method is proposed. The energy theory is proposed to optimize the decomposition mode number. Theories proofs and experiments verify the effectiveness of the model. Abstract: Wind energy has strong volatility and intermittent. Accurate wind speed prediction can not only improve the safety of the system, but also optimize dispatch and reduce economic losses. However, previous studies tend to ignore the influence of virtual components and lack effective identification of wind speed characteristics and a robust interval prediction scheme, resulting in poor results. To bridge these gaps, this paper proposes an energy theory method to solve the problem of modal over-decomposition. The study also combines effective modal recognition, uses different prediction methods according to modal characteristics and proposes a set of new optimization algorithms to improve nonlinear prediction capabilities. Finally, based on Monte Carlo theory, a set of interval prediction schemes that can adapt to different error characteristics are proposed. Under the verification of wind speed data in Changma, China and Sotavento, Spain. The mean absolute percentage error of wind speed deterministic prediction reaches 4.22% and 5.82%, respectively. The coverage rate of wind speed uncertainty prediction meets different confidence requirements, and the average interval width is still less than 2.5 m/s at 90% confidence. The results show that the forecasting system proposed in this paper is significantly better than all the comparative forecasting schemes, which can reduce the risk of fluctuations and improve the stability and safety of the wind power system.