Quantile deep learning model and multi-objective opposition elite marine predator optimization algorithm for wind speed prediction

• Published in: Applied mathematical modelling Vol. 115; pp. 56 - 79
• Main Authors: Wang, Jianzhou, Guo, Honggang, Li, Zhiwu, Song, Aiyi, Niu, Xinsong
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.03.2023
• Subjects: Data preprocessing; Multi-objective optimization; Quantile deep learning; Wind speed forecasting; Multi-objective optimization; Quantile deep learning; Data preprocessing; Wind speed forecasting
• ISSN: 0307-904X
• DOI: 10.1016/j.apm.2022.10.052

•A combined probability prediction system based on quantile prediction is proposed.•The quantile prediction component based on deep learning is constructed.•An innovation improved multi-objective optimization algorithm is proposed.•Opposition learning and elite level strategy are introduced in optimization algorithm. Wind speed prediction accuracy is critical for grid connection safety and intelligent wind farm management. However, most wind speed prediction studies mainly focus on the deterministic prediction, and are rarely discussed in wind speed uncertain prediction. Therefore, this paper proposes a wind speed combined probability prediction system that integrates data denoising technology and creatively introduces the concept of quantile into the deep learning model to construct the wind speed quantile prediction component. To ensemble the prediction components effectively, a novel multi-objective marine predator combination strategy is developed that circumvents the limitations of the traditional multi-objective optimization algorithm. The experimental results based on two wind speed datasets show that the proposed system can improve wind speed prediction accuracy, build a more appropriate wind speed prediction interval, efficiently measure and minimize the uncertainty of the forecast process.