A new combined model based on multi-objective salp swarm optimization for wind speed forecasting

• Published in: Applied soft computing Vol. 92; p. 106294
• Main Authors: Cheng, Zishu, Wang, Jiyang
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.07.2020
• Subjects: Combined model; Data preprocessing technique; Multi-objective optimization algorithm; Wind speed forecasting; Combined model; Multi-objective optimization algorithm; Data preprocessing technique; Wind speed forecasting
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2020.106294

Wind energy as the representative renewable energy sources attracted the global attention and wind power plays a significant role in power system. Thus, wind speed forecasting is highly critical in wind power grid management. The short-term wind speed prediction can effectively support power grid-management to reduce wind curtailments. In the past, lots of researches had often considered how to enhance the accuracy or stability in short wind speed forecasting. Nevertheless, just focus on one criterion is the inability to build an effective predictive system. In this paper, a novel combined forecasting system was proposed and effectively applied to address the issue of wind speed prediction while obtaining high precision and strong stability simultaneously at the same time. Four ANNs (artificial neural networks) were combined by the optimal weighting coefficients determined by MSSO (multi-objective salp swarm optimizer) in this system and data decomposition and denoising are included in the data preprocessing stage. The multi-objective optimization algorithm overcomes the weakness of the single-objective optimization algorithm that can only achieve one criterion. It can simultaneously optimize accuracy and stability. The 10-minute wind speed data of three data sets of Penglai, China were selected for multi-step forecasting to evaluate the effectiveness of the proposed combined model. And experimental results show that the proposed model not only achieves excellent precision and stability but also outperforms other proposed combined models. •Developed the novel combined model on data ensemble decomposition and reconstruction and a new swarm intelligence-based algorithm.•Provide an effective method for choosing the de-noising model.•Provide a novel deciding weight system based on leave-one-out strategy and multiple swarm intelligence-based evolutionary intelligence technology.