A novel spatio-temporal wind power forecasting framework based on multi-output support vector machine and optimization strategy. (1st May 2020)

• Record Type: Journal Article
• Authors: Lu, Peng; Ye, Lin; Zhong, Wuzhi; Qu, Ying; Zhai, Bingxu; Tang, Yong; Zhao, Yongning
• Is Part Of: Journal of cleaner production. Volume 254(2020)
• Publication Date: 2020-05-01
• Subjects: Wind power forecasting -- Spatio-temporal correlation -- Multi-output support vector machine -- Grey wolf optimizer -- Combined forecasting approaches; Factory and trade waste -- Management -- Periodicals; Manufactures -- Environmental aspects -- Periodicals; Déchets industriels -- Gestion -- Périodiques; Usines -- Aspect de l'environnement -- Périodiques; 628.5
• DOI: 10.1016/j.jclepro.2020.119993
• Languages: English
• ISSNs: 0959-6526

Abstract

Abstract: The integration of a large number of wind farms poses big challenges to the secure and economical operation of power systems, and ultra-short-term wind power forecasting is an effective solution. However, traditional approaches can only predict an individual wind farm power at a time and ignore the spatio-temporal correlation of wind farms. In this paper, a novel ultra-short-term forecasting framework based on spatio-temporal (ST) analysis, multi-output support vector machine (MSVM) and grey wolf optimizer (GWO) which defined ST-GWO-MSVM model is proposed to predict the output wind power from multiple wind farms; the ST-GWO-MSVM model includes data analysis stage, parameters optimization stage, and modeling stage. In the data analysis stage, the person correlation coefficient and partial autocorrelation function are used to analyze the spatio-temporal correlation of wind power. In the parameters optimization stage, to avoid obtaining the unreliable forecasting results due to the parameters are chosen empirically, the GWO algorithm is used to optimize the kernel function parameters of the MSVM model. In the modeling stage, an innovative forecasting model with optimal parameter of MSVM is proposed to predict the output wind power of 15 wind farms. Results show that the performance of ST-GWO-MSVM is better than other benchmark models in terms of multiple-error metrics including fractional bias, direction accuracy, and improvement percentages. Highlights: A novel spatio-temporal wind power forecasting model in ultra-short time scale is proposed. Spatio-temporal analysis is developed to study the characteristics of wind power cluster. A variable selection is established to determine the combined forecasting model input. A scientific and comprehensive evaluation module is provided for wind power error analysis.