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A novel spatio-temporal wind power forecasting framework based on multi-output support vector machine and optimization strategy

Lu, Peng, Ye, Lin, Zhong, Wuzhi, Qu, Ying, Zhai, Bingxu, Tang, Yong and Zhao, Yongning 2020. A novel spatio-temporal wind power forecasting framework based on multi-output support vector machine and optimization strategy. Journal of Cleaner Production 254 , 119993. 10.1016/j.jclepro.2020.119993

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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.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Engineering
Publisher: Elsevier
ISSN: 0959-6526
Date of Acceptance: 3 January 2020
Last Modified: 18 Feb 2020 10:24
URI: http://orca.cf.ac.uk/id/eprint/129668

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