A PSO-LSTM Model of Offshore Wind Power Forecast considering the Variation of Wind Speed in Second-Level Time Scale. (25th November 2021)

• Record Type: Journal Article
• Authors: Yuan, Chao; Tang, Yiming; Mei, Rui; Mo, Fei; Wang, Hong
• Other Names: Jauregui-Correa Juan C. Academic Editor.
• Is Part Of: Mathematical problems in engineering. Volume 2021(2021)
• Publication Date: 2021-11-25
• Subjects: Engineering mathematics -- Periodicals; 510.2462
• DOI: 10.1155/2021/2009062
• Languages: English
• ISSNs: 1024-123X

Abstract

Abstract : To enable power generation companies to make full use of effective wind energy resources and grid companies to correctly schedule wind power, this paper proposes a model of offshore wind power forecast considering the variation of wind speed in second-level time scale. First, data preprocessing is utilized to process the abnormal data and complete the normalization of offshore wind speed and wind power. Then, a wind speed prediction model is established in the second time scale through the differential smoothing power sequence. Finally, a rolling PSO-LSTM memory network is authorized to realize the prediction of second-level time scale wind speed and power. An offshore wind power case is utilized to illustrate and characterize the performance of the wind power forecast model.