Short‐term wind speed forecasting using wavelet transformation and AdaBoosting neural networks in Yunnan wind farm. Issue 4 (8th July 2016)

• Record Type: Journal Article
• Authors: Shao, Haijian; Wei, Haikun; Deng, Xing; Xing, Song
• Is Part Of: IET renewable power generation. Volume 11:Issue 4(2017)
• Page Start: 374
• Page End: 381
• Publication Date: 2016-07-08
• Subjects: wind power plants -- learning (artificial intelligence) -- neural nets -- wavelet transforms -- power engineering computing; short‐term wind speed forecasting -- wavelet transformation -- AdaBoosting neural networks -- Yunnan wind farm -- WT -- AdaBoost technique -- wind speeds distribution features -- seasonal pattern analysis -- power spectrum analysis -- wind speeds feature distribution -- energy distribution; Renewable energy sources -- Periodicals; 333.79405
• DOI: 10.1049/iet-rpg.2016.0118
• Languages: English
• ISSNs: 1752-1416

Abstract

Abstract : Wind speed presents a potential seasonal pattern revealed by the self‐similarity in wavelet periodogram with various scales. The corresponding seasonal pattern will promote the improvement of the short‐term wind speed forecasting accuracy. In this study, a novel method for short‐term wind speed forecasting using wavelet transformation (WT) and AdaBoost technique is proposed to analyse the wind speeds distribution features and promote the model configuration. Power spectrum and seasonal pattern analysis using the WT are presented to investigate the wind speeds feature distribution based on the scalogram percentage of energy distribution in different seasons. This procedure contributes to perfecting the investigation of wind speed seasonal pattern characteristics over time and promotes the sample division by computing the statistics measurement based on the estimated frequencies interval. The model order estimation based on the information criteria is processed to reflect the systems dynamical sustainability between the current outputs and historical data. Finally, the experiments based on the real data from Yunnan wind farm are given to verify the effectiveness of the proposed approach.