LAUSR.org creates dashboard-style pages of related content for over 1.5 million academic articles. Sign Up to like articles & get recommendations!

Short-term wind speed forecasting using wavelet transformation and AdaBoosting neural networks in Yunnan wind farm

Photo from wikipedia

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… Click to show full 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.

Keywords: speed; wind speed; term wind; speed forecasting; short term

Journal Title: Iet Renewable Power Generation
Year Published: 2017

Link to full text (if available)


Share on Social Media:                               Sign Up to like & get
recommendations!

Related content

More Information              News              Social Media              Video              Recommended



                Click one of the above tabs to view related content.