Title: A robust combination approach for short-term wind speed forecasting and analysis
Reporter: Jianming Hu (Guangzhou University)
Time: June 29, 2017 (Thursday) PM 14:00-15:00
Location: 307#Room, Research and Education Building
Contact: Hui Song (tel: 84708351-8123)
Abstract: With the increasing importance of wind power as a component of power systems, the problems induced by the stochastic and intermittent nature of wind speed have compelled system operators and researchers to search for more reliable techniques to forecast wind speed. This paper proposes a combination model for probabilistic short-term wind speed forecasting. In this proposed hybrid approach, EWT is employed to extract meaningful information from a wind speed series by designing an appropriate wavelet filter bank. The GPR model is utilized to combine independent forecasts generated by various forecasting engines (ARIMA, ELM, SVM and LSSVM) in a nonlinear way rather than the commonly used linear way. The proposed approach provides more probabilistic information for wind speed predictions besides improving the forecasting accuracy for single-value predictions. The effectiveness of the proposed approach is demonstrated with wind speed data from two wind farms in China. The results indicate that the individual forecasting engines do not consistently forecast short-term wind speed for the two sites, and the proposed combination method can generate a more reliable and accurate forecast.
The brief introduction to the reporter: Jianming Hu is associate professor of Guangzhou University. He graduated from the School of Mathematics and Statistics of Lanzhou University in December 2016. From august 2015 to august 2016, he went to the Queen's University in Canada to receive joint training and worked on through the multiple biological tags for cancer of therapeutic benefits. At present, he has published 7 SCI papers as the first author or the corresponding author and also is a reviewer of many SCI international journals. He majors in application of statistical theory and methods, time series and statistical.