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《Proceedings of the CSEE》 2008-26
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Optimization Algorithm of Short-term Multi-step Wind Speed Forecast

PAN Di-fu1, LIU Hui1, LI Yan-fei2 (1. School of Traffic & Transportation Engineering, Central South University, Changsha 410075, Hunan Province, China; 2. Key Laboratory of Traffic Safety on the Track (Central South University), Ministry of Education, Changsha 410075, Hunan Province, China)  
Giving a high precise wind speed forecast for wind farms, which can effectively relieves disadvantageous impact of wind power plants on power systems, enhances the competitive ability of wind power in electricity market. Using time series method to establish ARIMA (11,1,0) model for some wind speed directly measured from wind farms’ certain station in China. Then performed forecasting simulation by the established model. Aimed at the time delay of one-step forecast by ARIMA (11,1,0) model, authors proposed an improved algorithm named Kalman time-series method. Aimed also at the low accuracy of multi-step forecast by ARIMA (11,1,0) model, in addition authors proposed an improved algorithm named Rolling Amend Time-series method. Using the two improved methods to make calculative examples, which show that: ① Kalman time-series method not only solved the time-delay problem in some degree, but also has the mean forecast error of one-step forecast reducing from 6.49% to 3.19%; ②Rolling Amend Time-series method improved the accuracy of multi-step forecast, the mean absolute relative error of three-step, five-step, ten-step forecast respectively are only 7.01%, 7.63% and 8.42%. More important, the two proposed methods did not significantly increase the computation complexity.
【Fund】: “十五”国家科技支撑计划重大项目(2006BAC07B03)~~
【CateGory Index】: TM614
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