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《Proceedings of the CSEE》 2012-35
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Short-Term Wind Speed Forecasting For Multiple Wind Farms Using Bayesian Krigedkalman Mode

QING Xiangyun,YANG Fuwen,WANG Xingyu(Department of Automation,East China University of Science and Technology,Xuhui District,Shanghai 200237,China)  
Accurate short-term wind speed forecasting is critical to reliable and secure power system operations.Traditional forecasting approaches did not take account of the spatial neighborhoods information.However,wind speeds in multiple wind farms are correlated both in time and space.This paper introduced a short-term wind speed forecasting approach using Bayesian KrigedKalman model in which spatial structure with principal Kriging functions was modeled by a Bayesian hierarchical structure and time dynamic component was modeled by a state space process.Variational Bayesian method was applied to inference approximately and learn parameters of the model,which was a computationally efficient deterministic approach.One hour ahead forecasting was evaluated on publicly available wind data of multiple wind farms.The results were compared to persistence forecasting approach to show its improvement in terms of root mean square errors.
【Fund】: 中央高校基础研究基金项目(WH1114059 WJ0913001);; 国家自然科学基金项目(61074113 61174064);; 国家重点基础研究发展计划项目(973计划)(2012CB720502)~~
【CateGory Index】: TM614
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