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《Power System Technology》 2010-03
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Probabilistic Short-Term Load Forecasting Based on Dynamic Self-Adaptive Radial Basis Function Network

ZHOU Jian-zhong, ZHANG Ya-chao, LI Qing-qing, GUO Jun (College of Hydroelectric and Digitalization Engineering, Huazhong University of Science and Technology, Wuhan 430074, Hubei Province, China)  
According to the application of radial basis function (RBF) network in power system short-term load forecasting, a probabilistic short-term load forecasting method based on dynamic self-adaptive RBF network is proposed. The dynamic self-adaptive nearest neighbor-clustering learning algorithm is adopted to training the network for load forecasting. On this basis, by means of the statistics of error characteristics of historical load forecasting, a probability density function model for forecasting errors in load areas is established, and combining with the results of deterministic load forecasting the results of probabilistic load forecasting are obtained. The practicality and effectiveness of the proposed short-term load forecasting approach are verified by data analysis of actual power network.
【Fund】: “十一五”国家科技支撑计划重大项目(2008BAB29B08);; 国家自然科学基金雅砻江联合研究基金重点项目(50539140);; 科技部水利部公益性行业科研专项(200701008)~~
【CateGory Index】: TM715
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