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中文摘要：
针对径向基函数（radial basis function，RBF）网络在电力系统短期负荷预测中的应用，提出了一种基于动态白适应RBF网络的概率性短期负荷预测方法。采用动态自适应最近邻聚类学习算法训练网络实现负荷预测。在此基础上，通过对历史负荷预测误差特性的统计分析，对各负荷分区内预测误差的概率密度函数建模，并结合确定性预测结果获得概率性负荷预测结果。通过分析实际电网数据，验证了该方法的实用性与有效性。
英文摘要：
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.
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