基于径向基函数优化的短期负荷预测方法

Short-term load forecasting method based on RBF parameter optimization

为提高短期负荷预测模型的精确度,研究了一种基于径向基函数(radial basis function,RBF)神经网络参数优化的短期负荷预测方法。首先,对短期负荷影响因素进行分析,建立了计及温度累积效应的温度变量量化模型和计及负荷修正的日期类型变量量化模型;其次,建立基于RBF神经网络的短期负荷预测模型,分别基于近邻传播算法和遗传算法对RBF神经网络隐层节点的中心矢量和基宽参数进行优化;最后,基于某地区轻工业行业的夏季负荷数据进行了算例分析,结果表明,相比于未考虑参数优化的预测模型,可在一定程度上提高短期负荷的预测精度。

In order to improve the accuracy of short-term load forecasting model, a short-term load forecasting method based on radial basis function(RBF)neural network parameter optimization is studied. Firstly, the influencing factors of short-term load are analyzed, and the temperature variable quantitative model considering the accumulated temperature effect and the date type variable quantitative model considering the load correction are established. Secondly, a short-term load forecasting model based on RBF neural network is established, and the center vector and base width parameters of hidden layer nodes of RBF neural network are optimized based on the nearest neighbor propagation algorithm and genetic algorithm respectively. Finally, a case study is carried out based on the summer load data of a light processing industry in a certain area. The results show that the short-term load forecasting accuracy can be improved to a certain extent compared with the forecasting model without considering parameter optimization.