基于RBF神经网络与模糊控制的短期负荷预测

针对电力系统短期负荷预测，综合考虑温度、日期类型和天气等因素对短期电力负荷的影响，建立了径向基函数（Radial?Basis?Function，RBF）神经网络和模糊控制相结合的短期负荷预测模型。该模型利用RBF神经网络的非线性逼近能力对预测日负荷进行了预测，并采用在线自调整因子的模糊控制对预测误差进行在线智能修正。实际算例表明RBF神经网络与模糊控制相结合提高了预测精度。

Short-Term Load Forecasting Based on Radial Basis Function Neural Networks and Fuzzy Control

Aiming at short-term load forecasting of power system, considering the factors such as temperature, date type, weather status,etc, which influence the short-term electric load forecasting, a model is set up by combining Radial Basis Function(RBF)neural network with fuzzy control. The model forecasts the daily load by the nonlinear approaching capacity of the RBF neural network, than corrects the errors by on-line self-tuning factors of fuzzy control.The actual simulation shows that the method combining fuzzy control with neural network improves forecasting accuracy.