基于径向基神经网络和自适应神经模糊系统的电力短期负荷预测方法

针对实时电价对短期负荷的影响,建立了径向基（RBF）神经网络和自适应神经网络模糊系统（ANFIS）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不考虑电价因素的预测日负荷进行了预测,并根据近期实时电价的变化,应用ANFIS系统对RBF神经网络的负荷预测结果进行修正,以使固定电价时代的预测方法在电价敏感环境下也能达到较好的预测精度,克服了神经网络在电力市场下进行负荷预测时存在的不足.某电网实际预测结果表明,该方法具有较好的预测效果.

SHORT-TERM LOAD FORECASTING METHOD BASED ON RBF NEURAL NETWORK AND ANFIS SYSTEM

To counter the influence of real-time electric price on short-term load, a model for forecasting the short-term load is set up by combining Radial Basis Function (RBF)neural network with Adaptive Neural Fuzzy Inference System (ANFIS). The model first draws on the nonlinear approaching capacity of the RBF network to forecast the load on the prediction day which takes no account of the factor of electric price, and then, based on the recent changes of real-time price, uses the ANFIS system to modify the results of load forecasting obtained by using the RBF network so as to improve the forecasting accuracy and overcome the defect of the RBF network in price-sensitive environment. As the results of an example of factual forecasting show the model presented in this paper can work effectively.