Short-term Load Forecasting of Neural Network on Dempster Rule of Evidential Theory
Aiming that the traditional neural network has slow concentration speed and weak follow-time function, The distance changed BP network, RBF network and Elman network calculating ways are used in load forecasting at an area, according to the situation of weather factor. By analyzing the error of load forecasting, calculating ways have fusion through Dempster fusion rule of evidential theories. Through choosing the data of several days before estimated day as fusion samples and ruling the homologous basic belief degree function, belief degree allotment is fetched after the fusion.The load forecasting model is decided according to the corresponding time.The results of simulation demonstrate that the load forecasting calculating ways after fusion of evidential theories have higher accuracy.