RESEARCH OF THE COMBINATION FORECASTING MODEL FOR LOAD BASED ON ARTIFICIAL NEURAL NETWORK
XIE Kai-gui, LI Chun-yan, ZHOU Jia-qi (High Voltage and Electrical Engineering New Technology Key Laboratory of Ministry of Education, Chongqing University,Chongqing 400044,China)
This paper presents a non-stationary weights combination forecasting model(NWCFM) for load ,i.e., combination forecasting model based on artificial neural network. The corresponding artificial neural network (ANN)for this model is constructed using the nonlinear relationship between the forecasting values of various methods and the actual loads. The ANN has three layers and the output layer has only one neuron. The inputs of ANN are the forecasting values of all methods and the output is the original data. The ANN, which is trained by error back propagation (BP) algorithm with variable learning rate and variable momentum, has the forecasting function. At the same time, the stationary weights combination forecasting model(SWCFM) based on genetic algorithm is concisely introduced. The model can be used in year, month, hour load forecasting fields, and so on. The effectiveness and practicability of the models have been verified by some examples.
【CateGory Index】： TM714.1