Short-term electricity load forecast performance comparison based on four neural network models

Neural network methods are widely used in the prediction of chaos time series due to their versatility and small computation amount. In order to improve the prediction accuracy and real-time of all kinds of information in the short-term electricity load time series, four neural network methods with the ideal powerful capacity in non-linear modeling and predicting, such as back-propagation neural network (BPNN), ELMAN neural network, fuzzy neural network (FNN) and wavelet neural network (WNN), are used to realize the short-term electricity load forecast. Simulation experiments results and performance comparison analysis show the effectiveness of the proposed four time series prediction methods.