Use rough sets reduction algorithm to improve BP neural network load forecasting model

Traditional neural network load forecasting model is affected by the complexity of the network structure and complexity of the samples,easily leads to a "over-study" or low-generalization.The method uses several attribute reduction algorithms in rough sets theory to reduce the various historical data associated with load,eliminates the attributes that are not relevant to decision-making information.Examples prove that this method simplifies the BP neural network input variables,so as to shorten the neural network model of training time and improve the forecast performance.