Prediction of Electric Load for Users Based on BP Neural Network
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Yang Li, Xiangying Xie, Liang Shan, Dayan Ma, Deping Zhang
Power load forecasting is very important for power dispatching. Accurate load forecasting is of great significance for saving energy, reducing generating cost and improving social and economic benefits. In order to accurately predict the power load, based on BP neural network theory, combined with the advantages of Clementine in dealing with big data and preventing overfitting, a neural network prediction model for large data is constructed. The test results show that the model is effective and feasible, and achieves the expected results. It can achieve accurate prediction of user power consumption and greatly improve the accuracy of power grid dispatching.
BP Neural Network, Prediction