Research on Intelligent Analysis and Application of Power Data Based on Computational Intelligence
In order to improve the accuracy of smart grid load forecasting,a short-term power load forecasting model based on deep learning is proposed.On the basis of the long-short term memory network and convolutional neural network,a hybrid CNN-LSTM prediction model structure is constructed.The automatic encoder based on superposition convolution noise reduc-tion is used to extract the features of power data,and a load forecasting model with two stacked LSTM layers and a linear out-put layer is proposed.The 24 h short-term load forecasting results show that the MAE,RMSE,MAPE and R2 indicators of the proposed model are 232.08,292.19,0.0322 and 0.909,respectively,and the performance is improved by 74.8％,73.8％,70.8％and 10.9％,respectively,compared with XG Boost model.
万方数据
维普
