Power load prediction of smart grid based on deep learning

Abstract

Real electricity costs, weather, and historical load data are added as new reference data when analyzing the characteristics of the load change policy to improve the forecasting model. A load forecasting model for smart grid based on convolutional neural network(CNN) is proposed. The model first analyzes the characteristics of the variable model and determines the forecast model inputs using two methods of correlation analysis to understand the impact of actual energy costs, weather and other factors on the changes. Second, since Feedforward neural network is deficient in processing the correlation information between loads, the prediction model developed by the author was studied as CNN. After completing the training, input two sets of real datasets for final data analysis and comparison, and the results show that, LSTM, BP, SVR[UNK]MAPE[UNK]RMSE respectively 0.49%, 0.57%, 0.83%, 1.44% and 106.08 MW, 113.82MW, 169.77 MW, 285.61 MW. It can be seen that the prediction model proposed by the author has the highest accuracy and the best fit with the trend of actual load changes. It is proved that the CNN model has certain advantages when dealing with load forecasting problems related to time series.