Lightweight Short-term Load Forecasting Method Based on Xception Convolution and Weight Pruning
Accurate and fast load forecasting can promote the economic and stable operation of the power system.In order to make load forecasting model applicable to the resource-constrained side-terminal stations and to ensure good forecasting accuracy,a lightweight load forecasting model based on Xception convolutional neural network with weight pruning is pro-posed.First the daily type,historical load,temperature and humidity data from the historical data set are selected as mod-el inputs and transformed into feature matrices.Then a lightweight load forecasting model is constructed based on the Xception convolution and the attention mechanism according to the characteristics of the input feature matrices and the output vectors of the model.Finally a lightweight load forecasting model is established by weight pruning-based model training.Through comparative experiments,it is found that the number of parameters,computational volume and storage space of the lightweight load forecasting model in this paper are greatly reduced,while its accuracy is basically comparable to that of the large-scale model,which is more advantageous in side-end applications.
load forecastingXception convolutionpruninglightweight model
万方数据
维普
