A short-term load forecasting model based on mixup and transfer learning

When the amount of historical load data is insufficient, the use of deep learning for load forecasting is prone to overfitting. This paper proposes a short-term electric load forecasting model based on mixup and transfer learning. Mixup is introduced to achieve data enhancement, which improves the generalization ability of the forecasting model by expanding the distribution of training samples. Transfer the historical load of other users with similar consumption patterns to supplement data samples and avoid overfitting of the forecasting model. The similarity between the target load sequence and the source load sequence is measured by the maximal information coefficient (MIC). Finally, the expanded load data adopts a long short-term memory (LSTM) neural network that can learn the long-term dependence in the time series for load forecasting. Experimental results on the residential data set demonstrate that the model proposed in this paper improves the accuracy of load forecasting in scenarios where historical data is scarce.