Short-Term Load Forecasting Model Based on Ensemble Empirical Mode Decomposition and Q Learning Strategy

Short-term load forecasting is of great significance to the safe and stable operation of power systems. For that reason, a short-term load forecasting model based on ensemble empirical mode decomposition (abbr. EEMD) and Q learning strategy optimization was proposed. Firstly, the original load series was decomposed by EEMD to reduce the difficulty of forecasting. Secondly, on this basis, four deep learning models, namely, convolution neural network (abbr. CNN), residual neural network (abbr. ResNet), long short-term memory (abbr. LSTM) neural network and gated recurrent unit (abbr. GRU) were respectively used for forecasting to obtain four forecasting results, of which weighted combination was used to obtain the final load forecasting value. Thirdly, the combination weight was optimized by Q learning algorithm to maximize the forecasting performance of the combination model. Finally, the experiment was conducted using real collected load data from a certain region, and the results showed that the proposed combined forecasting model is superior to other forecasting models, and the effectiveness of the proposed model was verified.