A global forecasting method of heterogeneous household short-term load based on pre-trained autoencoder and deep-LSTM model

Short-term load forecasting (STLF) of heterogeneous multi-agents plays a significant role in smart grid. Faced with special difficulties of multi-agent STLF due to the high heterogeneity, uncertainty and volatility, traditional local methods usually make predictions based on load aggregation or clustering, the complexity of which will rise greatly with the increase of agent types. It has become more and more difficult for traditional methods to meet the STLF demand of smart grid. Meanwhile, global time series forecasting method emerges gradually in many fields, which can make predictions for many agents only by one model with much lower complexity. However, there is few researches on the multi-agent global STLF. This study proposes a global deep-Long Short-Term Memory (LSTM) STLF model based on a pretraining method. The model fully applies the information such as electricity consumption pattern, weather and calendar in a structural and orderly way. The empirical results show that our model can effectively predict the daily load of heterogeneous households, with prediction accuracies of 87.9–90.2% on the test set across various tasks. Compared with the base model, our model achieves an 8.7% higher accuracy with a much faster convergence speed. Furthermore, the fluctuations of accuracy on different tasks are within 2.3%, showing that our model is robust. The superiority of the global method is proved through the comparison with local method in the empirical results. This study is expected to make contribution to global STLF of heterogeneous households and providing experience for the global prediction of heterogeneous time series based on deep-LSTM and pretraining method.