Multi-objective LSTM ensemble model for household short-term load forecasting

With the development of smart grid, household load forecasting played an important role in power system operations. However, the household load forecasting is often inefficient due to its high volatility and uncertainty. Consequently, a multi-objective LSTM ensemble model based on the DBN combination strategy, is proposed in this paper. This method first builds a deep learning framework based on the LSTM network in order to generate several sub-models. With the diversity and accuracy of the sub-models as the objective functions, the improved MOEA/D algorithm is then used to optimize the parameters, in order to enhance the overall performance of the sub-models and ensure their differences. Finally, a DBN-based combination strategy is used to combine the single forecasts in order to form the ensemble forecast, and reduce the adverse effects of model uncertainty and data noise on the prediction accuracy. The experimental results show that the proposed method has several advantages in prediction accuracy and generalization capacity, compared with several current intelligent prediction methods.