Residual LSTM based short-term load forecasting

As the modern energy systems is becoming more complex and flexible, accurate load forecasting has been the key to scheduling power to meet customers’ needs, load switching, and infrastructure development. In this paper, we propose a neural network framework based on a modified deep residual network (DRN) and a long short-term memory (LSTM) recurrent neural network (RNN) for addressing the short-term load forecasting (STLF) problem. The proposed model not only inherits the DRN’s excellent characteristic to avoid vanishing gradient for training deeper neural networks, but also continues the LSTM’s strong ability to capture nonlinear patterns for time series forecasting. Moreover, through the dimension weighted units based on attention mechanism, the dimension-wise feature response is adaptively recalibrated by explicitly modeling the interdependencies between dimensions, so that we can jointly improve the performance of the model from three aspects: depth, time and feature dimension. The snapshot ensemble method has also been applied to improve the accuracy and robustness of the proposed model. By implementing multiple sets of experiments on two public datasets, we demonstrate that the proposed model has high accuracy, robustness and generalization capability, and can perform STLF better than the existing mainstream models.