An improved LSTM-Seq2Seq-based forecasting method for electricity load
Yangyang Mu, Ming Wang, Xuehan Zheng, He Gao
Abstract
Power load forecasting has gained considerable research interest in recent years. The power load is vulnerable to randomness and uncertainty during power grid operations. Therefore, it is crucial to effectively predict the electric load and improve the accuracy of the prediction. This study proposes a novel power load forecasting method based on an improved long short-term memory (LSTM) neural network. Thus, an long short-term memory neural network model is established for power load forecasting, which supports variable-length inputs and outputs. The conventional convolutional neural network (CNN) and recurrent neural network (RNN) cannot reflect the sequence dependence between the output labels. Therefore, the LSTM-Seq2Seq prediction model was established by combining the sequence-to-sequence (Seq2Seq) structure with that of the long short-term memory model to improve the prediction accuracy. Four prediction models, i.e., long short-term memory, deep belief network (DBN), support vector machine (SVM), and LSTM-Seq2Seq, were simulated and tested on two different datasets. The results demonstrated the effectiveness of the proposed LSTM-Seq2Seq method. In the future, this model can be extended to more prediction application scenarios.