Temporal Pattern Attention-Based Sequence to Sequence model for Multistep Individual Load Forecasting

摘要

Load forecasting plays a critical part in grid operation and planning. In particular, the importance of multistep load forecasting for individual power customer is increasingly prominent. Due to the strong volatility of individual consumers’ electricity consumption behavior, traditional machine learning methods that cannot capture time dependence are difficult to obtain good prediction results. The recurrent neural network (RNN) can capture the time correlations existing in the load data, and the sequence to sequence (Seq2Seq) model combining two RNNs of the encoder and decoder is very suitable for multistep prediction. The temporal pattern attention mechanism can further capture the periodic change pattern in historical load data, which further improves time series modeling. We combined their advantages to propose a new type of multistep individual load forecasting framework, called the temporal pattern attention based sequence to sequence (TPA-Seq2Seq) model. This model can overcome the difficulty of multi-step prediction and further capture the load change pattern. The proposed framework was tested on real residential smart meter data, the results show that the proposed model has good prediction accuracy and is well suited for longer prediction sequences.