Short-term water demand prediction based on decomposition technique optimization and a multihead attention mechanism

Abstract

Short-term water demand prediction is crucial for real-time optimal scheduling and leakage control in water distribution systems. This paper proposes a new deep learning-based method for short-term water demand prediction. The proposed method consists of four main parts: the variational mode decomposition method, the golden jackal optimization algorithm, the multihead attention mechanism, and the bidirectional gated recurrent unit (BiGRU) model. Furthermore, a seq2seq strategy was adopted for multistep prediction to avoid the error accumulation problem. Hourly water demand data collected from a real-world water distribution system were applied to investigate the potential of the proposed method. The results show that the proposed method can yield remarkably accurate and stable forecasts in single-step prediction (i.e., the mean absolute percentage error (MAPE) reaches 0.45%, and the root mean squared error (RMSE) is 25 m3/h). Moreover, the proposed method still achieves credible performance in 24-step prediction (i.e., the MAPE reaches 2.12%, and the RMSE is 126 m3/h). In general, for both single-step prediction and multistep prediction, the proposed method consistently outperforms other BiGRU-based methods. These findings suggest that the proposed method can provide a reliable alternative for short-term water demand prediction.