A neural network approach for short-term water demand forecasting based on a sparse autoencoder

This paper presents a backpropagation neural network (BPNN) approach based on the sparse autoencoder (SAE) for short-term water demand forecasting. In this method, the SAE is used as a feature learning method to extract useful information from hourly water demand data in an unsupervised manner. After that, the extracted information is employed to optimize the initial weights and thresholds of the BPNN. In addition, to enhance the effectiveness of the proposed method, data reconstruction is implemented to create suitable samples for the BPNN, and the early stopping method is employed to overcome the BPNN overfitting problem. Data collected from a real-world water distribution system are used to verify the effectiveness of the proposed method, and a comparison with the BPNN and other BPNN-based methods which integrate the BPNN with particle swarm optimization (PSO) and the mind evolutionary algorithm (MEA), respectively, is conducted. The results show that the proposed method can achieve fairly accurate and stable forecasts with a 2.31% mean absolute percentage error (MAPE) and 320 m3/h root mean squared error (RMSE). Compared with the BPNN, PSO–BPNN and MEA–BPNN models, the proposed method gains MAPE improvements of 5.80, 3.33 and 3.89%, respectively. In terms of the RMSE, promising improvements (i.e., 5.27, 2.73 and 3.33%, respectively) can be obtained.