Multivariable Time Series Forecasting for Urban Water Demand Based on Temporal Convolutional Network Combining Random Forest Feature Selection and Discrete Wavelet Transform

Urban water demand forecasting is crucial to reduce the waste of water resources and environmental protection. However, the non-stationarity and non-linearity of the water demand series under the influence of multivariate makes water demand prediction one of the long-standing challenges. This paper proposes a new hybrid forecasting model for urban water demand forecasting, which includes temporal convolution neural network (TCN), discrete wavelet transform (DWT) and random forest (RF). In order to improve the model's forecasting abilities, the RF method is used to rank the factors and remove the less important factors. The dimension of raw data is reduced to improve calculating efficiency and accuracy. Then, the original water demand series is decomposed into different characteristic sub-series of multiple variables with better-behavior by DWT to weaken the fluctuation of original series. At the core of the proposed model, TCN is utilized to establish appropriate prediction models. Finally, to test and validate the proposed model, a real-world multivariate dataset from a water plant in Suzhou, China, is used for comparison experiments with the most recent state-of-the-art models. The results show that the mean absolute percentage error (MAPE) of the proposed model is 1.22% which is smaller than the other benchmark models. The proposed model indicates the only 2.2% of the prediction results have a relative error of more than 5%. It shows that the reliable results of the proposed model can be a superior tool for urban water demand forecasting.