Improving short-term water demand forecasting using evolutionary algorithms

Modern solutions in water distribution systems are based on monitoring the quality and quantity of drinking water. Identifying the volume of water consumption is the main element of the tools embedded in water demand forecasting (WDF) systems. The crucial element in forecasting is the influence of random factors on the identification of water consumption, which includes, among others, weather conditions and anthropogenic aspects. The paper proposes an approach to forecasting water demand based on a linear regression model combined with evolutionary strategies to extract weekly seasonality and presents its results. A comparison is made between the author's model and solutions such as Support Vector Regression (SVR), Multilayer Perceptron (MLP), and Random Forest (RF). The implemented daily forecasting procedure allowed to minimize the MAPE error to even less than 2% for water consumption at the water supply zone level, that is the District Metered Area (DMA). The conducted research may be implemented as a component of WDF systems in water companies, especially at the stage of data preprocessing with the main goal of improving short-term water demand forecasting.