Integrated nonlinear daily water demand forecast model (case study: City of Guelph, Canada)

Urban water demand forecasting is a significant issue in the design, maintenance, and operation of a reliable and economical water supply system. The large variations in daily water demand are the leading source of health risks due to water pressure drops within the distribution network, increasing the likelihood of contamination leaking into the system and/or excessive retention time. This study presents an explicit mathematical equation, based on the air temperature and precipitation effects on the outdoors water uses and the seasonal and weekly trends in the domestic/industrial water demands that can accurately forecast the daily water demand fluctuations for a municipality. Recent time series prediction methods are based on linear, non-linear and hybrid methods that can be effectively applied to solve time series problems. However, due to the presence of the high-frequency values in the data, these methods show some limitations for practical applications. To solve these limitations, this study introduced a non-linear model through a grouping method of data handling for water demand management and tested for the case study City of Guelph, Ontario, Canada. The coefficient of determination and mean absolute percent error of the new water demand forecast model was 71.03 and 2.93, respectively, compared to 56.12 and 3.49 for the popular linear model, 58.91 and 3.37 for the non-linear model, 61.37 and 3.28 for the hybrid model (respectively). The proposed new model will allow optimizing the water storage costs versus pumping capacity costs of the water supply system to maximize the usage of the lower electricity costs for the production/pumping at the off-peak hours. Further, to assist the City with water demand management and reduce its peaking factors.