Water consumption analysis during night hours of residential customers

Abstract

Managing and reducing water losses should be a primary concern to ensure the sustainability of a water utility. Among all the potential strategies, the design and construction of district meter areas (DMA) is probably one of the most widely used for water loss assessment and control. This is because partitioning a water network into smaller portions significantly facilitates the analysis procedures and improves the speed at which bursts and leaks are detected and located. This analysis is typically done by processing and evaluating the time series of the inflows into the DMA. The Minimum Night Flow (MNF) represents the lowest flow into the DMA over a 24-h period. MNF typically occurs between 02:00 and 04:00 AM. During this period, most users do not intentionally use water and the inflows into the DMA are mainly composed of leakage at DMA pipes and private plumbing systems. Consequently, the analysis of the MNF allows for easy and accurate quantification of the magnitude of leakage in a particular DMA. The main difficulty in applying this methodology appears when trying to disaggregate the night flows into its fundamental components: 1) Leakage in mains and connection pipes belonging to the distribution network 2) Leakage at customers' facilities, and 3) Intentional use of water by customers. The first two components correspond to continuous flows that, in most cases, remain constant during the night hours. The third component is inherently random and may vary in magnitude and duration.Managing and reducing water losses should be a primary concern to ensure the sustainability of a water utility. Among all the potential strategies, the design and construction of district meter areas (DMA) is probably one of the most widely used for water loss assessment and control. This is because partitioning a water network into smaller portions significantly facilitates the analysis procedures and improves the speed at which bursts and leaks are detected and located. This analysis is typically done by processing and evaluating the time series of the inflows into the DMA. The Minimum Night Flow (MNF) represents the lowest flow into the DMA over a 24-h period. MNF typically occurs between 02:00 and 04:00 AM. During this period, most users do not intentionally use water and the inflows into the DMA are mainly composed of leakage at DMA pipes and private plumbing systems. Consequently, the analysis of the MNF allows for easy and accurate quantification of the magnitude of leakage in a particular DMA. The main difficulty in applying this methodology appears when trying to disaggregate the night flows into its fundamental components: 1) Leakage in mains and connection pipes belonging to the distribution network 2) Leakage at customers' facilities, and 3) Intentional use of water by customers. The first two components correspond to continuous flows that, in most cases, remain constant during the night hours. The third component is inherently random and may vary in magnitude and duration. In the proposed work, detailed data of one-year hourly readings from approximately 20,000 customers have been analyzed and disaggregated into leakage and intentional use. The aim is to improve the models that characterize the night consumption originated at the customers' facilities. Hence, the study's initial stage involves the development of algorithms that allow disaggregating the night consumption registered by the customers’ water meters into a baseline consumption due to leakage and the component caused by the intentional use of water. Probability function distributions of leakage flow rates at customers' plumbing systems have been obtained for various types of water users. Simplified probability distribution functions of intentional water use have also been developed to consider the duration of water consumption and the average flow of residential users. These probability functions allow the creation of synthetic consumption series that overlap with the baseline consumption caused by leakage inside the customers' premises. The novelty of the proposed methodology is that the probability functions obtained have been derived from actual water consumption data of nearly 20,000 customers, monitored for one year. The night consumption model developed may enable the water utility to better estimate this parameter in those DMAs where the customers' water meters cannot be read hourly.