Automatic Detection of Water Consumption Temporal Patterns in a Residential Area in Northen Italy

Abstract One of the main challenges for city development is to ensure a sustainable water resource management for the water supply system. A clear identification of the urban water consumption patterns supports policy and decision makers in managing the water resources, satisfying the total demand and, at the same time, reducing losses and identifying potential leakages or other issues in the distribution network. High resolution smart meters have widely shown to be an efficient tool to measure in-pipe water consumption. The collected data can be used to identify water demand patterns at different temporal and spatial scales, reaching the end-uses level. Water consumption patterns at building level can be influenced by multiple factors, such as socio-demographic aspects, seasonality, and house characteristics. The presence of a garden that requires summer irrigation strongly alters the daily consumption pattern. In this framework, we present an innovative approach to automatically detect the presence of garden irrigation, identifying daily average water consumption patterns with and without it. The proposed methodology was tested in a residential area in Northen Italy, where 23 smart meters recorded data at 1-minute resolution for two years. Results show very good performances in distinguishing between days with and without garden irrigation. The derived average normalized water consumption patterns for both scenarios can help decision makers and water managers to regulate the pressure regimes in the distribution network correctly.