Energy and flow demand analysis of domestic hot water in an apartment complex using a smart meter

Abstract

This study aims to reveal new perspectives on domestic hot water use regarding seasonal behavior, peak demand deviation with temporal metering resolution and time averaging, initially discarded characteristics, and the possibility of demand forecasting based on the previous outdoor temperature. To determine the use of domestic hot water in 918 households located at the study site, new smart meters and peripheral systems were used to collect sub-meter readings of energy/flow/discarded hot water demands and endpoint temperatures every 30 s. Collected data on individual households can provide quantitative information about seasonal changes, variations in hot water service quality among end-users, and end-user behaviors of initial use. Furthermore, statistical and machine learning analyses indicate the possibility of forecasting seasonal domestic hot water consumption through an understanding of the relationship between energy demand and previous outdoor temperatures. Through a clear understanding and with a proper use of the demand behavior as it pertains to domestic hot water, guidelines can be derived to enable the efficient use of domestic hot water by households as well as the efficient operation by heat suppliers.