Water and energy consumption in apartments in high-efficiency multifamily buildings
Creators: name: Jordán Cuebas, Francis
Publisher: name: No Publisher Supplied
Year: 2023

description: High-efficiency buildings are expected to decrease energy and water consumption, but their actual performance might exceed the designed energy and water consumption. Occupants, technical efficiency and building operation all affect water and energy consumption. However, the influence of occupants has become more prominent as the contribution of technology has been decreasing due to recent improvements in envelope and energy systems, and the installation of efficient water fixtures and appliances. Despite that, the contribution of tenants to water and energy consumption in high-efficiency buildings, especially in multistory buildings, is not well understood. Central to this dissertation are case studies of two economically diversegreen high-rise buildings in the northeastern US, the high-end Building #1 and the affordable Building #2. The objective of the first part of this work is to gain a better understanding of the drivers of indoor water consumption in urban multistory residential buildings, to more accurately predict residential water demand, and to identify water saving opportunities. Water meters at each fixture and appliance measured water consumption, infrared motion loggers recorded occupant presence, and interviews provided sociodemographic data and data about water consuming activities in selected apartments of the two case study buildings. The average apartment water consumption was 184 L/(cap ⋅ d) in Building #1 and 260 L/(cap ⋅ d) in Building #2. A principal component analysis determined that tenant presence in kitchen, kitchen faucet water consumption, cooking frequency, bathroom faucet water consumption, bathtub water consumption, percentage of tenants going to work/school, percentage of children, and percentage of seniors were major contributors to the variability in total per capita water consumption. A water end-use model taking these drivers into account demonstrated that preferences in bathtub and shower use exceeded the effects of dish washing and toilet leaks. When considered in relation to overall water consumption in the city, the tenant per capita water consumption in both buildings was similar. The second part evaluates one major water end-use, laundering, in depth and includes the primary energy consumption in this assessment to determine the relative contribution of habits and routines and laundry technology to water and energy consumption. The household habits and routines considered include frequency of use, fabric program, wash water temperature, load mass, extra rinse selection, final washer spin speed and dryness level. Water, hot water and electricity consumption and selected household habits and routines were monitored in apartments of Building #1 with the same high-efficiency washers and dryers (N=10). Electricity, water and hot water consumption of the same washer and dryer was also determined in the laboratory varying the user-controlled settings. A bottom-up model for water, hot water, and electricity consumption allowed the prediction of water and primary energy consumption for a range of laundry scenarios changing habits and routines and washer and dryer technology. Clothes washer water consumption in the apartments was 10 – 33 L/(cap ꞏ d) and hot water consumption 0.19 – 3.8 L/(cap ꞏ d), while electricity consumption was 0.02 – 0.17 kWh/(cap ꞏ d) for the clothes washer and 0.27 – 1.0 kWh/(cap ꞏ d) for the clothes dryer. Based on the scenario analysis, water consumption decreased in the following order: small loads in front-loading washer &gt; handwash program in efficient front-loading washer &gt; conventional top-loading washer &gt; efficient front-loading washer with extra rinse &gt; base case efficient front-loading washer &gt; front-loading washer with spray technology. Washer primary energy consumption decreased in the following order: small loads in front-loading washer &gt; maximum appropriate wash water temperature setting in front-loading washer &gt; hand wash fabric program only in front-loading washer &gt; base case of efficient front-loading washer &gt; front-loading washer with spray technology &gt; cold water in front-loading washer. Dryer primary energy consumption decreased in the following order: conventional top-loading washer which has a low final spin speed &gt; efficient top-loading washer &gt; small loads in front-loading washer &gt; lower final spin speed &gt; base case of efficient dryer &gt; heat pump clothes dryer &gt; 50 % of clothes line dried. Water and energy consumption efficiency is not achieved by technical improvements alone, household habits and routines are key in laundry water and energy conservation. The objective of the third and final part determines if lifestyle, which preserves the interdependence of variables related to occupant and building characteristics, is useful in assessing differences in electricity and water consumption in the two case study buildings. The buildingwide energy use intensity (energy use per floor area) of Building #1 [368 kWh/(m2 ꞏ year)] exceeded the energy use intensity of Building #2 [250 kWh/(m2 ꞏ year)]. However, the water use intensity of Building #1 [1.2 m3/(m2 ꞏ year)] was lower than the water use intensity of Building #2 [4.1 m3/(m2 ꞏ year)]. Utility meters provided the apartment electricity consumption and plug load meters the electricity consumption for appliances and equipment in selected apartments. Water meters at each fixture and appliance measured the water and hot water consumption. Infrared motion loggers recorded occupancy in the apartments and interviews provided household size and context. The first four factors in a multiple factor analysis explained 63.3 %, the first three factors 54.7 % and the first two factors 45.2 % of the variation of the data. Key drivers separating apartments along factor F1 were occupant presence, water consumptions and household size, with higher occupant presence mainly corresponding to Building #2. With an increase in factor F2, the electricity consumption increased. Building#1 was more linked to electricity consumption most likely because the apartments were larger and equipped with more electrical appliances and lighting fixtures. Occupant presence affected water and energy consumption differently, with occupant presence more related to water and hot water than to electricity consumption. A hierarchical cluster analysis showed that there are at least two clusters (lifestyles) in the buildings. The clustering gave similar although more distinctive separations of apartments by water consumption, energy consumption and occupancy than the separation by building. Separating more than two clusters was useful in explaining water and electricity consumption end-uses, but the additional clustering separated small clusters with unusual water consumption and electricity consumption behavior. The separation of water and energy consumptions by lifestyle was promising in this study of two case study buildings, suggesting the importance of confirming these results with larger datasets.