This study developed structured probabilistic statistical models to systematically reflect individual variations in the domestic hot water load factoring household characteristics and temporal variations in the hourly pattern of consumption. The hourly domestic hot water data of 15 households derived from the Korea Energy Agency’s public data were used. Models 1 and 2 were based on bilinear regression models to predict the daily average domestic hot water load based on the household characteristics and daily variations. Model 3 was based on the multivariate normal distribution to generate the average hourly domestic hot water load profile which varied per household. Model 4 used the beta distribution probability density function to randomly generate hourly variations from the average load profiles reflecting temporal variation. As a result of applying these four models, individual and temporal variations were reflected in the whole year hourly load prediction. The resulting probabilistic domestic hot water loads were compared with those determined using the deterministic method of the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE), ECO2 criteria and the single multivariate distribution model derived during the entire set of hourly load data across the 15 households. This comparison reflected that the structured probabilistic models predicted individual and temporal variations with sufficient accuracy.
Bibliographical notePublisher Copyright:
© 2022 Architectural Institute of Korea.
- Domestic hot water load
- Household variation
- Probabilistic model
- Residential community
- Temporal variation
ASJC Scopus subject areas
- Civil and Structural Engineering
- Building and Construction
- Engineering (miscellaneous)