Simulation-based evaluation of occupancy on energy consumption of multi-scale residential building archetypes

Abstract

Building archetypes are commonly used to identify areas with energy-inefficient buildings and estimate energy demand at different spatial scales. However, these archetypes often rely on conventional occupancy schedules, which are fixed and uniform throughout the year. This study proposes a new methodology that uses data from the Time Use Survey (TUS) to extract realistic occupancy profiles. The occupancy profiles are used to model the heat gains due to occupancy along with their interaction with lighting and heating systems. The developed occupancy schedules take into account factors such as the number of occupants, dwelling types, the month of the year, and the day of the year. Energy simulations were conducted to evaluate the impact of realistic occupancy schedules on multi-scale residential building archetypes at different spatial scales (postcode, county, and national). The results showed that the developed occupancy schedules were significantly different from conventional schedules, resulting in approximately 8%–10% variations in annual energy demand at various spatial scales compared to the base case. Monthly heating and electricity consumption also showed variations of approximately 25% and 32%, respectively, compared to the base case. With more granularity, the daily variations in heating and electricity consumption were reported as 50% and 12%, respectively, compared to the base case with standard and conventional schedules. These results highlight the importance of incorporating more realistic occupancy schedules in building archetypes for more reliable energy predictions at different spatial and temporal scales. Occupancy-integrated archetypes can help policymakers, local authorities, and urban planners to make informed decisions based on actual data and develop effective energy recommendations for direct retrofit investment and policies.