Using data-driven indoor temperature setpoints in energy simulations of existing buildings: a Swedish case study

Abstract

Abstract Building energy analyses of large samples or building stocks commonly use National building stock temperature averages in their calculations. However, such averages may not be representative of the conditions in a specific building type and may mask meaningful information found at building or dwelling level. Analysis of indoor temperature data from the Swedish housing stock showed that 25% out of approximately 1000 dwellings were heated at a temperature ≥23°C in wintertime. If indoor temperature management is considered as a potential energy saving measure for the building stock it may be more effective to explore implementation in these specific dwellings, than considering average temperature reduction across the entire building stock. This however would require more detailed input data on indoor temperatures. Would such an approach be worthwhile? To answer this question, two types of Swedish multifamily buildings were simulated with i) business-as-usual scenarios and ii) setpoints based on indoor temperature data from the last Swedish National Survey. The study shows that using data-driven, dwelling-specific indoor temperatures could lead to more effective decision making on indoor temperature management, targeting buildings and dwellings where temperature reduction would most likely cause the least compromise on comfort. Such a strategy however should be complementary to a wider plan of improved energy efficiency measures across the building stock.