Impact of Thermal Properties on Building Stock Energy Use Using Explainable Artificial Intelligence

As part of Sweden’s commitment to carbon neutrality, various municipalities have established energy efficiency targets. Achieving these targets requires decision-making knowledge at the local level, particularly concerning the energy retrofitting of existing building stocks. It includes understanding of the thermal properties (U-values) of building stock, their impact on energy use, and the potential energy retrofits. Our research focuses on assessing the thermal properties performance and their impact on energy use of residential building stocks in Umeå, Sweden. We employ explainable artificial intelligence (XAI) integrated with machine learning regression framework to elucidate how different building thermal features influence the building’s energy use and also the correlations among these features. The findings highlight the significant impact of building floor area on energy use, followed by location, age, etc. Among thermal properties, the exterior walls have high impact and attic floor has the lowest impact on the energy use of Umeå’s residential building stock. Ultimately, this study provides municipality-level decision-making insights for planning energy retrofitting initiatives for Umeå building stock.