Cluster analysis-based energy performance assessment for office building stock

Abstract

To achieve carbon neutrality at the national or city level, the energy performance and conservation measures of large buildings should be evaluated. However, the assessment of the energy performance of existing building stock is often based on annual energy use intensity calculated from energy bill data due to data acquisition limitations. This approach has limitations in analyzing seasonal effects and establishing effective energy conservation strategies. In this paper, we propose a novel energy performance assessment method for existing office building stock. Our method classifies monthly electricity, gas, and heat energy use patterns using clustering algorithms without requiring additional database construction beyond the National Building Energy Database in Korea. We discuss the clustering results and provide an application method to assist policymakers through a case study.