Smart meter-based archetypes for socioeconomically sensitive urban building energy modeling

Abstract

Urban building energy modeling (UBEM) and its associated tools have proliferated in recent years, leading to various model development, simulation, calibration approaches, and use cases. UBEM is becoming a valid policy support tool to guide planning and intervention efforts at the neighborhood and city scale. However, current UBEM workflows focus primarily on the physical properties of buildings and geospatial geometry data without consideration for socioeconomic factors or demographic characteristics of the modeled/studied areas. This limitation impedes UBEM's effectiveness as a policy support tool. This paper presents a novel method – using a combination of supervised and unsupervised learning techniques on smart meter and census data – to develop hourly usage schedules for different socioeconomic personas. The schedules can be used to refine building archetypes for UBEMs. The method is piloted in two cities with similar building stocks but different socioeconomic compositions. It was found that socioeconomic indicators are important in classifying building archetypes. Considering these indicators changes the predicted city-wide energy use for residential buildings by up to 10%. The method presented is scalable and applicable to cities and municipalities worldwide (large or small) and elucidates the importance of accounting for demographic and socioeconomic indicators to reflect lived realities accurately.