Uncertainty quantification of building energy use using a probabilistic spatiotemporal model of urban temperature

• We propagate uncertainty from urban temperature to building energy use • We integrate models of spatiotemporal urban temperature and forecasting of energy use • We implement a computationally efficient alternative to sampling-based methods • We analyze local temperature impacts on building energy use and sensitivity • Inaccurate temperature model of a location can underestimate energy use by 4.2–10.3 % Urban temperature significantly impacts energy use in the built environment. This paper presents a model coupling approach to quantify how uncertainty in urban temperature, particularly from localized weather effects such as the urban heat island, propagates through building energy systems and affects consumption patterns. The proposed method integrates two models: (1) a probabilistic spatiotemporal model (PSTM) to forecast near-surface temperature at a regional scale with sub-city resolution and (2) a time-series electrical load forecasting model at the building scale. The load forecasting model is calibrated using simulated energy consumption data for representative commercial and residential buildings in the United States. To improve computational efficiency, we also propose combining two uncertainty quantification algorithms: the first-order reliability method and the sequential compounding method, as an alternative to sampling-based approaches. As a numerical case study, we analyze the impact of hot weather in the City of Pittsburgh on energy consumption and the sensitivity of various building types. We also investigate how urban temperatures influence high energy use across different land use types. The numerical experiments on residential building models revealed that high energy consumption was underestimated by 4.2–10.3 %, depending on the building model, when compared to analyses based on temperatures that represent the typical weather of the region. This finding highlights that temperature conditions at a representative weather reference point, e.g., an airport, may underestimate energy use in urban areas, as it fails to account for localized weather such as the urban heat island.