Real-reference buildings for urban energy modelling: A multistage validation and diversification approach

Urban energy models are required to establish baselines, map buildings' performance, and explore energy and CO2 emissions reduction strategies. The authors propose a new reductive bottom-up model to estimate the final energy demand for educational building stocks. For this purpose, this model relies on data-driven validated realreference buildings and probability up-scaling. This model has three stages: (1) multivariate clustering techniques identify real-reference buildings from building stocks, (2) calibrated energy simulation estimate demand, uncertainty and allow to test scenarios, and (3) probability up-scaling diversifies building-level results regarding variations in their urban emplacement. Afterwards, the authors applied this model to two educational building stocks: a conditioned stock in Barcelona, Spain and a free-floating stock in Quito, Ecuador. Two real-reference schools represent 62% of the stock in Barcelona, with a final energy demand of 15.96 GWh/year. Likewise, two schools describe the entire educational stock in Quito with a final energy demand of 29.83 GWh/year. Results reflect Barcelona stock is more heterogeneous than Quito despite its lesser population. Specifically, their energy use intensity was 85.92 kWh/m2 with a 4.25% deviation in Barcelona and 40.23 kWh/m2 with a 0.11% deviation in Quito. This model is transferable among stocks because it relies on the characterization of buildings' thermal balance in their as-is state and provides good accuracy with building and urban energy records. This diversification procedure gave back part of the lost variability because of the reductive approach in the realreference building definition. Also, it produced lower prediction errors for aggregated final energy use.

Automated summary

Urban energy models are essential for establishing baselines, mapping building performance, and exploring energy reduction strategies. The authors introduce a new reductive bottom-up model that relies on real-reference buildings and probability up-scaling for estimating final energy demand. This model was applied to educational building stocks in Barcelona and Quito, revealing differences in energy use intensity and variability between the two urban areas.