Enrichment of building energy models using error domain constrained generative machine learning

We use a significant amount of energy to provide thermal comfort in buildings, and predicting this energy use is challenging due to the nature of building characteristics. Many energy-related characteristics are either uncertain or difficult to measure. This uncertainty hinders analysis of the current state and potential improvements. We propose a novel approach based on generative machine learning (ML) to estimate the values of energy-related characteristics. A generative ML network is trained to predict the sets of values of characteristics within predetermined constraints, corresponding to historical energy use. In addition, we build on an error-domain approach to include systematic modelling uncertainties. Three approaches − model inversion, generative ML, and generative ML with an error domain – are compared to predict sets of values of energy-related characteristics. The predicted sets of values obtained using a generative ML approach provide the most precise estimates of energy. However, these estimates can differ significantly from the measured energy use. While the predicted sets of values using an error domain approach are statistically conservative, they allowed an approximately correct estimate of energy use. Thus, leveraging a generative ML approach, we enriched energy models with relevant information that facilitates reliable energy analyses of the current state and predicted energy use.