Reconstructing building stock to replicate energy consumption data

The paper introduces an approach to replicate building stock energy data using energy survey data. For demonstration of the approach, the research uses energy consumption data for office buildings in Chicago from Commercial Building Energy Consumption Survey (CBECS) 2003. The replication starts from derivation of the energy use distribution for a building stock in a specific location from the survey data. Then probabilistic methods are used to map building stock model space to real-world data space reflecting a weather adjustment of the energy survey data. The approach leverages a linear surrogate model of the physics-based reduced order normative energy model. The normative building energy model can rapidly estimate the building energy performance with respect to its design and operational characteristics. The research investigates a statistical procedure to inversely estimate building parameters using regression and Bayesian inference model based on the Markov Chain Monte Carlo (MCMC) sampling techniques. The research serves a new paradigm of the building stock aggregation that can lead to an efficient energy model, which contributes the body of knowledge of energy modeling beyond the single building scale.