A multiple regression approach for calibration of residential building energy models

Nelson Fumo, Maria Josefina Torres, Kayla Broomfield

Research output: Contribution to journalArticlepeer-review

13 Scopus citations


When thinking on retrofitting a building, energy models are used to estimate energy consumption for the different options. However, the accuracy of the model may vary depending on the accuracy of the input parameters. To minimize the uncertainty of the input parameters a calibration process is needed. As a mean to reduce the time for calibration, a methodology using a regression analysis with parameters or independent variables that are a function of the physical driving forces for energy performance is proposed. The main novelty of the approach is that it can be considered a quasi-physical statistical approach that has advantage over pure traditional statistical approaches. This is achieved by incorporating the physical driven forces of the parameters contributing to the energy consumption. Other advantage is that it uses the well know and easy to implement multiple regression analysis and system of equations. The approach also can be implemented in stages based on a prioritized list of parameters; that is, a few parameters with more impact are first used for a coarse tuning, and then additional parameters are used for fine tuning. The approach is applied by running a series of simulations with the design input parameters to obtain data for a multiple regression analysis. Then, the regression coefficients are used in a system of equations which solution gives the magnitude of the parameters. The results of the methodology were compared with the traditional Bayesian approach as a mean to validate the approach. Results show that the proposed approach is as accurate as the Bayesian approach.

Original languageEnglish
Article number102874
JournalJournal of Building Engineering
StatePublished - Nov 2021


  • Energy model calibration
  • Regression analysis
  • Residential building energy analysis


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