Multi-linear Regression Models to Predict the Annual Energy Consumption of an Office Building with Different Shapes

Mohammad Mottahedi, Atefeh Mohammadpour, Shideh Shams Amiri, David Riley, Somayeh Asadi

Research output: Contribution to journalConference articlepeer-review

42 Scopus citations


The present study describes the development of a multi-linear regression model to predict the effect of building shape on total energy consumption in two different climate regions (i.e. cold-dry and warm-marine). Seven building shapes including H-shape, T-shape, rectangle, etc. were considered in this study. The simplified model can be used to conduct a parametric study in order to investigate the effect of building parameters on total heating and cooling load. Building simulation software programs, including eQUEST and DOE-2 were used to build and simulate individual building configuration that were generated using Monte Carlo simulation techniques. Ten thousand simulations for seven building shapes were performed to create a comprehensive dataset covering the full ranges of design parameters. Statistical analysis was performed using R statistical analysis program to develop a set of linear regression equations predicting energy consumption of each design scenario. In addition, the influence of several design parameters on building energy consumption was further investigated using the sensitivity analysis procedure. The difference between regression-predicted and DOE-2 simulated annual building energy consumption were largely within 5%. It is envisioned that the developed regression models can be used to estimate the total energy consumption in the early stages of the design when different building schemes and design concepts are being considered.

Original languageEnglish (US)
Pages (from-to)622-629
Number of pages8
JournalProcedia Engineering
StatePublished - 2015
EventInternational Conference on Sustainable Design, Engineering and Construction, ICSDEC 2015 - Chicago, United States
Duration: May 10 2015May 13 2015

All Science Journal Classification (ASJC) codes

  • General Engineering


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