Given the limited energy resources globally, energy optimization is crucial. A large part of this energy is consumed by
buildings. The aim of the research is to discover the effective factors simultaneously on the heating and cooling of buildings.
Research has been done on 768 residential buildings simulated with Ecotect software. This dataset is available under the title
of ”Energy Efficiency Data” on the machine learning repository website of the University of California, which has been used
in this article. Joint regression models and exploratory data analysis methods were used to identify the influencing factors of
the heating and cooling of buildings. Based on variables such as relative compactness, overall height, surface area, and roof
of the buildings, a new factor called ”type1” was introduced and shown to be one of the most important factors affecting the
heating and cooling of buildings. In the joint regression model, it is assumed that the responses follow a multivariate normal
distribution. Then, this model is compared with separate regression models (without assuming responses correlation) using
Akaike’s information criterion and deviance criterion, which point to the superiority of the joint regression model