Analysis of heating and cooling data of buildings and investigation of the influencing factors using a joint regression model

Given the limited energy resources globally, energy optimization is crucial. A significant portion of this energy is consumed by buildings. Therefore, the aim of this research is to explore the simultaneous factors affecting the heating and cooling of buildings. In the current research, 768 different residential buildings simulated with Ecotect software have been investigated. Joint regression model 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 variable called ”type” (building model) was introduced and shown to be one of the strongest factors affecting the heating and cooling of buildings. This variable is related to the shape of the building. 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) and using Akaike’s information criterion and deviance information criterion, pointing to the superiority of the joint regression model. Additionally, the model parameters are estimated using the maximum likelihood estimation method and the amount of Akaike model compared to the separate model is a decrease of 0.0072%, which shows the superiority of the joint regression model. The deviance information criterion is equal to 0.001736%, and in comparison with the chi distribution, the null hypothesis is rejected to test the superiority of the models, which is regressed to the superiority of the joint model.