Energy consumption model with energy use factors of tenants in commercial buildings using Gaussian process regression

Therefore, in this study we propose an energy consumption model using data on the energy use factors, such as occupant schedule, operation, and equipment, especially with a focus on the tenants in buildings. In this study, we analyzed the ranking of variable importance using the Random Forest algorithm and verified the energy consumption results of individual, office, and retail tenants in commercial buildings using a Gaussian process regression model. The main contribution of this study is the identification of the influence of energy use factors on the energy consumption of each tenant, both office and retail, thereby developing an energy model. This study established a method to identify the combination of variables that could estimate the energy consumption. Moreover, it can be seen that the significant variables to consider for developing an energy model differ depending on the tenant use class, i.e., office or retail.