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

Abstract Identification of the factors influencing energy consumption in buildings is crucial for energy efficient control in the operation stage. By using a multi-variate approach in energy performance prediction, we can characterize the building energy usage with a few available variables. However, very few studies have applied the variables related to building operation for energy consumption in buildings. Especially, the importance of the use factor, which affects the energy consumption, varies depending on the usage of tenants. However, there is a lack of sufficient research on the energy consumption based on energy use factors of each tenant in a building. 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.

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