To effectively establish and implement energy-saving plans for existing buildings, it is important to identify the determinants that influence actual energy consumption. Linear statistical models that have widely used in prior studies present a major limitation in treating nonlinear problems. Therefore, any determinant having nonlinear relationship with the energy consumption has been hardly found. To address this problem, this study proposes a novel approach to discover hidden determinants, using both linear and nonlinear models: multiple linear regression (MLR) and decision tree (DT). This study used energy consumption and characteristics data of 71 apartment units in Seoul, South Korea, which were collected by smart-metering and field survey. Through MLR and DT models, building, system, and occupant characteristics that significantly affect each of energy consumption for each end use were identified. In the results, some determinants were common in both models, while some determinants (e.g. the year of the building permit, coefficient of performance of air conditioners, etc.) were found only in the DT. The findings in this study imply that it is desirable to use nonlinear models such as a DT rather than relying only on linear models for comprehensive analysis of the relationships and interactions between variables.
- Data-driven approaches
- Decision tree
- Energy consumption by end use
- Multiple linear regression