Machine learning-based analysis of occupant-centric aspects: Critical elements in the energy consumption of residential buildings

Abstract

The housing sector consumes a significant amount of energy worldwide, which is mainly attributed to operating energy systems for the provision of thermally comfortable indoor environments. Although the literature in this field has focused on investigating critical factors in energy consumption, only a few studies have conducted a quantitative sensitivity analysis for thermal occupant factors (TOF) (i.e., metabolic rate and clothing level). Therefore, this paper introduces a framework for testing the criticality of TOF with a cross-comparison against building-related factors, considering the constraint of occupant thermal comfort. Using a building energy simulation model, the energy consumption of a case study is simulated, and building energy model alternatives are generated. The scope includes TOF and building envelope factors, with an established orthogonal experimental design. A popular branch of machine learning (ML) called linear genetic programming (LGP) is used to analyse the generated data from the experiment. Finally, a sensitivity analysis is conducted using the developed LGP model to determine and rank the criticality of the considered factors. The findings reveal that occupants' metabolic rate and clothing level have relevancy factors of −0.48 and −0.38 respectively, which ranked them 2nd and 3rd against building envelope factors for achieving energy-efficient comfortable houses. This research contributes to the literature by introducing a framework that couples orthogonal experiment design with ML techniques to quantify the criticality of TOF and rank them against building-envelope factors.