Building Occupant Energy Labels (Oel): Capturing the Human Factors in Buildings for Energy Efficiency

Abstract

Occupancy is one of the primary contributors to the energy performance gap, defined as the difference between actual and predicted energy usage, in buildings. This paper limits its scope to residential buildings, where occupant-centric consumption often goes unaccounted for in standard energy metrics. This paper starts from the hypothesis that a simple occupant energy efficiency label is needed to capture the essence of occupant behaviour. Such a label would help researchers and practitioners study a wide range of behavioural patterns and may better frame occupant interventions, potentially contributing more than expected to the field. Focusing on the residential sector, this research recognises that the complexity of occupant behaviour and its links to different scientific calculations requires that researchers deal with several intricate factors in their building performance assessments. Moreover, complexity arising from changing attitudes and behaviours-based on building typology, social environment, seasonal effects, and personal comfort levels-further complicates the challenge. Starting with these problems, this paper proposes a framework for an occupant energy labelling (OEL) model to overcome these issues. The contribution of the paper is twofold. Firstly, the literature is reviewed in depth to reveal current research related to occupant behaviour for labelling of humans based on their energy consumption. Secondly, a case study with energy simulations is implemented in the UK, using the CREST tool, to demonstrate the feasibility and potential of OEL. The results show that labelling occupants may help societies reduce building energy consumption by combining insights from energy statistics, surveys, and bills gathered with less effort, and can assist decision-makers in determining the best match between buildings and occupants. While the focus of this study is on residential buildings, future research is recommended to explore the applicability of OEL in office environments, where occupant behaviour and energy dynamics may differ significantly.