A Meta-Analysis Review of Occupant Behaviour Models for Assessing Demand-Side Energy Consumption

Deepu Krishnan, Scott Kelly and Yohan Kim
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Deepu Krishnan: Institute for Sustainable Futures, University of Technology Sydney, Building 10, 235 Jones St, Ultimo, NSW 2007, Australia
Yohan Kim: Institute for Sustainable Futures, University of Technology Sydney, Building 10, 235 Jones St, Ultimo, NSW 2007, Australia

Energies, 2022, vol. 15, issue 3, 1-23

Abstract: Occupant behaviour plays a significant role in shaping the dynamics of energy consumption in buildings, but the complex nature of occupant behaviour has hindered a deeper understanding of its influence. A meta-analysis was conducted on 65 published studies that used data-driven quantitative assessments to assess energy-related occupant behaviour using the Knowledge Discovery and Data Mining (KDD) framework. Hierarchical clustering was utilised to categorise different modelling techniques based on the intended outcomes of the model and the types of parameters used in various models. This study will assist researchers in selecting the most appropriate parameters and methods under various data constraints and research questions. The research revealed two distinct model categories being used to study occupant behaviour-driven energy consumption, namely (i) occupancy status models and (ii) energy-related behaviour models. Multiple studies have identified limitations on data collection and privacy concerns as constraints of modelling occupant behaviour in residential buildings. The “regression model” and its variants were found to be the preferred model types for research that models “energy-related behaviour”, and “classification models” were found to be preferable for modelling “occupancy” status. There were only limited instances of data-driven studies that modelled occupant behaviour in low-income households, and there is a need to generate region-specific models to accurately model energy-related behaviour.

Keywords: occupant behaviour; occupancy; low-income households; hierarchical clustering; knowledge discovery and data mining; residential energy consumption (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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