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Distinguishing Household Groupings within a Precinct Based on Energy Usage Patterns Using Machine Learning Analysis

Author

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  • Troy Malatesta

    (Sustainability Policy Institute, School of Design and Built Environment, Curtin University, Building 418 Level 4, Kent St., Bentley, WA 6102, Australia)

  • Qilin Li

    (School of Electrical Engineering, Computer and Math Science, Curtin University, Building 314 Level 4, Kent St., Bentley, WA 6102, Australia)

  • Jessica K. Breadsell

    (Sustainability Policy Institute, School of Design and Built Environment, Curtin University, Building 418 Level 4, Kent St., Bentley, WA 6102, Australia)

  • Christine Eon

    (Sustainability Policy Institute, School of Design and Built Environment, Curtin University, Building 418 Level 4, Kent St., Bentley, WA 6102, Australia)

Abstract

The home can be a complex environment to understand, as well as to model and predict, due to inherent variability between people’s routines and practices. A one-size-fits-all approach does not consider people’s contextual and institutional influences that contribute to their daily routines. These contextual and institutional factors relate to the household structure and relationship between occupants, as well as the working lifestyle of the occupants. One household can consume resources and live quite differently compared to a similar size household with the same number of occupants due to these factors. Predictive analysis of consumption data can identify this difference to create household-specific modelling to predict occupant routines and practices. Using post-occupancy data from the Fairwater Living Laboratory in Sydney that monitored 39 homes built in a green-star community, this research has utilised machine learning approaches and a K-Means clustering method complemented by t-distributed Stochastic Neighbour Embedding (t-SNE) to show how households follow different daily routines and activities resulting in resource consumption. This analysis has identified energy usage patterns and household groupings with each group following similar daily routines and consumption. The comparison between modelling the precinct as a whole and modelling households individually shows how detail can be lost when aggregating household data at a precinct/community level. This detail can explain why policies or technologies are not as effective as their design due to ignoring the delicate aspects of household routines and practices. These household groupings can provide insight for policymakers to help them understand the different profiles that may be present in the community. These findings are useful for net-zero developments and decarbonization of the built environment through modelling occupant behaviour accurately and developing policies and technologies to suit.

Suggested Citation

  • Troy Malatesta & Qilin Li & Jessica K. Breadsell & Christine Eon, 2023. "Distinguishing Household Groupings within a Precinct Based on Energy Usage Patterns Using Machine Learning Analysis," Energies, MDPI, vol. 16(10), pages 1-25, May.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:10:p:4119-:d:1148054
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    References listed on IDEAS

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