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Residential Electricity Demand Modelling: Validation of a Behavioural Agent-Based Approach

Author

Listed:
  • Baxter L. M. Williams

    (Department of Mechanical Engineering, University of Canterbury, Private Bag 4800, Christchurch 8140, New Zealand)

  • R. J. Hooper

    (Maidstone Associates Ltd., 2/17 Kahu Road, Christchurch 8140, New Zealand)

  • Daniel Gnoth

    (Ara Ake, 8 Young Street, New Plymouth 4310, New Zealand)

  • J. G. Chase

    (Department of Mechanical Engineering, University of Canterbury, Private Bag 4800, Christchurch 8140, New Zealand)

Abstract

The targets for reducing greenhouse gas emissions, combined with increased electrification and the increased use of intermittent renewable energy sources, create significant challenges in matching supply and demand within distribution grid constraints. Demand response (DR) can shift electricity demand to align with constraints, reducing peak loads and increasing the utilisation of renewable generation. In countries like Aotearoa (New Zealand), peak loads are driven primarily by the residential sector, which is a prime candidate for DR. However, traditional deterministic and stochastic models do not account for the important variability in behavioural-driven residential demand and thus cannot be used to design or optimise DR. This paper presents a behavioural agent-based model (ABM) of residential electricity demand, which is validated using real electricity demand data from residential distribution transformers owned by Powerco, an electricity distributor in Aotearoa (New Zealand). The model accurately predicts demand in three neighbourhoods and matches the changes caused by seasonal variation, as well as the effects of COVID-19 lockdowns. The Pearson correlation coefficients between the median modelled and real demand are above 0.8 in 83% of cases, and the total median energy use variation is typically within 1–4%. Thus, this model provides a robust platform for network planning, scenario analysis, and DR program design or optimisation.

Suggested Citation

  • Baxter L. M. Williams & R. J. Hooper & Daniel Gnoth & J. G. Chase, 2025. "Residential Electricity Demand Modelling: Validation of a Behavioural Agent-Based Approach," Energies, MDPI, vol. 18(6), pages 1-22, March.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:6:p:1314-:d:1607319
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    2. Silvia Trimarchi & Fabio Casamatta & Laura Gamba & Francesco Grimaccia & Marco Lorenzo & Alessandro Niccolai, 2025. "A Review of Agent-Based Models for Energy Commodity Markets and Their Natural Integration with RL Models," Energies, MDPI, vol. 18(12), pages 1-23, June.

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