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Predicting residential electricity consumption patterns based on smart meter and household data: A case study from the Republic of Ireland

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  • Guo, Zhifeng
  • O'Hanley, Jesse R.
  • Gibson, Stuart

Abstract

We use machine learning algorithms to investigate various aspects of residential electricity consumption for households in the Republic of Ireland. Temperature, day of week, and month of year have an apparent causal effect on consumption. The prevalence of six distinct intra-day load profiles, identified by clustering, changes dramatically between weekdays and weekends as well as seasonally. Key socio-demographic and dwelling characteristics associated with annual load profiles include household makeup and size and occupation of the primary income earner. We further discuss policy and management implications of our findings and propose avenues for future research.

Suggested Citation

  • Guo, Zhifeng & O'Hanley, Jesse R. & Gibson, Stuart, 2022. "Predicting residential electricity consumption patterns based on smart meter and household data: A case study from the Republic of Ireland," Utilities Policy, Elsevier, vol. 79(C).
    • Handle: RePEc:eee:juipol:v:79:y:2022:i:c:s0957178722001102
    DOI: 10.1016/j.jup.2022.101446
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    References listed on IDEAS

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    2. Li, Zhen & Niu, Shuwen & Halleck Vega, Sol Maria & Wang, Jinnian & Wang, Dakang & Yang, Xiankun, 2024. "Electrification and residential well-being in China," Energy, Elsevier, vol. 294(C).
    3. Atif Maqbool Khan & Artur Wyrwa, 2024. "A Survey of Quantitative Techniques in Electricity Consumption—A Global Perspective," Energies, MDPI, vol. 17(19), pages 1-38, September.
    4. Brown, Alastair & Hampton, Harrison & Foley, Aoife & Furszyfer Del Rio, Dylan & Lowans, Christopher & Caulfield, Brian, 2023. "Understanding domestic consumer attitude and behaviour towards energy: A study on the Island of Ireland," Energy Policy, Elsevier, vol. 181(C).

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