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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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    1. Pratik Mochi & Kartik Pandya & Joao Soares & Zita Vale, 2023. "Optimizing Power Exchange Cost Considering Behavioral Intervention in Local Energy Community," Mathematics, MDPI, vol. 11(10), pages 1-15, May.

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