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

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, 79 . Article Number 101446. ISSN 0957-1787. (doi:10.1016/j.jup.2022.101446) (KAR id:97579)

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.

Item Type: Article
DOI/Identification number: 10.1016/j.jup.2022.101446
Uncontrolled keywords: Residential electricity consumption; household load profiles; machine learning
Subjects: H Social Sciences
Divisions: Divisions > Kent Business School - Division > Department of Analytics, Operations and Systems
Funders: University of Kent (https://ror.org/00xkeyj56)
Depositing User: Jesse O'Hanley
Date Deposited: 25 Oct 2022 09:38 UTC
Last Modified: 19 Sep 2023 15:03 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/97579 (The current URI for this page, for reference purposes)

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