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)
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Official URL: https://doi.org/10.1016/j.jup.2022.101446 |
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 |
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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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