Probability density distributions for household air source heat pump electricity demand

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Title: Probability density distributions for household air source heat pump electricity demand
Authors: Chesser, MichaelLyons, PádraigO'Reilly, PadraicCarroll, Paula
Permanent link: http://hdl.handle.net/10197/12527
Date: 12-Aug-2020
Online since: 2021-09-30T15:09:41Z
Abstract: The Irish government is implementing policies to transition Ireland to a low carbon and environmentally sustainable economy by 2050. Ireland has sectoral targets of 600,000 installed heat pumps by 2030, currently roughly 28,000 are installed. Such a high target of heat pumps will not only have a significant effect on electricity demand but also on the management and operation of the grid. In this paper we explore the demand from homes heated by air source heat pumps using an innovative dataset from a field trial in Ireland. To assess the impact of large-scale adoption of heat pumps, this paper estimates the after diversity maximum demand per heat pump heated home. In particular we explore statistical distributions to best model coincident demand, and estimate after diversity maximum demand per home. We use the software package RStudio to model several different distributions. Based on goodness-of-fit statistics and criteria, a Gamma distribution is the best fit. We apply our methodology to data from a similar heat pump trial in the UK to complement our results.
Funding Details: Enterprise Ireland
Funding Details: Sustainable Energy Authority of Ireland (SEAI)
The International Energy Research Centre (IERC)
Type of material: Journal Article
Publisher: Elsevier
Journal: Procedia Computer Science
Volume: 175
Start page: 468
End page: 475
Copyright (published version): 2020 the Authors
Keywords: Air source heat pumpProbability density distributionsHousehold electricity useIreland
DOI: 10.1016/j.procs.2020.07.067
Other versions: http://cs-conferences.acadiau.ca/seit-20/
Language: en
Status of Item: Peer reviewed
Conference Details: The 10th International Conference on Sustainable Energy Information Technology (SEIT 2020), Leuven, Belgium, 9-12 August 2020
ISSN: 1877-0509
This item is made available under a Creative Commons License: https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
Appears in Collections:Business Research Collection
Energy Institute Research Collection

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