Creating and Characterising Electricity Load Profiles of Residential Buildings

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Title: Creating and Characterising Electricity Load Profiles of Residential Buildings
Authors: Fitzpatrick, JamesCarroll, PaulaAjwani, Deepak
Permanent link: http://hdl.handle.net/10197/12526
Date: 18-Sep-2020
Online since: 2021-09-30T14:15:08Z
Abstract: Intelligent planning, control and forecasting of electricity usage has become a vitally important element of the modern conception of the energy grid. Electricity smart-meters permit the sequential measurement of electricity usage at an aggregate level within a dwelling at regular time intervals. Electricity distributors or suppliers are interested in making general decisions that apply to large groups of customers, making it necessary to determine an appropriate electricity usage behaviour-based clustering of these data to determine appropriate aggregate load profiles. We perform a clustering of time series data associated with 3670 residential smart meters from an Irish customer behaviour trial and attempt to establish the relationship between the characteristics of each cluster based upon responses provided in an accompanying survey. Our analysis provides interesting insights into general electricity usage behaviours of residential consumers and the salient characteristics that affect those behaviours. Our characterisation of the usage profiles at a fine-granularity level and the resultant insights have the potential to improve the decisions made by distribution and supply companies, policy makers and other stakeholders, allowing them, for example, to optimise pricing, electricity usage, network investment strategies and to plan policies to best affect social behavior.
Funding Details: Science Foundation Ireland
Type of material: Conference Publication
Publisher: Springer
Series/Report no.: Lecture Notes in Computer Science; 12588
Copyright (published version): 2020 Springer Nature
Keywords: Smart-meterLoad-profilingTime series clustering
DOI: 10.1007/978-3-030-65742-0_13
Other versions: https://project.inria.fr/aaltd20/
Language: en
Status of Item: Peer reviewed
Is part of: Lemaire V., Malinowski S., Bagnall A., Guyet T., Tavenard R., Ifrim G. (eds.). Advanced Analytics and Learning on Temporal Data. AALTD 2020
Conference Details: The 2020 International Workshop on Advanced Analytics and Learning on Temporal Data (AALTD 2020),Ghent, Belgium (held online due to coronavirus outbreak), 18 September 2020
This item is made available under a Creative Commons License: https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
Appears in Collections:Computer Science Research Collection
Business Research Collection
Energy Institute Research Collection

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