CORE
🇺🇦
make metadata, not war
Services
Services overview
Explore all CORE services
Access to raw data
API
Dataset
FastSync
Content discovery
Recommender
Discovery
OAI identifiers
OAI Resolver
Managing content
Dashboard
Bespoke contracts
Consultancy services
Support us
Support us
Membership
Sponsorship
Community governance
Advisory Board
Board of supporters
Research network
About
About us
Our mission
Team
Blog
FAQs
Contact us
A reduced-dimension feature extraction method to represent retail store electricity profiles
Authors
CJ Axon
R Granell
M Kolokotroni
DCH Wallom
Publication date
1 January 2022
Publisher
'Elsevier BV'
Doi
Abstract
Copyright © 2022 The Author(s). Characterising the inter-seasonal energy performance of buildings is a useful tool for a business to understand what is ‘normal’ for its portfolio of premises and to detect anomalous patterns of energy demand. When adding a new building to the portfolio, it will be useful to predict what will be the likely energy use as part of on-going monitoring of the site. For a large portfolio of buildings with, say, half-hourly energy use measurements (48 dimensions), analysis and prediction will require machine learning tools. Even so, it is advantageous to minimise the amount of data and number of dimensions and features required to find useful patterns in the measurement stream. Our aim is to devise a reduced feature set that can generate a statistically reasonable representation of daily electricity load profiles of retail stores and small supermarkets. We then test if our method is sufficiently accurate to predict and cluster measured patterns of demand. We propose an automatic method to extract features such as times and average demands from electricity load profiles. We used four regression models for prediction and six clustering methods to compare with the results obtained using all of the readings in the load profile. We found that the reduced feature set gave a good representation of the load profile, with only small prediction and clustering errors. The results are robust as prediction is supervised learning and clustering is unsupervised. This simplified feature set is a concise way to represent profiles without using small variances of the demand that do not add useful information to the overall picture. As modern sensor systems increase the volume, availability, and immediacy of data, using reduced dimensional datasets will be key to extracting useful information from high-resolution data streams
Similar works
Full text
Open in the Core reader
Download PDF
Available Versions
Supporting member
Oxford University Research Archive
See this paper in CORE
Go to the repository landing page
Download from data provider
oai:ora.ox.ac.uk:uuid:fe1c21f3...
Last time updated on 04/10/2022
ORA - Oxford University Research Archive
See this paper in CORE
Go to the repository landing page
Download from data provider
oai:ora.ox.ac.uk:uuid:fe1c21f3...
Last time updated on 20/10/2022
Sustaining member
Brunel University Research Archive
See this paper in CORE
Go to the repository landing page
Download from data provider
oai:bura.brunel.ac.uk:2438/252...
Last time updated on 10/10/2022