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Journal article

A data-driven approach for electricity load profile prediction of new supermarkets

Abstract:

Predicting the electricity demand of new supermarkets will help with design, planning, and future energy management. Instead of creating complex site-specific thermal engineering models, simplified statistical energy prediction models as we propose can be useful to energy managers. We have designed and implemented a data-driven method to predict the ’electricity daily load profile’ (EDLP) for new stores. Our preliminary work exploits a data-set of hourly electricity meter readings for 196 UK ...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1016/j.egypro.2019.02.087

Authors


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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Sub department:
Oxford e-Research Centre
Role:
Author
ORCID:
0000-0001-9572-0972
More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
Role:
Author
More from this funder
Funding agency for:
Granell, R
Grant:
EP/R511742/1
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Funding agency for:
Wallom, D
Grant:
EP/R511742/1
Publisher:
Elsevier Publisher's website
Journal:
Energy Procedia Journal website
Volume:
161
Pages:
242-250
Publication date:
2019-03-18
Acceptance date:
2018-08-15
DOI:
EISSN:
1876-6102
ISSN:
1876-6102
Source identifiers:
935870
Keywords:
Pubs id:
pubs:935870
UUID:
uuid:3abdb19c-b475-446a-b1d3-d72148f744b7
Local pid:
pubs:935870
Deposit date:
2018-10-31

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