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Impacts of building load dispersion level on its load forecasting accuracy: Data or algorithms? Importance of reliability and interpretability in machine learning

Abstract:

Data-driven forecasting techniques have been widely used for building load forecasting due to their accuracy and wide availability of operational data. Recent advances have been underpinned by the increased capability of machine learning (ML) algorithms; however, most studies only tested ML techniques on a single or a small number of buildings over short periods, lacking reliable tests. Moreover, few studies focused on the effects of characteristics of building load profiles on forecast accur...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1016/j.enbuild.2023.112896

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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Oxford college:
Lady Margaret Hall
Role:
Author
ORCID:
0000-0001-7527-3407
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Name:
Engineering and Physical Sciences Research Council
Grant:
RKES 180969
Publisher:
Elsevier
Journal:
Energy and Buildings More from this journal
Volume:
285
Article number:
112896
Publication date:
2023-02-15
Acceptance date:
2023-02-10
DOI:
EISSN:
1872-6178
ISSN:
0378-7788
Language:
English
Keywords:
Pubs id:
1328527
Local pid:
pubs:1328527
Deposit date:
2023-02-14

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