Journal article icon

Journal article

Predicting winning and losing businesses when changing electricity tariffs

Abstract:

By using smart meters, more data about how businesses use energy is becoming available to energy retailers (providers). This is enabling innovation in the structure and type of tariffs on offer in the energy market. We have applied Artificial Neural Networks, Support Vector Machines, and Naive Bayesian Classifiers to a data set of the electrical power use by 12,000 businesses (in 44 sectors) to investigate predicting which businesses will gain or lose by switching between tariffs (a two-class...

Expand abstract
Publication status:
Published
Peer review status:
Peer reviewed
Version:
Publisher's version

Actions


Access Document


Files:
Publisher copy:
10.1016/j.apenergy.2014.07.098

Authors


More by this author
Institution:
University of Oxford
Department:
Oxford, MPLS, Oxford e-Research Centre
Wallom, DCH More by this author
Publisher:
Elsevier Ltd. Publisher's website
Journal:
Applied Energy Journal website
Volume:
133
Pages:
298-307
Publication date:
2014-08-16
Acceptance date:
2014-07-25
DOI:
ISSN:
0306-2619
URN:
uuid:2fdac4a1-b818-459c-b1e4-cfe5a18addf0
Source identifiers:
483241
Local pid:
pubs:483241

Terms of use


Metrics



If you are the owner of this record, you can report an update to it here: Report update to this record

TO TOP