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http://bura.brunel.ac.uk/handle/2438/10586
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DC Field | Value | Language |
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dc.contributor.author | Granell, R | - |
dc.contributor.author | Axon, CJ | - |
dc.contributor.author | Wallom, DCH | - |
dc.date.accessioned | 2015-04-17T09:57:30Z | - |
dc.date.available | 2014-11-15 | - |
dc.date.available | 2015-04-17T09:57:30Z | - |
dc.date.issued | 2014 | - |
dc.identifier.citation | Applied Energy, 2014, 133 pp. 298 - 307 | en_US |
dc.identifier.issn | S0306261914007892 | - |
dc.identifier.issn | S0306261914007892 | - |
dc.identifier.issn | 0306-2619 | - |
dc.identifier.uri | http://bura.brunel.ac.uk/handle/2438/10586 | - |
dc.description.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-classes problem). We have used only three features of each company: their business sector, load profile category, and mean power use. We are particularly interested in the switch between a static tariff (fixed price or time-of-use) and a dynamic tariff (half-hourly pricing). We have extended the two-classes problem to include a price elasticity factor (a three-classes problem). We show how the classification error for the two- and three-classes problems varies with the amount of available data. Furthermore, we used Ordinary Least Squares and Support Vector Regression models to compute the exact values of the amount gained or lost by a business if it switched tariff types. Our analysis suggests that the machine learning classifiers required less data to reach useful performance levels than the regression models. | en_US |
dc.format.extent | 298 - 307 | - |
dc.format.extent | 298 - 307 | - |
dc.language | eng | - |
dc.language.iso | en | en_US |
dc.publisher | Elsevier Ltd | en_US |
dc.subject | Classification | en_US |
dc.subject | Energy | en_US |
dc.subject | Neural Networks | en_US |
dc.subject | Regression models | en_US |
dc.subject | Support Vector Machines | en_US |
dc.subject | Tariff switching | en_US |
dc.title | Predicting winning and losing businesses when changing electricity tariffs | en_US |
dc.type | Article | en_US |
dc.identifier.doi | http://dx.doi.org/10.1016/j.apenergy.2014.07.098 | - |
dc.relation.isPartOf | Applied Energy | - |
dc.relation.isPartOf | Applied Energy | - |
pubs.volume | 133 | - |
pubs.volume | 133 | - |
pubs.organisational-data | /Brunel | - |
pubs.organisational-data | /Brunel/Brunel Staff by College/Department/Division | - |
pubs.organisational-data | /Brunel/Brunel Staff by College/Department/Division/College of Engineering, Design and Physical Sciences | - |
pubs.organisational-data | /Brunel/Brunel Staff by College/Department/Division/College of Engineering, Design and Physical Sciences/Dept of Mechanical, Aerospace and Civil Engineering | - |
pubs.organisational-data | /Brunel/Brunel Staff by College/Department/Division/College of Engineering, Design and Physical Sciences/Dept of Mechanical, Aerospace and Civil Engineering/Mechanical and Aerospace Engineering | - |
pubs.organisational-data | /Brunel/Brunel Staff by Institute/Theme | - |
pubs.organisational-data | /Brunel/Brunel Staff by Institute/Theme/Institute of Energy Futures | - |
pubs.organisational-data | /Brunel/Brunel Staff by Institute/Theme/Institute of Energy Futures/Resource Efficient Future Cities | - |
Appears in Collections: | Dept of Electronic and Electrical Engineering Research Papers |
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FullText.pdf | 1.24 MB | Unknown | View/Open |
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