Predicting winning and losing businesses when changing electricity tariffs
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 Bayes...
| Main Authors: | , , |
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| Format: | Journal article |
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Elsevier
2014
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| _version_ | 1826265676310904832 |
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| author | Granell, R Axon, C Wallom, D |
| author_facet | Granell, R Axon, C Wallom, D |
| author_sort | Granell, R |
| collection | OXFORD |
| description | 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. © 2014 The Authors. |
| first_indexed | 2024-03-06T20:27:25Z |
| format | Journal article |
| id | oxford-uuid:2fdac4a1-b818-459c-b1e4-cfe5a18addf0 |
| institution | University of Oxford |
| last_indexed | 2024-03-06T20:27:25Z |
| publishDate | 2014 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | oxford-uuid:2fdac4a1-b818-459c-b1e4-cfe5a18addf02022-03-26T12:58:01ZPredicting winning and losing businesses when changing electricity tariffsJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:2fdac4a1-b818-459c-b1e4-cfe5a18addf0Symplectic Elements at OxfordElsevier2014Granell, RAxon, CWallom, DBy 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. © 2014 The Authors. |
| spellingShingle | Granell, R Axon, C Wallom, D Predicting winning and losing businesses when changing electricity tariffs |
| title | Predicting winning and losing businesses when changing electricity tariffs |
| title_full | Predicting winning and losing businesses when changing electricity tariffs |
| title_fullStr | Predicting winning and losing businesses when changing electricity tariffs |
| title_full_unstemmed | Predicting winning and losing businesses when changing electricity tariffs |
| title_short | Predicting winning and losing businesses when changing electricity tariffs |
| title_sort | predicting winning and losing businesses when changing electricity tariffs |
| work_keys_str_mv | AT granellr predictingwinningandlosingbusinesseswhenchangingelectricitytariffs AT axonc predictingwinningandlosingbusinesseswhenchangingelectricitytariffs AT wallomd predictingwinningandlosingbusinesseswhenchangingelectricitytariffs |