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Predicting winning and losing businesses when changing electricity tariffs

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  • Granell, Ramon
  • Axon, Colin J.
  • Wallom, David C.H.

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.

Suggested Citation

  • Granell, Ramon & Axon, Colin J. & Wallom, David C.H., 2014. "Predicting winning and losing businesses when changing electricity tariffs," Applied Energy, Elsevier, vol. 133(C), pages 298-307.
    • Handle: RePEc:eee:appene:v:133:y:2014:i:c:p:298-307
    DOI: 10.1016/j.apenergy.2014.07.098
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    References listed on IDEAS

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    Cited by:

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    2. Ibtihal Abdelmotteleb & Tomás Gómez & Javier Reneses, 2017. "Evaluation Methodology for Tariff Design under Escalating Penetrations of Distributed Energy Resources," Energies, MDPI, vol. 10(6), pages 1-16, June.
    3. Tebello Mathaba & Xiaohua Xia, 2015. "A Parametric Energy Model for Energy Management of Long Belt Conveyors," Energies, MDPI, vol. 8(12), pages 1-19, December.
    4. Pihnastyi, Oleh & Khodusov, Valery, 2020. "Development of the controlling speed algorithm of the conveyor belt based on TOU-tariffs," MPRA Paper 104681, University Library of Munich, Germany, revised 12 Nov 2020.
    5. Pihnastyi, Oleh & Chernіavska, Svіtlana, 2022. "Improvement of methods for description of a three-bunker collection conveyor," MPRA Paper 115529, University Library of Munich, Germany, revised 15 Oct 2022.

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