IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v133y2014icp298-307.html
   My bibliography  Save this article

Predicting winning and losing businesses when changing electricity tariffs

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261914007892
    Download Restriction: Full text for ScienceDirect subscribers only

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Hayashi, Yukinari & Yoshihara, Naoki & Okushima, Shinichiro & Yamada, Akira, 2011. "Moral Motivation in Public Economic Activities," Economic Review, Hitotsubashi University, vol. 62(1), pages 1-19, January.
    2. Thorsnes, Paul & Williams, John & Lawson, Rob, 2012. "Consumer responses to time varying prices for electricity," Energy Policy, Elsevier, vol. 49(C), pages 552-561.
    3. Hartmann, Patrick & Apaolaza Ibanez, Vanessa, 2007. "Managing customer loyalty in liberalized residential energy markets: The impact of energy branding," Energy Policy, Elsevier, vol. 35(4), pages 2661-2672, April.
    4. Severin Borenstein, 2007. "Wealth Transfers Among Large Customers from Implementing Real-Time Retail Electricity Pricing," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2), pages 131-150.
    5. Bartusch, Cajsa & Alvehag, Karin, 2014. "Further exploring the potential of residential demand response programs in electricity distribution," Applied Energy, Elsevier, vol. 125(C), pages 39-59.
    6. Woo, C.K. & Sreedharan, P. & Hargreaves, J. & Kahrl, F. & Wang, J. & Horowitz, I., 2014. "A review of electricity product differentiation," Applied Energy, Elsevier, vol. 114(C), pages 262-272.
    7. James Mixon, 2010. "GRETL: an econometrics package for teaching and research," Managerial Finance, Emerald Group Publishing, vol. 36(1), pages 71-81, February.
    8. Ericson, Torgeir, 2011. "Households' self-selection of dynamic electricity tariffs," Applied Energy, Elsevier, vol. 88(7), pages 2541-2547, July.
    9. Hartway, Rob & Price, Snuller & Woo, C.K, 1999. "Smart meter, customer choice and profitable time-of-use rate option," Energy, Elsevier, vol. 24(10), pages 895-903.
    10. Christmann, Andreas & Steinwart, Ingo & Hubert, Mia, 2007. "Robust learning from bites for data mining," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 347-361, September.
    11. Ahmad Faruqui & Sanem Sergici, 2010. "Household response to dynamic pricing of electricity: a survey of 15 experiments," Journal of Regulatory Economics, Springer, vol. 38(2), pages 193-225, October.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. repec:gam:jeners:v:10:y:2017:i:6:p:778-:d:100587 is not listed on IDEAS
    2. Tebello Mathaba & Xiaohua Xia, 2015. "A Parametric Energy Model for Energy Management of Long Belt Conveyors," Energies, MDPI, Open Access Journal, vol. 8(12), pages 1-19, December.
    3. repec:gam:jeners:v:8:y:2015:i:12:p:13590-13608:d:59719 is not listed on IDEAS

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:133:y:2014:i:c:p:298-307. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Dana Niculescu). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.