Predicting winning and losing businesses when changing electricity tariffs
Open accessAbstract
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.
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Clustering can help identify permit allocation schemes that maximize social welfare, and supervised learning has been used to predict winners and losers from changing electricity tariff schemes.
“Here, clustering can help identify permit allocation schemes that maximize social welfare [855], and supervised learning has been used to predict winners and losers from changing electricity tariff schemes [302].”
From Tackling climate change with machine learning (2023) · cites this paper as [302]
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Tackling climate change with machine learning
Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A. S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., et al. (2023) · ACM Computing Surveys · doi:10.1145/3485128 · Cited by 336 · see citing works
- Authors
- Granell, R., Axon, C. J., & Wallom, D. C. H.
- Affiliations
- Brunel University of London (GB)
- University of Oxford (GB)
- Corresponding author
- Wallom, David C.H.
- Venue
- Applied Energy
- Publisher
- Elsevier BV
- Published
- 2014-11
- Volume
- 133
- Pages
- 298-307
- ISSN
- 0306-2619 (ISSN-L)
- OpenAlex
- W2012784982
- Cited by
- 30
- References
- 32
- Citations by year
- 2025: 1 · 2024: 1 · 2023: 4 · 2022: 6 · 2021: 5 · 2020: 1 · 2019: 2 · 2018: 2 · 2017: 1 · 2016: 3 · 2015: 2 · 2014: 1 · 2013: 1
- FWCI
- 1.24
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Applied Energy (publishedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- Brunel University Research Archive (BURA) (Brunel University London) (submittedVersion)
- Funders
- UK Engineering and Physical Science Research Council (EP/I000194/1)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Smart Grid Energy Management
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Energy Load and Power Forecasting
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Electric Vehicles and Infrastructure
- Keywords
- electricity tariffs, smart meters, dynamic tariffs, time-of-use pricing, price elasticity, electricity profile, energy retailers, support vector regression, support vector machine, artificial neural networks, naive Bayesian classifier
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