Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/10586
Title: Predicting winning and losing businesses when changing electricity tariffs
Authors: Granell, R
Axon, CJ
Wallom, DCH
Keywords: Classification;Energy;Neural Networks;Regression models;Support Vector Machines;Tariff switching
Issue Date: 2014
Publisher: Elsevier Ltd
Citation: Applied Energy, 2014, 133 pp. 298 - 307
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.
URI: http://bura.brunel.ac.uk/handle/2438/10586
DOI: http://dx.doi.org/10.1016/j.apenergy.2014.07.098
ISSN: S0306261914007892
S0306261914007892
0306-2619
Appears in Collections:Dept of Electronic and Computer Engineering Research Papers

Files in This Item:
File Description SizeFormat 
FullText.pdf1.24 MBUnknownView/Open


Items in BURA are protected by copyright, with all rights reserved, unless otherwise indicated.