Dragana Milan Knežević, Marija Blagojević

DOI Number
First page
Last page


This paper explains the process of using neural networks, as one of numerous data mining techniques, for the classification of electricity consumers. The processed data comprised more than a million recordings of electricity consumption for 21,643 consumers over the period of four years and eight months. Using a data subset (70% of the entire dataset), the network was trained for the classification of consumers according to the type of the electric meter they possess (single-rate or dual-rate) and the zone they live in (city or village). The network input data in both cases included: consumer code, reading period from-to, current and previous meter reading for both low and high tariff, dual and single rate tariff consumption for that period and their total amount, as independent variables, whereas the network output comprised dependent variable classes (zone or type of electric meter). The results show that a network created in this way can be trained so well that it achieves high precision when evaluated using the test dataset. Using the available recordings about electricity consumption, the type of the electric meter consumers possess and the zone they live in can be predicted with the accuracy of 77% and 82%, respectively. These findings can provide the basis for further research using other data mining techniques.


data mining, neural network, classification, prediction, electricity, R programming

Full Text:



U. Ali, C. Buccella and C. Cecati, “Households electricity consumption analysis with data mining techniques”, Department of Information Engineering, Computer Science and Mathematics, University of L’Aquila, Italy, 2016.

D. Shi, J. Guan, J. Zurada and A. Manikas, “A Data-Mining Approach to Identification of Risk Factors in Safety Management Systems”, Journal of Management Information Systems, vol. 34, no. 4, pp. 1054–1081, 2017.

M. Blagojević, “Appliance of web mining in education”, Technics and Informatics in Education, Čačak, 2010.

S. Shadroo and M. A. Rahmani, “Systematic survey of big data and data mining in internet of things”, Computer Networks, 2018.

G. Prati, L. Pietrantoni and F. Fraboni, “Using data mining techniques to predict the severity of bicycle crashes”, Accident Analysis & Prevention, Elsevier Ltd, 2017, pp. 44–54.

M. Carpita, M. Sandri, A. Simonetto and P. Zuccolotto, “Data Mining Applications with R”, Research Center “Data, Methods and Systems” Department of Economics and Management of the University of Brescia, Italy, 2014.

Z. Guo, K. Zhou,X. Zhang, S. Yang and Z. Shao, “Data mining based framework for exploring household electricity consumption patterns: A case study in China context”, Journal of Cleaner Production, Elsevier Ltd, 2018.

R. Rathod and R. D. Garg, “Regional electricity consumption analysis for consumers using data mining techniques and consumer meter reading data”, International Journal of Electrical Power & Energy Systems, vol. 78, pp. 368–374, 2016.

S. K. Barai, “Data mining applications in transportation engineering”, Journal Transport, 2003.

C. Da Cunha, B. Agard and A. Kusiak, ”Data mining for improvement of product quality”, International Journal of Production Research, vol. 44, no. 18–19, pp. 4027–4041, 2006.

C. Djeraba, “Data mining from multimedia”, International Journal Parallel Emergent Distributed System, vol. 22, pp. 405–406, 2007.

S. Xiaogang, ”Data Mining Methods and Models”, The American Statistician, vol. 62, no. 1, pp. 91, 2012.

A. Ghasemi, M. Gitizadeh, “Detection of illegal consumers using pattern classification approach combined with Levenberg-Marquardt method in smart grid”, International journal of electrical power and energy systems, vol. 99, pp. 363–375, 2018.

S. Ramos; J. M. Duarte; F. J. Duarte; Z. Vale, “A data-mining-based methodology to support MV electricity customers’ characterization”, Elsevier BV, 2015.

Z. Jiang, R. Lin, F. Yang, “A Hybrid Machine Learning Model for Electricity Consumer Categorization Using Smart Meter Data”, Energies, vol. 11, p. 2235, 2018.

F. Günther, “neuralnet: Training of Neural Networks”, Stefan Fritsch, The R Journal , vol. 2/1, 2010.

L. Bing, Web Data Mining. Exploring Hyperlinks, Contents, and Usage Data,Secon Edition, Springer, 2011.

G. Ciaburro and B. Venkateswaran, “Neural networks with R”, Packt Publishing Ltd, Birmingham, 2017.

Software on web address:, accessed in: October 2018.


  • There are currently no refbacks.

ISSN: 0353-3670