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Data Mining Applications in Understanding Electricity Consumers’ Behavior: A Case Study of Tulkarm District, Palestine

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  • Maher AbuBaker

    (Department of Management Information Systems, Faculty of Engineering and Information Technology, An-Najah National University, Nablus, Palestine
    Current address: An-Najah National University, P.O.Box: 7, Nablus, Palestine.)

Abstract

This paper presents a comprehensive data analysis and visualization of electricity consumers’ prepaid bills of Tulkarm district. We analyzed 250,000 electricity consumers’ prepaid bills covering the time period from June to December 2018. The application of data mining techniques for understanding electricity consumers’ behavior in electricity consumption and their behavior in charging their electricity meter’s smart cards in terms of quantities charged and charging frequencies in different time periods, areas and tariffs are used. Understanding consumers’ behavior will support planning and decision making at strategic, tactical and operational levels. This analysis is useful for predicting and forecasting future demand with a certain degree of accuracy. Monthly, weekly, daily and hourly time periods are covered in the analysis. Outliers detection using visualization tools such as box plot is applied. K-means unsupervised machine learning clustering algorithm is implemented. The support vector machine classification method is applied. As a result of this study, electricity consumers’ behavior in different areas, tariffs and timing periods is understood and presented by numbers and graphs and new electricity consumer segmentation is proposed.

Suggested Citation

  • Maher AbuBaker, 2019. "Data Mining Applications in Understanding Electricity Consumers’ Behavior: A Case Study of Tulkarm District, Palestine," Energies, MDPI, vol. 12(22), pages 1-29, November.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:22:p:4287-:d:285706
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    References listed on IDEAS

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    1. Kakoli Bandyopadhyay, 2008. "User acceptance of prepayment metering systems in India," International Journal of Indian Culture and Business Management, Inderscience Enterprises Ltd, vol. 1(4), pages 450-465.
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    Cited by:

    1. Maher AbuBaker, 2021. "Household Electricity Load Forecasting Toward Demand Response Program Using Data Mining Techniques in A Traditional Power Grid," International Journal of Energy Economics and Policy, Econjournals, vol. 11(4), pages 132-148.
    2. Gokturk Poyrazoglu, 2021. "Determination of Price Zones during Transition from Uniform to Zonal Electricity Market: A Case Study for Turkey," Energies, MDPI, vol. 14(4), pages 1-13, February.
    3. Akilu Yunusa-Kaltungo & Ruifeng Cao, 2020. "Towards Developing an Automated Faults Characterisation Framework for Rotating Machines. Part 1: Rotor-Related Faults," Energies, MDPI, vol. 13(6), pages 1-20, March.
    4. Hyun Cheol Jeong & Jaesung Jung & Byung O Kang, 2020. "Development of Operational Strategies of Energy Storage System Using Classification of Customer Load Profiles under Time-of-Use Tariffs in South Korea," Energies, MDPI, vol. 13(7), pages 1-17, April.
    5. Luo, X.J. & Oyedele, Lukumon O. & Ajayi, Anuoluwapo O. & Akinade, Olugbenga O. & Owolabi, Hakeem A. & Ahmed, Ashraf, 2020. "Feature extraction and genetic algorithm enhanced adaptive deep neural network for energy consumption prediction in buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    6. Liwen Shi & Zhonglin Fu & Wei Guo & Jing Zhang & Jiang Sun, 2023. "Exploring the Factors That Promote Sustainable Growth in Regional Sales of New Energy Vehicles: An Empirical Study of China," Sustainability, MDPI, vol. 15(8), pages 1-16, April.

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