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Household Electricity Load Forecasting Toward Demand Response Program Using Data Mining Techniques in A Traditional Power Grid

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  • Maher AbuBaker

    (An-Najah National University, Nablus, Palestine.)

Abstract

At present, the continuous increase of household electricity demand is strategic and crucial in electricity demand management. Household electricity consumers can play an important role in this issue. The rationalization of electricity consumption might be achieved by using an efficient Demand Response (DR) program. In this paper a new methodology is suggested using a combination of data mining techniques namely K-means clustering, K-Nearest Neighbors (K-NN) classification and ARIMA for electricity load forecasting using consumers electricity prepaid bills data set of an ordinary electricity grid with prepaid electricity meters. As a result of applying this methodology, various DR programs are recommended as an attempt to assist the management of electricity system to manage the electricity demand issues from demand-side in an efficient and effective manner, which can be put into practice. A case study has been carried out in Tulkarm District, Palestine. The performance of applying the suggested methodology is measured, and the results are considered very well.

Suggested Citation

  • 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.
    • Handle: RePEc:eco:journ2:2021-04-18
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    File URL: https://www.econjournals.com/index.php/ijeep/article/view/11192/5900
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    References listed on IDEAS

    as
    1. 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.
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    Cited by:

    1. Felix Ghislain Yem Souhe & Camille Franklin Mbey & Alexandre Teplaira Boum & Pierre Ele, 2021. "Forecasting of Electrical Energy Consumption of Households in a Smart Grid," International Journal of Energy Economics and Policy, Econjournals, vol. 11(6), pages 221-233.
    2. Wulfran Fendzi Mbasso & Reagan Jean Jacques Molu & Serge Raoul Dzonde Naoussi & Saatong Kenfack, 2022. "Demand-Supply Forecasting based on Deep Learning for Electricity Balance in Cameroon," International Journal of Energy Economics and Policy, Econjournals, vol. 12(4), pages 99-103, July.

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    More about this item

    Keywords

    Demand Response (DR); K-means Clustering; K-Nearest Neighbor classification (K-NN); ARIMA model; Prepaid electricity meters;
    All these keywords.

    JEL classification:

    • Q4 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting
    • Q49 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Other

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