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Isolated Areas Consumption Short-Term Forecasting Method

Author

Listed:
  • Guillaume Guerard

    (Research Center, Leonard de Vinci Pole Universitaire, 92916 Paris La Défense, France)

  • Hugo Pousseur

    (Research Center, Leonard de Vinci Pole Universitaire, 92916 Paris La Défense, France
    Student in Master Thesis.)

  • Ihab Taleb

    (Research Center, Leonard de Vinci Pole Universitaire, 92916 Paris La Défense, France)

Abstract

Forecasting consumption in isolated areas represents a challenging problem typically resolved using deep learning or huge mathematical models with various dimensions. Those models require expertise in metering and algorithms and the equipment needs to be frequently maintained. In the context of the MAESHA H2020 project, most of the consumers and producers are isolated. Forecasting becomes more difficult due to the lack of external data and the significant impact of human behaviors on those small systems. The proposed approach is based on data sequencing, sequential mining, and pattern mining to infer the results into a Hidden Markov Model. It only needs the consumption and production curve as a time series and adapts itself to provide the forecast. Our method gives a better forecast than other prediction machines and deep-learning methods used in literature review.

Suggested Citation

  • Guillaume Guerard & Hugo Pousseur & Ihab Taleb, 2021. "Isolated Areas Consumption Short-Term Forecasting Method," Energies, MDPI, vol. 14(23), pages 1-23, November.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:23:p:7914-:d:688081
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    References listed on IDEAS

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    1. Zhao, Hai-xiang & Magoulès, Frédéric, 2012. "A review on the prediction of building energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(6), pages 3586-3592.
    2. Kim, Tae-Young & Cho, Sung-Bae, 2019. "Predicting residential energy consumption using CNN-LSTM neural networks," Energy, Elsevier, vol. 182(C), pages 72-81.
    3. Massoud Amin, 2013. "The smart-grid solution," Nature, Nature, vol. 499(7457), pages 145-147, July.
    4. Wang, Jian Qi & Du, Yu & Wang, Jing, 2020. "LSTM based long-term energy consumption prediction with periodicity," Energy, Elsevier, vol. 197(C).
    5. Deb, Chirag & Zhang, Fan & Yang, Junjing & Lee, Siew Eang & Shah, Kwok Wei, 2017. "A review on time series forecasting techniques for building energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 902-924.
    6. Raza, Muhammad Qamar & Khosravi, Abbas, 2015. "A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 50(C), pages 1352-1372.
    7. Amasyali, Kadir & El-Gohary, Nora M., 2018. "A review of data-driven building energy consumption prediction studies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1192-1205.
    8. Hong, Tao & Fan, Shu, 2016. "Probabilistic electric load forecasting: A tutorial review," International Journal of Forecasting, Elsevier, vol. 32(3), pages 914-938.
    9. Wang, Zeyu & Srinivasan, Ravi S., 2017. "A review of artificial intelligence based building energy use prediction: Contrasting the capabilities of single and ensemble prediction models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 796-808.
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    Cited by:

    1. Zbigniew Leonowicz & Michal Jasinski, 2022. "Machine Learning and Data Mining Applications in Power Systems," Energies, MDPI, vol. 15(5), pages 1-2, February.

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