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Expect : EXplainable Prediction Model for Energy ConsumpTion

Author

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  • Amira Mouakher

    (IT Institute, Corvinus University of Budapest, 1093 Budapest, Hungary
    Corvinus Institute for Advanced Studies, Corvinus University of Budapest, 1093 Budapest, Hungary)

  • Wissem Inoubli

    (Department of Software Science, Tallinn University of Technology, 12618 Tallinn, Estonia)

  • Chahinez Ounoughi

    (Department of Software Science, Tallinn University of Technology, 12618 Tallinn, Estonia
    Faculty of Sciences of Tunis, University of Tunis El Manar, LIPAH-LR11ES14, Tunis 2092, Tunisia)

  • Andrea Ko

    (IT Institute, Corvinus University of Budapest, 1093 Budapest, Hungary)

Abstract

With the steady growth of energy demands and resource depletion in today’s world, energy prediction models have gained more and more attention recently. Reducing energy consumption and carbon footprint are critical factors for achieving efficiency in sustainable cities. Unfortunately, traditional energy prediction models focus only on prediction performance. However, explainable models are essential to building trust and engaging users to accept AI-based systems. In this paper, we propose an explainable deep learning model, called Expect , to forecast energy consumption from time series effectively. Our results demonstrate our proposal’s robustness and accuracy when compared to the baseline methods.

Suggested Citation

  • Amira Mouakher & Wissem Inoubli & Chahinez Ounoughi & Andrea Ko, 2022. "Expect : EXplainable Prediction Model for Energy ConsumpTion," Mathematics, MDPI, vol. 10(2), pages 1-21, January.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:2:p:248-:d:724446
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    References listed on IDEAS

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    1. 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.
    2. Fumo, Nelson & Rafe Biswas, M.A., 2015. "Regression analysis for prediction of residential energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 47(C), pages 332-343.
    3. Beccali, Marco & Ciulla, Giuseppina & Lo Brano, Valerio & Galatioto, Alessandra & Bonomolo, Marina, 2017. "Artificial neural network decision support tool for assessment of the energy performance and the refurbishment actions for the non-residential building stock in Southern Italy," Energy, Elsevier, vol. 137(C), pages 1201-1218.
    4. Zhong, Hai & Wang, Jiajun & Jia, Hongjie & Mu, Yunfei & Lv, Shilei, 2019. "Vector field-based support vector regression for building energy consumption prediction," Applied Energy, Elsevier, vol. 242(C), pages 403-414.
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    1. Agnieszka Kowalska-Styczeń & Tomasz Owczarek & Janusz Siwy & Adam Sojda & Maciej Wolny, 2022. "Analysis of Business Customers’ Energy Consumption Data Registered by Trading Companies in Poland," Energies, MDPI, vol. 15(14), pages 1-23, July.

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