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Adaptive Machine Learning for Automated Modeling of Residential Prosumer Agents

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  • David Toquica

    (Department of Electrical and Computer Engineering, Université du Québec à Trois-Rivières, 3351, Boul. des Forges, Trois-Rivières, QC G8Z 4M3, Canada)

  • Kodjo Agbossou

    (Department of Electrical and Computer Engineering, Université du Québec à Trois-Rivières, 3351, Boul. des Forges, Trois-Rivières, QC G8Z 4M3, Canada)

  • Roland Malhamé

    (Department of Electrical Engineering, Polytechnique Montréal, C.P. 6079, Succ. Centre-Ville, Montréal, QC H3C 3A7, Canada)

  • Nilson Henao

    (Department of Electrical and Computer Engineering, Université du Québec à Trois-Rivières, 3351, Boul. des Forges, Trois-Rivières, QC G8Z 4M3, Canada)

  • Sousso Kelouwani

    (Department of Mechanical Engineering, Université du Québec à Trois-Rivières, 3351, Boul. des Forges, Trois-Rivières, QC G8Z 4M3, Canada)

  • Alben Cardenas

    (Department of Electrical and Computer Engineering, Université du Québec à Trois-Rivières, 3351, Boul. des Forges, Trois-Rivières, QC G8Z 4M3, Canada)

Abstract

An efficient participation of prosumers in power system management depends on the quality of information they can obtain. Prosumers actions can be performed by automated agents that are operating in time-changing environments. Therefore, it is essential for them to deal with data stream problems in order to make reliable decisions based on the most accurate information. This paper provides an in-depth investigation of data and concept drift issues in accordance with residential prosumer agents. Additionally, the adaptation techniques, forgetting mechanisms, and learning strategies employed to handle these issues are explored. Accordingly, an approach is proposed to adapt the prosumer agent models to overcome the gradual and sudden concept drift concurrently. The suggested method is based on triggered adaptation techniques and performance-based forgetting mechanism. The results obtained in this study demonstrate that the proposed approach is capable of constructing efficient prosumer agents models with regard to the concept drift problem.

Suggested Citation

  • David Toquica & Kodjo Agbossou & Roland Malhamé & Nilson Henao & Sousso Kelouwani & Alben Cardenas, 2020. "Adaptive Machine Learning for Automated Modeling of Residential Prosumer Agents," Energies, MDPI, vol. 13(9), pages 1-19, May.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:9:p:2250-:d:353812
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    References listed on IDEAS

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    1. Farzan, Farbod & Jafari, Mohsen A. & Gong, Jie & Farzan, Farnaz & Stryker, Andrew, 2015. "A multi-scale adaptive model of residential energy demand," Applied Energy, Elsevier, vol. 150(C), pages 258-273.
    2. Jaeyeong Yoo & Byungsung Park & Kyungsung An & Essam A. Al-Ammar & Yasin Khan & Kyeon Hur & Jong Hyun Kim, 2012. "Look-Ahead Energy Management of a Grid-Connected Residential PV System with Energy Storage under Time-Based Rate Programs," Energies, MDPI, vol. 5(4), pages 1-19, April.
    3. Keshtkar, Azim & Arzanpour, Siamak, 2017. "An adaptive fuzzy logic system for residential energy management in smart grid environments," Applied Energy, Elsevier, vol. 186(P1), pages 68-81.
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    Cited by:

    1. Jaroslaw Krzywanski, 2022. "Advanced AI Applications in Energy and Environmental Engineering Systems," Energies, MDPI, vol. 15(15), pages 1-3, August.
    2. Fernando V. Cerna & Mahdi Pourakbari-Kasmaei & Luizalba S. S. Pinheiro & Ehsan Naderi & Matti Lehtonen & Javier Contreras, 2021. "Intelligent Energy Management in a Prosumer Community Considering the Load Factor Enhancement," Energies, MDPI, vol. 14(12), pages 1-24, June.

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