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Article

DevOps Model Appproach for Monitoring Smart Energy Systems

1
LI-PARAD Laboratory EA 7432, Versailles University, 55 Avenue de Paris, 78035 Versailles, France
2
Energisme, 88 Avenue du Général Leclerc, 92100 Boulogne-Billancourt, France
3
Ecole Pratique des Hautes Etudes, PSL Research University, 4-14 Rue Ferrus, 75014 Paris, France
4
De Vinci Research Center, Pole Universitaire Léonard de Vinci, 12 Avenue Léonard de Vinci, 92400 Courbevoie, France
*
Author to whom correspondence should be addressed.
Academic Editor: Abu-Siada Ahmed
Energies 2022, 15(15), 5516; https://doi.org/10.3390/en15155516
Received: 1 July 2022 / Revised: 22 July 2022 / Accepted: 25 July 2022 / Published: 29 July 2022
Energy systems are often socio-technical complex systems that are facing new challenges regarding technological and environmental changes. Because of their complex nature, they cannot be approached solely through analytical modeling, hence the inefficiency of most classical modeling approaches. In this article, a Hybrid Approach based on both systemic and analytical modeling is presented and applied to a case study. From this novel approach, a model—the Multi-Institution Building Energy System—is presented. It allowed us to highlight and detail the need for greater governance of energy systems. The socio-technical solutions identified to answer the issues of governance (Accuracy, Reliability and Fairness) were DevOps methodology and the use of Distributed Microservices Architecture. Based on this framework, the design of a Decision Support System assuring and exploiting state-of-the-art scalable tools for data management and machine learning factories is described in this article. Moreover, we wish to set up the conceptual basis necessary for the design of a generic theoretical framework of optimization applicable to complex socio-technical systems in the context of the management of a shared resource. View Full-Text
Keywords: smart grid; complex system; recommender system; automated machine learning; clustering; profiling; DevOps; monitoring smart grid; complex system; recommender system; automated machine learning; clustering; profiling; DevOps; monitoring
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MDPI and ACS Style

Lévy, L.-N.; Bosom, J.; Guerard, G.; Amor, S.B.; Bui, M.; Tran, H. DevOps Model Appproach for Monitoring Smart Energy Systems. Energies 2022, 15, 5516. https://doi.org/10.3390/en15155516

AMA Style

Lévy L-N, Bosom J, Guerard G, Amor SB, Bui M, Tran H. DevOps Model Appproach for Monitoring Smart Energy Systems. Energies. 2022; 15(15):5516. https://doi.org/10.3390/en15155516

Chicago/Turabian Style

Lévy, Loup-Noé, Jérémie Bosom, Guillaume Guerard, Soufian Ben Amor, Marc Bui, and Hai Tran. 2022. "DevOps Model Appproach for Monitoring Smart Energy Systems" Energies 15, no. 15: 5516. https://doi.org/10.3390/en15155516

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