DevOps Model Appproach for Monitoring Smart Energy Systems

Loup-Noé Lévy, Jérémie Bosom, Guillaume Guerard, Soufian Ben Amor, Marc Bui and Hai Tran
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Loup-Noé Lévy: LI-PARAD Laboratory EA 7432, Versailles University, 55 Avenue de Paris, 78035 Versailles, France
Jérémie Bosom: Energisme, 88 Avenue du Général Leclerc, 92100 Boulogne-Billancourt, France
Guillaume Guerard: De Vinci Research Center, Pole Universitaire Léonard de Vinci, 12 Avenue Léonard de Vinci, 92400 Courbevoie, France
Soufian Ben Amor: LI-PARAD Laboratory EA 7432, Versailles University, 55 Avenue de Paris, 78035 Versailles, France
Marc Bui: Ecole Pratique des Hautes Etudes, PSL Research University, 4-14 Rue Ferrus, 75014 Paris, France
Hai Tran: Energisme, 88 Avenue du Général Leclerc, 92100 Boulogne-Billancourt, France

Energies, 2022, vol. 15, issue 15, 1-27

Abstract: 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.

Keywords: smart grid; complex system; recommender system; automated machine learning; clustering; profiling; DevOps; monitoring (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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