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.

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In 2019, the French government announced, in the energy renovation plan for buildings, the goal of achieving carbon neutrality by 2050. Reducing the consumption of buildings therefore represents a critical issue.Improving the energy performance of buildings remains a complex task due to the massification, heterogeneity and multiplicity of related data (Zou et al. 2018). The visualization and analysis of the remotely collected data allows a detailed understanding of the buildings. It is therefore crucial to understand, structure and analyze the data efficiently.The company Energisme presents itself as a trusted third-party for energy measurement and performance. As such, Energisme offers a platform for the collection and deployment of algorithmic solutions for optimized energy management in private and public organizations. The problematic proposed by Energisme is the following: How to provide a collective intelligence emerging from a platform gathering algorithms and energy databases?This problem raises four technical and scientific challenges:

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