Portrait de Francesco Morri

Francesco Morri

Postdoctorat - Polytechnique
Superviseur⋅e principal⋅e
Margarida Carvalho
Co-supervisor
Quentin Cappart
Sujets de recherche
Apprentissage par renforcement
Systèmes multi-agents
Théorie des jeux
Site web
Google Scholar
GitHub

Publications

Winning the 2023 CityLearn Challenge: A Community-Based Hierarchical Energy Systems Coordination Algorithm
Andoni I. Garmendia
Francesco Morri
Quentin Cappart
Hélène Le Cadre
. The effective management and control of building energy systems are crucial for reducing the energy consumption peak loads, CO 2 emissions… (voir plus), and ensuring the stability of the power grid, while maintaining optimal comfort levels within buildings. The difficulty to accommodate this trade-off is amplified by dynamic environmental conditions and the need for scalable solutions that can adapt across various building types and geographic locations. Acknowledging the importance of this problem, NeurIPS conference hosted since 2020 the CityLearn control challenge to foster the design of innovative solutions in building energy management. Participants were tasked with developing strategies that not only enhance energy efficiency but also prioritize sustainability and occupant comfort. This paper introduces the Community-based Hierarchical Energy Systems Co-ordination Algorithm ( CHESCA ), the winning approach of the 2023 edition. We rely on a hierarchical approach adaptable to an arbitrary number of buildings, first optimizing building-level metrics individually, and later refining these through a central community-level controller to improve grid-related metrics. Compared to the other high-ranked competitors, our approach demonstrated fast inference capabilities like learning-based methods, while offering a better interpretability and a superior generalization capabilities with minimal data requirements. This paper details our approach, supported by comprehensive experimental results and ablation studies.
2023-12-31
ECAI (publié)
doi.org