Winning the 2023 CityLearn Challenge: A Community-Based Hierarchical Energy Systems Coordination Algorithm
(2024) 27th European Conference on Artificial Intelligence
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- The effective management and control of building energy systems are crucial for reducing the energy consumption peak loads, CO2 emissions, 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 arbi- trary number of buildings, first optimizing building-level metrics in- dividually, 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 in- terpretability and a superior generalization capabilities with mini- mal data requirements. This paper details our approach, supported by comprehensive experimental results and ablation studies.
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Citations
I. Garmendia, A., Morri, F., Cappart, Q., & Le Cadre, H. (2024). Winning the 2023 CityLearn Challenge: A Community-Based Hierarchical Energy Systems Coordination Algorithm. In Endriss, U., Melo, F.S., Bach, K., Bugarín-Diz, A., Alonso-Moral, J.M., Barro, S., Heintz, F. (eds.) (ed.), Frontiers in Artificial Intelligence and Applications : ECAI 2024 (p. p. 2330-2337). I O S Press. https://doi.org/10.3233/faia240757