The mission of this internship is to investigate the potentials of employing MARL and HILL in MAS to design self-adaptive energy systems that can learn to adapt themselves in response to dynamics. The proposed solutions will be applied in the management of local energy communities that are equipped with renewable energy sources (RES). More precisely, we aim at increasing RES penetration and electricity cost saving by optimising energy consumption to improve self-consumption in the communities and to support grid constraints (e.g., through demand response schemes), while ensuring user comfort. MARL and HILL will be employed to learn user preferences from data or observations and to consider dynamic changes in the system, which aids agents in their decision making. Various on-going projects in the lab on energy communities could provide further specifics on the use-cases and data for this internship.

Date de début souhaitée : dès que possible

Durée : 6 mois

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