Resident-Oriented Green Energy Optimization Using a Multi-objective Evolutionary Algorithm

The European Green Deal has set ambitious short-term targets for reducing \(CO_2\) emissions and achieving climate neutrality. In communal living spaces, the associated challenges involve the exploitation of energy from renewable sources in order to reduce indirect \(CO_2\) emissions caused by grid electricity consumption, and the satisfaction of the residents, with their individual appliance-scheduling preferences that often conflict with their objective of minimizing associated billing charges. This paper tackles this multi-objective optimization problem by proposing a multi-objective evolutionary algorithm based on decomposition with decision making. The algorithm produces a set of optimal trade-offs between maximizing the satisfaction of resident appliance-scheduling preferences and minimizing their billing charges, with decision making opting for the trade-off offering minimal deviation from the use of green energy, consequently limiting the \(CO_2\) footprint. Our experimental evaluation, based on the energy consumption patterns of 10 UK households as recorded in the REFIT public dataset, demonstrates that the proposed approach clearly outperforms alternative state-of-the-art approaches.