@InProceedings{10.1007/978-3-031-63227-3_32,
author="Papakyriakou, Thalis
and Pamboris, Andreas
and Konstantinidis, Andreas",
editor="Maglogiannis, Ilias
and Iliadis, Lazaros
and Karydis, Ioannis
and Papaleonidas, Antonios
and Chochliouros, Ioannis",
title="Resident-Oriented Green Energy Optimization Using a Multi-objective Evolutionary Algorithm",
booktitle="Artificial Intelligence Applications and Innovations. AIAI 2024 IFIP WG 12.5 International Workshops",
year="2024",
publisher="Springer Nature Switzerland",
address="Cham",
pages="447--461",
abstract="The European Green Deal has set ambitious short-term targets for reducing {\$}{\$}CO{\_}2{\$}{\$}CO2emissions 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{\$}{\$}CO2emissions 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{\$}{\$}CO2footprint. 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.",
isbn="978-3-031-63227-3"
}