Abstract
The energy consumption of households accounts for approximately 30% of the total global energy consumption, leading to a significant portion of CO2 emissions from energy production. Enhancing energy efficiency by managing demand, such as through load shifting, presents a viable strategy for reducing CO2 emissions. This study introduces an innovative activity-based multi-agent recommendation system aimed at reducing CO2 emissions in households. By shifting household activities rather than individual appliance usage, we propose a more intuitive approach to energy efficiency grounded in the social practices of domestic life. Using real-world data, the system provides personalized, actionable recommendations. Our contributions encompass the development of an Activity Agent, the introduction of a performance measure, and a practical implementation strategy requiring minimal user input. Our approach not only encourages sustainable behavior among households but also contributes to the IS field by demonstrating how AI can play a pivotal role in addressing climate change challenges.
Recommended Citation
Zharova, Alona; Löschmann, Laura; and Lessmann, Stefan, "Optimizing Household Energy Use: An Activity-based Recommendation System for Reducing CO2 Emissions" (2024). Wirtschaftsinformatik 2024 Proceedings. 87.
https://aisel.aisnet.org/wi2024/87