Optimizing Household Energy Use: An Activity-Based Recommendation System for Reducing CO2 Emissions

Alona Zharova (), Laura Löschmann and Stefan Lessmann
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Alona Zharova: Humboldt-Universität zu Berlin, Chair of Information Systems
Laura Löschmann: Humboldt-Universität zu Berlin, Chair of Information Systems
Stefan Lessmann: Humboldt-Universität zu Berlin, Chair of Information Systems

A chapter in Artificial Intelligence, Data, and Decision-Making, 2026, pp 95-111 from Springer

Abstract: Abstract The energy consumption of households accounts for approximately 30% of the total global energy consumption, leading to a significant portion of CO2 emis-sions from energy production. Enhancing energy efficiency by managing de-mand, 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, ac-tionable recommendations. Our contributions encompass the development of an Activity Agent, the introduction of a performance measure, and a practical im-plementation strategy requiring minimal user input. Our approach not only en-courages 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.

Keywords: Activity-based systems; Multi-agent systems; Personalized recommendations; User-centric system design; Energy efficiency (search for similar items in EconPapers)
Date: 2026
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DOI: 10.1007/978-3-032-08480-4_7

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