# Utility-Based Context-Aware Multi-Agent Recommendation System for Energy Efficiency in Residential Buildings

arXiv: https://arxiv.org/abs/2205.02704 · HTML: https://mlsanity.com/paper/2205.02704/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 May 2022.

## Built on by (most cited first, 3 of 3)

- [A Survey on Context-Aware Multi-Agent Systems: Techniques, Challenges and Future Directions](https://mlsanity.com/paper/2402.01968/) (2024)
- [Recommender systems and reinforcement learning for human-building interaction and context-aware support: A text mining-driven review of scientific literature](https://mlsanity.com/paper/2411.08734/) (2024)
- [Contextual Scalarisation Thompson Sampling for multi-objective decisions in public media](https://mlsanity.com/paper/2605.31291/) (2026)

## Abstract (arXiv)

A significant part of CO2 emissions is due to high electricity consumption in residential buildings. Using load shifting can help to improve the households' energy efficiency. To nudge changes in energy consumption behavior, simple but powerful architectures are vital. This paper presents a novel algorithm of a recommendation system generating device usage recommendations and suggests a framework for evaluating its performance by analyzing potential energy cost savings. As a utility-based recommender system, it models user preferences depending on habitual device usage patterns, user availability, and device usage costs. As a context-aware system, it requires an external hourly electricity price signal and appliance-level energy consumption data. Due to a multi-agent architecture, it provides flexibility and allows for adjustments and further enhancements. Empirical results show that the system can provide energy cost savings of 18% and more for most studied households.