  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.
