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

TL;DR

This paper introduces a utility-based, context-aware multi-agent recommendation system that optimizes device usage in residential buildings to reduce energy costs and CO2 emissions, demonstrating significant savings.

Contribution

It presents a novel multi-agent framework for personalized energy-saving recommendations based on user habits, costs, and external electricity prices.

Findings

Achieves over 18% energy cost savings in case studies

Models user preferences using habitual device usage patterns

Provides a flexible, extendable multi-agent architecture

Abstract

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.