论文标题
基于公用事业的上下文感知的多代理推荐系统，用于住宅建筑中的能源效率
Utility-Based Context-Aware Multi-Agent Recommendation System for Energy Efficiency in Residential Buildings
论文作者
论文摘要
二氧化碳排放的很大一部分是由于住宅建筑中的高电量。使用载荷转移可以帮助提高家庭的能源效率。为了推动能源消耗行为的变化，简单但强大的体系结构至关重要。本文提出了一种建议系统生成设备使用建议的新算法，并提出了一个通过分析势能成本节省来评估其性能的框架。作为基于公用事业的推荐系统，它根据习惯设备的使用模式，用户可用性和设备使用成本对用户偏好进行建模。作为一种上下文感知的系统，它需要外部每小时电力价格信号和设备级别的能源消耗数据。由于多代理体系结构，它提供了灵活性并可以进行调整和进一步的增强。经验结果表明，该系统可以为大多数研究的家庭提供18％及以上的能源成本。
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.
