Home Energy Recommendation System (HERS): A Deep Reinforcement Learning Method Based on Residents’ Feedback and Activity
Home Energy Recommendation System (HERS): A Deep Reinforcement Learning Method Based on Residents’ Feedback and Activity
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DOI：
10.1109/tsg.2022.3158814
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发表时间：
2022-07
影响因子：
9.6
通讯作者：
S. S. Shuvo-S.;Yasin Yılmaz
中科院分区：
文献类型：
--
作者：
S. S. Shuvo-S.;Yasin Yılmaz
智能家用电器可以智能地指挥和行动，使它们适合实施优化技术。这些智能设备的人工智能（AI）控制实现了电力消耗的需求侧管理（DSM）。通过在决策过程中整合人类反馈和活动，这项工作提出了一种深度强化学习（RL）方法，用于管理智能设备，以优化电力成本并舒适居民。我们的贡献是双重的。首先，我们将人类的反馈，我们的DSM技术，我们命名为家庭能源推荐系统（HERS）的目标函数。其次，我们在RL状态定义中包含人类活动数据，以提高能量优化性能。我们进行了全面的实验分析，将所提出的深度RL方法与缺乏上述关键决策功能的现有方法进行比较。建议的模型是强大的居民活动和喜好不同，适用于广泛的家庭与不同的居民配置文件。
Smart home appliances can take command and act intelligently, making them suitable for implementing optimization techniques. Artificial intelligence (AI) based control of these smart devices enables demand-side management (DSM) of electricity consumption. By integrating human feedback and activity in the decision process, this work proposes a deep Reinforcement Learning (RL) method for managing smart devices to optimize electricity cost and comfort residents. Our contributions are twofold. Firstly, we incorporate human feedback in the objective function of our DSM technique that we name Home Energy Recommendation System (HERS). Secondly, we include human activity data in the RL state definition to enhance the energy optimization performance. We perform comprehensive experimental analyses to compare the proposed deep RL approach with existing approaches that lack the aforementioned critical decision-making features. The proposed model is robust to varying resident activities and preferences and applicable to a broad spectrum of homes with different resident profiles.