Learning-based framework for sensor fault-tolerant building HVAC control with model-assisted learning
Learning-based framework for sensor fault-tolerant building HVAC control with model-assisted learning
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DOI：
10.1145/3486611.3486644
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发表时间：
2021-06
期刊：
影响因子：
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通讯作者：
Shichao Xu;Yangyang Fu;Yixuan Wang;Zheng O’Neill;Qi Zhu
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文献类型：
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作者：
Shichao Xu;Yangyang Fu;Yixuan Wang;Zheng O’Neill;Qi Zhu
随着人们在室内花费多达87％的时间，建筑物中的智能供暖，通风和空调（HVAC）系统对于维持乘员舒适和减少能源消耗至关重要。这些智能建筑物中的HVAC系统依赖于“实时传感器读数，实际上经常会遭受各种故障，也可能容易受到恶意攻击。这种错误的传感器输入可能导致违反室内环境要求（例如温度，湿度等）和能耗的增加。尽管文献中已经提出了许多基于模型的方法来构建HVAC控制，但开发准确的物理模型来确保其性能，甚至更具挑战性来解决传感器故障的影响是昂贵的。在这项工作中，我们为传感器故障耐受性HVAC控制提供了一个新颖的基于学习的框架，其中包括三个基于深度学习的组件，用于1）考虑使用可能的传感器故障，2）根据评估其准确性选择其中一项提案； 3）将强化学习应用于选定的温度建议。此外，为了应对培训与建筑有关任务的培训数据不足的挑战，我们提出了一种模型辅助学习方法，以利用一种抽象的构建物理动态模型。通过广泛的实验，我们证明了拟议的耐断层HVAC控制框架可以在各种传感器断层模式下显着降低建筑温度的侵犯，同时维持能源效率。
As people spend up to 87% of their time indoors, intelligent Heating, Ventilation, and Air Conditioning (HVAC) systems in buildings are essential for maintaining occupant comfort and reducing energy consumption. These HVAC systems in smart buildings rely 'on real-time sensor readings, which in practice often suffer from various faults and could also be vulnerable to malicious attacks. Such faulty sensor inputs may lead to the violation of indoor environment requirements (e.g., temperature, humidity, etc.) and the increase of energy consumption. While many model-based approaches have been proposed in the literature for building HVAC control, it is costly to develop accurate physical models for ensuring their performance and even more challenging to address the impact of sensor faults. In this work, we present a novel learning-based framework for sensor fault-tolerant HVAC control, which includes three deep learning based components for 1) generating temperature proposals with the consideration of possible sensor faults, 2) selecting one of the proposals based on the assessment of their accuracy, and 3) applying reinforcement learning with the selected temperature proposal. Moreover, to address the challenge of training data insufficiency in building-related tasks, we propose a model-assisted learning method leveraging an abstract model of building physical dynamics. Through extensive experiments, we demonstrate that the proposed fault-tolerant HVAC control framework can significantly reduce building temperature violations under a variety of sensor fault patterns while maintaining energy efficiency.