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Learning-based framework for sensor fault-tolerant building HVAC control with model-assisted learning

Authors:

Qi ZhuAuthors Info & Claims

BuildSys '21: Proceedings of the 8th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation

Pages 1 - 10

https://doi.org/10.1145/3486611.3486644

Published: 17 November 2021 Publication History

Abstract

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.

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Cited By

Roda-Sanchez LCirillo FSolmaz GJacobs TGarrido-Hidalgo COlivares TKovacs E(2024)Building a Smart Campus Digital Twin: System, Analytics, and Lessons Learned From a Real-World ProjectIEEE Internet of Things Journal10.1109/JIOT.2023.330044711:3(4614-4627)Online publication date: 1-Feb-2024
https://doi.org/10.1109/JIOT.2023.3300447
Li GRen LPradhan OO’Neill ZWen JYang ZFu YChu MHuang JWu TCandan KAdetola VZhu Q(2024)Emulation and detection of physical faults and cyber-attacks on building energy systems through real-time hardware-in-the-loop experimentsEnergy and Buildings10.1016/j.enbuild.2024.114596(114596)Online publication date: Jul-2024
https://doi.org/10.1016/j.enbuild.2024.114596
Wang YZhan SJiao RWang ZJin WYang ZWang ZHuang CZhu QKrause ABrunskill ECho KEngelhardt BSabato SScarlett J(2023)Enforcing hard constraints with soft barriersProceedings of the 40th International Conference on Machine Learning10.5555/3618408.3619930(36593-36604)Online publication date: 23-Jul-2023
https://dl.acm.org/doi/10.5555/3618408.3619930
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Index Terms

Learning-based framework for sensor fault-tolerant building HVAC control with model-assisted learning
1. Computer systems organization
  1. Embedded and cyber-physical systems
2. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Reinforcement learning

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cover image ACM Conferences

BuildSys '21: Proceedings of the 8th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation

November 2021

388 pages

ISBN:9781450391146

DOI:10.1145/3486611

General Chair:
Xiaofan (Fred) Jiang
Columbia University
,
Program Chairs:
Omprakash Gnawali
University of Houston
,
Zoltan Nagy
The University of Texas Austin

Copyright © 2021 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 17 November 2021

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BuildSys '21

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BuildSys '21: The 8th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation

November 17 - 18, 2021

Coimbra, Portugal

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BuildSys '21 Paper Acceptance Rate 28 of 107 submissions, 26%;

Overall Acceptance Rate 148 of 500 submissions, 30%

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The 22nd ACM Conference on Embedded Networked Sensor Systems

November 4 - 7, 2024

Hangzhou , China

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Cited By

Roda-Sanchez LCirillo FSolmaz GJacobs TGarrido-Hidalgo COlivares TKovacs E(2024)Building a Smart Campus Digital Twin: System, Analytics, and Lessons Learned From a Real-World ProjectIEEE Internet of Things Journal10.1109/JIOT.2023.330044711:3(4614-4627)Online publication date: 1-Feb-2024
https://doi.org/10.1109/JIOT.2023.3300447
Li GRen LPradhan OO’Neill ZWen JYang ZFu YChu MHuang JWu TCandan KAdetola VZhu Q(2024)Emulation and detection of physical faults and cyber-attacks on building energy systems through real-time hardware-in-the-loop experimentsEnergy and Buildings10.1016/j.enbuild.2024.114596(114596)Online publication date: Jul-2024
https://doi.org/10.1016/j.enbuild.2024.114596
Wang YZhan SJiao RWang ZJin WYang ZWang ZHuang CZhu QKrause ABrunskill ECho KEngelhardt BSabato SScarlett J(2023)Enforcing hard constraints with soft barriersProceedings of the 40th International Conference on Machine Learning10.5555/3618408.3619930(36593-36604)Online publication date: 23-Jul-2023
https://dl.acm.org/doi/10.5555/3618408.3619930
Wang ZHuang CZhu QElkind E(2023)Efficient global robustness certification of neural networks via interleaving twin-network encoding (extended abstract)Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence10.24963/ijcai.2023/727(6498-6503)Online publication date: 19-Aug-2023
https://dl.acm.org/doi/10.24963/ijcai.2023/727
Wang YZhou WFan JWang ZLi JChen XHuang CLi WZhu Q(2023)POLAR-Express: Efficient and Precise Formal Reachability Analysis of Neural-Network Controlled SystemsIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2023.333121543:3(994-1007)Online publication date: 8-Nov-2023
https://dl.acm.org/doi/10.1109/TCAD.2023.3331215
Li GRen LFu YYang ZAdetola VWen JZhu QWu TCandan KO'Neill Z(2023)A critical review of cyber-physical security for building automation systemsAnnual Reviews in Control10.1016/j.arcontrol.2023.02.00455(237-254)Online publication date: 2023
https://doi.org/10.1016/j.arcontrol.2023.02.004
Xu SWang LWang YZhu Q(2021)Weak Adaptation Learning: Addressing Cross-domain Data Insufficiency with Weak Annotator2021 IEEE/CVF International Conference on Computer Vision (ICCV)10.1109/ICCV48922.2021.00879(8897-8906)Online publication date: Oct-2021
https://doi.org/10.1109/ICCV48922.2021.00879

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