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

Shichao Xu, Yangyang Fu, Yixuan Wang, Zheng O'Neill, Qi Zhu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationBuildSys 2021 - Proceedings of the 2021 ACM International Conference on Systems for Energy-Efficient Built Environments
PublisherAssociation for Computing Machinery, Inc
Pages1-10
Number of pages10
ISBN (Electronic)9781450391146
DOIs
StatePublished - Nov 17 2021
Event8th ACM International Conference on Systems for Energy-Efficient Built Environments, BuildSys 2021 - Virtual, Online, Portugal
Duration: Nov 17 2021Nov 18 2021

Publication series

NameBuildSys 2021 - Proceedings of the 2021 ACM International Conference on Systems for Energy-Efficient Built Environments

Conference

Conference8th ACM International Conference on Systems for Energy-Efficient Built Environments, BuildSys 2021
Country/TerritoryPortugal
CityVirtual, Online
Period11/17/2111/18/21

Keywords

  • HVAC control
  • deep learning
  • sensor fault-tolerant

ASJC Scopus subject areas

  • Information Systems
  • Renewable Energy, Sustainability and the Environment
  • Electrical and Electronic Engineering
  • Building and Construction
  • Computer Networks and Communications
  • Architecture

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