  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.

Systems and Control;Machine Learning;Systems and Control