A Privacy-Preserving Framework for Cloud-Based HVAC Control

The objective of this work is (i) to develop an encrypted cloud-based HVAC control framework to ensure the privacy of occupancy information, (ii) to reduce the communication and computation costs of encrypted HVAC control. Occupancy of a building is sensitive and private information that can be accurately inferred by cloud-based HVAC controllers. To ensure the privacy of the privacy information, in our framework, the measurements of an HVAC system are encrypted by a fully homomorphic encryption prior to communication with the cloud controller. We first develop an encrypted fast gradient algorithm that allows the cloud controller to regulate the indoor temperature and CO$_2$ of a building by solving two model predictive control problems. We next develop an event-triggered control policy to reduce the communication and computation costs of the encrypted HVAC control. We cast the optimal design of the event-triggered policy as an optimal control problem wherein the objective is to minimize a linear combination of the control and communication costs. Using Bellman's optimality principle, we study the structural properties of the optimal event-triggered policy and show that the optimal triggering policy is a function of the current state, the last communicated state with the cloud, and the time since the last communication with the cloud. We also show that the optimal design of the event-triggered policy can be transformed into a Markov decision process by introducing two new states. We finally study the performance of the developed encrypted HVAC control framework using the TRNSYS simulator. Our numerical results show that the proposed framework not only ensures efficient control of the indoor temperature and CO$_2$ but also reduces the computation and communication costs of encrypted HVAC control by at least 60%.