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Privacy-aware Fully Model-Free Event-triggered Cloud-based HVAC Control

Zhenan Feng, Ehsan Nekouei

TL;DR

An encrypted fully model-free event-triggered cloud-based HVAC control framework that ensures the privacy of occupancy information and minimizes the communication and computation overhead associated with encrypted HVAC control is proposed.

Abstract

Privacy is a major concern when computing-as-a-service (CaaS) platforms, e.g., cloud-computing platforms, are utilized for building automation, as CaaS platforms can infer sensitive information, such as occupancy, using the sensor measurements of a building. Although the existing encrypted model-based control algorithms can ensure the security and privacy of sensor measurements, they are highly complex to implement and require high computational resources, which result in a high cost of using CaaS platforms. To address these issues, in this paper, we propose an encrypted fully model-free event-triggered cloud-based HVAC control framework that ensures the privacy of occupancy information and minimizes the communication and computation overhead associated with encrypted HVAC control. To this end, we first develop a model-free controller for regulating indoor temperature and CO2 levels. We then design a model-free event-triggering unit which reduces the communication and computation costs of encrypted HVAC control using an optimal triggering policy. Finally, we evaluate the performance of the proposed encrypted fully model-free event-triggered cloud-based HVAC control framework using the TRNSYS simulator, comparing it to an encrypted model-based event-triggered control framework, which uses model predictive control to regulate the indoor climate. Our numerical results demonstrate that, compared to the encrypted model-based method, the proposed fully model-free framework improves the control performance while reducing the communication and computation costs. More specifically, it reduces the communication between the system and the CaaS platform by 64% amount, and its computation time is 75% less than that of the model-based control.

Privacy-aware Fully Model-Free Event-triggered Cloud-based HVAC Control

TL;DR

An encrypted fully model-free event-triggered cloud-based HVAC control framework that ensures the privacy of occupancy information and minimizes the communication and computation overhead associated with encrypted HVAC control is proposed.

Abstract

Privacy is a major concern when computing-as-a-service (CaaS) platforms, e.g., cloud-computing platforms, are utilized for building automation, as CaaS platforms can infer sensitive information, such as occupancy, using the sensor measurements of a building. Although the existing encrypted model-based control algorithms can ensure the security and privacy of sensor measurements, they are highly complex to implement and require high computational resources, which result in a high cost of using CaaS platforms. To address these issues, in this paper, we propose an encrypted fully model-free event-triggered cloud-based HVAC control framework that ensures the privacy of occupancy information and minimizes the communication and computation overhead associated with encrypted HVAC control. To this end, we first develop a model-free controller for regulating indoor temperature and CO2 levels. We then design a model-free event-triggering unit which reduces the communication and computation costs of encrypted HVAC control using an optimal triggering policy. Finally, we evaluate the performance of the proposed encrypted fully model-free event-triggered cloud-based HVAC control framework using the TRNSYS simulator, comparing it to an encrypted model-based event-triggered control framework, which uses model predictive control to regulate the indoor climate. Our numerical results demonstrate that, compared to the encrypted model-based method, the proposed fully model-free framework improves the control performance while reducing the communication and computation costs. More specifically, it reduces the communication between the system and the CaaS platform by 64% amount, and its computation time is 75% less than that of the model-based control.
Paper Structure (27 sections, 25 equations, 11 figures, 1 algorithm)

This paper contains 27 sections, 25 equations, 11 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. Motivation
  3. Contributions
  4. Related Work
  5. Organization
  6. Encrypted fully model-free HVAC control
  7. System Description
  8. The Design of Model-free Controller
  9. Actor
  10. Critics
  11. Secure Training and Implementation of Model-Free HVAC Controller
  12. Encrypted Implementation of Linear Layers
  13. Encrypted Implementation of Activation
  14. The Design of Model-Free Event-triggering Unit
  15. Numerical Results
  16. ...and 12 more sections

Figures (11)

  • Figure 1: An overview of the encrypted fully model-free event-triggered cloud-based HVAC control framework.
  • Figure 2: The forward and backward process for training the model-free controller.
  • Figure 3: Encrypted matrix-vector multiplication using the diagonal method.
  • Figure 4: The outlay of rooms in the building. Note that the shadow parts are the walls of the building.
  • Figure 5: The control performance of temperature and CO$_2$ obtained by the proposed optimal fully model-free policy and the optimal model-based policy.
  • ...and 6 more figures