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arXiv · 2312.03588

Vulnerability of Building Energy Management against Targeted False Data Injection Attacks:Model Predictive Control vs. Proportional Integral

Xiaoyu Ge·Kamelia Norouzi·Faegheh Moazeni·Mirel Sehic·Javad Khazaei·Parv Venkitasubramaniam·Rick Blum
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Abstract

Cybersecurity in building energy management is crucial for protecting infrastructure, ensuring data integrity, and preventing unauthorized access or manipulation. This paper investigates the energy efficiency and cybersecurity of building energy management systems (BMS) against false data injection (FDI) attacks using proportional-integral (PI) and model predictive control (MPC) methods. Focusing on a commercial building model with five rooms, vulnerability of PI-based BMS and nonlinear MPC-based BMS against FDIs on sensors and actuators is studied. The study aims to assess the effectiveness of these control strategies in maintaining system performance and lifespan, highlighting the potential of MPC in enhancing system resilience against cyber threats. Our case studies demonstrate that even a short term FDIA can cause a 12% reduction in lifetime of a heat-pump under an MPC controller, and cause a near thirty-fold overuse of flow valves under a PI controller.

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Xiaoyu Ge, Kamelia Norouzi, Faegheh Moazeni, Mirel Sehic, Javad Khazaei, Parv Venkitasubramaniam, Rick Blum. 2023-12-06. Vulnerability of Building Energy Management against Targeted False Data Injection Attacks:Model Predictive Control vs. Proportional Integral. https://arxiv.org/abs/2312.03588

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