A machine-learning-based event-triggered model predictive control for building energy management. (1st April 2023)

• Record Type: Journal Article
• Authors: Yang, Shiyu; Chen, Wanyu; Wan, Man Pun
• Is Part Of: Building and environment. Volume 233(2023)
• Publication Date: 2023-04-01
• Subjects: Event-triggered mechanism -- Model predictive control -- Machine learning -- Air conditioning -- Building energy management; Buildings -- Environmental engineering -- Periodicals; Building -- Research -- Periodicals; Constructions -- Technique de l'environnement -- Périodiques; Electronic journals; 696
• DOI: 10.1016/j.buildenv.2023.110101
• Languages: English
• ISSNs: 0360-1323

Abstract

Abstract: Model predictive control (MPC) for building energy management has exhibited a huge potential for largely cutting down energy use and improving human comfort. However, the high demand for computational power for solving the optimization is challenging the widespread deployment of MPC in real buildings. Typical MPC employs a time-triggered mechanism (TTM) that runs optimization recurrently at each time step without considering the necessity, which could cause excessive computation resource usage. This paper proposes an event-triggered mechanism (ETM) that only runs optimization when triggering events occur. Contrary to conventional ETMs that only considers the current information (e.g., room conditions), the ETM proposed is based on a cost function that covers the past, current, and future information. Based on the proposed ETM, a machine-learning-based event-triggered model predictive control (ETMPC) system that optimizes both building energy efficiency and thermal comfort is developed. The developed ETMPC system is then employed for air-conditioning control for performance evaluation through simulations. The control performance of the proposed ETMPC is compared to an MPC employing TTM and a common thermostat. Compared to the MPC employing TTM, the proposed ETMPC reduced the computational load in terms of the number of optimization runs by 77.6%–88.2%, meanwhile, achieving similar energy savings (more than 9.3% energy saving over the thermostat) as the MPC employing TTM does (9% energy saving over the thermostat). With the significant reduction of computational load, the ETMPC achieves similar thermal comfort performance as the MPC employing TTM does, and it is significantly better over the thermostat. Highlights: An event-triggered machine-learning-based model predictive control is proposed. An event-triggered mechanism that runs optimization only when necessary is proposed. The event-triggered mechanism considers past, current, and future information. The proposed approach can cut down the computational load by 77.6%–88.2%.