Field demonstration and implementation analysis of model predictive control in an office HVAC system. (15th July 2022)

• Record Type: Journal Article
• Authors: Blum, David; Wang, Zhe; Weyandt, Chris; Kim, Donghun; Wetter, Michael; Hong, Tianzhen; Piette, Mary Ann
• Is Part Of: Applied energy. Volume 318(2022)
• Publication Date: 2022-07-15
• Subjects: Model predictive control -- HVAC -- Modelica -- Optimization -- MPCPy; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2022.119104
• Languages: English
• ISSNs: 0306-2619

Abstract

Abstract: Model Predictive Control (MPC) is a promising technique to address growing needs for heating, ventilation, and air-conditioning (HVAC) systems to operate more efficiently and with greater flexibility. However, due to a number of factors, including the required implementation expertise, lack of high quality data, and a risk-adverse industry, MPC has yet to gain widespread adoption. While many previous studies have shown the advantages of MPC, few analyzed the implementation effort and associated practical challenges. In addition, previous work has developed an open-source, Modelica-based tool-chain that automatically generates optimal control, parameter estimation, and state estimation problems aimed at facilitating MPC implementation. Therefore, this study demonstrates usage of this tool-chain to implement MPC in a real office building, discusses practical challenges of implementing MPC, and estimates the implementation effort associated with various tasks in order to inform the development of future workflows and serve as an initial benchmark for their impact on reducing implementation effort. This study finds that the implemented MPC saves approximately 40% of HVAC energy over the existing control during a two-month trial period and that tasks related to data collection and controller deployment activities can each require as much effort as model generation. Highlights: MPC saved energy compared to existing control in a real office building HVAC system. Open-source software was used for the MPC implementation. Effort for data collection and MPC deployment was comparable to model generation. Workflows that streamline implementation processes would help reduce effort.