Journal
APPLIED ENERGY
Volume 318, Issue -, Pages -Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.apenergy.2022.119104
Keywords
Model predictive control; HVAC; Modelica; Optimization; MPCPy
Categories
Funding
- Assistant Secretary for Energy Efficiency and Renewable Energy, Office of Building Technologies of the U.S. Department of Energy [DE-AC02-05CH11231]
- U.S.-China Clean Energy Research Center for Building Energy Efficiency (CERC-BEE)
- Lawrence Berkeley National Laboratory, USA
- Erika Gupta of Building Technologies Office of United States Department of Energy
- U.S. Department of Energy
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Model Predictive Control (MPC) is a promising technique for HVAC systems, but has not been widely adopted due to factors such as high implementation expertise requirements, lack of high quality data, and industry risk aversion. This study demonstrates the implementation of MPC using a developed tool-chain in a real office building, discussing challenges and estimating implementation effort to inform future workflow development.
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.
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