@article{35225,
  keywords = {HVAC, Optimization, Modelica, Model predictive control (MPC), MPCPy},
  author = {David Blum and Zhe Wang and Chris Weyandt and Donghun Kim and Michael Wetter and Tianzhen Hong and Mary Ann Piette},
  title = {Field demonstration and implementation analysis of model predictive control in an office HVAC system},
  abstract = {<p><span><a class="topic-link" title="Learn more about Model Predictive Control from ScienceDirect's AI-generated Topic Pages" href="https://www.sciencedirect.com/topics/engineering/predictive-control-model">Model Predictive Control</a>&nbsp;(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&nbsp;</span><a class="topic-link" title="Learn more about HVAC from ScienceDirect's AI-generated Topic Pages" href="https://www.sciencedirect.com/topics/engineering/air-conditioning">HVAC</a><span>&nbsp;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.</span></p>},
  year = {2022},
  journal = {Applied Energy},
  volume = {318},
  pages = {119104},
  month = {07/2022},
  issn = {03062619},
  url = {https://linkinghub.elsevier.com/retrieve/pii/S0306261922004895},
  doi = {10.1016/j.apenergy.2022.119104},
  language = {eng},
}