@article{32257,
  keywords = {Energy performance, Model predictive control (MPC), FLEXLAB, Radiant slab systems, Experimental study},
  author = {Xiufeng Pang and Carlos Duarte and Philip Haves and Frank Chuang},
  title = {Testing and demonstration of model predictive control applied to a radiant slab cooling system in a building test facility},
  abstract = {<p>Radiant slab systems have the potential to significantly reduce energy consumption in buildings. However, control of radiant slab systems is challenging. Classical feedback control is inadequate due to the large thermal inertia of the systems and heuristic feed-forward control often leads to unacceptable indoor comfort and may not achieve the full energy savings potential. Model predictive control (MPC) is now attracting increasing interest in the building industry and holds promise for radiant systems. However, an often-cited barrier to its implementation in the building industry is the high computational cost and complexity relative to the feedback controls used in conventional systems. The objectives of this study were to (i) verify the correct operation of an open source MPC toolchain developed for radiant slab systems, and (ii) demonstrate its efficacy in a test facility. A matched pair of cells in the FLEXLAB building test facility at the Lawrence Berkeley National Laboratory was used in the study. The proposed MPC toolchain was implemented in one cell and the performance compared to that of the other cell, which used a conventional heuristic control strategy. The results showed that the simplified MPC approach applied in the toolchain worked as expected and realized energy savings over the conventional control strategy. The MPC yielded 42% chilled water pump power reduction and 16% cooling thermal energy savings, while maintaining equal or better indoor comfort.</p>},
  year = {2018},
  journal = {Energy and Buildings},
  volume = {172},
  pages = {432 - 441},
  month = {08/2018},
  issn = {03787788},
  url = {https://linkinghub.elsevier.com/retrieve/pii/S0378778818304201},
  doi = {10.1016/j.enbuild.2018.05.013},
  language = {eng},
}