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Massimo Fiorentini

Model Predictive Control (MPC) for enhancing building and HVAC system energy efficiency: Problem formulation, applications and opportunities

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperReviewResearchpeer-review


  • Gianluca Serale, Polytechnic University of Turin
  • ,
  • Massimo Fiorentini
  • Alfonso Capozzoli, Polytechnic University of Turin
  • ,
  • Daniele Bernardini, ODYS S.r.l
  • ,
  • Alberto Bemporad, IMT Institute for Advanced Studies Lucca

In the last few years, the application of Model Predictive Control (MPC) for energy management in buildings has received significant attention from the research community. MPC is becoming more and more viable because of the increase in computational power of building automation systems and the availability of a significant amount of monitored building data. MPC has found successful implementation in building thermal regulation, fully exploiting the potential of building thermal mass. Moreover, MPC has been positively applied to active energy storage systems, as well as to the optimal management of on-site renewable energy sources. MPC also opens up several opportunities for enhancing energy efficiency in the operation of Heating Ventilation and Air Conditioning (HVAC) systems because of its ability to consider constraints, prediction of disturbances and multiple conflicting objectives, such as indoor thermal comfort and building energy demand. Despite the application of MPC algorithms in building control has been thoroughly investigated in various works, a unified framework that fully describes and formulates the implementation is still lacking. Firstly, this work introduces a common dictionary and taxonomy that gives a common ground to all the engineering disciplines involved in building design and control. Secondly the main scope of this paper is to define the MPC formulation framework and critically discuss the outcomes of different existing MPC algorithms for building and HVAC system management. The potential benefits of the application of MPC in improving energy efficiency in buildings were highlighted.

Original languageEnglish
Article number631
Publication statusPublished - 25 Feb 2018
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2017 by the authors.

    Research areas

  • Building management system (BMS), HVAC system thermal management, Model predictive control (MPC), Performance optimization, Renewable energy system (RES), Review

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