Model predictive control for demand side management in buildings: A survey

Meisam Farrokhifar, Hamidreza Bahmani, Behdad Faridpak, Amin Safari, David Pozo, Marco Aiello

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

Buildings are responsible for a large portion of the world's energy consumption. Any measure that can be taken to optimize the use of energy related to them must be considered. Demand Side Management (DSM) can be used to shave demand peaks and to avoid bootstrapping highly polluting fast ramp-up generators. This though brings a control problem that is complicated by the increasing diffusion of small-scale, renewable energy sources and local storage facilities which are decentralized and, in general, hard to predict reliably. The overall goal of the control strategy is to balance energy, demand/supply, and to minimize costs. This survey focuses on control strategies to support DSM, considering buildings as the load to be managed. Among the various control strategies, model predictive control (MPC) has a predominant role due to its broad applicability and easy portability to many diverse contexts. The method is suitable for any nonlinear, multi-variable, and linear parameter varying system. The survey provides a general, unifying mathematical characterization of the approaches and lays the foundations for comparing and evaluating MPC-based DSM in buildings.

Original languageEnglish
Article number103381
JournalSustainable Cities and Society
Volume75
DOIs
Publication statusPublished - Dec 2021

Keywords

  • Building management system
  • Demand side management
  • Model predictive control
  • Optimization
  • Renewable energy sources

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