Model Predictive Control Optimization via Genetic Algorithm Using a Detailed Building Energy Model

Germán Ramos Ruiz, Eva Lucas Segarra and Carlos Fernández Bandera
Additional contact information
Germán Ramos Ruiz: School of Architecture, University of Navarra, 31009 Pamplona, Spain
Eva Lucas Segarra: School of Architecture, University of Navarra, 31009 Pamplona, Spain
Carlos Fernández Bandera: School of Architecture, University of Navarra, 31009 Pamplona, Spain

Energies, 2018, vol. 12, issue 1, 1-18

Abstract: There is growing concern about how to mitigate climate change in which the reduction of CO 2 emissions plays an important role. Buildings have gained attention in recent years since they are responsible for around 30% of greenhouse gases. In this context, advance control strategies to optimize HVAC systems are necessary because they can provide significant energy savings whilst maintaining indoor thermal comfort. Simulation-based model predictive control (MPC) procedures allow an increase in building energy performance through the smart control of HVAC systems. The paper presents a methodology that overcomes one of the critical issues in using detailed building energy models in MPC optimizations—computational time. Through a case study, the methodology explains how to resolve this issue. Three main novel approaches are developed: a reduction in the search space for the genetic algorithm (NSGA-II) thanks to the use of the curve of free oscillation; a reduction in convergence time based on a process of two linked stages; and, finally, a methodology to measure, in a combined way, the temporal convergence of the algorithm and the precision of the obtained solution.

Keywords: model predictive control (MPC); detailed building energy models (BEM); setpoint-objective optimization; genetic algorithm (NSGA-II); white box models; EnergyPlus; MPC computational time) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://www.mdpi.com/1996-1073/12/1/34/pdf (application/pdf)
https://www.mdpi.com/1996-1073/12/1/34/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:12:y:2018:i:1:p:34-:d:192725

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().