Genetic and Swarm Algorithms for Optimizing the Control of Building HVAC Systems Using Real Data: A Comparative Study

Alberto Garces-Jimenez, Jose-Manuel Gomez-Pulido, Nuria Gallego-Salvador and Alvaro-Jose Garcia-Tejedor
Additional contact information
Alberto Garces-Jimenez: Centro de Innovación Experimental del Conocimiento (CEIEC), Universidad Francisco de Vitoria (UFV), Carretera Pozuelo-Majadahonda, Km. 1.8, 28223 Pozuelo de Alarcón, Madrid, Spain
Jose-Manuel Gomez-Pulido: Departamento de Ciencias de la Computación, Universidad de Alcala (UAH), Carretera Madrid-Barcelona, Km. 33.6, 28805 Alcalá de Henares, Madrid, Spain
Nuria Gallego-Salvador: Departamento de Ciencias de la Computación, Universidad de Alcala (UAH), Carretera Madrid-Barcelona, Km. 33.6, 28805 Alcalá de Henares, Madrid, Spain
Alvaro-Jose Garcia-Tejedor: Centro de Innovación Experimental del Conocimiento (CEIEC), Universidad Francisco de Vitoria (UFV), Carretera Pozuelo-Majadahonda, Km. 1.8, 28223 Pozuelo de Alarcón, Madrid, Spain

Mathematics, 2021, vol. 9, issue 18, 1-24

Abstract: Buildings consume a considerable amount of electrical energy, the Heating, Ventilation, and Air Conditioning (HVAC) system being the most demanding. Saving energy and maintaining comfort still challenge scientists as they conflict. The control of HVAC systems can be improved by modeling their behavior, which is nonlinear, complex, and dynamic and works in uncertain contexts. Scientific literature shows that Soft Computing techniques require fewer computing resources but at the expense of some controlled accuracy loss. Metaheuristics-search-based algorithms show positive results, although further research will be necessary to resolve new challenging multi-objective optimization problems. This article compares the performance of selected genetic and swarm-intelligence-based algorithms with the aim of discerning their capabilities in the field of smart buildings. MOGA, NSGA-II/III, OMOPSO, SMPSO, and Random Search, as benchmarking, are compared in hypervolume, generational distance, ?-indicator, and execution time. Real data from the Building Management System of Teatro Real de Madrid have been used to train a data model used for the multiple objective calculations. The novelty brought by the analysis of the different proposed dynamic optimization algorithms in the transient time of an HVAC system also includes the addition, to the conventional optimization objectives of comfort and energy efficiency, of the coefficient of performance, and of the rate of change in ambient temperature, aiming to extend the equipment lifecycle and minimize the overshooting effect when passing to the steady state. The optimization works impressively well in energy savings, although the results must be balanced with other real considerations, such as realistic constraints on chillers’ operational capacity. The intuitive visualization of the performance of the two families of algorithms in a real multi-HVAC system increases the novelty of this proposal.

Keywords: multi-objective optimization; genetic algorithms; evolutionary computation; swarm intelligence; Heating, Ventilation and Air Conditioning (HVAC); metaheuristics search; bio-inspired algorithms; smart building; soft computing (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.mdpi.com/2227-7390/9/18/2181/pdf (application/pdf)
https://www.mdpi.com/2227-7390/9/18/2181/ (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:jmathe:v:9:y:2021:i:18:p:2181-:d:630481

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

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