IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v9y2021i18p2181-d630481.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    These authors equally contributed.)

  • 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
    These authors equally contributed.)

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, ε-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.

Suggested Citation

  • Alberto Garces-Jimenez & Jose-Manuel Gomez-Pulido & Nuria Gallego-Salvador & Alvaro-Jose Garcia-Tejedor, 2021. "Genetic and Swarm Algorithms for Optimizing the Control of Building HVAC Systems Using Real Data: A Comparative Study," Mathematics, MDPI, vol. 9(18), pages 1-24, September.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:18:p:2181-:d:630481
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/9/18/2181/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/9/18/2181/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jose Aguilar & Douglas Ardila & Andrés Avendaño & Felipe Macias & Camila White & José Gomez-Pulido & José Gutierrez de Mesa & Alberto Garces-Jimenez, 2020. "An Autonomic Cycle of Data Analysis Tasks for the Supervision of HVAC Systems of Smart Building," Energies, MDPI, vol. 13(12), pages 1-24, June.
    2. Farinaz Behrooz & Norman Mariun & Mohammad Hamiruce Marhaban & Mohd Amran Mohd Radzi & Abdul Rahman Ramli, 2018. "Review of Control Techniques for HVAC Systems—Nonlinearity Approaches Based on Fuzzy Cognitive Maps," Energies, MDPI, vol. 11(3), pages 1-41, February.
    3. Reynolds, Jonathan & Rezgui, Yacine & Kwan, Alan & Piriou, Solène, 2018. "A zone-level, building energy optimisation combining an artificial neural network, a genetic algorithm, and model predictive control," Energy, Elsevier, vol. 151(C), pages 729-739.
    4. Marzieh Keshavarz & Seyed Hamid Reza Pasandideh, 2018. "Multi-objective optimisation of continuous review inventory system under mixture of lost sales and backorders within different constraints," International Journal of Logistics Systems and Management, Inderscience Enterprises Ltd, vol. 29(3), pages 327-348.
    5. Shaikh, Pervez Hameed & Nor, Nursyarizal Bin Mohd & Nallagownden, Perumal & Elamvazuthi, Irraivan & Ibrahim, Taib, 2014. "A review on optimized control systems for building energy and comfort management of smart sustainable buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 34(C), pages 409-429.
    6. Aste, Niccolò & Manfren, Massimiliano & Marenzi, Giorgia, 2017. "Building Automation and Control Systems and performance optimization: A framework for analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 313-330.
    7. Ana Castillo-Martinez & Jose Ramon Almagro & Alberto Gutierrez-Escolar & Antonio Del Corte & José Luis Castillo-Sequera & José Manuel Gómez-Pulido & José-María Gutiérrez-Martínez, 2017. "Particle Swarm Optimization for Outdoor Lighting Design," Energies, MDPI, vol. 10(1), pages 1-11, January.
    8. Gao, Lei & Hwang, Yunho & Cao, Tao, 2019. "An overview of optimization technologies applied in combined cooling, heating and power systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 114(C), pages 1-1.
    9. Deepak Kumar & Sushil Kumar & Rohit Bansal & Parveen Singla, 2017. "A Survey to Nature Inspired Soft Computing," International Journal of Information System Modeling and Design (IJISMD), IGI Global, vol. 8(2), pages 112-133, April.
    10. Afroz, Zakia & Shafiullah, GM & Urmee, Tania & Higgins, Gary, 2018. "Modeling techniques used in building HVAC control systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 83(C), pages 64-84.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Flavio Muñoz & Ramon Garcia-Hernandez & Jose Ruelas & Juan E. Palomares-Ruiz & Carlos Álvarez-Macías, 2022. "Optimal Operation for Reduced Energy Consumption of an Air Conditioning System Using Neural Inverse Optimal Control," Mathematics, MDPI, vol. 10(5), pages 1-15, February.
    2. Nadia Jahanafroozi & Saman Shokrpour & Fatemeh Nejati & Omrane Benjeddou & Mohammad Worya Khordehbinan & Afshin Marani & Moncef L. Nehdi, 2022. "New Heuristic Methods for Sustainable Energy Performance Analysis of HVAC Systems," Sustainability, MDPI, vol. 14(21), pages 1-14, November.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Gianluca Serale & Massimo Fiorentini & Alfonso Capozzoli & Daniele Bernardini & Alberto Bemporad, 2018. "Model Predictive Control (MPC) for Enhancing Building and HVAC System Energy Efficiency: Problem Formulation, Applications and Opportunities," Energies, MDPI, vol. 11(3), pages 1-35, March.
    2. Amasyali, Kadir & El-Gohary, Nora M., 2021. "Real data-driven occupant-behavior optimization for reduced energy consumption and improved comfort," Applied Energy, Elsevier, vol. 302(C).
    3. Yang, Shiyu & Wan, Man Pun, 2022. "Machine-learning-based model predictive control with instantaneous linearization – A case study on an air-conditioning and mechanical ventilation system," Applied Energy, Elsevier, vol. 306(PB).
    4. Clara Ceccolini & Roozbeh Sangi, 2022. "Benchmarking Approaches for Assessing the Performance of Building Control Strategies: A Review," Energies, MDPI, vol. 15(4), pages 1-30, February.
    5. Moudgil, Vipul & Hewage, Kasun & Hussain, Syed Asad & Sadiq, Rehan, 2023. "Integration of IoT in building energy infrastructure: A critical review on challenges and solutions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 174(C).
    6. Halhoul Merabet, Ghezlane & Essaaidi, Mohamed & Ben Haddou, Mohamed & Qolomany, Basheer & Qadir, Junaid & Anan, Muhammad & Al-Fuqaha, Ala & Abid, Mohamed Riduan & Benhaddou, Driss, 2021. "Intelligent building control systems for thermal comfort and energy-efficiency: A systematic review of artificial intelligence-assisted techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    7. Aguilar, J. & Garces-Jimenez, A. & R-Moreno, M.D. & García, Rodrigo, 2021. "A systematic literature review on the use of artificial intelligence in energy self-management in smart buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 151(C).
    8. Jahangir Hossain & Aida. F. A. Kadir & Ainain. N. Hanafi & Hussain Shareef & Tamer Khatib & Kyairul. A. Baharin & Mohamad. F. Sulaima, 2023. "A Review on Optimal Energy Management in Commercial Buildings," Energies, MDPI, vol. 16(4), pages 1-40, February.
    9. Zhan, Sicheng & Chong, Adrian, 2021. "Data requirements and performance evaluation of model predictive control in buildings: A modeling perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 142(C).
    10. Reynolds, Jonathan & Ahmad, Muhammad Waseem & Rezgui, Yacine & Hippolyte, Jean-Laurent, 2019. "Operational supply and demand optimisation of a multi-vector district energy system using artificial neural networks and a genetic algorithm," Applied Energy, Elsevier, vol. 235(C), pages 699-713.
    11. Yang, Shiyu & Wan, Man Pun & Chen, Wanyu & Ng, Bing Feng & Dubey, Swapnil, 2020. "Model predictive control with adaptive machine-learning-based model for building energy efficiency and comfort optimization," Applied Energy, Elsevier, vol. 271(C).
    12. Iddio, E. & Wang, L. & Thomas, Y. & McMorrow, G. & Denzer, A., 2020. "Energy efficient operation and modeling for greenhouses: A literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 117(C).
    13. Ali Sadollah & Mohammad Nasir & Zong Woo Geem, 2020. "Sustainability and Optimization: From Conceptual Fundamentals to Applications," Sustainability, MDPI, vol. 12(5), pages 1-34, March.
    14. Kathirgamanathan, Anjukan & De Rosa, Mattia & Mangina, Eleni & Finn, Donal P., 2021. "Data-driven predictive control for unlocking building energy flexibility: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    15. Qiang, Guofeng & Tang, Shu & Hao, Jianli & Di Sarno, Luigi & Wu, Guangdong & Ren, Shaoxing, 2023. "Building automation systems for energy and comfort management in green buildings: A critical review and future directions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 179(C).
    16. Wenquan Jin & Israr Ullah & Shabir Ahmad & Dohyeun Kim, 2019. "Occupant Comfort Management Based on Energy Optimization Using an Environment Prediction Model in Smart Homes," Sustainability, MDPI, vol. 11(4), pages 1-18, February.
    17. Panagiotis Michailidis & Iakovos Michailidis & Dimitrios Vamvakas & Elias Kosmatopoulos, 2023. "Model-Free HVAC Control in Buildings: A Review," Energies, MDPI, vol. 16(20), pages 1-45, October.
    18. Chen, Zhelun & O’Neill, Zheng & Wen, Jin & Pradhan, Ojas & Yang, Tao & Lu, Xing & Lin, Guanjing & Miyata, Shohei & Lee, Seungjae & Shen, Chou & Chiosa, Roberto & Piscitelli, Marco Savino & Capozzoli, , 2023. "A review of data-driven fault detection and diagnostics for building HVAC systems," Applied Energy, Elsevier, vol. 339(C).
    19. Anass Berouine & Radouane Ouladsine & Mohamed Bakhouya & Mohamed Essaaidi, 2020. "Towards a Real-Time Predictive Management Approach of Indoor Air Quality in Energy-Efficient Buildings," Energies, MDPI, vol. 13(12), pages 1-16, June.
    20. Yang, Shiyu & Wan, Man Pun & Ng, Bing Feng & Dubey, Swapnil & Henze, Gregor P. & Chen, Wanyu & Baskaran, Krishnamoorthy, 2020. "Experimental study of model predictive control for an air-conditioning system with dedicated outdoor air system," Applied Energy, Elsevier, vol. 257(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:9:y:2021:i:18:p:2181-:d:630481. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.