IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v10y2017i3p321-d92390.html
   My bibliography  Save this article

An Economic Model-Based Predictive Control to Manage the Users’ Thermal Comfort in a Building

Author

Listed:
  • Yaser Imad Alamin

    (Department of Informatics, University of Almería, Agrifood Campus of International Excellence (ceiA3) CIESOL Joint Centre University of Almería—CIEMAT, 04120 Almería, Spain)

  • María Del Mar Castilla

    (Department of System Engineering and Automation, School of Engineering, University of Seville, 41092 Seville, Spain)

  • José Domingo Álvarez

    (Department of Informatics, University of Almería, Agrifood Campus of International Excellence (ceiA3) CIESOL Joint Centre University of Almería—CIEMAT, 04120 Almería, Spain)

  • Antonio Ruano

    (Faculty of Science and Technology, University of Algarve, Faro, Portugal and, IDMEC, Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisboa, Portugal)

Abstract

The goal of maintaining users’ thermal comfort conditions in indoor environments may require complex regulation procedures and a proper energy management. This problem is being widely analyzed, since it has a direct effect on users’ productivity. This paper presents an economic model-based predictive control (MPC) whose main strength is the use of the day-ahead price (DAP) in order to predict the energy consumption associated with the heating, ventilation and air conditioning (HVAC). In this way, the control system is able to maintain a high thermal comfort level by optimizing the use of the HVAC system and to reduce, at the same time, the energy consumption associated with it, as much as possible. Later, the performance of the proposed control system is tested through simulations with a non-linear model of a bioclimatic building room. Several simulation scenarios are considered as a test-bed. From the obtained results, it is possible to conclude that the control system has a good behavior in several situations, i.e., it can reach the users’ thermal comfort for the analyzed situations, whereas the HVAC use is adjusted through the DAP; therefore, the energy savings associated with the HVAC is increased.

Suggested Citation

  • Yaser Imad Alamin & María Del Mar Castilla & José Domingo Álvarez & Antonio Ruano, 2017. "An Economic Model-Based Predictive Control to Manage the Users’ Thermal Comfort in a Building," Energies, MDPI, vol. 10(3), pages 1-18, March.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:3:p:321-:d:92390
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/10/3/321/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/10/3/321/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Diakaki, Christina & Grigoroudis, Evangelos & Kabelis, Nikos & Kolokotsa, Dionyssia & Kalaitzakis, Kostas & Stavrakakis, George, 2010. "A multi-objective decision model for the improvement of energy efficiency in buildings," Energy, Elsevier, vol. 35(12), pages 5483-5496.
    2. Edorta Carrascal & Izaskun Garrido & Aitor J. Garrido & José María Sala, 2016. "Optimization of the Heating System Use in Aged Public Buildings via Model Predictive Control," Energies, MDPI, vol. 9(4), pages 1-20, March.
    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. Grzegorz Majewski & Łukasz J. Orman & Marek Telejko & Norbert Radek & Jacek Pietraszek & Agata Dudek, 2020. "Assessment of Thermal Comfort in the Intelligent Buildings in View of Providing High Quality Indoor Environment," Energies, MDPI, vol. 13(8), pages 1-20, April.
    2. Awais Shah & Deqing Huang & Tianpeng Huang & Umar Farid, 2018. "Optimization of BuildingsEnergy Consumption by Designing Sliding Mode Control for Multizone VAV Air Conditioning Systems," Energies, MDPI, vol. 11(11), pages 1-18, October.
    3. Awais Shah & Deqing Huang & Yixing Chen & Xin Kang & Na Qin, 2017. "Robust Sliding Mode Control of Air Handling Unit for Energy Efficiency Enhancement," Energies, MDPI, vol. 10(11), pages 1-21, November.
    4. Giovanni Pau & Mario Collotta & Antonio Ruano & Jiahu Qin, 2017. "Smart Home Energy Management," Energies, MDPI, vol. 10(3), pages 1-5, March.
    5. Dongsu Kim & Jongman Lee & Sunglok Do & Pedro J. Mago & Kwang Ho Lee & Heejin Cho, 2022. "Energy Modeling and Model Predictive Control for HVAC in Buildings: A Review of Current Research Trends," Energies, MDPI, vol. 15(19), pages 1-30, October.
    6. Liu, Xinrui & Hou, Min & Sun, Siluo & Wang, Jiawei & Sun, Qiuye & Dong, Chaoyu, 2022. "Multi-time scale optimal scheduling of integrated electricity and district heating systems considering thermal comfort of users: An enhanced-interval optimization method," Energy, Elsevier, vol. 254(PB).

    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. Minjeong Sim & Dongjun Suh & Marc-Oliver Otto, 2021. "Multi-Objective Particle Swarm Optimization-Based Decision Support Model for Integrating Renewable Energy Systems in a Korean Campus Building," Sustainability, MDPI, vol. 13(15), pages 1-18, August.
    2. García Kerdan, Iván & Raslan, Rokia & Ruyssevelt, Paul & Morillón Gálvez, David, 2017. "A comparison of an energy/economic-based against an exergoeconomic-based multi-objective optimisation for low carbon building energy design," Energy, Elsevier, vol. 128(C), pages 244-263.
    3. Jeong, Kwangbok & Hong, Taehoon & Kim, Jimin & Cho, Kyuman, 2019. "Development of a multi-objective optimization model for determining the optimal CO2 emissions reduction strategies for a multi-family housing complex," Renewable and Sustainable Energy Reviews, Elsevier, vol. 110(C), pages 118-131.
    4. Tharindu Prabatha & Kasun Hewage & Rehan Sadiq, 2023. "An Incentives Planning Framework for Residential Energy Retrofits: A Life Cycle Thinking-Based Analysis under Uncertainty," Sustainability, MDPI, vol. 15(6), pages 1-29, March.
    5. Diakaki, Christina & Grigoroudis, Evangelos & Kolokotsa, Dionyssia, 2013. "Performance study of a multi-objective mathematical programming modelling approach for energy decision-making in buildings," Energy, Elsevier, vol. 59(C), pages 534-542.
    6. Edgar Lorenzo-Sáez & José-Vicente Oliver-Villanueva & Eloina Coll-Aliaga & Lenin-Guillermo Lemus-Zúñiga & Victoria Lerma-Arce & Antonio Reig-Fabado, 2020. "Energy Efficiency and GHG Emissions Mapping of Buildings for Decision-Making Processes against Climate Change at the Local Level," Sustainability, MDPI, vol. 12(7), pages 1-17, April.
    7. Karmellos, M. & Kiprakis, A. & Mavrotas, G., 2015. "A multi-objective approach for optimal prioritization of energy efficiency measures in buildings: Model, software and case studies," Applied Energy, Elsevier, vol. 139(C), pages 131-150.
    8. Lombardi, P. & Sokolnikova, T. & Suslov, K. & Voropai, N. & Styczynski, Z.A., 2016. "Isolated power system in Russia: A chance for renewable energies?," Renewable Energy, Elsevier, vol. 90(C), pages 532-541.
    9. 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.
    10. Rockstuhl, Sebastian & Wenninger, Simon & Wiethe, Christian & Häckel, Björn, 2021. "Understanding the risk perception of energy efficiency investments: Investment perspective vs. energy bill perspective," Energy Policy, Elsevier, vol. 159(C).
    11. Aste, Niccolò & Leonforte, Fabrizio & Manfren, Massimiliano & Mazzon, Manlio, 2015. "Thermal inertia and energy efficiency – Parametric simulation assessment on a calibrated case study," Applied Energy, Elsevier, vol. 145(C), pages 111-123.
    12. Alexander Melnik & Kirill Ermolaev, 2020. "Strategy Context of Decision Making for Improved Energy Efficiency in Industrial Energy Systems," Energies, MDPI, vol. 13(7), pages 1-28, March.
    13. Zhixing Li & Mimi Tian & Xiaoqing Zhu & Shujing Xie & Xin He, 2022. "A Review of Integrated Design Process for Building Climate Responsiveness," Energies, MDPI, vol. 15(19), pages 1-35, September.
    14. Ascione, Fabrizio & Bianco, Nicola & De Stasio, Claudio & Mauro, Gerardo Maria & Vanoli, Giuseppe Peter, 2016. "Multi-stage and multi-objective optimization for energy retrofitting a developed hospital reference building: A new approach to assess cost-optimality," Applied Energy, Elsevier, vol. 174(C), pages 37-68.
    15. Yaw-Shyan Tsay & Chiu-Yu Yeh & Yu-Han Chen & Mei-Chen Lu & Yu-Chen Lin, 2021. "A Machine Learning-Based Prediction Model of LCCO 2 for Building Envelope Renovation in Taiwan," Sustainability, MDPI, vol. 13(15), pages 1-18, July.
    16. Giulia Mancò & Elisa Guelpa & Alessandro Colangelo & Alessandro Virtuani & Tommaso Morbiato & Vittorio Verda, 2021. "Innovative Renewable Technology Integration for Nearly Zero-Energy Buildings within the Re-COGNITION Project," Sustainability, MDPI, vol. 13(4), pages 1-24, February.
    17. Walter, Travis & Sohn, Michael D., 2016. "A regression-based approach to estimating retrofit savings using the Building Performance Database," Applied Energy, Elsevier, vol. 179(C), pages 996-1005.
    18. Yan, Chengchu & Wang, Shengwei & Xiao, Fu & Gao, Dian-ce, 2015. "A multi-level energy performance diagnosis method for energy information poor buildings," Energy, Elsevier, vol. 83(C), pages 189-203.
    19. Xu, Wenjie & Svetozarevic, Bratislav & Di Natale, Loris & Heer, Philipp & Jones, Colin N., 2024. "Data-driven adaptive building thermal controller tuning with constraints: A primal–dual contextual Bayesian optimization approach," Applied Energy, Elsevier, vol. 358(C).
    20. Małgorzata Basińska & Dobrosława Kaczorek & Halina Koczyk, 2021. "Economic and Energy Analysis of Building Retrofitting Using Internal Insulations," Energies, MDPI, vol. 14(9), pages 1-18, April.

    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:jeners:v:10:y:2017:i:3:p:321-:d:92390. 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.