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

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  • 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
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    References listed on IDEAS

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    1. 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.
    2. 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.
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    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).

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