IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v271y2020ics0306261920307674.html
   My bibliography  Save this article

Evaluation of energy-saving potential for optimal time response of HVAC control system in smart buildings

Author

Listed:
  • Homod, Raad Z.
  • Gaeid, Khalaf S.
  • Dawood, Suroor M.
  • Hatami, Alireza
  • Sahari, Khairul S.

Abstract

In some fields, such as the semiconductor manufacturing process, museum, pharmaceutical, and medicine manufacturing industry, the HVAC system needs a very fast response time to protect products and more energy-efficient buildings than traditional controllers. So, the proposed controller is designed to overcome such problems by using integrated fuzzy PI-PD Mamdani-type (FPIPDM) and cluster adaptive training based on Takagi-Sugeno-Kang (CABTSK) type. The spans of the fuzzy membership functions of the FPIPDM are tuned online by the Nelder-Mead simplex search (NMSS) algorithm to minimize time response, while the CABTSK model is tuned offline and online using a gradient descent (GD) algorithm to enhance the stability of the overall system and reject disturbances. Then, the integration framework is used to enable the concept of time-optimal based on the bang-bang code delegation. In this sense, a selected switch delegates the execution of proper control code to the action processor that provides computational resources to control indoor conditions. The predicted mean vote (PMV) index provides a higher comfort level than the temperature, as it considers six variables related to thermal comfort. The results of the proposed structure show that it improves the overall output accuracy and significantly reduces the response time. Furthermore, it increases the robustness of the indoor conditions and it is quite applicable to the MIMO HVAC systems processes with strong coupling actions between temperature and humidity, large time delay, noise, disturbances, nonlinearities, and imprecise identification model.

Suggested Citation

  • Homod, Raad Z. & Gaeid, Khalaf S. & Dawood, Suroor M. & Hatami, Alireza & Sahari, Khairul S., 2020. "Evaluation of energy-saving potential for optimal time response of HVAC control system in smart buildings," Applied Energy, Elsevier, vol. 271(C).
    • Handle: RePEc:eee:appene:v:271:y:2020:i:c:s0306261920307674
    DOI: 10.1016/j.apenergy.2020.115255
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261920307674
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2020.115255?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Homod, Raad Z. & Sahari, Khairul Salleh Mohamed & Almurib, Haider A.F., 2014. "Energy saving by integrated control of natural ventilation and HVAC systems using model guide for comparison," Renewable Energy, Elsevier, vol. 71(C), pages 639-650.
    2. Bianchini, Gianni & Casini, Marco & Pepe, Daniele & Vicino, Antonio & Zanvettor, Giovanni Gino, 2019. "An integrated model predictive control approach for optimal HVAC and energy storage operation in large-scale buildings," Applied Energy, Elsevier, vol. 240(C), pages 327-340.
    3. Zhao, B.Y. & Zhao, Z.G. & Li, Y. & Wang, R.Z. & Taylor, R.A., 2019. "An adaptive PID control method to improve the power tracking performance of solar photovoltaic air-conditioning systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.
    4. Homod, Raad Z., 2018. "Analysis and optimization of HVAC control systems based on energy and performance considerations for smart buildings," Renewable Energy, Elsevier, vol. 126(C), pages 49-64.
    5. Chiroma, Haruna & Abdulkareem, Sameem & Herawan, Tutut, 2015. "Evolutionary Neural Network model for West Texas Intermediate crude oil price prediction," Applied Energy, Elsevier, vol. 142(C), pages 266-273.
    6. Afram, Abdul & Janabi-Sharifi, Farrokh, 2015. "Gray-box modeling and validation of residential HVAC system for control system design," Applied Energy, Elsevier, vol. 137(C), pages 134-150.
    7. Chen, Weiming & Lei, Yalin & Feng, Kuishuang & Wu, Sanmang & Li, Li, 2019. "Provincial emission accounting for CO2 mitigation in China: Insights from production, consumption and income perspectives," Applied Energy, Elsevier, vol. 255(C).
    8. Amjad Almusaed & Asaad Almssad & Raad Z. Homod & Ibrahim Yitmen, 2020. "Environmental Profile on Building Material Passports for Hot Climates," Sustainability, MDPI, vol. 12(9), pages 1-20, May.
    9. Homod, Raad Z., 2014. "Assessment regarding energy saving and decoupling for different AHU (air handling unit) and control strategies in the hot-humid climatic region of Iraq," Energy, Elsevier, vol. 74(C), pages 762-774.
    10. Barbeito, Inés & Zaragoza, Sonia & Tarrío-Saavedra, Javier & Naya, Salvador, 2017. "Assessing thermal comfort and energy efficiency in buildings by statistical quality control for autocorrelated data," Applied Energy, Elsevier, vol. 190(C), pages 1-17.
    11. Mei, Jun & Xia, Xiaohua, 2017. "Energy-efficient predictive control of indoor thermal comfort and air quality in a direct expansion air conditioning system," Applied Energy, Elsevier, vol. 195(C), pages 439-452.
    12. Yoon, Ah-Yun & Kim, Young-Jin & Zakula, Tea & Moon, Seung-Ill, 2020. "Retail electricity pricing via online-learning of data-driven demand response of HVAC systems," Applied Energy, Elsevier, vol. 265(C).
    13. Du, Zhimin & Jin, Xinqiao & Fang, Xing & Fan, Bo, 2016. "A dual-benchmark based energy analysis method to evaluate control strategies for building HVAC systems," Applied Energy, Elsevier, vol. 183(C), pages 700-714.
    14. Jiang, Yuliang & Wang, Xinli & Zhao, Hongxia & Wang, Lei & Yin, Xiaohong & Jia, Lei, 2020. "Dynamic modeling and economic model predictive control of a liquid desiccant air conditioning," Applied Energy, Elsevier, vol. 259(C).
    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. Liu, Yiren & Zhao, Xiangyu & Qin, S. Joe, 2024. "Dynamically engineered multi-modal feature learning for predictions of office building cooling loads," Applied Energy, Elsevier, vol. 355(C).
    2. Lee, Minjung & Ham, Jeonggyun & Lee, Jeong-Won & Cho, Honghyun, 2023. "Analysis of thermal comfort, energy consumption, and CO2 reduction of indoor space according to the type of local heating under winter rest conditions," Energy, Elsevier, vol. 268(C).
    3. Mpho J. Lencwe & SP Daniel Chowdhury & Sipho Mahlangu & Maxwell Sibanyoni & Louwrance Ngoma, 2021. "An Efficient HVAC Network Control for Safety Enhancement of a Typical Uninterrupted Power Supply Battery Storage Room," Energies, MDPI, vol. 14(16), pages 1-23, August.
    4. Esmaeilzadeh, Ahmad & Deal, Brian & Yousefi-Koma, Aghil & Zakerzadeh, Mohammad Reza, 2023. "How combination of control methods and renewable energies leads a large commercial building to a zero-emission zone – A case study in U.S," Energy, Elsevier, vol. 263(PD).
    5. Lei, Yue & Zhan, Sicheng & Ono, Eikichi & Peng, Yuzhen & Zhang, Zhiang & Hasama, Takamasa & Chong, Adrian, 2022. "A practical deep reinforcement learning framework for multivariate occupant-centric control in buildings," Applied Energy, Elsevier, vol. 324(C).
    6. Alexandre Correia & Luís Miguel Ferreira & Paulo Coimbra & Pedro Moura & Aníbal T. de Almeida, 2022. "Smart Thermostats for a Campus Microgrid: Demand Control and Improving Air Quality," Energies, MDPI, vol. 15(4), pages 1-21, February.
    7. Krzysztof Lalik & Mateusz Kozek & Szymon Podlasek & Rafał Figaj & Paweł Gut, 2021. "Q-Learning Neural Controller for Steam Generator Station in Micro Cogeneration Systems," Energies, MDPI, vol. 14(17), pages 1-13, August.
    8. 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).
    9. Homod, Raad Z. & Mohammed, Hayder Ibrahim & Abderrahmane, Aissa & Alawi, Omer A. & Khalaf, Osamah Ibrahim & Mahdi, Jasim M. & Guedri, Kamel & Dhaidan, Nabeel S. & Albahri, A.S. & Sadeq, Abdellatif M. , 2023. "Deep clustering of Lagrangian trajectory for multi-task learning to energy saving in intelligent buildings using cooperative multi-agent," Applied Energy, Elsevier, vol. 351(C).
    10. García Vázquez, C.A. & Cotfas, D.T. & González Santos, A.I. & Cotfas, P.A. & León Ávila, B.Y., 2024. "Reduction of electricity consumption in an AHU using mathematical modelling for controller tuning," Energy, Elsevier, vol. 293(C).
    11. Giuseppe Anastasi & Carlo Bartoli & Paolo Conti & Emanuele Crisostomi & Alessandro Franco & Sergio Saponara & Daniele Testi & Dimitri Thomopulos & Carlo Vallati, 2021. "Optimized Energy and Air Quality Management of Shared Smart Buildings in the COVID-19 Scenario," Energies, MDPI, vol. 14(8), pages 1-17, April.
    12. Homod, Raad Z. & Munahi, Basil Sh. & Mohammed, Hayder Ibrahim & Albadr, Musatafa Abbas Abbood & Abderrahmane, AISSA & Mahdi, Jasim M. & Ben Hamida, Mohamed Bechir & Alhasnawi, Bilal Naji & Albahri, A., 2024. "Deep clustering of reinforcement learning based on the bang-bang principle to optimize the energy in multi-boiler for intelligent buildings," Applied Energy, Elsevier, vol. 356(C).
    13. Homod, Raad Z. & Togun, Hussein & Ateeq, Adnan A. & Al-Mousawi, Fadhel Noraldeen & Yaseen, Zaher Mundher & Al-Kouz, Wael & Hussein, Ahmed Kadhim & Alawi, Omer A. & Goodarzi, Marjan & Ahmadi, Goodarz, 2022. "An innovative clustering technique to generate hybrid modeling of cooling coils for energy analysis: A case study for control performance in HVAC systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 166(C).
    14. Waseem, Muhammad & Lin, Zhenzhi & Liu, Shengyuan & Zhang, Zhi & Aziz, Tarique & Khan, Danish, 2021. "Fuzzy compromised solution-based novel home appliances scheduling and demand response with optimal dispatch of distributed energy resources," Applied Energy, Elsevier, vol. 290(C).
    15. V. S. K. V. Harish & Arun Kumar & Tabish Alam & Paolo Blecich, 2021. "Assessment of State-Space Building Energy System Models in Terms of Stability and Controllability," Sustainability, MDPI, vol. 13(21), pages 1-26, October.
    16. Homod, Raad Z. & Togun, Hussein & Kadhim Hussein, Ahmed & Noraldeen Al-Mousawi, Fadhel & Yaseen, Zaher Mundher & Al-Kouz, Wael & Abd, Haider J. & Alawi, Omer A. & Goodarzi, Marjan & Hussein, Omar A., 2022. "Dynamics analysis of a novel hybrid deep clustering for unsupervised learning by reinforcement of multi-agent to energy saving in intelligent buildings," Applied Energy, Elsevier, vol. 313(C).

    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. Homod, Raad Z. & Togun, Hussein & Kadhim Hussein, Ahmed & Noraldeen Al-Mousawi, Fadhel & Yaseen, Zaher Mundher & Al-Kouz, Wael & Abd, Haider J. & Alawi, Omer A. & Goodarzi, Marjan & Hussein, Omar A., 2022. "Dynamics analysis of a novel hybrid deep clustering for unsupervised learning by reinforcement of multi-agent to energy saving in intelligent buildings," Applied Energy, Elsevier, vol. 313(C).
    2. Homod, Raad Z. & Togun, Hussein & Ateeq, Adnan A. & Al-Mousawi, Fadhel Noraldeen & Yaseen, Zaher Mundher & Al-Kouz, Wael & Hussein, Ahmed Kadhim & Alawi, Omer A. & Goodarzi, Marjan & Ahmadi, Goodarz, 2022. "An innovative clustering technique to generate hybrid modeling of cooling coils for energy analysis: A case study for control performance in HVAC systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 166(C).
    3. Homod, Raad Z. & Mohammed, Hayder Ibrahim & Abderrahmane, Aissa & Alawi, Omer A. & Khalaf, Osamah Ibrahim & Mahdi, Jasim M. & Guedri, Kamel & Dhaidan, Nabeel S. & Albahri, A.S. & Sadeq, Abdellatif M. , 2023. "Deep clustering of Lagrangian trajectory for multi-task learning to energy saving in intelligent buildings using cooperative multi-agent," Applied Energy, Elsevier, vol. 351(C).
    4. Homod, Raad Z., 2018. "Analysis and optimization of HVAC control systems based on energy and performance considerations for smart buildings," Renewable Energy, Elsevier, vol. 126(C), pages 49-64.
    5. Raman, Naren Srivaths & Devaprasad, Karthikeya & Chen, Bo & Ingley, Herbert A. & Barooah, Prabir, 2020. "Model predictive control for energy-efficient HVAC operation with humidity and latent heat considerations," Applied Energy, Elsevier, vol. 279(C).
    6. Asad, Hussain Syed & Yuen, Richard Kwok Kit & Huang, Gongsheng, 2017. "Multiplexed real-time optimization of HVAC systems with enhanced control stability," Applied Energy, Elsevier, vol. 187(C), pages 640-651.
    7. da Fonseca, André L.A. & Chvatal, Karin M.S. & Fernandes, Ricardo A.S., 2021. "Thermal comfort maintenance in demand response programs: A critical review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 141(C).
    8. Homod, Raad Z. & Munahi, Basil Sh. & Mohammed, Hayder Ibrahim & Albadr, Musatafa Abbas Abbood & Abderrahmane, AISSA & Mahdi, Jasim M. & Ben Hamida, Mohamed Bechir & Alhasnawi, Bilal Naji & Albahri, A., 2024. "Deep clustering of reinforcement learning based on the bang-bang principle to optimize the energy in multi-boiler for intelligent buildings," Applied Energy, Elsevier, vol. 356(C).
    9. Hussain, Syed Asad & Huang, Gongsheng & Yuen, Richard Kwok Kit & Wang, Wei, 2020. "Adaptive regression model-based real-time optimal control of central air-conditioning systems," Applied Energy, Elsevier, vol. 276(C).
    10. Alexandre Correia & Luís Miguel Ferreira & Paulo Coimbra & Pedro Moura & Aníbal T. de Almeida, 2022. "Smart Thermostats for a Campus Microgrid: Demand Control and Improving Air Quality," Energies, MDPI, vol. 15(4), pages 1-21, February.
    11. Jiang, Yuliang & Zhu, Shanying & Xu, Qimin & Yang, Bo & Guan, Xinping, 2023. "Hybrid modeling-based temperature and humidity adaptive control for a multi-zone HVAC system," Applied Energy, Elsevier, vol. 334(C).
    12. Kritana Prueksakorn & Cheng-Xu Piao & Hyunchul Ha & Taehyeung Kim, 2015. "Computational and Experimental Investigation for an Optimal Design of Industrial Windows to Allow Natural Ventilation during Wind-Driven Rain," Sustainability, MDPI, vol. 7(8), pages 1-22, August.
    13. Wang, Xin & Sun, Mei, 2021. "A novel prediction model of multi-layer symbolic pattern network: Based on causation entropy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 575(C).
    14. Gao, D.C. & Sun, Y.J. & Ma, Z. & Ren, H., 2021. "A review on integration and design of desiccant air-conditioning systems for overall performance improvements," Renewable and Sustainable Energy Reviews, Elsevier, vol. 141(C).
    15. Donghua Wang & Tianhui Fang, 2022. "Forecasting Crude Oil Prices with a WT-FNN Model," Energies, MDPI, vol. 15(6), pages 1-21, March.
    16. Lork, Clement & Li, Wen-Tai & Qin, Yan & Zhou, Yuren & Yuen, Chau & Tushar, Wayes & Saha, Tapan K., 2020. "An uncertainty-aware deep reinforcement learning framework for residential air conditioning energy management," Applied Energy, Elsevier, vol. 276(C).
    17. Zhao, Jing & Yang, Zilan & Shi, Linyu & Liu, Dehan & Li, Haonan & Mi, Yumiao & Wang, Hongbin & Feng, Meili & Hutagaol, Timothy Joseph, 2024. "Photovoltaic capacity dynamic tracking model predictive control strategy of air-conditioning systems with consideration of flexible loads," Applied Energy, Elsevier, vol. 356(C).
    18. Karasu, Seçkin & Altan, Aytaç, 2022. "Crude oil time series prediction model based on LSTM network with chaotic Henry gas solubility optimization," Energy, Elsevier, vol. 242(C).
    19. Radosław Puka & Bartosz Łamasz & Marek Michalski, 2021. "Effectiveness of Artificial Neural Networks in Hedging against WTI Crude Oil Price Risk," Energies, MDPI, vol. 14(11), pages 1-26, June.
    20. Liu, Hong & Zhao, Yue & Gu, Chenghong & Ge, Shaoyun & Yang, Zan, 2021. "Adjustable capability of the distributed energy system: Definition, framework, and evaluation model," Energy, Elsevier, vol. 222(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:eee:appene:v:271:y:2020:i:c:s0306261920307674. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.