IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v91y2012i1p290-300.html
   My bibliography  Save this article

Dynamic modeling and control of a direct expansion air conditioning system using artificial neural network

Author

Listed:
  • Li, Ning
  • Xia, Liang
  • Shiming, Deng
  • Xu, Xiangguo
  • Chan, Ming-Yin

Abstract

An artificial neural network (ANN)-based dynamic model for an experimental variable speed direct expansion (DX) air conditioning (A/C) system has been developed, linking the indoor air temperature and humidity controlled by the DX A/C system with the variations of compressor and supply fan speeds. The values of average relative error (ARE) and maximum relative error (MRE) when validating the ANN-based dynamic model developed under three different input patterns were 0.33%, 0.27%, 0.27% and 0.89%, 0.99%, 1.15%, respectively, indicating the high accuracy of the ANN-based dynamic model developed. An ANN-based controller was then developed for controlling the indoor air temperature and humidity simultaneously by varying the compressor speed and supply fan speed in a space served by the experimental DX A/C system. The controllability tests including command following test and disturbance rejection test were carried out using the experimental DX A/C system, and the test results showed that the ANN-based controller developed was able to track the changes in setpoints and to resist the disturbances.

Suggested Citation

  • Li, Ning & Xia, Liang & Shiming, Deng & Xu, Xiangguo & Chan, Ming-Yin, 2012. "Dynamic modeling and control of a direct expansion air conditioning system using artificial neural network," Applied Energy, Elsevier, vol. 91(1), pages 290-300.
    • Handle: RePEc:eee:appene:v:91:y:2012:i:1:p:290-300
    DOI: 10.1016/j.apenergy.2011.09.037
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261911006428
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2011.09.037?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. Fong, K.F. & Yuen, S.Y. & Chow, C.K. & Leung, S.W., 2010. "Energy management and design of centralized air-conditioning systems through the non-revisiting strategy for heuristic optimization methods," Applied Energy, Elsevier, vol. 87(11), pages 3494-3506, November.
    2. Wong, S.L. & Wan, Kevin K.W. & Lam, Tony N.T., 2010. "Artificial neural networks for energy analysis of office buildings with daylighting," Applied Energy, Elsevier, vol. 87(2), pages 551-557, February.
    3. Mohanraj, M. & Jayaraj, S. & Muraleedharan, C., 2009. "Performance prediction of a direct expansion solar assisted heat pump using artificial neural networks," Applied Energy, Elsevier, vol. 86(9), pages 1442-1449, September.
    4. Mahmoud, Mohamed A. & Alajmi, Ali F., 2010. "Quantitative assessment of energy conservation due to public awareness campaigns using neural networks," Applied Energy, Elsevier, vol. 87(1), pages 220-228, January.
    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. 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.
    2. Talebian-Kiakalaieh, Amin & Amin, Nor Aishah Saidina & Zarei, Alireza & Noshadi, Iman, 2013. "Transesterification of waste cooking oil by heteropoly acid (HPA) catalyst: Optimization and kinetic model," Applied Energy, Elsevier, vol. 102(C), pages 283-292.
    3. Yan, Huaxia & Pan, Yan & Li, Zhao & Deng, Shiming, 2018. "Further development of a thermal comfort based fuzzy logic controller for a direct expansion air conditioning system," Applied Energy, Elsevier, vol. 219(C), pages 312-324.
    4. 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.
    5. Ascione, Fabrizio & Bellia, Laura & Capozzoli, Alfonso, 2013. "A coupled numerical approach on museum air conditioning: Energy and fluid-dynamic analysis," Applied Energy, Elsevier, vol. 103(C), pages 416-427.
    6. Lim, Dae Kyu & Ahn, Byoung Ha & Jeong, Ji Hwan, 2018. "Method to control an air conditioner by directly measuring the relative humidity of indoor air to improve the comfort and energy efficiency," Applied Energy, Elsevier, vol. 215(C), pages 290-299.
    7. Fan, Hongming & Shao, Shuangquan & Tian, Changqing, 2014. "Performance investigation on a multi-unit heat pump for simultaneous temperature and humidity control," Applied Energy, Elsevier, vol. 113(C), pages 883-890.
    8. Yan, Huaxia & Xia, Yudong & Deng, Shiming, 2017. "Simulation study on a three-evaporator air conditioning system for simultaneous indoor air temperature and humidity control," Applied Energy, Elsevier, vol. 207(C), pages 294-304.
    9. Miklos Kassai, 2019. "Energy Performance Investigation of a Direct Expansion Ventilation Cooling System with a Heat Wheel," Energies, MDPI, vol. 12(22), pages 1-16, November.
    10. Ma, Nan & Aviv, Dorit & Guo, Hongshan & Braham, William W., 2021. "Measuring the right factors: A review of variables and models for thermal comfort and indoor air quality," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    11. Huang, Yanjun & Khajepour, Amir & Bagheri, Farshid & Bahrami, Majid, 2016. "Optimal energy-efficient predictive controllers in automotive air-conditioning/refrigeration systems," Applied Energy, Elsevier, vol. 184(C), pages 605-618.
    12. Chen, Wenjing & Chan, Ming-yin & Weng, Wenbing & Yan, Huaxia & Deng, Shiming, 2018. "An experimental study on the operational characteristics of a direct expansion based enhanced dehumidification air conditioning system," Applied Energy, Elsevier, vol. 225(C), pages 922-933.
    13. Qinghong Peng & Qungui Du, 2016. "Progress in Heat Pump Air Conditioning Systems for Electric Vehicles—A Review," Energies, MDPI, vol. 9(4), pages 1-17, March.
    14. Huang, Xianghui & Li, Kuining & Xie, Yi & Liu, Bin & Liu, Jiangyan & Liu, Zhaoming & Mou, Lunjie, 2022. "A novel multistage constant compressor speed control strategy of electric vehicle air conditioning system based on genetic algorithm," Energy, Elsevier, vol. 241(C).
    15. Kang, Won Hee & Lee, Jong Man & Yeon, Sang Hun & Park, Min Kyeong & Kim, Chul Ho & Lee, Je Hyeon & Moon, Jin Woo & Lee, Kwang Ho, 2020. "Modeling, calibration, and sensitivity analysis of direct expansion AHU-Water source VRF system," Energy, Elsevier, vol. 199(C).
    16. Mei, Jun & Xia, Xiaohua & Song, Mengjie, 2018. "An autonomous hierarchical control for improving indoor comfort and energy efficiency of a direct expansion air conditioning system," Applied Energy, Elsevier, vol. 221(C), pages 450-463.

    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. Benedetti, Miriam & Cesarotti, Vittorio & Introna, Vito & Serranti, Jacopo, 2016. "Energy consumption control automation using Artificial Neural Networks and adaptive algorithms: Proposal of a new methodology and case study," Applied Energy, Elsevier, vol. 165(C), pages 60-71.
    2. Dongjun Suh & Seongju Chang, 2012. "An Energy and Water Resource Demand Estimation Model for Multi-Family Housing Complexes in Korea," Energies, MDPI, vol. 5(11), pages 1-20, November.
    3. Sun, Alexander Y., 2020. "Optimal carbon storage reservoir management through deep reinforcement learning," Applied Energy, Elsevier, vol. 278(C).
    4. Alexandru Pîrjan & Simona-Vasilica Oprea & George Căruțașu & Dana-Mihaela Petroșanu & Adela Bâra & Cristina Coculescu, 2017. "Devising Hourly Forecasting Solutions Regarding Electricity Consumption in the Case of Commercial Center Type Consumers," Energies, MDPI, vol. 10(11), pages 1-36, October.
    5. Leung, Philip C.M. & Lee, Eric W.M., 2013. "Estimation of electrical power consumption in subway station design by intelligent approach," Applied Energy, Elsevier, vol. 101(C), pages 634-643.
    6. Nutkiewicz, Alex & Yang, Zheng & Jain, Rishee K., 2018. "Data-driven Urban Energy Simulation (DUE-S): A framework for integrating engineering simulation and machine learning methods in a multi-scale urban energy modeling workflow," Applied Energy, Elsevier, vol. 225(C), pages 1176-1189.
    7. Abou-Ziyan, Hosny Z. & Alajmi, Ali F., 2014. "Effect of load-sharing operation strategy on the aggregate performance of existed multiple-chiller systems," Applied Energy, Elsevier, vol. 135(C), pages 329-338.
    8. Amasyali, Kadir & El-Gohary, Nora M., 2018. "A review of data-driven building energy consumption prediction studies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1192-1205.
    9. Mohanraj, M. & Belyayev, Ye. & Jayaraj, S. & Kaltayev, A., 2018. "Research and developments on solar assisted compression heat pump systems – A comprehensive review (Part A: Modeling and modifications)," Renewable and Sustainable Energy Reviews, Elsevier, vol. 83(C), pages 90-123.
    10. Buratti, C. & Barbanera, M. & Palladino, D., 2014. "An original tool for checking energy performance and certification of buildings by means of Artificial Neural Networks," Applied Energy, Elsevier, vol. 120(C), pages 125-132.
    11. Chen, Qun & Wang, Yi-Fei & Xu, Yun-Chao, 2015. "A thermal resistance-based method for the optimal design of central variable water/air volume chiller systems," Applied Energy, Elsevier, vol. 139(C), pages 119-130.
    12. Abokersh, Mohamed Hany & Vallès, Manel & Cabeza, Luisa F. & Boer, Dieter, 2020. "A framework for the optimal integration of solar assisted district heating in different urban sized communities: A robust machine learning approach incorporating global sensitivity analysis," Applied Energy, Elsevier, vol. 267(C).
    13. Liu, Zengkai & Liu, Yonghong & Zhang, Dawei & Cai, Baoping & Zheng, Chao, 2015. "Fault diagnosis for a solar assisted heat pump system under incomplete data and expert knowledge," Energy, Elsevier, vol. 87(C), pages 41-48.
    14. Wei, Yixuan & Xia, Liang & Pan, Song & Wu, Jinshun & Zhang, Xingxing & Han, Mengjie & Zhang, Weiya & Xie, Jingchao & Li, Qingping, 2019. "Prediction of occupancy level and energy consumption in office building using blind system identification and neural networks," Applied Energy, Elsevier, vol. 240(C), pages 276-294.
    15. Wahiba Yaïci & Michela Longo & Evgueniy Entchev & Federica Foiadelli, 2017. "Simulation Study on the Effect of Reduced Inputs of Artificial Neural Networks on the Predictive Performance of the Solar Energy System," Sustainability, MDPI, vol. 9(8), pages 1-14, August.
    16. Taslimi-Renani, Ehsan & Modiri-Delshad, Mostafa & Elias, Mohamad Fathi Mohamad & Rahim, Nasrudin Abd., 2016. "Development of an enhanced parametric model for wind turbine power curve," Applied Energy, Elsevier, vol. 177(C), pages 544-552.
    17. Sharma, Naveen & Varun, & Siddhartha,, 2012. "Stochastic techniques used for optimization in solar systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(3), pages 1399-1411.
    18. 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).
    19. Ascione, Fabrizio & Bianco, Nicola & De Stasio, Claudio & Mauro, Gerardo Maria & Vanoli, Giuseppe Peter, 2017. "Artificial neural networks to predict energy performance and retrofit scenarios for any member of a building category: A novel approach," Energy, Elsevier, vol. 118(C), pages 999-1017.
    20. Badiei, A. & Golizadeh Akhlaghi, Y. & Zhao, X. & Shittu, S. & Xiao, X. & Li, J. & Fan, Y. & Li, G., 2020. "A chronological review of advances in solar assisted heat pump technology in 21st century," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(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:91:y:2012:i:1:p:290-300. 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.