IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i24p9295-d996781.html
   My bibliography  Save this article

Research on Central Air Conditioning Systems and an Intelligent Prediction Model of Building Energy Load

Author

Listed:
  • Lin Pan

    (School of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan 430063, China
    GREE, State Key Laboratory of Air-Conditioning Equipment and System Energy Conservation, GREE Electric Appliances Inc. of Zhuhai, Zhuhai 519070, China
    Hainan Institute, Wuhan University of Technology, Sanya 572025, China
    Hebei Huifeng Network Technology Development Co., Ltd., Shijiazhuang 050092, China)

  • Sheng Wang

    (GREE, State Key Laboratory of Air-Conditioning Equipment and System Energy Conservation, GREE Electric Appliances Inc. of Zhuhai, Zhuhai 519070, China
    These authors contributed equally to this work.)

  • Jiying Wang

    (School of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan 430063, China
    These authors contributed equally to this work.)

  • Min Xiao

    (School of Computer Science and Artificial Intelligence, Wuhan University of Technology, Wuhan 430063, China
    These authors contributed equally to this work.)

  • Zhirong Tan

    (School of Navigation, Wuhan University of Technology, Wuhan 430063, China
    Hubei Key Laboratory of Inland Shipping Technology, Wuhan 430063, China
    These authors contributed equally to this work.)

Abstract

The central air conditioning system provides city dwellers with an efficient and comfortable environment. Meanwhile, coinciding with their use, the building electricity load is increased, as central air conditioners consume a lot of electricity. It has become necessary to control central air conditioners for storage and to analyze the energy saving optimization of central air conditioner operation. This study investigates the energy consumption background of central air conditioning systems, and proposes an intelligent load prediction method. With a back propagation (BP) neural network, we use the data collected in the actual project to build the cooling load prediction model for central air conditioning. The network model is also trained using the Levenberg–Marquardt (LM) algorithm, and the established model is trained, tested, and predicted by importing a portion of the sample data, which is filtered by preprocessing. The experimental results show that most of the data errors for training, testing, and prediction are within 10%, indicating that the accuracy achievable by the model can meet the practical requirements, and can be used in real engineering projects.

Suggested Citation

  • Lin Pan & Sheng Wang & Jiying Wang & Min Xiao & Zhirong Tan, 2022. "Research on Central Air Conditioning Systems and an Intelligent Prediction Model of Building Energy Load," Energies, MDPI, vol. 15(24), pages 1-31, December.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:24:p:9295-:d:996781
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/24/9295/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/24/9295/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Lixia Wang & Pawan Kumar & Mamookho Elizabeth Makhatha & Vishal Jagota, 2022. "Numerical simulation of air distribution for monitoring the central air conditioning in large atrium," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(1), pages 340-352, March.
    2. Jing Zhao & Yu Shan, 2020. "A Fuzzy Control Strategy Using the Load Forecast for Air Conditioning System," Energies, MDPI, vol. 13(3), pages 1-17, January.
    3. Federico Divina & Miguel García Torres & Francisco A. Goméz Vela & José Luis Vázquez Noguera, 2019. "A Comparative Study of Time Series Forecasting Methods for Short Term Electric Energy Consumption Prediction in Smart Buildings," Energies, MDPI, vol. 12(10), pages 1-23, May.
    4. Chua, K.J. & Chou, S.K. & Yang, W.M. & Yan, J., 2013. "Achieving better energy-efficient air conditioning – A review of technologies and strategies," Applied Energy, Elsevier, vol. 104(C), pages 87-104.
    5. Chaudhuri, Tanaya & Soh, Yeng Chai & Li, Hua & Xie, Lihua, 2019. "A feedforward neural network based indoor-climate control framework for thermal comfort and energy saving in buildings," Applied Energy, Elsevier, vol. 248(C), pages 44-53.
    6. Ji Li & Yuanwei Liu & Ruixue Zhang & Zhijian Liu & Wei Xu & Biao Qiao & Xiaomei Feng, 2018. "Load Distribution of Semi-Central Evaporative Cooling Air-Conditioning System Based on the TRNSYS Platform," Energies, MDPI, vol. 11(5), pages 1-15, May.
    7. Qingsong Ma & Hiroatsu Fukuda & Myonghyang Lee & Takumi Kobatake & Yuko Kuma & Akihito Ozaki & Xindong Wei, 2018. "Experimental Analysis of the Thermal Performance of a Sunspace Attached to a House with a Central Air Conditioning System," Sustainability, MDPI, vol. 10(5), pages 1-17, May.
    8. Yu, Yanzhe & You, Shijun & Zhang, Huan & Ye, Tianzhen & Wang, Yaran & Wei, Shen, 2021. "A review on available energy saving strategies for heating, ventilation and air conditioning in underground metro stations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 141(C).
    9. Li, Wenqiang & Gong, Guangcai & Ren, Zhongjun & Ouyang, Qianwu & Peng, Pei & Chun, Liang & Fang, Xi, 2022. "A method for energy consumption optimization of air conditioning systems based on load prediction and energy flexibility," Energy, Elsevier, vol. 243(C).
    10. Barone, Giovanni & Buonomano, Annamaria & Forzano, Cesare & Palombo, Adolfo, 2020. "Enhancing trains envelope – heating, ventilation, and air conditioning systems: A new dynamic simulation approach for energy, economic, environmental impact and thermal comfort analyses," Energy, Elsevier, vol. 204(C).
    11. Kashish Kumar & Alok Singh & Saboor Shaik & C Ahamed Saleel & Abdul Aabid & Muneer Baig, 2022. "Comparative Analysis on Dehumidification Performance of KCOOH–LiCl Hybrid Liquid Desiccant Air-Conditioning System: An Energy-Saving Approach," Sustainability, MDPI, vol. 14(6), pages 1-22, March.
    12. Ching-Jui Tien & Chung-Yuen Yang & Ming-Tang Tsai & Hong-Jey Gow, 2022. "Development of Fault Diagnosing System for Ice-Storage Air-Conditioning Systems," Energies, MDPI, vol. 15(11), pages 1-13, May.
    13. Zahra Parhizi & Hamed Karami & Iman Golpour & Mohammad Kaveh & Mariusz Szymanek & Ana M. Blanco-Marigorta & José Daniel Marcos & Esmail Khalife & Stanisław Skowron & Nashwan Adnan Othman & Yousef Darv, 2022. "Modeling and Optimization of Energy and Exergy Parameters of a Hybrid-Solar Dryer for Basil Leaf Drying Using RSM," Sustainability, MDPI, vol. 14(14), pages 1-27, July.
    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. Siyue Lu & Baoqun Zhang & Longfei Ma & Hui Xu & Yuantong Li & Shaobing Yang, 2023. "Economic Load-Reduction Strategy of Central Air Conditioning Based on Convolutional Neural Network and Pre-Cooling," Energies, MDPI, vol. 16(13), pages 1-22, June.

    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. Cheng, Fanyong & Cui, Can & Cai, Wenjian & Zhang, Xin & Ge, Yuan & Li, Bingxu, 2022. "A novel data-driven air balancing method with energy-saving constraint strategy to minimize the energy consumption of ventilation system," Energy, Elsevier, vol. 239(PB).
    2. Jing, Gang & Cai, Wenjian & Zhang, Xin & Cui, Can & Yin, Xiaohong & Xian, Huacai, 2019. "An energy-saving oriented air balancing strategy for multi-zone demand-controlled ventilation system," Energy, Elsevier, vol. 172(C), pages 1053-1065.
    3. Amal A. Al-Shargabi & Abdulbasit Almhafdy & Dina M. Ibrahim & Manal Alghieth & Francisco Chiclana, 2021. "Tuning Deep Neural Networks for Predicting Energy Consumption in Arid Climate Based on Buildings Characteristics," Sustainability, MDPI, vol. 13(22), pages 1-20, November.
    4. Juana Isabel Méndez & Adán Medina & Pedro Ponce & Therese Peffer & Alan Meier & Arturo Molina, 2022. "Evolving Gamified Smart Communities in Mexico to Save Energy in Communities through Intelligent Interfaces," Energies, MDPI, vol. 15(15), pages 1-29, July.
    5. Tong, Zheming & Chen, Yujiao & Malkawi, Ali & Liu, Zhu & Freeman, Richard B., 2016. "Energy saving potential of natural ventilation in China: The impact of ambient air pollution," Applied Energy, Elsevier, vol. 179(C), pages 660-668.
    6. Sun, Hongchang & Niu, Yanlei & Li, Chengdong & Zhou, Changgeng & Zhai, Wenwen & Chen, Zhe & Wu, Hao & Niu, Lanqiang, 2022. "Energy consumption optimization of building air conditioning system via combining the parallel temporal convolutional neural network and adaptive opposition-learning chimp algorithm," Energy, Elsevier, vol. 259(C).
    7. Zu, Kan & Qin, Menghao & Cui, Shuqing, 2020. "Progress and potential of metal-organic frameworks (MOFs) as novel desiccants for built environment control: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
    8. Dylan Norbert Gono & Herlina Napitupulu & Firdaniza, 2023. "Silver Price Forecasting Using Extreme Gradient Boosting (XGBoost) Method," Mathematics, MDPI, vol. 11(18), pages 1-15, September.
    9. Mortazavi, Mehdi & Schmid, Michael & Moghaddam, Saeed, 2017. "Compact and efficient generator for low grade solar and waste heat driven absorption systems," Applied Energy, Elsevier, vol. 198(C), pages 173-179.
    10. Mahmood, Muhammad H. & Sultan, Muhammad & Miyazaki, Takahiko & Koyama, Shigeru & Maisotsenko, Valeriy S., 2016. "Overview of the Maisotsenko cycle – A way towards dew point evaporative cooling," Renewable and Sustainable Energy Reviews, Elsevier, vol. 66(C), pages 537-555.
    11. Adamczyk, Janusz & Dylewski, Robert, 2017. "The impact of thermal insulation investments on sustainability in the construction sector," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 421-429.
    12. Laslett, Dean & Carter, Craig & Creagh, Chris & Jennings, Philip, 2017. "A large-scale renewable electricity supply system by 2030: Solar, wind, energy efficiency, storage and inertia for the South West Interconnected System (SWIS) in Western Australia," Renewable Energy, Elsevier, vol. 113(C), pages 713-731.
    13. Zu, Kan & Qin, Menghao, 2021. "Experimental and modeling investigation of water adsorption of hydrophilic carboxylate-based MOF for indoor moisture control," Energy, Elsevier, vol. 228(C).
    14. Ruparathna, Rajeev & Hewage, Kasun & Sadiq, Rehan, 2016. "Improving the energy efficiency of the existing building stock: A critical review of commercial and institutional buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 1032-1045.
    15. Wang, Ran & Lu, Shilei & Feng, Wei, 2020. "A novel improved model for building energy consumption prediction based on model integration," Applied Energy, Elsevier, vol. 262(C).
    16. Thangavelu, Sundar Raj & Myat, Aung & Khambadkone, Ashwin, 2017. "Energy optimization methodology of multi-chiller plant in commercial buildings," Energy, Elsevier, vol. 123(C), pages 64-76.
    17. Wang, Cheng & Liu, Chuang & Lin, Yuzhang & Bi, Tianshu, 2020. "Day-ahead dispatch of integrated electric-heat systems considering weather-parameter-driven residential thermal demands," Energy, Elsevier, vol. 203(C).
    18. Xu, Peng & Ma, Xiaoli & Zhao, Xudong & Fancey, Kevin, 2017. "Experimental investigation of a super performance dew point air cooler," Applied Energy, Elsevier, vol. 203(C), pages 761-777.
    19. Jim, C.Y., 2014. "Air-conditioning energy consumption due to green roofs with different building thermal insulation," Applied Energy, Elsevier, vol. 128(C), pages 49-59.
    20. Huang, Yanjun & Khajepour, Amir & Ding, Haitao & Bagheri, Farshid & Bahrami, Majid, 2017. "An energy-saving set-point optimizer with a sliding mode controller for automotive air-conditioning/refrigeration systems," Applied Energy, Elsevier, vol. 188(C), pages 576-585.

    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:15:y:2022:i:24:p:9295-:d:996781. 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.