IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v75y2015icp115-122.html
   My bibliography  Save this article

Experimental investigation and simulation analysis of the thermal performance of a balcony wall integrated solar water heating unit

Author

Listed:
  • Li, Rui
  • Dai, Yanjun
  • Wang, Ruzhu

Abstract

A balcony wall type solar water heater system was designed and constructed in a high-rise building. The U-type evacuated glass tube solar collector is fixed vertically on the balcony wall. The water, heated in the solar collector, flows through the exchanger coil in the water tank and then flows back to the solar collector. With regard to the hot water supply system, the cold water, heated by the heat exchanger, is sent to the point of use. Considering storeys and water consumption pattern, four apartments are selected for testing. Meanwhile, the theoretical analysis with TRNSYS was presented. According to the experimental results, mean daily collector efficiency is about 40%. Solar fraction is high in summer and autumn for the relative high radiation and high ambient temperature. Under given conditions, the annual energy extracted from tank is 2805.3 MJ/m2, and the annual solar fraction is 40.5%. When the tank volume-to-collector area ratio is decreased to 37.5 L/m2, the solar fraction can be increased to 50%. The results show that the family to use water all day round gets higher solar fraction than the family using hot water mostly in the morning and night.

Suggested Citation

  • Li, Rui & Dai, Yanjun & Wang, Ruzhu, 2015. "Experimental investigation and simulation analysis of the thermal performance of a balcony wall integrated solar water heating unit," Renewable Energy, Elsevier, vol. 75(C), pages 115-122.
    • Handle: RePEc:eee:renene:v:75:y:2015:i:c:p:115-122
    DOI: 10.1016/j.renene.2014.09.023
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148114005886
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2014.09.023?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. Chow, T.T. & Fong, K.F. & Chan, A.L.S. & Lin, Z., 2006. "Potential application of a centralized solar water-heating system for a high-rise residential building in Hong Kong," Applied Energy, Elsevier, vol. 83(1), pages 42-54, January.
    2. Benli, Hüseyin, 2013. "Experimentally derived efficiency and exergy analysis of a new solar air heater having different surface shapes," Renewable Energy, Elsevier, vol. 50(C), pages 58-67.
    3. Kalogirou, Soteris A., 2000. "Long-term performance prediction of forced circulation solar domestic water heating systems using artificial neural networks," Applied Energy, Elsevier, vol. 66(1), pages 63-74, May.
    4. Kalogirou, Soteris A. & Bojic, Milorad, 2000. "Artificial neural networks for the prediction of the energy consumption of a passive solar building," Energy, Elsevier, vol. 25(5), pages 479-491.
    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. Wang, Chuyao & Ji, Jie, 2023. "Comprehensive performance analysis of a rural building integrated PV/T wall in hot summer and cold winter region," Energy, Elsevier, vol. 282(C).
    2. Buonomano, Annamaria & Calise, Francesco & Palombo, Adolfo & Vicidomini, Maria, 2019. "Transient analysis, exergy and thermo-economic modelling of façade integrated photovoltaic/thermal solar collectors," Renewable Energy, Elsevier, vol. 137(C), pages 109-126.
    3. Chen, Fei & Liu, Yang, 2022. "Model construction and performance investigation of multi-section compound parabolic concentrator with solar vacuum tube," Energy, Elsevier, vol. 250(C).
    4. Vieira, Abel S. & Stewart, Rodney A. & Lamberts, Roberto & Beal, Cara D., 2018. "Residential solar water heaters in Brisbane, Australia: Key performance parameters and indicators," Renewable Energy, Elsevier, vol. 116(PA), pages 120-132.
    5. Vassiliades, C. & Agathokleous, R. & Barone, G. & Forzano, C. & Giuzio, G.F. & Palombo, A. & Buonomano, A. & Kalogirou, S., 2022. "Building integration of active solar energy systems: A review of geometrical and architectural characteristics," Renewable and Sustainable Energy Reviews, Elsevier, vol. 164(C).
    6. Lamnatou, Chr. & Cristofari, C. & Chemisana, D. & Canaletti, J.L., 2016. "Building-integrated solar thermal systems based on vacuum-tube technology: Critical factors focusing on life-cycle environmental profile," Renewable and Sustainable Energy Reviews, Elsevier, vol. 65(C), pages 1199-1215.
    7. Shan, M. & Yu, T. & Yang, X., 2016. "Assessment of an integrated active solar and air-source heat pump water heating system operated within a passive house in a cold climate zone," Renewable Energy, Elsevier, vol. 87(P3), pages 1059-1066.

    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. Rodríguez-Hidalgo, M.C. & Rodríguez-Aumente, P.A. & Lecuona, A. & Legrand, M. & Ventas, R., 2012. "Domestic hot water consumption vs. solar thermal energy storage: The optimum size of the storage tank," Applied Energy, Elsevier, vol. 97(C), pages 897-906.
    2. He, Zhaoyu & Guo, Weimin & Zhang, Peng, 2022. "Performance prediction, optimal design and operational control of thermal energy storage using artificial intelligence methods," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    3. Kalogirou, Soteris A., 2001. "Artificial neural networks in renewable energy systems applications: a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 5(4), pages 373-401, December.
    4. Movagharnejad, Kamyar & Mehdizadeh, Bahman & Banihashemi, Morteza & Kordkheili, Masoud Sheikhi, 2011. "Forecasting the differences between various commercial oil prices in the Persian Gulf region by neural network," Energy, Elsevier, vol. 36(7), pages 3979-3984.
    5. Wu, Sheng-Ju & Shiah, Sheau-Wen & Yu, Wei-Lung, 2009. "Parametric analysis of proton exchange membrane fuel cell performance by using the Taguchi method and a neural network," Renewable Energy, Elsevier, vol. 34(1), pages 135-144.
    6. Wang, Zhangyuan & Yang, Wansheng & Qiu, Feng & Zhang, Xiangmei & Zhao, Xudong, 2015. "Solar water heating: From theory, application, marketing and research," Renewable and Sustainable Energy Reviews, Elsevier, vol. 41(C), pages 68-84.
    7. Jha, Sunil Kr. & Bilalovic, Jasmin & Jha, Anju & Patel, Nilesh & Zhang, Han, 2017. "Renewable energy: Present research and future scope of Artificial Intelligence," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 297-317.
    8. Ghritlahre, Harish Kumar & Prasad, Radha Krishna, 2018. "Application of ANN technique to predict the performance of solar collector systems - A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 84(C), pages 75-88.
    9. Kalogirou, S.A. & Mathioulakis, E. & Belessiotis, V., 2014. "Artificial neural networks for the performance prediction of large solar systems," Renewable Energy, Elsevier, vol. 63(C), pages 90-97.
    10. Thirugnanasambandam, Mirunalini & Iniyan, S. & Goic, Ranko, 2010. "A review of solar thermal technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(1), pages 312-322, January.
    11. Singh, Ramkishore & Lazarus, Ian J. & Souliotis, Manolis, 2016. "Recent developments in integrated collector storage (ICS) solar water heaters: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 54(C), pages 270-298.
    12. Lazrak, Amine & Leconte, Antoine & Chèze, David & Fraisse, Gilles & Papillon, Philippe & Souyri, Bernard, 2015. "Numerical and experimental results of a novel and generic methodology for energy performance evaluation of thermal systems using renewable energies," Applied Energy, Elsevier, vol. 158(C), pages 142-156.
    13. Kicsiny, R. & Nagy, J. & Szalóki, Cs., 2014. "Extended ordinary differential equation models for solar heating systems with pipes," Applied Energy, Elsevier, vol. 129(C), pages 166-176.
    14. Altan Dombaycı, Ömer & Gölcü, Mustafa, 2009. "Daily means ambient temperature prediction using artificial neural network method: A case study of Turkey," Renewable Energy, Elsevier, vol. 34(4), pages 1158-1161.
    15. Kalogirou, Soteris A., 2000. "Applications of artificial neural-networks for energy systems," Applied Energy, Elsevier, vol. 67(1-2), pages 17-35, September.
    16. Zhijian Liu & Hao Li & Xinyu Zhang & Guangya Jin & Kewei Cheng, 2015. "Novel Method for Measuring the Heat Collection Rate and Heat Loss Coefficient of Water-in-Glass Evacuated Tube Solar Water Heaters Based on Artificial Neural Networks and Support Vector Machine," Energies, MDPI, vol. 8(8), pages 1-21, August.
    17. Yeo, In-Ae & Yee, Jurng-Jae, 2014. "A proposal for a site location planning model of environmentally friendly urban energy supply plants using an environment and energy geographical information system (E-GIS) database (DB) and an artifi," Applied Energy, Elsevier, vol. 119(C), pages 99-117.
    18. Vakili, Masoud & Yahyaei, Masood & Ramsay, James & Aghajannezhad, Pouria & Paknezhad, Behnaz, 2021. "Adaptive neuro-fuzzy inference system modeling to predict the performance of graphene nanoplatelets nanofluid-based direct absorption solar collector based on experimental study," Renewable Energy, Elsevier, vol. 163(C), pages 807-824.
    19. Şencan, Arzu & Yakut, Kemal A. & Kalogirou, Soteris A., 2006. "Thermodynamic analysis of absorption systems using artificial neural network," Renewable Energy, Elsevier, vol. 31(1), pages 29-43.
    20. Ghobadian, B. & Rahimi, H. & Nikbakht, A.M. & Najafi, G. & Yusaf, T.F., 2009. "Diesel engine performance and exhaust emission analysis using waste cooking biodiesel fuel with an artificial neural network," Renewable Energy, Elsevier, vol. 34(4), pages 976-982.

    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:renene:v:75:y:2015:i:c:p:115-122. 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.journals.elsevier.com/renewable-energy .

    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.