IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v97y2012icp897-906.html
   My bibliography  Save this article

Domestic hot water consumption vs. solar thermal energy storage: The optimum size of the storage tank

Author

Listed:
  • Rodríguez-Hidalgo, M.C.
  • Rodríguez-Aumente, P.A.
  • Lecuona, A.
  • Legrand, M.
  • Ventas, R.

Abstract

Many efforts have been made in order to adequate the production of a solar thermal collector field to the consumption of domestic hot water of the inhabitants of a building. In that sense, much has been achieved in different domains: research agencies, government policies and manufacturers. However, most of the design rules of the solar plants are based on steady state models, whereas solar irradiance, consumption and thermal accumulation are inherently transient processes. As a result of this lack of physical accuracy, thermal storage tanks are sometimes left to be as large as the designer decides without any aforementioned precise recommendation. This can be a problem if solar thermal systems are meant to be implemented in nowadays buildings, where there is a shortage of space. In addition to that, an excessive storage volume could not result more efficient in many residential applications, but costly, extreme in space consumption and in some cases too heavy.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:appene:v:97:y:2012:i:c:p:897-906
    DOI: 10.1016/j.apenergy.2011.12.088
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261911008944
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2011.12.088?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. Tanton, D. M. & Cohen, R. R. & Probert, S. D., 1987. "Improving the effectiveness of a domestic central-heating boiler by the use of heat storage," Applied Energy, Elsevier, vol. 27(1), pages 53-82.
    2. Meyer, J.P & Tshimankinda, M, 1998. "Domestic hot-water consumption in South African apartments," Energy, Elsevier, vol. 23(1), pages 61-66.
    3. 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.
    4. 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.
    5. Vine, Edward & Diamond, Rick & Szydlowski, Rich, 1987. "Domestic hot water consumption in four low-income apartment buildings," Energy, Elsevier, vol. 12(6), pages 459-467.
    6. Cardinale, N. & Piccininni, F. & Stefanizzi, P., 2003. "Economic optimization of low-flow solar domestic hot water plants," Renewable Energy, Elsevier, vol. 28(12), pages 1899-1914.
    7. Bojić, M. & Kalogirou, S. & Petronijević, K., 2002. "Simulation of a solar domestic water heating system using a time marching model," Renewable Energy, Elsevier, vol. 27(3), pages 441-452.
    8. Ghaddar, N.K., 1994. "Stratified storage tank influence on performance of solar water heating system tested in Beirut," Renewable Energy, Elsevier, vol. 4(8), pages 911-925.
    9. Wolf, D. & Tamir, A. & kudish, A.I., 1980. "A central solar domestic hot water system. Performance and economic analysis," Energy, Elsevier, vol. 5(2), pages 191-205.
    10. 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.
    11. Wolf, D. & Sembira, A.N. & Kudish, A.I., 1984. "Dynamic simulation and parametric sensitivity studies on a central solar domestic hot water system," Energy, Elsevier, vol. 9(2), pages 169-181.
    12. Lima, Juliana Benoni Arruda & Prado, Racine T.A. & Montoro Taborianski, Vanessa, 2006. "Optimization of tank and flat-plate collector of solar water heating system for single-family households to assure economic efficiency through the TRNSYS program," Renewable Energy, Elsevier, vol. 31(10), pages 1581-1595.
    Full references (including those not matched with items on IDEAS)

    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. Naspolini, Helena F. & Rüther, Ricardo, 2012. "Assessing the technical and economic viability of low-cost domestic solar hot water systems (DSHWS) in low-income residential dwellings in Brazil," Renewable Energy, Elsevier, vol. 48(C), pages 92-99.
    2. 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.
    3. Meireles, I. & Sousa, V. & Bleys, B. & Poncelet, B., 2022. "Domestic hot water consumption pattern: Relation with total water consumption and air temperature," Renewable and Sustainable Energy Reviews, Elsevier, vol. 157(C).
    4. 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.
    5. 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.
    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. 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.
    9. Kalogirou, Soteris A., 2000. "Applications of artificial neural-networks for energy systems," Applied Energy, Elsevier, vol. 67(1-2), pages 17-35, September.
    10. 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.
    11. Suárez, I. & Prieto, M.M. & Fernández, F.J., 2013. "Analysis of potential energy, economic and environmental savings in residential buildings: Solar collectors combined with microturbines," Applied Energy, Elsevier, vol. 104(C), pages 128-136.
    12. Katarzyna Ratajczak & Katarzyna Michalak & Michał Narojczyk & Łukasz Amanowicz, 2021. "Real Domestic Hot Water Consumption in Residential Buildings and Its Impact on Buildings’ Energy Performance—Case Study in Poland," Energies, MDPI, vol. 14(16), pages 1-22, August.
    13. 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.
    14. Pomianowski, M.Z. & Johra, H. & Marszal-Pomianowska, A. & Zhang, C., 2020. "Sustainable and energy-efficient domestic hot water systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 128(C).
    15. Chandra, Yogender Pal & Matuska, Tomas, 2020. "Numerical prediction of the stratification performance in domestic hot water storage tanks," Renewable Energy, Elsevier, vol. 154(C), pages 1165-1179.
    16. 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).
    17. 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.
    18. Gautam, Abhishek & Chamoli, Sunil & Kumar, Alok & Singh, Satyendra, 2017. "A review on technical improvements, economic feasibility and world scenario of solar water heating system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 68(P1), pages 541-562.
    19. 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.
    20. Jebaraj, S. & Iniyan, S., 2006. "A review of energy models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 10(4), pages 281-311, August.

    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:97:y:2012:i:c:p:897-906. 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.