IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v129y2014icp166-176.html
   My bibliography  Save this article

Extended ordinary differential equation models for solar heating systems with pipes

Author

Listed:
  • Kicsiny, R.
  • Nagy, J.
  • Szalóki, Cs.

Abstract

There is no doubt on the importance to investigate and develop solar heating systems. Mathematical modeling is the theoretically established tool for this purpose. In this study, improved, validated ordinary differential equation (ODE) models (an extended linear and a nonlinear ones) are proposed for a wide sort of solar heating systems with a solar collector, a heat exchanger, a storage and pipes. The pipes are considered as separated components in the model, so their thermal and delaying effects are taken into account. The advantages of the proposed ODE models, compared to other ODE, partial differential equation and delay differential equation (DDE) approaches are discussed. Based on the comparison of measured and simulated data of a real solar heating system, the validation and the efficiency of the proposed ODE models are demonstrated.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:appene:v:129:y:2014:i:c:p:166-176
    DOI: 10.1016/j.apenergy.2014.04.108
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261914004711
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.apenergy.2014.04.108?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. Kalogirou, Soteris A., 2004. "Optimization of solar systems using artificial neural-networks and genetic algorithms," Applied Energy, Elsevier, vol. 77(4), pages 383-405, April.
    2. Fiedler, Frank & Persson, Tomas, 2009. "Carbon monoxide emissions of combined pellet and solar heating systems," Applied Energy, Elsevier, vol. 86(2), pages 135-143, February.
    3. Buonomano, A. & Calise, F. & Palombo, A., 2013. "Solar heating and cooling systems by CPVT and ET solar collectors: A novel transient simulation model," Applied Energy, Elsevier, vol. 103(C), pages 588-606.
    4. Jie, Pengfei & Tian, Zhe & Yuan, Shanshan & Zhu, Neng, 2012. "Modeling the dynamic characteristics of a district heating network," Energy, Elsevier, vol. 39(1), pages 126-134.
    5. Chow, T.T. & Bai, Y. & Fong, K.F. & Lin, Z., 2012. "Analysis of a solar assisted heat pump system for indoor swimming pool water and space heating," Applied Energy, Elsevier, vol. 100(C), pages 309-317.
    6. Calise, F. & Palombo, A. & Vanoli, L., 2010. "Maximization of primary energy savings of solar heating and cooling systems by transient simulations and computer design of experiments," Applied Energy, Elsevier, vol. 87(2), pages 524-540, February.
    7. 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.
    8. 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.
    9. Saman, Namir & Mahdi, Hashim, 1996. "Analysis of the delay hot/cold water problem," Energy, Elsevier, vol. 21(5), pages 395-400.
    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. Araújo, António & Pereira, Vítor, 2017. "Solar thermal modeling for rapid estimation of auxiliary energy requirements in domestic hot water production: Proportional flow rate control," Energy, Elsevier, vol. 138(C), pages 668-681.
    2. Araújo, António & Silva, Rui, 2020. "Energy modeling of solar water heating systems with on-off control and thermally stratified storage using a fast computation algorithm," Renewable Energy, Elsevier, vol. 150(C), pages 891-906.
    3. Araújo, António & Pereira, Vítor, 2017. "Solar thermal modeling for rapid estimation of auxiliary energy requirements in domestic hot water production: On-off flow rate control," Energy, Elsevier, vol. 119(C), pages 637-651.
    4. Kicsiny, Richárd, 2015. "Transfer functions of solar heating systems for dynamic analysis and control design," Renewable Energy, Elsevier, vol. 77(C), pages 64-78.
    5. Ntsaluba, Sula & Zhu, Bing & Xia, Xiaohua, 2016. "Optimal flow control of a forced circulation solar water heating system with energy storage units and connecting pipes," Renewable Energy, Elsevier, vol. 89(C), pages 108-124.
    6. Duquette, Jean & Rowe, Andrew & Wild, Peter, 2016. "Thermal performance of a steady state physical pipe model for simulating district heating grids with variable flow," Applied Energy, Elsevier, vol. 178(C), pages 383-393.
    7. Vesipa, Riccardo & Ridolfi, Luca, 2019. "Overshoots in the water-level control of hydropower plants," Renewable Energy, Elsevier, vol. 131(C), pages 800-810.

    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. 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.
    2. Calise, Francesco & Dentice d'Accadia, Massimo & Figaj, Rafal Damian & Vanoli, Laura, 2016. "A novel solar-assisted heat pump driven by photovoltaic/thermal collectors: Dynamic simulation and thermoeconomic optimization," Energy, Elsevier, vol. 95(C), pages 346-366.
    3. 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.
    4. 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).
    5. 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.
    6. Calise, Francesco & Dentice d'Accadia, Massimo & Libertini, Luigi & Quiriti, Edoardo & Vicidomini, Maria, 2017. "A novel tool for thermoeconomic analysis and optimization of trigeneration systems: A case study for a hospital building in Italy," Energy, Elsevier, vol. 126(C), pages 64-87.
    7. Reda, Francesco & Viot, Maxime & Sipilä, Kari & Helm, Martin, 2016. "Energy assessment of solar cooling thermally driven system configurations for an office building in a Nordic country," Applied Energy, Elsevier, vol. 166(C), pages 27-43.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. Francesco Calise & Rafal Damian Figaj & Laura Vanoli, 2018. "Energy and Economic Analysis of Energy Savings Measures in a Swimming Pool Centre by Means of Dynamic Simulations," Energies, MDPI, vol. 11(9), pages 1-27, August.
    13. 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.
    14. Mohan, Gowtham & Kumar, Uday & Pokhrel, Manoj Kumar & Martin, Andrew, 2016. "A novel solar thermal polygeneration system for sustainable production of cooling, clean water and domestic hot water in United Arab Emirates: Dynamic simulation and economic evaluation," Applied Energy, Elsevier, vol. 167(C), pages 173-188.
    15. 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.
    16. 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.
    17. Sözen, Adnan & Arcaklioglu, Erol & Özalp, Mehmet & Yücesu, Serdar, 2005. "Performance parameters of an ejector-absorption heat transformer," Applied Energy, Elsevier, vol. 80(3), pages 273-289, March.
    18. 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.
    19. 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.
    20. Calise, Francesco & Dentice d'Accadia, Massimo & Piacentino, Antonio, 2014. "A novel solar trigeneration system integrating PVT (photovoltaic/thermal collectors) and SW (seawater) desalination: Dynamic simulation and economic assessment," Energy, Elsevier, vol. 67(C), pages 129-148.

    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:129:y:2014:i:c:p:166-176. 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.