IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v93y2015ip1p908-922.html
   My bibliography  Save this article

Artificial neural network modeling of a photovoltaic-thermal evaporator of solar assisted heat pumps

Author

Listed:
  • Gunasekar, N.
  • Mohanraj, M.
  • Velmurugan, V.

Abstract

In this work, the artificial neural network model was developed to predict the energy performance of a photovoltaic-thermal evaporator used in solar assisted heat pumps. The experiments were carried out under the meteorological conditions of Coimbatore city (latitude of 10.98°N and longitude of 76.96°E) in India. The energy performance parameters of a photovoltaic-thermal evaporator such as, evaporator heat gain, solar energy input ratio, photovoltaic efficiency and photovoltaic panel temperature were observed with reference to four ambient parameters such as, solar intensity, ambient temperature, ambient wind velocity and ambient relative humidity. The experimental results were used as training data for the network. The multilayer feed forward network is optimized to 4-15-4 configuration for predicting the energy performance of the photovoltaic-thermal evaporator. Analysis of variance was carried out to identify the significant ambient parameter influencing the energy performance of photovoltaic-thermal evaporators. The network predictions are found to be closer to the experimental values with the maximum fraction of absolute variance values, minimum root mean square errors and minimum coefficient of variance values. The analysis of variance results confirmed that solar intensity and ambient temperature are the most influencing parameters affecting the energy performance of photovoltaic-thermal evaporators.

Suggested Citation

  • Gunasekar, N. & Mohanraj, M. & Velmurugan, V., 2015. "Artificial neural network modeling of a photovoltaic-thermal evaporator of solar assisted heat pumps," Energy, Elsevier, vol. 93(P1), pages 908-922.
    • Handle: RePEc:eee:energy:v:93:y:2015:i:p1:p:908-922
    DOI: 10.1016/j.energy.2015.09.078
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544215012888
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2015.09.078?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. & 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.
    2. 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.
    3. Shariah, Adnan & Al-Akhras, M-Ali & Al-Omari, I.A., 2002. "Optimizing the tilt angle of solar collectors," Renewable Energy, Elsevier, vol. 26(4), pages 587-598.
    4. Mohanraj, M. & Jayaraj, S. & Muraleedharan, C., 2012. "Applications of artificial neural networks for refrigeration, air-conditioning and heat pump systems—A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(2), pages 1340-1358.
    5. Tyagi, V.V. & Kaushik, S.C. & Tyagi, S.K., 2012. "Advancement in solar photovoltaic/thermal (PV/T) hybrid collector technology," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(3), pages 1383-1398.
    6. Sivasakthivel, T. & Murugesan, K. & Thomas, H.R., 2014. "Optimization of operating parameters of ground source heat pump system for space heating and cooling by Taguchi method and utility concept," Applied Energy, Elsevier, vol. 116(C), pages 76-85.
    7. 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.
    8. Sivasakthivel, T. & Murugesan, K. & Sahoo, P.K., 2014. "Optimization of ground heat exchanger parameters of ground source heat pump system for space heating applications," Energy, Elsevier, vol. 78(C), pages 573-586.
    9. Dubey, Swapnil & Sandhu, G.S. & Tiwari, G.N., 2009. "Analytical expression for electrical efficiency of PV/T hybrid air collector," Applied Energy, Elsevier, vol. 86(5), pages 697-705, May.
    10. Amori, Karima E. & Abd-AlRaheem, Mustafa Adil, 2014. "Field study of various air based photovoltaic/thermal hybrid solar collectors," Renewable Energy, Elsevier, vol. 63(C), pages 402-414.
    11. 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.
    12. Chow, T.T., 2010. "A review on photovoltaic/thermal hybrid solar technology," Applied Energy, Elsevier, vol. 87(2), pages 365-379, February.
    13. Kalogirou, Soteris A., 2000. "Applications of artificial neural-networks for energy systems," Applied Energy, Elsevier, vol. 67(1-2), pages 17-35, September.
    14. Singh, G.K., 2013. "Solar power generation by PV (photovoltaic) technology: A review," Energy, Elsevier, vol. 53(C), pages 1-13.
    15. Ji, Jie & Liu, Keliang & Chow, Tin-tai & Pei, Gang & He, Wei & He, Hanfeng, 2008. "Performance analysis of a photovoltaic heat pump," Applied Energy, Elsevier, vol. 85(8), pages 680-693, August.
    16. Mekhilef, S. & Saidur, R. & Kamalisarvestani, M., 2012. "Effect of dust, humidity and air velocity on efficiency of photovoltaic cells," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(5), pages 2920-2925.
    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. Abbas, Sajid & Yuan, Yanping & Zhou, Jinzhi & Hassan, Atazaz & Yu, Min & Yasheng, Ji, 2022. "Experimental and analytical analysis of the impact of different base plate materials and design parameters on the performance of the photovoltaic/thermal system," Renewable Energy, Elsevier, vol. 187(C), pages 522-536.
    2. Del Amo, Alejandro & Martínez-Gracia, Amaya & Bayod-Rújula, Angel A. & Cañada, Marta, 2019. "Performance analysis and experimental validation of a solar-assisted heat pump fed by photovoltaic-thermal collectors," Energy, Elsevier, vol. 169(C), pages 1214-1223.
    3. Song, Zhiying & Ji, Jie & Cai, Jingyong & Zhao, Bin & Li, Zhaomeng, 2021. "Investigation on a direct-expansion solar-assisted heat pump with a novel hybrid compound parabolic concentrator/photovoltaic/fin evaporator," Applied Energy, Elsevier, vol. 299(C).
    4. 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.
    5. 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.
    6. Dimri, Neha & Tiwari, Arvind & Tiwari, G.N., 2019. "Comparative study of photovoltaic thermal (PVT) integrated thermoelectric cooler (TEC) fluid collectors," Renewable Energy, Elsevier, vol. 134(C), pages 343-356.
    7. Rana, Mashud & Sethuvenkatraman, Subbu & Heidari, Rahmat & Hands, Stuart, 2022. "Solar thermal generation forecast via deep learning and application to buildings cooling system control," Renewable Energy, Elsevier, vol. 196(C), pages 694-706.
    8. Cui, Yuanlong & Zhu, Jie & Zoras, Stamatis & Zhang, Jizhe, 2021. "Comprehensive review of the recent advances in PV/T system with loop-pipe configuration and nanofluid," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    9. 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).
    10. Bugała, A. & Zaborowicz, M. & Boniecki, P. & Janczak, D. & Koszela, K. & Czekała, W. & Lewicki, A., 2018. "Short-term forecast of generation of electric energy in photovoltaic systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 306-312.
    11. 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.
    12. Yao, Jian & Zheng, Sihang & Chen, Daochuan & Dai, Yanjun & Huang, Mingjun, 2021. "Performance improvement of vapor-injection heat pump system by employing PVT collector/evaporator for residential heating in cold climate region," Energy, Elsevier, vol. 219(C).
    13. Kong, Xiangqiang & Jiang, Kailin & Dong, Shandong & Li, Ying & Li, Jianbo, 2018. "Control strategy and experimental analysis of a direct-expansion solar-assisted heat pump water heater with R134a," Energy, Elsevier, vol. 145(C), pages 17-24.
    14. Siwei Lou & Wenqiang Chen & Danny H.W. Li & Mo Wang & Hainan Chen & Isaac Y.F. Lun & Dawei Xia, 2019. "Tilted Photovoltaic Energy Outputs in Outdoor Environments," Sustainability, MDPI, vol. 11(21), pages 1-17, October.
    15. Wang, Lu & Guo, Leihong & Ren, Jianlin & Kong, Xiangfei, 2022. "Using of heat thermal storage of PCM and solar energy for distributed clean building heating: A multi-level scale-up research," Applied Energy, Elsevier, vol. 321(C).
    16. Ma, Juanli & Fung, Alan S. & Brands, Monica & Abul Moyeed, Osama Mohammad & Mhanna, Ahmad & Juan, Neil, 2021. "Effects of photovoltaic/thermal (PV/T) control strategies on the performance of liquid-based PV/T assisted heat pump for space heating," Renewable Energy, Elsevier, vol. 172(C), pages 753-764.
    17. Wang, Hai & Huang, Jin & Song, Mengjie & Yan, Jian, 2019. "Effects of receiver parameters on the optical performance of a fixed-focus Fresnel lens solar concentrator/cavity receiver system in solar cooker," Applied Energy, Elsevier, vol. 237(C), pages 70-82.
    18. Han, Youhua & Ma, Liangdong & Zhang, Jili & Mi, Peiyuan & Guo, Xiaochao, 2024. "Superheat matching control method for a roll-bond photovoltaic-thermal heat pump system," Energy, Elsevier, vol. 290(C).
    19. Arun, K.R. & Srinivas, M. & Saleel, C.A. & Jayaraj, S., 2020. "Influence of the location of discrete macro-encapsulated thermal energy storage on the performance of a double pass solar plate collector system," Renewable Energy, Elsevier, vol. 146(C), pages 675-686.
    20. Sholahudin, S. & Han, Hwataik, 2016. "Simplified dynamic neural network model to predict heating load of a building using Taguchi method," Energy, Elsevier, vol. 115(P3), pages 1672-1678.
    21. Chao Zhou & Ahmad Riaz & Jingjing Wang & Jili Zhang & Lin Xu, 2023. "Photovoltaic Thermal Heat Pump Assessment for Power and Domestic Hot Water Generation," Energies, MDPI, vol. 16(19), pages 1-21, October.
    22. Song, Zhiying & Ji, Jie & Zhang, Yuzhe & Li, Yunhai & Li, Jing & Zhao, Xudong, 2023. "Annual analysis of the photovoltaic direct-expansion heat pump assisted by double condensing equipment for secondary power generation," Renewable Energy, Elsevier, vol. 209(C), pages 169-183.
    23. Xu, Yingjie & Mao, Chengbin & Huang, Yuangong & Shen, Xi & Xu, Xiaoxiao & Chen, Guangming, 2021. "Performance evaluation and multi-objective optimization of a low-temperature CO2 heat pump water heater based on artificial neural network and new economic analysis," Energy, Elsevier, vol. 216(C).
    24. Hu, Mingke & Zhao, Bin & Li, Jing & Wang, Yunyun & Pei, Gang, 2017. "Preliminary thermal analysis of a combined photovoltaic–photothermic–nocturnal radiative cooling system," Energy, Elsevier, vol. 137(C), pages 419-430.

    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. 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.
    2. 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.
    3. Makki, Adham & Omer, Siddig & Sabir, Hisham, 2015. "Advancements in hybrid photovoltaic systems for enhanced solar cells performance," Renewable and Sustainable Energy Reviews, Elsevier, vol. 41(C), pages 658-684.
    4. Hwang, Jun Kwon & Yun, Geun Young & Lee, Sukho & Seo, Hyeongjoon & Santamouris, Mat, 2020. "Using deep learning approaches with variable selection process to predict the energy performance of a heating and cooling system," Renewable Energy, Elsevier, vol. 149(C), pages 1227-1245.
    5. Kumar, Anil & Baredar, Prashant & Qureshi, Uzma, 2015. "Historical and recent development of photovoltaic thermal (PVT) technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 42(C), pages 1428-1436.
    6. 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.
    7. 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.
    8. 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.
    9. Mohanraj, M. & Jayaraj, S. & Muraleedharan, C., 2012. "Applications of artificial neural networks for refrigeration, air-conditioning and heat pump systems—A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(2), pages 1340-1358.
    10. Buratti, Cinzia & Barelli, Linda & Moretti, Elisa, 2012. "Application of artificial neural network to predict thermal transmittance of wooden windows," Applied Energy, Elsevier, vol. 98(C), pages 425-432.
    11. Jani, D.B. & Mishra, Manish & Sahoo, P.K., 2017. "Application of artificial neural network for predicting performance of solid desiccant cooling systems – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 352-366.
    12. Ata, Rasit, 2015. "Artificial neural networks applications in wind energy systems: a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 534-562.
    13. Pang, Wei & Cui, Yanan & Zhang, Qian & Wilson, Gregory.J. & Yan, Hui, 2020. "A comparative analysis on performances of flat plate photovoltaic/thermal collectors in view of operating media, structural designs, and climate conditions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 119(C).
    14. 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.
    15. Mollahosseini, Arash & Hosseini, Seyed Amid & Jabbari, Mostafa & Figoli, Alberto & Rahimpour, Ahmad, 2017. "Renewable energy management and market in Iran: A holistic review on current state and future demands," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 774-788.
    16. Xia, Lei & Ma, Zhenjun & Kokogiannakis, Georgios & Wang, Shugang & Gong, Xuemei, 2018. "A model-based optimal control strategy for ground source heat pump systems with integrated solar photovoltaic thermal collectors," Applied Energy, Elsevier, vol. 228(C), pages 1399-1412.
    17. 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.
    18. Abdul Mujeebu, Muhammad & Alshamrani, Othman Subhi, 2016. "Prospects of energy conservation and management in buildings – The Saudi Arabian scenario versus global trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 1647-1663.
    19. Tomar, Vivek & Norton, Brian & Tiwari, G.N., 2019. "A novel approach towards investigating the performance of different PVT configurations integrated on test cells: An experimental study," Renewable Energy, Elsevier, vol. 137(C), pages 93-108.
    20. Almonacid, F. & Fernández, Eduardo F. & Rodrigo, P. & Pérez-Higueras, P.J. & Rus-Casas, C., 2013. "Estimating the maximum power of a High Concentrator Photovoltaic (HCPV) module using an Artificial Neural Network," Energy, Elsevier, vol. 53(C), pages 165-172.

    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:energy:v:93:y:2015:i:p1:p:908-922. 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/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.