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Long-term performance prediction of forced circulation solar domestic water heating systems using artificial neural networks

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  • Kalogirou, Soteris A.

Abstract

The objective of this work is to use Artificial Neural Networks (ANNs) for the long-term performance prediction of forced circulation type solar domestic water heating (SDWH) systems. ANNs have been used in diverse applications and they have been shown to be particularly useful in system modelling and for system identification. Three SDWH systems have been tested and modelled according to the procedures outlined in the standard ISO 9459-2 at three locations in Greece. Two ANNs have been trained using the monthly data produced by the modelling program supplied with the standard. Different networks were used due to the different natures of the output required in each case. The first network was trained to estimate the solar energy output of the system for a draw-off quantity equal to the storage tank capacity and the second network was trained to estimate the solar energy output of the system and the average quantity of hot water per month, at demand temperatures of 35 and 40°C. The data presented as input to both networks are similar to the data used in the program supplied with the standard. The statistical coefficient of multiple determination (R2-value) obtained for the training data set was equal to 0.9972 for the first network and equal to 0.9878 and 0.9973 for the second network for the two output parameters, solar energy output and hot water quantity, respectively. Other data, unknown to the network, were subsequently used to evaluate the accuracy of the prediction. Predictions with R2-values equal to 0.9945 for the first network and 0.9825 and 0.9910 for the second were obtained. The maximum percentage differences were 1.9 and 5.5% for the two networks respectively. These results indicate that the proposed method can successfully be used for the prediction of the long-term performance of forced circulation water heating solar systems. The advantages of this approach compared to the conventional algorithmic methods are speed, simplicity, and the capacity of the network to learn from examples. This is done by embedding experiential knowledge in the network.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:appene:v:66:y:2000:i:1:p:63-74
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    1. 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.
    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. Cadenas, Erasmo & Rivera, Wilfrido, 2009. "Short term wind speed forecasting in La Venta, Oaxaca, México, using artificial neural networks," Renewable Energy, Elsevier, vol. 34(1), pages 274-278.
    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. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. Kalogirou, Soteris A., 2000. "Applications of artificial neural-networks for energy systems," Applied Energy, Elsevier, vol. 67(1-2), pages 17-35, September.
    15. 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.
    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. 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.
    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. 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.
    21. 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.
    22. 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.
    23. 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.

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