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All issues Volume 680 (2025) E3S Web Conf., 680 (2025) 00149 References
Open Access
Issue
E3S Web Conf.
Volume 680, 2025
The 4th International Conference on Energy and Green Computing (ICEGC’2025)
Article Number 00149
Number of page(s) 16
DOI https://doi.org/10.1051/e3sconf/202568000149
Published online 19 December 2025
  1. Y. El Alami, E. Baghaz, R. Nasrin, M. Al-Damook, R. Bendaoud, T. Bouragba, M. Melhaoui, M. Benhmida, Up-to-Date Review on Flat-Plate Solar Hybrid Photovoltaic Thermal Systems: Absorber Designs and Fabrication Materials, Int. J. Energy Res. 2025 (2025) 5547555. https://doi.org/10.1155/er/5547555. [Google Scholar]
  2. M.S. Hossain, A.K. Pandey, J. Selvaraj, N.A. Rahim, M.M. Islam, V.V. Tyagi, Two side serpentine flow based photovoltaic-thermal-phase change materials (PVT-PCM) system: Energy, exergy and economic analysis, Renew. Energy 136 (2019) 1320–1336. [Google Scholar]
  3. J.J. Michael, I. S, R. Goic, Flat plate solar photovoltaic–thermal (PV/T) systems: A reference guide, Renew. Sustain. Energy Rev. 51 (2015) 62–88. https://doi.org/10.1016/j.rser.2015.06.022. [CrossRef] [Google Scholar]
  4. A. Kazemian, A. Taheri, A. Sardarabadi, T. Ma, M. Passandideh-Fard, J. Peng, Energy, exergy and environmental analysis of glazed and unglazed PVT system integrated with phase change material: An experimental approach, Sol. Energy 201 (2020) 178–189. https://doi.org/10.1016/j.solener.2020.02.096. [CrossRef] [Google Scholar]
  5. A. Dhaundiyal, D. Atsu, Energy assessment of photovoltaic modules, Sol. Energy 218 (2021) 337–345. https://doi.org/10.1016/j.solener.2021.02.055. [Google Scholar]
  6. S. Motahar, H. Bagheri-Esfeh, Artificial neural network based assessment of grid-connected photovoltaic thermal systems in heating dominated regions of Iran, Sustain. Energy Technol. Assess. 39 (2020) 100694. https://doi.org/10.1016/j.seta.2020.100694. [Google Scholar]
  7. S.A. Namuq, J.M. Mahdi, Boosting thermal regulation of phase change materials in photovoltaic-thermal systems through solid and porous fins, Int. J. Renew. Energy Dev. 13 (2024) 179–190. https://doi.org/10.61435/ijred.2024.59986. [Google Scholar]
  8. A. Sharma, P.K. Singh, E. Makki, J. Giri, T. Sathish, A comprehensive review of critical analysis of biodegradable waste PCM for thermal energy storage systems using machine learning and deep learning to predict dynamic behavior, Heliyon 10 (2024) e25800. https://doi.org/10.1016/j.heliyon.2024.e25800. [Google Scholar]
  9. J. Liu, F. Tavakoli, S.M. Sajadi, M.Z. Mahmoud, B. Heidarshenas, H.Ş. Aybar, Numerical evaluation and artificial neural network modeling of the effect of oval PCM compartment dimensions around a triple lithiumion battery pack despite forced airflow, Eng. Anal. Bound. Elem. 142 (2022) 71–92. https://doi.org/10.1016/j.enganabound.2022.05.006. [Google Scholar]
  10. A.H. Elsheikh, S.W. Sharshir, M.E. Mostafa, F.A. Essa, M.K. Ahmed Ali, Applications of nanofluids in solar energy: A review of recent advances, Renew. Sustain. Energy Rev. 82 (2018) 3483–3502. https://doi.org/10.1016/j.rser.2017.10.108. [Google Scholar]
  11. A. Shahsavar, P. Jha, M. Arici, P. Estellé, Experimental investigation of the usability of the rifled serpentine tube to improve energy and exergy performances of a nanofluid-based photovoltaic/thermal system, Renew. Energy 170 (2021) 410–425. https://doi.org/10.1016/j.renene.2021.01.117. [Google Scholar]
  12. R. Asghar, F. Riganti-Fulginei, M. Quercio, A. Mahrouch, Artificial Neural Networks for Photovoltaic Power Forecasting: A Review of Five Promising Models, IEEE Access 12 (2024) 90461–90485. https://doi.org/10.1109/ACCESS.2024.3420693. [Google Scholar]
  13. S.H. Bandaru, V. Becerra, S. Khanna, J. Radulovic, D. Hutchinson, R. Khusainov, A Review of Photovoltaic Thermal (PVT) Technology for Residential Applications: Performance Indicators, Progress, and Opportunities, Energies 14 (2021) 3853. https://doi.org/10.3390/en14133853. [Google Scholar]
  14. M.G.M. Abdolrasol, S.M.S. Hussain, T.S. Ustun, M.R. Sarker, M.A. Hannan, R. Mohamed, J.A. Ali, S. Mekhilef, A. Milad, Artificial Neural Networks Based Optimization Techniques: A Review, Electronics 10 (2021) 2689. https://doi.org/10.3390/electronics10212689. [Google Scholar]
  15. S.Y. Heng, W.M. Ridwan, P. Kumar, A.N. Ahmed, C.M. Fai, A.H. Birima, A. El-Shafie, Artificial neural network model with different backpropagation algorithms and meteorological data for solar radiation prediction, Sci. Rep. 12 (2022) 10457. https://doi.org/10.1038/s41598-022-13532-3. [Google Scholar]
  16. S.-C. Lim, J.-H. Huh, S.-H. Hong, C.-Y. Park, J.-C. Kim, Solar Power Forecasting Using CNN-LSTM Hybrid Model, Energies 15 (2022) 8233. https://doi.org/10.3390/en15218233. [Google Scholar]
  17. H. Madhi, S. Aljabair, A.A. Imran, A review of photovoltaic/thermal system cooled using mono and hybrid nanofluids, Int. J. Thermofluids 22 (2024) 100679. https://doi.org/10.1016/j.ijft.2024.100679. [Google Scholar]
  18. A.K. Tiwari, K. Chatterjee, S. Agrawal, G.K. Singh, A comprehensive review of photovoltaic-thermal (PVT) technology: Performance evaluation and contemporary development, Energy Rep. 10 (2023) 2655–2679. https://doi.org/10.1016/j.egyr.2023.09.043. [Google Scholar]
  19. Y.E. Alami, H. Dahmani, F. Ouerradi, B. Zohal, R. Bendaoud, E. Baghaz, Impact of Heat Exchanger Materials on the Performance of Solar Hybrid Photovoltaic Thermal (PVT) Systems: 3D Analysis, in: B. Hajji, A. Gagliano, A. Mellit, A. Rabhi, M. Calì (Eds.), Proc. 4th Int. Conf. Electron. Eng. Renew. Energy Syst. - Vol. 2, Springer Nature, Singapore, 2025: pp. 609–619. https://doi.org/10.1007/978-981-97-9975-6_58. [Google Scholar]
  20. R. Jogineedi, K. Biswas, S. Shrestha, Experimental Study of the Behavior of Phase Change Materials during Interrupted Phase Change Processes, Energies 14 (2021) 8021. https://doi.org/10.3390/en14238021. [Google Scholar]
  21. M. Ghamari, C.H. See, D. Hughes, T. Mallick, K.S. Reddy, K. Patchigolla, S. Sundaram, Advancing sustainable building through passive cooling with phase change materials, a comprehensive literature review, Energy Build. 312 (2024) 114164. https://doi.org/10.1016/j.enbuild.2024.114164. [Google Scholar]
  22. M. Ghazy, E.M.M. Ibrahim, A.S.A. Mohamed, A.A. Askalany, Cooling technologies for enhancing photovoltaic–thermal (PVT) performance: a state of the art, Int. J. Energy Environ. Eng. 13 (2022) 1205–1235. https://doi.org/10.1007/s40095-022-00491-8. [Google Scholar]
  23. K. Park, Y. Seong, Y. Jung, I. Youn, C.K. Choi, Development of Water Level Prediction Improvement Method Using Multivariate Time Series Data by GRU Model, Water 15 (2023) 587. https://doi.org/10.3390/w15030587. [Google Scholar]
  24. B. Abu-Jdayil, A.-H. Mourad, W. Hittini, M. Hassan, S. Hameedi, Traditional, state-of-the-art and renewable thermal building insulation materials: An overview, Constr. Build. Mater. 214 (2019) 709–735. https://doi.org/10.1016/j.conbuildmat.2019.04.102. [CrossRef] [Google Scholar]
  25. M.M. Umair, Y. Zhang, K. Iqbal, S. Zhang, B. Tang, Novel strategies and supporting materials applied to shape-stabilize organic phase change materials for thermal energy storage–A review, Appl. Energy 235 (2019) 846–873. https://doi.org/10.1016/j.apenergy.2018.11.017. [Google Scholar]
  26. A.H.A. Al-Waeli, M.T. Chaichan, K. Sopian, H.A. Kazem, H.B. Mahood, A.A. Khadom, Modeling and experimental validation of a PVT system using nanofluid coolant and nano-PCM, Sol. Energy 177 (2019) 178–191. https://doi.org/10.1016/j.solener.2018.11.016. [Google Scholar]
  27. A.K. Pandey, I. Ali Laghari, R. Reji Kumar, K. Chopra, M. Samykano, A.M. Abusorrah, K. Sharma, V.V. Tyagi, Energy, exergy, exergoeconomic and enviroeconomic (4-E) assessment of solar water heater with/without phase change material for building and other applications: A comprehensive review, Sustain. Energy Technol. Assess. 45 (2021) 101139. https://doi.org/10.1016/j.seta.2021.101139. [Google Scholar]
  28. M. Hosseinzadeh, M. Sardarabadi, M. Passandideh-Fard, Energy and exergy analysis of nanofluid based photovoltaic thermal system integrated with phase change material, Energy 147 (2018) 636–647. https://doi.org/10.1016/j.energy.2018.01.073. [Google Scholar]
  29. H. Fayaz, N.A. Rahim, M. Hasanuzzaman, A. Rivai, R. Nasrin, Numerical and outdoor real time experimental investigation of performance of PCM based PVT system, Sol. Energy 179 (2019) 135–150. https://doi.org/10.1016/j.solener.2018.12.057. [Google Scholar]
  30. M. Carmona, A. Palacio Bastos, J.D. García, Experimental evaluation of a hybrid photovoltaic and thermal solar energy collector with integrated phase change material (PVT-PCM) in comparison with a traditional photovoltaic (PV) module, Renew. Energy 172 (2021) 680–696. https://doi.org/10.1016/j.renene.2021.03.022. [Google Scholar]
  31. W.S. McCulloch, W. Pitts, A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics 5, 115–133 (1943). https://doi.org/10.1007/BF02478259 [CrossRef] [Google Scholar]
  32. A.R. Pazikadin, D. Rifai, K. Ali, M.Z. Malik, A.N. Abdalla, M.A. Faraj, Solar irradiance measurement instrumentation and power solar generation forecasting based on Artificial Neural Networks (ANN): A review of five years research trend, Sci. Total Environ. 715 (2020) 136848. https://doi.org/10.1016/j.scitotenv.2020.136848. [Google Scholar]
  33. J. Kumar, R. Goomer, A.K. Singh, Long Short Term Memory Recurrent Neural Network (LSTM-RNN) Based Workload Forecasting Model For Cloud Datacenters, Procedia Comput. Sci. 125 (2018) 676–682. https://doi.org/10.1016/j.procs.2017.12.087. [Google Scholar]
  34. K. Greff, R.K. Srivastava, J. Koutník, B.R. Steunebrink, J. Schmidhuber, LSTM: A Search Space Odyssey, IEEE Trans. Neural Netw. Learn. Syst. 28 (2017) 2222–2232. https://doi.org/10.1109/TNNLS.2016.2582924. [Google Scholar]
  35. Muhammed Musab Bayat, Ertan Buyruk, Modeling of Photovoltaic/Thermal System by Artificial Neural Network Based on The Experimental Study, (2023). https://doi.org/10.5281/ZENODO.10259964. [Google Scholar]
  36. J. Schmidhuber, Deep Learning in Neural Networks: An Overview, Neural Netw. 61 (2015) 85–117. https://s://doi.org/10.1016/j.neunet.2014.09.003. [CrossRef] [Google Scholar]
  37. H.A. Kazem, Performance evaluation of solar photovoltaic/thermal system performance: An experimental and artificial neural network approach, Case Stud. Therm. Eng. (2024). [Google Scholar]
  38. Roheen Qamar, Baqar Ali Zardari, Artificial Neural Networks: An Overview, Mesopotamian J. Comput. Sci. 2023 (2023) 124–133. https://doi.org/10.58496/mjcsc/2023/015. [Google Scholar]
  39. A. Mahrouch, M. Ouassaid, Primary Frequency Regulation Based on Deloaded Control, ANN, and 3D-Fuzzy Logic Controller for Hybrid Autonomous Microgrid, Technol. Econ. Smart Grids Sustain. Energy 7 (2022) 1. https://doi.org/10.1007/s40866-022-00125-2. [Google Scholar]
  40. S. Maqekeni, K. Obileke, O. Ndiweni, P. Mukumba, Application of Fuzzy Logic Techniques in Solar Energy Systems: A Review, Appl. Syst. Innov. 8 (2025) 144. https://doi.org/10.3390/asi8050144. [Google Scholar]
  41. T. Deng, Effect of the Number of Hidden Layer Neurons on the Accuracy of the Back Propagation Neural Network, Highlights Sci. Eng. Technol. 74 (2023) 462–468. https://doi.org/10.54097/nbra6h45. [Google Scholar]
  42. B.H. Vu, I.-Y. Chung, Optimal generation scheduling and operating reserve management for PV generation using RNN-based forecasting models for stand-alone microgrids, Renew. Energy 195 (2022) 1137–1154. https://doi.org/10.1016/j.renene.2022.06.086. [Google Scholar]
  43. C. Feng, J. Zhang, SolarNet: A sky image-based deep convolutional neural network for intra-hour solar forecasting, Sol. Energy 204 (2020) 71–78. https://doi.org/10.1016/j.solener.2020.03.083. [Google Scholar]
  44. X.-H. Le, H.V. Ho, G. Lee, S. Jung, Application of Long Short-Term Memory (LSTM) Neural Network for Flood Forecasting, Water 11 (2019) 1387. https://doi.org/10.3390/w11071387. [Google Scholar]
  45. H. Gholamalinejad, H. Khosravi, Vehicle classification using a real-time convolutional structure based on DWT pooling layer and SE blocks, Expert Syst. Appl. 183 (2021) 115420. https://doi.org/10.1016/j.eswa.2021.115420. [Google Scholar]
  46. H. Salehinejad, S. Sankar, J. Barfett, E. Colak, S. Valaee, Recent Advances in Recurrent Neural Networks, (2018). https://doi.org/10.48550/arXiv.1801.01078. [Google Scholar]
  47. T. Mikolov, A. Joulin, S. Chopra, M. Mathieu, M. Ranzato, Learning Longer Memory in Recurrent Neural Networks, (2015). https://doi.org/10.48550/arXiv.1412.7753. [Google Scholar]
  48. J. Pöppelbaum, A. Schwung, Improving quaternion neural networks with quaternionic activation functions, Knowl.-Based Syst. 305 (2024) 112619. https://doi.org/10.1016/j.knosys.2024.112619. [Google Scholar]
  49. M. Herrando, K. Wang, G. Huang, T. Otanicar, O.B. Mousa, R.A. Agathokleous, Y. Ding, S. Kalogirou, N. Ekins-Daukes, R.A. Taylor, C.N. Markides, A review of solar hybrid photovoltaic-thermal (PV-T) collectors and systems, Prog. Energy Combust. Sci. 97 (2023) 101072. https://doi.org/10.1016/j.pecs.2023.101072. [CrossRef] [Google Scholar]
  50. D. Raina, A. Dawange, T. Bandha, A. Kaur, R. Wasekar, K. Verma, S. Verma, K. Dhingra, Convoluted neural network and transfer learning algorithm for improved brain tumor classifications in MRI, J. Knowl. Learn. Sci. Technol. ISSN 2959-6386 Online 3 (2024) 200–212. https://doi.org/10.60087/jklst.v3.n4.p200. [Google Scholar]
  51. A.H.S. Weerakoon, M. Assadi, Artificial Neural Network (ANN) driven Techno-Economic Predictions for Micro Gas Turbines (MGT) based Energy Applications, Energy AI 20 (2025) 100483. https://doi.org/10.1016/j.egyai.2025.100483. [Google Scholar]
  52. A. Mellit, A.M. Pavan, A 24-h forecast of solar irradiance using artificial neural network: Application for performance prediction of a grid-connected PV plant at Trieste, Italy, Sol. Energy 84 (2010) 807–821. https://doi.org/10.1016/j.solener.2010.02.006. [Google Scholar]
  53. L. Liu, F. Liu, Y. Zheng, A Novel Ultra-Short-Term PV Power Forecasting Method Based on DBN-Based Takagi-Sugeno Fuzzy Model, Energies 14 (2021) 6447. https://doi.org/10.3390/en14206447. [Google Scholar]
  54. H.A. Kazem, M.T. Chaichan, H.S. Abd, A.H.A. Al-Waeli, M.T. Mahdi, H.H. Fadhil, I.I. Mohd, A.A. Khadom, Performance evaluation of solar photovoltaic/thermal system performance: An experimental and artificial neural network approach, Case Stud. Therm. Eng. 61 (2024) 104860. https://doi.org/10.1016/j.csite.2024.104860. [Google Scholar]
  55. H.R. Sarhaddi Saeid Farahat, Faramarz, Artificial Neural Network Based Model of Photovoltaic Thermal (pvt) Collector, (n.d.). https://doi.org/10.22436/jmcs.04.03.15. [Google Scholar]
  56. B. Souayeh, S. Bhattacharyya, N. Hdhiri, M. Waqas Alam, Heat and Fluid Flow Analysis and ANN-Based Prediction of A Novel Spring Corrugated Tape, Sustainability 13 (2021) 3023. https://doi.org/10.3390/su13063023. [Google Scholar]
  57. O. Rejeb, B. Lamrani, R. Lamba, T. Kousksou, T. Salameh, A. Jemni, A. Hamid, M. Bettayeb, C. Ghenai, Numerical investigations of concentrated photovoltaic thermal system integrated with thermoelectric power generator and phase change material, J. Energy Storage null (2023) null. https://doi.org/10.1016/j.est.2023.106820. [Google Scholar]
  58. S. Metlek, C. Kandilli, K. Kayaalp, Prediction of the effect of temperature on electric power in photovoltaic thermal systems based on natural zeolite plates, Int. J. Energy Res. 46 (2022) 6370–6382. https://doi.org/10.1002/er.7575. [Google Scholar]
  59. I.B. Askari, A. Shahsavar, M. Jamei, F. Calise, M. Karbasi, A parametric assessing and intelligent forecasting of the energy and exergy performances of a dish concentrating photovoltaic/thermal collector considering six different nanofluids and applying two meticulous soft computing paradigms, Renew. Energy 193 (2022) 149–166. https://doi.org/10.1016/j.renene.2022.04.155. [Google Scholar]
  60. A. Ali, K. Aurangzeb, M. Shoaib, M. Alhussein, M.Z. Malik, Second-law analysis of nanofluid-based photovoltaic/thermal system modeling and forecasting model based on artificial neural network, Eng. Anal. Bound. Elem. 157 (2023) 342–352. https://doi.org/10.1016/j.enganabound.2023.09.018. [Google Scholar]
  61. O. Büyükalaca, H.M. Kılıç, U. Olmuş, Y.E. Güzelel, K.N. Çerçi, Numerical investigation and ANN modeling of performance for hexagonal boron Nitride-water nanofluid PVT collectors, Therm. Sci. Eng. Prog. 43 (2023) 101997. https://doi.org/10.1016/j.tsep.2023.101997. [Google Scholar]
  62. H. Kalani, M. Sardarabadi, M. Passandideh-Fard, Using artificial neural network models and particle swarm optimization for manner prediction of a photovoltaic thermal nanofluid based collector, Appl. Therm. Eng. 113 (2017) 1170–1177. https://doi.org/10.1016/j.applthermaleng.2016.11.105. [Google Scholar]

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