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The role of nanotechnology and artificial intelligence in optimizing thermal energy systems

Author

Listed:
  • Mohammed, Hayder I.
  • Rashid, Farhan Lafta
  • Togun, Hussein
  • Agyekum, Ephraim Bonah
  • Ameen, Arman
  • Hammoodi, Karrar A.
  • Parveen, Rujda
  • Kadhim, Saif Ali
  • Abbas, Walaa N.

Abstract

The growing demand for clean energy and the limitations of conventional thermal systems necessitates the integration of advanced technologies to enhance efficiency, adaptability, and sustainability. This review critically examines recent advancements in the application of nanotechnology and artificial intelligence for optimizing thermal energy systems, including solar collectors, heat exchangers, and latent heat storage units. Nanotechnology (particularly the use of nano-enhanced phase change materials and nanofluids such as Al₂O₃ and CuO) has shown to improve thermal conductivity by up to 28.8 %, accelerating energy absorption and storage rates. Concurrently, artificial intelligence algorithms, especially artificial neural networks and particle swarm optimization, enable predictive modelling, real-time system control, and fault detection, with some models achieving prediction accuracies above 97 % under complex operational conditions. The review emphasizes the synergistic potential of combining these technologies to create intelligent, self-regulating thermal energy systems. However, the paper also identifies critical challenges including computational overhead, cost of nanoparticle synthesis, lack of reproducibility in artificial intelligence implementations, and insufficient validation under extreme scenarios. Commercial deployment case studies (such as artificial intelligence-driven phase change material-based heating, ventilation, and air conditioning systems in smart buildings) are discussed to illustrate practical viability, reporting energy savings of up to 28 % with return-on-investment periods under three years. The paper concludes by proposing integrated research directions that combine multiscale material innovation with robust artificial intelligence training on dynamic datasets. This dual approach is essential to developing scalable, cost-effective, and resilient thermal energy systems capable of supporting global energy transitions.

Suggested Citation

  • Mohammed, Hayder I. & Rashid, Farhan Lafta & Togun, Hussein & Agyekum, Ephraim Bonah & Ameen, Arman & Hammoodi, Karrar A. & Parveen, Rujda & Kadhim, Saif Ali & Abbas, Walaa N., 2025. "The role of nanotechnology and artificial intelligence in optimizing thermal energy systems," Applied Energy, Elsevier, vol. 400(C).
    • Handle: RePEc:eee:appene:v:400:y:2025:i:c:s0306261925013066
    DOI: 10.1016/j.apenergy.2025.126576
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    1. Ali Motevali & Mohammadreza Hasandust Rostami & Gholamhassan Najafi & Wei-Mon Yan, 2021. "Evaluation and Improvement of PCM Melting in Double Tube Heat Exchangers Using Different Combinations of Nanoparticles and PCM (The Case of Renewable Energy Systems)," Sustainability, MDPI, vol. 13(19), pages 1-19, September.
    2. Xu, Weiyan & Tu, Jielei & Xu, Ning & Liu, Zuming, 2024. "Predicting daily heating energy consumption in residential buildings through integration of random forest model and meta-heuristic algorithms," Energy, Elsevier, vol. 301(C).
    3. Szoplik, Jolanta, 2015. "Forecasting of natural gas consumption with artificial neural networks," Energy, Elsevier, vol. 85(C), pages 208-220.
    4. Guo, Junfei & Liu, Zhan & Du, Zhao & Yu, Jiabang & Yang, Xiaohu & Yan, Jinyue, 2021. "Effect of fin-metal foam structure on thermal energy storage: An experimental study," Renewable Energy, Elsevier, vol. 172(C), pages 57-70.
    5. Hajialigol, Najmeh & Fattahi, Abolfazl & Karimi, Nader & Jamali, Mostafa & Keighobadi, Shervin, 2024. "Hybridized power-hydrogen generation using various configurations of Brayton-organic flash Rankine cycles fed by a sustainable fuel: Exergy and exergoeconomic analyses with ANN prediction," Energy, Elsevier, vol. 290(C).
    6. Sardari, Pouyan Talebizadeh & Mohammed, Hayder I. & Giddings, Donald & walker, Gavin S. & Gillott, Mark & Grant, David, 2019. "Numerical study of a multiple-segment metal foam-PCM latent heat storage unit: Effect of porosity, pore density and location of heat source," Energy, Elsevier, vol. 189(C).
    7. Miró, Laia & Gasia, Jaume & Cabeza, Luisa F., 2016. "Thermal energy storage (TES) for industrial waste heat (IWH) recovery: A review," Applied Energy, Elsevier, vol. 179(C), pages 284-301.
    8. Amaral, C. & Vicente, R. & Marques, P.A.A.P. & Barros-Timmons, A., 2017. "Phase change materials and carbon nanostructures for thermal energy storage: A literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 79(C), pages 1212-1228.
    9. Navarro, Lidia & de Gracia, Alvaro & Colclough, Shane & Browne, Maria & McCormack, Sarah J. & Griffiths, Philip & Cabeza, Luisa F., 2016. "Thermal energy storage in building integrated thermal systems: A review. Part 1. active storage systems," Renewable Energy, Elsevier, vol. 88(C), pages 526-547.
    10. Ahmed Faris Amiri & Sofiane Kichou & Houcine Oudira & Aissa Chouder & Santiago Silvestre, 2024. "Fault Detection and Diagnosis of a Photovoltaic System Based on Deep Learning Using the Combination of a Convolutional Neural Network (CNN) and Bidirectional Gated Recurrent Unit (Bi-GRU)," Sustainability, MDPI, vol. 16(3), pages 1-21, January.
    11. Cox, Sam J. & Kim, Dongsu & Cho, Heejin & Mago, Pedro, 2019. "Real time optimal control of district cooling system with thermal energy storage using neural networks," Applied Energy, Elsevier, vol. 238(C), pages 466-480.
    12. Vassiliades, C. & Agathokleous, R. & Barone, G. & Forzano, C. & Giuzio, G.F. & Palombo, A. & Buonomano, A. & Kalogirou, S., 2022. "Building integration of active solar energy systems: A review of geometrical and architectural characteristics," Renewable and Sustainable Energy Reviews, Elsevier, vol. 164(C).
    13. Halhoul Merabet, Ghezlane & Essaaidi, Mohamed & Ben Haddou, Mohamed & Qolomany, Basheer & Qadir, Junaid & Anan, Muhammad & Al-Fuqaha, Ala & Abid, Mohamed Riduan & Benhaddou, Driss, 2021. "Intelligent building control systems for thermal comfort and energy-efficiency: A systematic review of artificial intelligence-assisted techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    14. Salimian Rizi, Behzad & Pavlak, Gregory & Cushing, Vincent & Heidarinejad, Mohammad, 2023. "Predicting uncertainty of a chiller plant power consumption using quantile random forest: A commercial building case study," Energy, Elsevier, vol. 283(C).
    15. Aml Sayed & Mohamed Ebeed & Ziad M. Ali & Adel Bedair Abdel-Rahman & Mahrous Ahmed & Shady H. E. Abdel Aleem & Adel El-Shahat & Mahmoud Rihan, 2021. "A Hybrid Optimization Algorithm for Solving of the Unit Commitment Problem Considering Uncertainty of the Load Demand," Energies, MDPI, vol. 14(23), pages 1-21, November.
    16. Cao, Guohua & Wu, Lijuan, 2016. "Support vector regression with fruit fly optimization algorithm for seasonal electricity consumption forecasting," Energy, Elsevier, vol. 115(P1), pages 734-745.
    17. Moghtader Gilvaei, Zoleikha & Haghighi Poshtiri, Amin & Mirzazade Akbarpoor, Ali, 2022. "A novel passive system for providing natural ventilation and passive cooling: Evaluating thermal comfort and building energy," Renewable Energy, Elsevier, vol. 198(C), pages 463-483.
    18. Lee, Chien-Chiang & Yan, Jingyang, 2024. "Will artificial intelligence make energy cleaner? Evidence of nonlinearity," Applied Energy, Elsevier, vol. 363(C).
    19. Faraj, Khaireldin & Khaled, Mahmoud & Faraj, Jalal & Hachem, Farouk & Castelain, Cathy, 2020. "Phase change material thermal energy storage systems for cooling applications in buildings: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 119(C).
    20. Sajid, Muhammad Usman & Ali, Hafiz Muhammad, 2019. "Recent advances in application of nanofluids in heat transfer devices: A critical review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 103(C), pages 556-592.
    21. Yayuan Feng & Youxian Huang & Haifeng Shang & Junwei Lou & Ala deen Knefaty & Jian Yao & Rongyue Zheng, 2022. "Prediction of Hourly Air-Conditioning Energy Consumption in Office Buildings Based on Gaussian Process Regression," Energies, MDPI, vol. 15(13), pages 1-19, June.
    22. Jomehzadeh, Fatemeh & Nejat, Payam & Calautit, John Kaiser & Yusof, Mohd Badruddin Mohd & Zaki, Sheikh Ahmad & Hughes, Ben Richard & Yazid, Muhammad Noor Afiq Witri Muhammad, 2017. "A review on windcatcher for passive cooling and natural ventilation in buildings, Part 1: Indoor air quality and thermal comfort assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 736-756.
    23. Bastien, Diane & Athienitis, Andreas K., 2018. "Passive thermal energy storage, part 1: Design concepts and metrics," Renewable Energy, Elsevier, vol. 115(C), pages 1319-1327.
    24. Khajavi, Hamed & Rastgoo, Amir, 2023. "Improving the prediction of heating energy consumed at residential buildings using a combination of support vector regression and meta-heuristic algorithms," Energy, Elsevier, vol. 272(C).
    25. Amasyali, Kadir & El-Gohary, Nora M., 2018. "A review of data-driven building energy consumption prediction studies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1192-1205.
    26. William Nelson & Charles Culp, 2022. "Machine Learning Methods for Automated Fault Detection and Diagnostics in Building Systems—A Review," Energies, MDPI, vol. 15(15), pages 1-20, July.
    27. Bianchi, M. & De Pascale, A. & Melino, F., 2013. "Performance analysis of an integrated CHP system with thermal and Electric Energy Storage for residential application," Applied Energy, Elsevier, vol. 112(C), pages 928-938.
    28. Connolly, D. & Lund, H. & Mathiesen, B.V. & Leahy, M., 2010. "A review of computer tools for analysing the integration of renewable energy into various energy systems," Applied Energy, Elsevier, vol. 87(4), pages 1059-1082, April.
    29. Konstantinos Mira & Francesca Bugiotti & Tatiana Morosuk, 2023. "Artificial Intelligence and Machine Learning in Energy Conversion and Management," Energies, MDPI, vol. 16(23), pages 1-36, November.
    30. Fumey, B. & Weber, R. & Baldini, L., 2019. "Sorption based long-term thermal energy storage – Process classification and analysis of performance limitations: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 111(C), pages 57-74.
    31. Azadeh, A. & Babazadeh, R. & Asadzadeh, S.M., 2013. "Optimum estimation and forecasting of renewable energy consumption by artificial neural networks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 27(C), pages 605-612.
    32. Jiang, Junyu & Yu, Yuanbin & Min, Haitao & Cao, Qiming & Sun, Weiyi & Zhang, Zhaopu & Luo, Chunqi, 2023. "Trip-level energy consumption prediction model for electric bus combining Markov-based speed profile generation and Gaussian processing regression," Energy, Elsevier, vol. 263(PD).
    33. Navarro, Lidia & de Gracia, Alvaro & Niall, Dervilla & Castell, Albert & Browne, Maria & McCormack, Sarah J. & Griffiths, Philip & Cabeza, Luisa F., 2016. "Thermal energy storage in building integrated thermal systems: A review. Part 2. Integration as passive system," Renewable Energy, Elsevier, vol. 85(C), pages 1334-1356.
    34. Kijo-Kleczkowska, Agnieszka & Gnatowski, Adam & Krzywanski, Jaroslaw & Gajek, Marcin & Szumera, Magdalena & Tora, Barbara & Kogut, Krzysztof & Knaś, Krzysztof, 2024. "Experimental research and prediction of heat generation during plastics, coal and biomass waste combustion using thermal analysis methods," Energy, Elsevier, vol. 290(C).
    35. Rocha, Helder R.O. & Fiorotti, Rodrigo & Louzada, Danilo M. & Silvestre, Leonardo J. & Celeste, Wanderley C. & Silva, Jair A.L., 2024. "Net Zero Energy cost Building system design based on Artificial Intelligence," Applied Energy, Elsevier, vol. 355(C).
    36. Tarragona, Joan & Pisello, Anna Laura & Fernández, Cèsar & de Gracia, Alvaro & Cabeza, Luisa F., 2021. "Systematic review on model predictive control strategies applied to active thermal energy storage systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    37. Abdollahi, Nasrin & Rahimi, Masoud, 2020. "Potential of water natural circulation coupled with nano-enhanced PCM for PV module cooling," Renewable Energy, Elsevier, vol. 147(P1), pages 302-309.
    38. Sree Harsha Bandaru & Victor Becerra & Sourav Khanna & Jovana Radulovic & David Hutchinson & Rinat Khusainov, 2021. "A Review of Photovoltaic Thermal (PVT) Technology for Residential Applications: Performance Indicators, Progress, and Opportunities," Energies, MDPI, vol. 14(13), pages 1-48, June.
    39. Fadi Alnaimat & Yasir Rashid, 2019. "Thermal Energy Storage in Solar Power Plants: A Review of the Materials, Associated Limitations, and Proposed Solutions," Energies, MDPI, vol. 12(21), pages 1-19, October.
    40. 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).
    41. Beyne, W. & T'Jollyn, I. & Lecompte, S. & Cabeza, L.F. & De Paepe, M., 2023. "Standardised methods for the determination of key performance indicators for thermal energy storage heat exchangers," Renewable and Sustainable Energy Reviews, Elsevier, vol. 176(C).
    42. Shuyu Li & Rongrong Li, 2017. "Comparison of Forecasting Energy Consumption in Shandong, China Using the ARIMA Model, GM Model, and ARIMA-GM Model," Sustainability, MDPI, vol. 9(7), pages 1-19, July.
    43. Rostami, Sara & Afrand, Masoud & Shahsavar, Amin & Sheikholeslami, M. & Kalbasi, Rasool & Aghakhani, Saeed & Shadloo, Mostafa Safdari & Oztop, Hakan F., 2020. "A review of melting and freezing processes of PCM/nano-PCM and their application in energy storage," Energy, Elsevier, vol. 211(C).
    44. Benamar Bouyeddou & Fouzi Harrou & Bilal Taghezouit & Ying Sun & Amar Hadj Arab, 2022. "Improved Semi-Supervised Data-Mining-Based Schemes for Fault Detection in a Grid-Connected Photovoltaic System," Energies, MDPI, vol. 15(21), pages 1-22, October.
    45. Guelpa, Elisa & Verda, Vittorio, 2019. "Thermal energy storage in district heating and cooling systems: A review," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    46. Cai, Wei & Wen, Xiaodong & Li, Chaoen & Shao, Jingjing & Xu, Jianguo, 2023. "Predicting the energy consumption in buildings using the optimized support vector regression model," Energy, Elsevier, vol. 273(C).
    47. Kingsley Ukoba & Kehinde O. Olatunji & Eyitayo Adeoye & Tien-Chien Jen & Daniel M. Madyira, 2024. "Optimizing renewable energy systems through artificial intelligence: Review and future prospects," Energy & Environment, , vol. 35(7), pages 3833-3879, November.
    48. Singh, Davinder & Rugamba, Tanguy & Katara, Harsh & Grewal, Kuljeet Singh, 2025. "Computationally effective machine learning approach for modular thermal energy storage design," Applied Energy, Elsevier, vol. 377(PA).
    49. Wanruo Lou & Lingai Luo & Yuchao Hua & Yilin Fan & Zhenyu Du, 2021. "A Review on the Performance Indicators and Influencing Factors for the Thermocline Thermal Energy Storage Systems," Energies, MDPI, vol. 14(24), pages 1-19, December.
    50. Zhong, Hai & Wang, Jiajun & Jia, Hongjie & Mu, Yunfei & Lv, Shilei, 2019. "Vector field-based support vector regression for building energy consumption prediction," Applied Energy, Elsevier, vol. 242(C), pages 403-414.
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