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Performance prediction, optimal design and operational control of thermal energy storage using artificial intelligence methods

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  • He, Zhaoyu
  • Guo, Weimin
  • Zhang, Peng

Abstract

Capable of storing and redistributing energy, thermal energy storage (TES) shows a promising applicability in energy systems. Recently, artificial intelligence (AI) technique is gradually playing an important role in automation, information retrieval, decision making, intelligent recognition, monitoring and management. With the assistance of AI techniques, TES systems can become more and more reasonable and intelligent, which paves a new path for the researches on TES. In the present review, a comprehensive literature summarization and analysis on the application of AI techniques to TES is presented. Performance prediction, optimal design, control and operation by means of AI for the TES systems with various applications are discussed and compared. This review shows that AI-based prediction models, like artificial neural network and support vector machine, can accurately estimate the TES performance and the properties of TES materials in a very fast fashion. AI-based optimization algorithms, such as genetic algorithm, particle swarm optimization, and teaching-learning-based optimization are able to optimize the design and operation of the TES systems towards the objectives like higher system efficiency, cost savings, more renewable energy utilization and less environmental impacts. Fuzzy logic can be utilized to properly design and control the TES systems where uncertain and imprecise factors are inevitably present. General strategies of the AI-based TES performance modelling and the completely AI-based design and control of the TES are summarized, while the main limitations are that AI cannot be used to directly unveil the unknown physical mechanism of the TES and that the lack of the comprehensive TES performance database hinders the real-world implementation. On the way to completely intelligent TES systems, further investigations on the enhancement of adaptation and self-improvement capability are necessary. In addition, the potential research topics are pointed out for the future development and deployment referring to the needs of the future smart energy system, intelligent and zero energy building, and smart home.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:rensus:v:156:y:2022:i:c:s1364032121012417
    DOI: 10.1016/j.rser.2021.111977
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    as
    1. Reynolds, Jonathan & Ahmad, Muhammad Waseem & Rezgui, Yacine & Hippolyte, Jean-Laurent, 2019. "Operational supply and demand optimisation of a multi-vector district energy system using artificial neural networks and a genetic algorithm," Applied Energy, Elsevier, vol. 235(C), pages 699-713.
    2. Wang, Jiangjiang & Zhai, Zhiqiang (John) & Jing, Youyin & Zhang, Chunfa, 2010. "Particle swarm optimization for redundant building cooling heating and power system," Applied Energy, Elsevier, vol. 87(12), pages 3668-3679, December.
    3. Yaïci, Wahiba & Entchev, Evgueniy, 2016. "Adaptive Neuro-Fuzzy Inference System modelling for performance prediction of solar thermal energy system," Renewable Energy, Elsevier, vol. 86(C), pages 302-315.
    4. Razavi, M. & Dehghani-sanij, A.R. & Khani, M.R. & Dehghani, M.R., 2015. "Comparing meshless local Petrov–Galerkin and artificial neural networks methods for modeling heat transfer in cisterns," Renewable and Sustainable Energy Reviews, Elsevier, vol. 43(C), pages 521-529.
    5. Sanaye, Sepehr & Fardad, Abbasali & Mostakhdemi, Masoud, 2011. "Thermoeconomic optimization of an ice thermal storage system for gas turbine inlet cooling," Energy, Elsevier, vol. 36(2), pages 1057-1067.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. Wang, Haixin & Yang, Junyou & Chen, Zhe & Li, Gen & Liang, Jun & Ma, Yiming & Dong, Henan & Ji, Huichao & Feng, Jiawei, 2020. "Optimal dispatch based on prediction of distributed electric heating storages in combined electricity and heat networks," Applied Energy, Elsevier, vol. 267(C).
    11. 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.
    12. 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.
    13. Wei, Gaosheng & Wang, Gang & Xu, Chao & Ju, Xing & Xing, Lijing & Du, Xiaoze & Yang, Yongping, 2018. "Selection principles and thermophysical properties of high temperature phase change materials for thermal energy storage: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 1771-1786.
    14. Bravo, Ruben & Ortiz, Carlos & Chacartegui, Ricardo & Friedrich, Daniel, 2021. "Multi-objective optimisation and guidelines for the design of dispatchable hybrid solar power plants with thermochemical energy storage," Applied Energy, Elsevier, vol. 282(PB).
    15. Souliotis, M. & Kalogirou, S. & Tripanagnostopoulos, Y., 2009. "Modelling of an ICS solar water heater using artificial neural networks and TRNSYS," Renewable Energy, Elsevier, vol. 34(5), pages 1333-1339.
    16. Zhou, Yuekuan & Zheng, Siqian & Zhang, Guoqiang, 2020. "Machine-learning based study on the on-site renewable electrical performance of an optimal hybrid PCMs integrated renewable system with high-level parameters’ uncertainties," Renewable Energy, Elsevier, vol. 151(C), pages 403-418.
    17. Zhou, Yuekuan & Zheng, Siqian & Zhang, Guoqiang, 2020. "Machine learning-based optimal design of a phase change material integrated renewable system with on-site PV, radiative cooling and hybrid ventilations—study of modelling and application in five clima," Energy, Elsevier, vol. 192(C).
    18. Srikanth, R. & Nemani, Pavan & Balaji, C., 2015. "Multi-objective geometric optimization of a PCM based matrix type composite heat sink," Applied Energy, Elsevier, vol. 156(C), pages 703-714.
    19. Zhijian Liu & Kejun Liu & Hao Li & Xinyu Zhang & Guangya Jin & Kewei Cheng, 2015. "Artificial Neural Networks-Based Software for Measuring Heat Collection Rate and Heat Loss Coefficient of Water-in-Glass Evacuated Tube Solar Water Heaters," PLOS ONE, Public Library of Science, vol. 10(12), pages 1-16, December.
    20. Seddegh, Saeid & Wang, Xiaolin & Henderson, Alan D. & Xing, Ziwen, 2015. "Solar domestic hot water systems using latent heat energy storage medium: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 517-533.
    21. Lee, Wen-Shing & Chen, Yi -Ting & Wu, Ting-Hau, 2009. "Optimization for ice-storage air-conditioning system using particle swarm algorithm," Applied Energy, Elsevier, vol. 86(9), pages 1589-1595, September.
    22. Kalogirou, Soteris A & Panteliou, Sofia & Dentsoras, Argiris, 1999. "Artificial neural networks used for the performance prediction of a thermosiphon solar water heater," Renewable Energy, Elsevier, vol. 18(1), pages 87-99.
    23. Gil, Antoni & Oró, Eduard & Peiró, Gerard & Álvarez, Servando & Cabeza, Luisa F., 2013. "Material selection and testing for thermal energy storage in solar cooling," Renewable Energy, Elsevier, vol. 57(C), pages 366-371.
    24. González-Roubaud, Edouard & Pérez-Osorio, David & Prieto, Cristina, 2017. "Review of commercial thermal energy storage in concentrated solar power plants: Steam vs. molten salts," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 133-148.
    25. 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.
    26. Lizana, Jesús & Chacartegui, Ricardo & Barrios-Padura, Angela & Valverde, José Manuel, 2017. "Advances in thermal energy storage materials and their applications towards zero energy buildings: A critical review," Applied Energy, Elsevier, vol. 203(C), pages 219-239.
    27. Mayer, Martin János & Szilágyi, Artúr & Gróf, Gyula, 2020. "Environmental and economic multi-objective optimization of a household level hybrid renewable energy system by genetic algorithm," Applied Energy, Elsevier, vol. 269(C).
    28. Barbieri, Enrico Saverio & Melino, Francesco & Morini, Mirko, 2012. "Influence of the thermal energy storage on the profitability of micro-CHP systems for residential building applications," Applied Energy, Elsevier, vol. 97(C), pages 714-722.
    29. Revesz, Akos & Jones, Phil & Dunham, Chris & Davies, Gareth & Marques, Catarina & Matabuena, Rodrigo & Scott, Jim & Maidment, Graeme, 2020. "Developing novel 5th generation district energy networks," Energy, Elsevier, vol. 201(C).
    30. Cavallaro, Fausto, 2010. "Fuzzy TOPSIS approach for assessing thermal-energy storage in concentrated solar power (CSP) systems," Applied Energy, Elsevier, vol. 87(2), pages 496-503, February.
    31. 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.
    32. Buffa, Simone & Cozzini, Marco & D’Antoni, Matteo & Baratieri, Marco & Fedrizzi, Roberto, 2019. "5th generation district heating and cooling systems: A review of existing cases in Europe," Renewable and Sustainable Energy Reviews, Elsevier, vol. 104(C), pages 504-522.
    33. 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.
    34. Keykhah, Sajjad & Assareh, Ehsanolah & Moltames, Rahim & Izadi, Mohsen & Ali, Hafiz Muhammad, 2020. "Heat transfer and fluid flow for tube included a porous media: Assessment and Multi-Objective Optimization Using Particle Swarm Optimization (PSO) Algorithm," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).
    35. 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.
    36. Zhou, Yuekuan & Zheng, Siqian & Liu, Zhengxuan & Wen, Tao & Ding, Zhixiong & Yan, Jun & Zhang, Guoqiang, 2020. "Passive and active phase change materials integrated building energy systems with advanced machine-learning based climate-adaptive designs, intelligent operations, uncertainty-based analysis and optim," Renewable and Sustainable Energy Reviews, Elsevier, vol. 130(C).
    37. Cai, Genchun & Liang, Yunmin & Liu, Zhichun & Liu, Wei, 2020. "Design and optimization of bio-inspired wave-like channel for a PEM fuel cell applying genetic algorithm," Energy, Elsevier, vol. 192(C).
    38. Scapino, Luca & Zondag, Herbert A. & Diriken, Jan & Rindt, Camilo C.M. & Van Bael, Johan & Sciacovelli, Adriano, 2019. "Modeling the performance of a sorption thermal energy storage reactor using artificial neural networks," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    39. Zhou, Yuekuan & Zheng, Siqian, 2020. "Multi-level uncertainty optimisation on phase change materials integrated renewable systems with hybrid ventilations and active cooling," Energy, Elsevier, vol. 202(C).
    40. Sharma, Sumedha & Dua, Amit & Singh, Mukesh & Kumar, Neeraj & Prakash, Surya, 2018. "Fuzzy rough set based energy management system for self-sustainable smart city," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 3633-3644.
    41. Luo, Na & Hong, Tianzhen & Li, Hui & Jia, Ruoxi & Weng, Wenguo, 2017. "Data analytics and optimization of an ice-based energy storage system for commercial buildings," Applied Energy, Elsevier, vol. 204(C), pages 459-475.
    42. Denis Sidorov & Fang Liu & Yonghui Sun, 2020. "Machine Learning for Energy Systems," Energies, MDPI, vol. 13(18), pages 1-6, September.
    43. Chang Liu & Mao-Song Cheng & Bing-Chen Zhao & Zhi-Min Dai, 2017. "A Wind Power Plant with Thermal Energy Storage for Improving the Utilization of Wind Energy," Energies, MDPI, vol. 10(12), pages 1-20, December.
    44. Eltamaly, Ali M. & Al-Saud, M.S. & Abokhalil, Ahmed G. & Farh, Hassan M.H., 2020. "Simulation and experimental validation of fast adaptive particle swarm optimization strategy for photovoltaic global peak tracker under dynamic partial shading," Renewable and Sustainable Energy Reviews, Elsevier, vol. 124(C).
    45. Palacios, A. & Barreneche, C. & Navarro, M.E. & Ding, Y., 2020. "Thermal energy storage technologies for concentrated solar power – A review from a materials perspective," Renewable Energy, Elsevier, vol. 156(C), pages 1244-1265.
    46. Lin, Wenye & Ma, Zhenjun & Ren, Haoshan & Gschwander, Stefan & Wang, Shugang, 2019. "Multi-objective optimisation of thermal energy storage using phase change materials for solar air systems," Renewable Energy, Elsevier, vol. 130(C), pages 1116-1129.
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