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Applications of artificial neural networks for refrigeration, air-conditioning and heat pump systems—A review

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  1. Noye, Sarah & Mulero Martinez, Rubén & Carnieletto, Laura & De Carli, Michele & Castelruiz Aguirre, Amaia, 2022. "A review of advanced ground source heat pump control: Artificial intelligence for autonomous and adaptive control," Renewable and Sustainable Energy Reviews, Elsevier, vol. 153(C).
  2. Noushabadi, Abolfazl Sajadi & Lay, Ebrahim Nemati & Dashti, Amir & Mohammadi, Amir H. & Chofreh, Abdoulmohammad Gholamzadeh & Goni, Feybi Ariani & Klemeš, Jiří Jaromír, 2023. "Insights into modelling and evaluation of thermodynamic and transport properties of refrigerants using machine-learning methods," Energy, Elsevier, vol. 262(PA).
  3. Belman-Flores, J.M. & Barroso-Maldonado, J.M. & Rodríguez-Muñoz, A.P. & Camacho-Vázquez, G., 2015. "Enhancements in domestic refrigeration, approaching a sustainable refrigerator – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 955-968.
  4. Tomasz Tietze & Piotr Szulc & Daniel Smykowski & Andrzej Sitka & Romuald Redzicki, 2021. "Application of Phase Change Material and Artificial Neural Networks for Smoothing of Heat Flux Fluctuations," Energies, MDPI, vol. 14(12), pages 1-17, June.
  5. 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.
  6. 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.
  7. Anjan Rao Puttige & Staffan Andersson & Ronny Östin & Thomas Olofsson, 2020. "A Novel Analytical-ANN Hybrid Model for Borehole Heat Exchanger," Energies, MDPI, vol. 13(23), pages 1-19, November.
  8. Jin Woo Moon & Sung Kwon Jung & Yong Oh Lee & Sangsun Choi, 2015. "Prediction Performance of an Artificial Neural Network Model for the Amount of Cooling Energy Consumption in Hotel Rooms," Energies, MDPI, vol. 8(8), pages 1-18, August.
  9. Huang, Yanjun & Khajepour, Amir & Ding, Haitao & Bagheri, Farshid & Bahrami, Majid, 2017. "An energy-saving set-point optimizer with a sliding mode controller for automotive air-conditioning/refrigeration systems," Applied Energy, Elsevier, vol. 188(C), pages 576-585.
  10. 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.
  11. 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.
  12. Legorburu, Gabriel & Smith, Amanda D., 2018. "Energy modeling framework for optimizing heat recovery in a seasonal food processing facility," Applied Energy, Elsevier, vol. 229(C), pages 151-162.
  13. Tomasz Halon & Ewa Pelinska-Olko & Malgorzata Szyc & Bartosz Zajaczkowski, 2019. "Predicting Performance of a District Heat Powered Adsorption Chiller by Means of an Artificial Neural Network," Energies, MDPI, vol. 12(17), pages 1-11, August.
  14. Zini, Marco & Carcasci, Carlo, 2023. "Machine learning-based monitoring method for the electricity consumption of a healthcare facility in Italy," Energy, Elsevier, vol. 262(PB).
  15. 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).
  16. 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.
  17. Song, Mengjie & Deng, Shiming & Dang, Chaobin & Mao, Ning & Wang, Zhihua, 2018. "Review on improvement for air source heat pump units during frosting and defrosting," Applied Energy, Elsevier, vol. 211(C), pages 1150-1170.
  18. Han, Youhua & Liu, Yang & Lu, Shixiang & Basalike, Pie & Zhang, Jili, 2021. "Electrical performance and power prediction of a roll-bond photovoltaic thermal array under dewing and frosting conditions," Energy, Elsevier, vol. 237(C).
  19. Rong, Xiangyang & Long, Weiguo & Jia, Jikang & Liu, Lianhua & Si, Pengfei & Shi, Lijun & Yan, Jinyue & Liu, Boran & Zhao, Mishen, 2023. "Experimental study on a multi-evaporator mutual defrosting system for air source heat pumps," Applied Energy, Elsevier, vol. 332(C).
  20. Abubaker, Ahmad M. & Darwish Ahmad, Adnan & Salaimeh, Ahmad A. & Akafuah, Nelson K. & Saito, Kozo, 2022. "A novel solar combined cycle integration: An exergy-based optimization using artificial neural network," Renewable Energy, Elsevier, vol. 181(C), pages 914-932.
  21. 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.
  22. Song, Mengjie & Pan, Dongmei & Li, Ning & Deng, Shiming, 2015. "An experimental study on the negative effects of downwards flow of the melted frost over a multi-circuit outdoor coil in an air source heat pump during reverse cycle defrosting," Applied Energy, Elsevier, vol. 138(C), pages 598-604.
  23. Maiorino, Angelo & Del Duca, Manuel Gesù & Aprea, Ciro, 2022. "ART.I.CO. (ARTificial Intelligence for COoling): An innovative method for optimizing the control of refrigeration systems based on Artificial Neural Networks," Applied Energy, Elsevier, vol. 306(PB).
  24. Angelo Maiorino & Manuel Gesù Del Duca & Jaka Tušek & Urban Tomc & Andrej Kitanovski & Ciro Aprea, 2019. "Evaluating Magnetocaloric Effect in Magnetocaloric Materials: A Novel Approach Based on Indirect Measurements Using Artificial Neural Networks," Energies, MDPI, vol. 12(10), pages 1-22, May.
  25. 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.
  26. 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.
  27. Touš, Michal & Pavlas, Martin & Putna, Ondřej & Stehlík, Petr & Crha, Lukáš, 2015. "Combined heat and power production planning in a waste-to-energy plant on a short-term basis," Energy, Elsevier, vol. 90(P1), pages 137-147.
  28. 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.
  29. Huang, Yanjun & Khajepour, Amir & Bagheri, Farshid & Bahrami, Majid, 2016. "Optimal energy-efficient predictive controllers in automotive air-conditioning/refrigeration systems," Applied Energy, Elsevier, vol. 184(C), pages 605-618.
  30. Shi, Lei & Zhang, Shuai & Arshad, Adeel & Hu, Yanwei & He, Yurong & Yan, Yuying, 2021. "Thermo-physical properties prediction of carbon-based magnetic nanofluids based on an artificial neural network," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
  31. Siddiqui, M.U. & Said, S.A.M., 2015. "A review of solar powered absorption systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 42(C), pages 93-115.
  32. Carlos Amaris & Maria E. Alvarez & Manel Vallès & Mahmoud Bourouis, 2020. "Performance Assessment of an NH 3 /LiNO 3 Bubble Plate Absorber Applying a Semi-Empirical Model and Artificial Neural Networks," Energies, MDPI, vol. 13(17), pages 1-20, August.
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