Application of artificial neural network for predicting performance of solid desiccant cooling systems – A review
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DOI: 10.1016/j.rser.2017.05.169
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- Fong, K.F. & Lee, C.K., 2018. "Impact of adsorbent characteristics on performance of solid desiccant wheel," Energy, Elsevier, vol. 144(C), pages 1003-1012.
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- Feng, Y.H. & Dai, Y.J. & Wang, R.Z. & Ge, T.S., 2022. "Insights into desiccant-based internally-cooled dehumidification using porous sorbents: From a modeling viewpoint," Applied Energy, Elsevier, vol. 311(C).
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- Su, Minqi & Han, Xiaoqu & Dai, Yanbing & Wang, Jinshi & Liu, Jiping & Yan, Junjie, 2024. "Investigation on recirculated regenerative solid desiccant-assisted dehumidification system: Impact of system configurations and desiccant materials," Energy, Elsevier, vol. 286(C).
- 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.
- Gun Il Kim & Beakcheol Jang, 2023. "Petroleum Price Prediction with CNN-LSTM and CNN-GRU Using Skip-Connection," Mathematics, MDPI, vol. 11(3), pages 1-16, January.
- 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.
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Keywords
ANN; Desiccant cooling; Dehumidifier; COP; Regeneration;All these keywords.
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