Modeling of Geothermal EAHE Outputs Using  Convolutional Neural Network and Artificial Neural Network in Ghardaia

BOUAMER, Chaima; KIFOUCHE, Abdessalam                         Encadrant

dc.contributor.author	BOUAMER, Chaima
dc.contributor.author	KIFOUCHE, Abdessalam Encadrant
dc.date.accessioned	2025-09-21T09:09:12Z
dc.date.available	2025-09-21T09:09:12Z
dc.date.issued	2025
dc.identifier.uri	https://dspace.univ-ghardaia.edu.dz/xmlui/handle/123456789/9835
dc.description	Spécialité : Automatique	EN_en
dc.description.abstract	Artificial Intelligence (AI) and thermal energy systems are two distinct fields and the role of both in advancing scientific research and supporting sustainable development, making this study saturated with knowledge diversity, where deep learning was exploited to optimise the performance of geothermal heat exchangers. A feed-forward back-propagation network and a convolutional neural network were used to use the data obtained in a specific period to build an accurate predictive model that helps optimise the system performance.	EN_en
dc.language.iso	en	EN_en
dc.publisher	université Ghardaia	EN_en
dc.subject	Earth-Air Heat Exchanger (EAHE), Geothermal Energy, Convolutional Neural Network (CNN),and Artificial Neural Network (ANN) , Deep Learning, Heat Transfer.	EN_en
dc.subject	Échangeur de chaleur terre-air (EAHE), énergie géothermique, réseau neuronal convolutif (CNN), réseau neuronal artificiel (ANN), apprentissage profond, transfert de chaleur.	EN_en
dc.title	Modeling of Geothermal EAHE Outputs Using Convolutional Neural Network and Artificial Neural Network in Ghardaia	EN_en
dc.type	Thesis	EN_en