DEEP LEARNING MODELS FOR NATURAL GAS DEMAND FORECASTING: A COMPARATIVE STUDY OF MLP, CNN AND LSTM

DEEP LEARNING MODELS FOR NATURAL GAS DEMAND FORECASTING: A COMPARATIVE STUDY OF MLP, CNN AND LSTM

This study aims to investigate the use of various deep learning techniques to predict future residential natural gas consumption in Italy, with a particular emphasis on the correlation between gas consumption and temperature. Four models were evaluated, including Multi-layer Perceptron (MLP), Convol...

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Bibliographic Details
Main Authors: Artemis Aidoni, Konstantinos Kofidis, Catalina Lucia Cocianu, Lazar Avram
Format: Article
Language:English
Published: Petroleum-Gas University of Ploiesti 2023-04-01
Series:Romanian Journal of Petroleum & Gas Technology
Subjects:
gas consumption
forecasting
temperature
deep learning algorithms
Online Access:http://jpgt.upg-ploiesti.ro/wp-content/uploads/2023/04/12_Articol-jurnal-UPG-nr-1_2023_AidoniAetal.pdf

Internet

http://jpgt.upg-ploiesti.ro/wp-content/uploads/2023/04/12_Articol-jurnal-UPG-nr-1_2023_AidoniAetal.pdf

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