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A comprehensive review on computing methods for the prediction of energy cost in Kingdom of Saudi Arabia
Nayeemuddin MOHAMMED, Andi ASIZ, Mohammad Ali KHASAWNEH, Feroz SHAIK, Hiren MEWADA, Tasneem SULTANA
download PDFAbstract. Addressing the increasing demand for energy in the Kingdom of Saudi Arabia (KSA) poses challenges and opportunities. This necessitates effective energy planning, diversification of energy sources, and implementation of energy-efficient technologies. This study presents the energy scenario in the KSA. Later, various technical algorithms used for energy prediction from past data, including regression models, statistical models, machine learning and deep learning networks, are presented. The present study revealed that learnable models, specifically neural networks, outperformed statistical and regression networks in predicting energy demands. In addition, statistical models lack predictability and lack adoption with new data.
Keywords
Energy, Artificial Neural Network, Prediction Models, Regression, Statistical, Deep Learning
Published online 7/15/2024, 8 pages
Copyright © 2024 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: Nayeemuddin MOHAMMED, Andi ASIZ, Mohammad Ali KHASAWNEH, Feroz SHAIK, Hiren MEWADA, Tasneem SULTANA, A comprehensive review on computing methods for the prediction of energy cost in Kingdom of Saudi Arabia, Materials Research Proceedings, Vol. 43, pp 156-163, 2024
DOI: https://doi.org/10.21741/9781644903216-21
The article was published as article 21 of the book Renewable Energy: Generation and Application
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
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