Open Access
A comparative study of machine learning approaches for an accurate predictive modeling of solar energy generation
Publication type: Journal Article
Publication date: 2024-12-01
scimago Q1
wos Q2
SJR: 1.172
CiteScore: 11.7
Impact factor: 5.1
ISSN: 23524847
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Metrics
12
Total citations:
12
Citations from 2024:
11
(91.67%)
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GOST
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Chaaban A. K., Chaaban K., Alfadl N. A comparative study of machine learning approaches for an accurate predictive modeling of solar energy generation // Energy Reports. 2024. Vol. 12. pp. 1293-1302.
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Chaaban A. K., Chaaban K., Alfadl N. A comparative study of machine learning approaches for an accurate predictive modeling of solar energy generation // Energy Reports. 2024. Vol. 12. pp. 1293-1302.
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RIS
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TY - JOUR
DO - 10.1016/j.egyr.2024.07.010
UR - https://linkinghub.elsevier.com/retrieve/pii/S2352484724004347
TI - A comparative study of machine learning approaches for an accurate predictive modeling of solar energy generation
T2 - Energy Reports
AU - Chaaban, Alain K.
AU - Chaaban, Khaled
AU - Alfadl, Najd
PY - 2024
DA - 2024/12/01
PB - Elsevier
SP - 1293-1302
VL - 12
SN - 2352-4847
ER -
Cite this
BibTex (up to 50 authors)
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@article{2024_Chaaban,
author = {Alain K. Chaaban and Khaled Chaaban and Najd Alfadl},
title = {A comparative study of machine learning approaches for an accurate predictive modeling of solar energy generation},
journal = {Energy Reports},
year = {2024},
volume = {12},
publisher = {Elsevier},
month = {dec},
url = {https://linkinghub.elsevier.com/retrieve/pii/S2352484724004347},
pages = {1293--1302},
doi = {10.1016/j.egyr.2024.07.010}
}