2021
DOI: 10.1016/j.solener.2021.08.049
|View full text |Cite
|
Sign up to set email alerts
|

Projection of future daily global horizontal irradiance under four RCP scenarios: An assessment through newly developed temperature and rainfall-based empirical model

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2

Citation Types

0
2
0

Year Published

2023
2023
2024
2024

Publication Types

Select...
4

Relationship

0
4

Authors

Journals

citations
Cited by 4 publications
(2 citation statements)
references
References 71 publications
0
2
0
Order By: Relevance
“…In an attempt to use climatic variables in the vulnerability assessment, general circulation models (GCMs) are the most used methods to project future climate [16][17][18]. GCMs simulate the climate variables by solving complex differential equations capable of basing their projections on representative concentration pathway (RCP) scenarios [19] that represent greenhouse gas emissions. GCMs are major sources used to explore the complexity of climate and give quantitative measures of future weather [18].…”
Section: Introductionmentioning
confidence: 99%
See 1 more Smart Citation
“…In an attempt to use climatic variables in the vulnerability assessment, general circulation models (GCMs) are the most used methods to project future climate [16][17][18]. GCMs simulate the climate variables by solving complex differential equations capable of basing their projections on representative concentration pathway (RCP) scenarios [19] that represent greenhouse gas emissions. GCMs are major sources used to explore the complexity of climate and give quantitative measures of future weather [18].…”
Section: Introductionmentioning
confidence: 99%
“…GCM downscaling via flexible methods, such as artificial neural networks (ANNs), have become popular in recent years [20]. The downscaling technique has been applied to adapt the GCM projections to a basin scale [18,19,21]. Authors such as [22][23][24] recommend investigating the uncertainties associated with the projections of meteorological variables with ANN.…”
Section: Introductionmentioning
confidence: 99%