A new approach for predicting solar radiation based on a pattern search algorithm A new approach for predicting solar radiation based on a pattern search algorithm

Over the years, accurate prediction of global solar radiation (GSR) is a crucial concern for the design and planning of solar energy systems. Various academic research studies are conducted to precisely predict the GSR, particularly during the development and operational phases of solar energy syste...

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Bibliographic Details
Published in:Theoretical and applied climatology Vol. 156; no. 1; p. 31
Main Authors: Kaplan, Yusuf Alper, Tolun, Gülizar Gizem, Kaplan, Ayşe Gül
Format: Journal Article
Language:English
Published: Vienna Springer Vienna 01.01.2025
Springer Nature B.V
Subjects:
Algorithms
Aquatic Pollution
Atmospheric Protection/Air Quality Control/Air Pollution
Atmospheric Sciences
Climatology
Earth and Environmental Science
Earth Sciences
Energy
Pattern search
Performance evaluation
power generation
prediction
Prediction models
Predictions
Radiation
Search algorithms
Search methods
Solar energy
Solar power
Solar power generation
Solar radiation
Statistical analysis
Statistical models
Statistical tests
System effectiveness
Waste Water Technology
Water Management
Water Pollution Control
ISSN:0177-798X, 1434-4483
Online Access:Get full text
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Summary:Over the years, accurate prediction of global solar radiation (GSR) is a crucial concern for the design and planning of solar energy systems. Various academic research studies are conducted to precisely predict the GSR, particularly during the development and operational phases of solar energy systems. Especially, the amount of solar energy that may be generated would enhance the effectiveness of solar power generation systems and have a favourable impact on investments in solar energy systems. The pattern search algorithm (PSA) is utilised throughout multiple fields in numerous applied and engineering disciplines. Upon examining the literature, it is evident that researchers employ a broad variety of techniques and algorithms as models for predicting the GSR. However, the GSR prediction model was initially constructed using the PSA, and its performance was thoroughly evaluated through multiple statistical tests. This paper examines the performance of the pattern search method in constructing a GSR prediction model. The implementation of the PSA approach, which is rarely the primary preference in GSR prediction models, introduces originality to the study and illuminates a unique path in the literature. The acquired test outcomes were contrasted with many GSR prediction models commonly employed in the literature. The results clearly demonstrated that this technique is applicable as a GSR prediction model.
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ISSN:0177-798X
1434-4483
DOI:10.1007/s00704-024-05234-9