Integrating experimental and theoretical approaches for enhanced machine learning modeling of solar radiation
This study presents a novel hybrid framework for estimating solar radiation components—critical for optimizing solar energy systems—by integrating theoretical solar parameters with meteorological data, an approach seldom explored in literature. A one-year dataset, recorded at 15-minute intervals, wa...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-10-01
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| Series: | Engineering Science and Technology, an International Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2215098625002113 |
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| Summary: | This study presents a novel hybrid framework for estimating solar radiation components—critical for optimizing solar energy systems—by integrating theoretical solar parameters with meteorological data, an approach seldom explored in literature. A one-year dataset, recorded at 15-minute intervals, was collected from a weather station at The Australian University, Kuwait. The theoretical global solar radiation values were validated against 8,784 hourly averaged experimental data points, incorporating key solar parameters such as local time, solar declination, the Equation of Time (EoT), hour angle, and solar zenith angle. These theoretical inputs, combined with meteorological parameters (temperature, humidity, wind speed, and wind direction), were used to train multiple machine learning (ML) models. To improve model accuracy, a four-scenario data treatment strategy was applied, including Gaussian fitting and zero-removal techniques. In total, 28 ML models were evaluated, encompassing linear regression, support vector machines, Gaussian process regression, and neural networks. The Exponential GPR model emerged as the most accurate, achieving R2 values > 0.99 and RMSE as low as 0.13 W/m2. These results represent a significant performance improvement over existing models in the literature, many of which report RMSE values in the range of 20–60 W/m2 and R2 values between 0.85 and 0.98. The findings demonstrate substantial improvements over existing methods and offer a scalable solution for a more precise solar forecasting in various environments. |
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| ISSN: | 2215-0986 |