Horizontal-to-tilted conversion of solar radiation data using machine learning algorithms
Solar radiation is the main input of system design algorithms in solar energy engineering. Solar radiation is usually measured on horizontal surfaces. However, in majority of solar energy applications such as photovoltaics, surfaces are either fixed at certain angles or continuously track the sun fo...
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| Vydáno v: | Engineering applications of artificial intelligence Ročník 153; s. 110951 |
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| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Elsevier Ltd
01.08.2025
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| Témata: | |
| ISSN: | 0952-1976 |
| On-line přístup: | Získat plný text |
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| Shrnutí: | Solar radiation is the main input of system design algorithms in solar energy engineering. Solar radiation is usually measured on horizontal surfaces. However, in majority of solar energy applications such as photovoltaics, surfaces are either fixed at certain angles or continuously track the sun for maximizing energy input. Therefore, converting solar radiation data from horizontal to tilted surfaces is essential. Conventionally, conversion of solar radiation from horizontal to tilted is carried out using analytical methods. As with many other disciplines in science and technology, machine learning has recently been successfully applied also to solar radiation modelling to solve various problems such as in-advance forecasting of solar radiation. In the present article, solar radiation collected on horizontal surface is converted to tilted surface by machine learning algorithms and compared to solar radiation measured at a tilted surface. Eight different machine learning algorithms have been presently used for the conversion of solar radiation data. Accuracy of the models has been assessed based on a total of seven statistical metrics commonly used in literature. Overall, extra trees algorithm led to the best results as indicated by the statistical metrics used, for example, the mean absolute error of 7.3219 and coefficient of determination 0.9964. Based on the results presently obtained, it is demonstrated that machine learning led to an improved prediction when compared to the analytical models. The present research highlights the crucial significance of such advanced techniques, emphasizing their potential to drive a paradigm shift in solar energy engineering. |
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| ISSN: | 0952-1976 |
| DOI: | 10.1016/j.engappai.2025.110951 |