Machine learning - driven solar forecasting in dust-prone regions for sustainable energy systems
This research focuses on improving solar energy forecasting in dust-affected regions such as the UAE, where frequent dust storms reduce photovoltaic (PV) efficiency by scattering and absorbing sunlight. Many existing models overlook the impact of dust events, leading to inaccurate forecasts during s...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-01-01
|
| Series: | Solar Energy Advances |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S266711312500021X |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849725580557156352 |
|---|---|
| author | Kadhim Hayawi Husna Maliakkal Neethu Venugopal Thanveer Musthafa Hussain Gomathi Bhavani Rajagopalan |
| author_facet | Kadhim Hayawi Husna Maliakkal Neethu Venugopal Thanveer Musthafa Hussain Gomathi Bhavani Rajagopalan |
| author_sort | Kadhim Hayawi |
| collection | DOAJ |
| description | This research focuses on improving solar energy forecasting in dust-affected regions such as the UAE, where frequent dust storms reduce photovoltaic (PV) efficiency by scattering and absorbing sunlight. Many existing models overlook the impact of dust events, leading to inaccurate forecasts during such conditions. To address this, the study develops machine learning models—including LSTM, GRU, and hybrid LSTM-GRU architectures—that incorporate solar, weather, and dust-related features. The models were evaluated across multiple forecasti24 hoursons (1, 6, 12, and 24 hours), demonstrating that including dust-related variables significantly enhances prediction accuracy, particularly for short-term forecasts. Temporal and seasonal analyses revealed that dust events, most frequent in the late afternoon and early spring, correlate with substantial drops in solar power output. The LSTM model consistently outperformed the others, achieving a Mean Absolute Error (MAE) of 0.018034 for a 1-hour horizon when dust features were included. Statistical tests confirmed that dust events significantly affect forecasting accuracy, reinforcing the importance of dust-related features for reliable predictions. This research contributes to optimizing PV power generation in challenging environments, supporting sustainable energy systems and decarbonization efforts. It also offers insights for further model refinement and the inclusion of additional environmental variables. |
| format | Article |
| id | doaj-art-dd2a99956a9f4e02837b7480707e8096 |
| institution | DOAJ |
| issn | 2667-1131 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Solar Energy Advances |
| spelling | doaj-art-dd2a99956a9f4e02837b7480707e80962025-08-20T03:10:25ZengElsevierSolar Energy Advances2667-11312025-01-01510010810.1016/j.seja.2025.100108Machine learning - driven solar forecasting in dust-prone regions for sustainable energy systemsKadhim Hayawi0Husna Maliakkal1Neethu Venugopal2Thanveer Musthafa Hussain3Gomathi Bhavani Rajagopalan4College of Interdisciplinary Studies, Computational Sciences, Zayed University, Abu Dhabi, United Arab Emirates; Correspondence author at. Zayed University, Abu Dhabi Campus, United Arab Emirates.College of Interdisciplinary Studies, Computational Sciences, Zayed University, Abu Dhabi, United Arab EmiratesCollege of Interdisciplinary Studies, Computational Sciences, Zayed University, Abu Dhabi, United Arab EmiratesDepartment of Electrical and Electronics Engineering, Birla Institute of Technology and Science, Pilani, Dubai, United Arab EmiratesDepartment of Electrical and Electronics Engineering, Birla Institute of Technology and Science, Pilani, Dubai, United Arab EmiratesThis research focuses on improving solar energy forecasting in dust-affected regions such as the UAE, where frequent dust storms reduce photovoltaic (PV) efficiency by scattering and absorbing sunlight. Many existing models overlook the impact of dust events, leading to inaccurate forecasts during such conditions. To address this, the study develops machine learning models—including LSTM, GRU, and hybrid LSTM-GRU architectures—that incorporate solar, weather, and dust-related features. The models were evaluated across multiple forecasti24 hoursons (1, 6, 12, and 24 hours), demonstrating that including dust-related variables significantly enhances prediction accuracy, particularly for short-term forecasts. Temporal and seasonal analyses revealed that dust events, most frequent in the late afternoon and early spring, correlate with substantial drops in solar power output. The LSTM model consistently outperformed the others, achieving a Mean Absolute Error (MAE) of 0.018034 for a 1-hour horizon when dust features were included. Statistical tests confirmed that dust events significantly affect forecasting accuracy, reinforcing the importance of dust-related features for reliable predictions. This research contributes to optimizing PV power generation in challenging environments, supporting sustainable energy systems and decarbonization efforts. It also offers insights for further model refinement and the inclusion of additional environmental variables.http://www.sciencedirect.com/science/article/pii/S266711312500021XSustainable energySolar energy forecastingDust impactPhotovoltaic (PV) efficiencyMachine learning |
| spellingShingle | Kadhim Hayawi Husna Maliakkal Neethu Venugopal Thanveer Musthafa Hussain Gomathi Bhavani Rajagopalan Machine learning - driven solar forecasting in dust-prone regions for sustainable energy systems Solar Energy Advances Sustainable energy Solar energy forecasting Dust impact Photovoltaic (PV) efficiency Machine learning |
| title | Machine learning - driven solar forecasting in dust-prone regions for sustainable energy systems |
| title_full | Machine learning - driven solar forecasting in dust-prone regions for sustainable energy systems |
| title_fullStr | Machine learning - driven solar forecasting in dust-prone regions for sustainable energy systems |
| title_full_unstemmed | Machine learning - driven solar forecasting in dust-prone regions for sustainable energy systems |
| title_short | Machine learning - driven solar forecasting in dust-prone regions for sustainable energy systems |
| title_sort | machine learning driven solar forecasting in dust prone regions for sustainable energy systems |
| topic | Sustainable energy Solar energy forecasting Dust impact Photovoltaic (PV) efficiency Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S266711312500021X |
| work_keys_str_mv | AT kadhimhayawi machinelearningdrivensolarforecastingindustproneregionsforsustainableenergysystems AT husnamaliakkal machinelearningdrivensolarforecastingindustproneregionsforsustainableenergysystems AT neethuvenugopal machinelearningdrivensolarforecastingindustproneregionsforsustainableenergysystems AT thanveermusthafahussain machinelearningdrivensolarforecastingindustproneregionsforsustainableenergysystems AT gomathibhavanirajagopalan machinelearningdrivensolarforecastingindustproneregionsforsustainableenergysystems |