AI-Driven precision in solar forecasting: Breakthroughs in machine learning and deep learning
The need for accurate solar energy forecasting is paramount as the global push towards renewable energy intensifies. We aimed to provide a comprehensive analysis of the latest advancements in solar energy forecasting, focusing on Machine Learning (ML) and Deep Learning (DL) techniques. The novelty o...
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| Format: | Article |
| Language: | English |
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AIMS Press
2024-09-01
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| Series: | AIMS Geosciences |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/geosci.2024035 |
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| author | Ayesha Nadeem Muhammad Farhan Hanif Muhammad Sabir Naveed Muhammad Tahir Hassan Mustabshirha Gul Naveed Husnain Jianchun Mi |
| author_facet | Ayesha Nadeem Muhammad Farhan Hanif Muhammad Sabir Naveed Muhammad Tahir Hassan Mustabshirha Gul Naveed Husnain Jianchun Mi |
| author_sort | Ayesha Nadeem |
| collection | DOAJ |
| description | The need for accurate solar energy forecasting is paramount as the global push towards renewable energy intensifies. We aimed to provide a comprehensive analysis of the latest advancements in solar energy forecasting, focusing on Machine Learning (ML) and Deep Learning (DL) techniques. The novelty of this review lies in its detailed examination of ML and DL models, highlighting their ability to handle complex and nonlinear patterns in Solar Irradiance (SI) data. We systematically explored the evolution from traditional empirical, including machine learning (ML), and physical approaches to these advanced models, and delved into their real-world applications, discussing economic and policy implications. Additionally, we covered a variety of forecasting models, including empirical, image-based, statistical, ML, DL, foundation, and hybrid models. Our analysis revealed that ML and DL models significantly enhance forecasting accuracy, operational efficiency, and grid reliability, contributing to economic benefits and supporting sustainable energy policies. By addressing challenges related to data quality and model interpretability, this review underscores the importance of continuous innovation in solar forecasting techniques to fully realize their potential. The findings suggest that integrating these advanced models with traditional approaches offers the most promising path forward for improving solar energy forecasting. |
| format | Article |
| id | doaj-art-1b57db6540284cdca68e387f8de3231b |
| institution | Kabale University |
| issn | 2471-2132 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | AIMS Geosciences |
| spelling | doaj-art-1b57db6540284cdca68e387f8de3231b2025-01-24T01:13:55ZengAIMS PressAIMS Geosciences2471-21322024-09-0110468473410.3934/geosci.2024035AI-Driven precision in solar forecasting: Breakthroughs in machine learning and deep learningAyesha Nadeem0Muhammad Farhan Hanif1Muhammad Sabir Naveed2Muhammad Tahir Hassan3Mustabshirha Gul4Naveed Husnain5Jianchun Mi6Department of Mechanical and Manufacturing Engineering, Pak-Austria Fachhochschule-Institute of Applied Sciences and Technology, Mang, Haripur 22621, Khyber Pakhtunkhwa, PakistanDepartment of Energy & Resource Engineering, College of Engineering, Peking University, Beijing 100871, ChinaDepartment of Energy & Resource Engineering, College of Engineering, Peking University, Beijing 100871, ChinaDepartment of Mechanical Engineering, FE&T, Bahauddin Zakariya University, Multan 60000, PakistanDepartment of Mechanical Engineering, FE&T, Bahauddin Zakariya University, Multan 60000, PakistanDepartment of Mechanical Engineering, FE&T, Bahauddin Zakariya University, Multan 60000, PakistanDepartment of Energy & Resource Engineering, College of Engineering, Peking University, Beijing 100871, ChinaThe need for accurate solar energy forecasting is paramount as the global push towards renewable energy intensifies. We aimed to provide a comprehensive analysis of the latest advancements in solar energy forecasting, focusing on Machine Learning (ML) and Deep Learning (DL) techniques. The novelty of this review lies in its detailed examination of ML and DL models, highlighting their ability to handle complex and nonlinear patterns in Solar Irradiance (SI) data. We systematically explored the evolution from traditional empirical, including machine learning (ML), and physical approaches to these advanced models, and delved into their real-world applications, discussing economic and policy implications. Additionally, we covered a variety of forecasting models, including empirical, image-based, statistical, ML, DL, foundation, and hybrid models. Our analysis revealed that ML and DL models significantly enhance forecasting accuracy, operational efficiency, and grid reliability, contributing to economic benefits and supporting sustainable energy policies. By addressing challenges related to data quality and model interpretability, this review underscores the importance of continuous innovation in solar forecasting techniques to fully realize their potential. The findings suggest that integrating these advanced models with traditional approaches offers the most promising path forward for improving solar energy forecasting.https://www.aimspress.com/article/doi/10.3934/geosci.2024035transformersmachine learning (ml)deep learning (dl)solar irradiance (si)artificial neural network (ann)solar forecasting |
| spellingShingle | Ayesha Nadeem Muhammad Farhan Hanif Muhammad Sabir Naveed Muhammad Tahir Hassan Mustabshirha Gul Naveed Husnain Jianchun Mi AI-Driven precision in solar forecasting: Breakthroughs in machine learning and deep learning AIMS Geosciences transformers machine learning (ml) deep learning (dl) solar irradiance (si) artificial neural network (ann) solar forecasting |
| title | AI-Driven precision in solar forecasting: Breakthroughs in machine learning and deep learning |
| title_full | AI-Driven precision in solar forecasting: Breakthroughs in machine learning and deep learning |
| title_fullStr | AI-Driven precision in solar forecasting: Breakthroughs in machine learning and deep learning |
| title_full_unstemmed | AI-Driven precision in solar forecasting: Breakthroughs in machine learning and deep learning |
| title_short | AI-Driven precision in solar forecasting: Breakthroughs in machine learning and deep learning |
| title_sort | ai driven precision in solar forecasting breakthroughs in machine learning and deep learning |
| topic | transformers machine learning (ml) deep learning (dl) solar irradiance (si) artificial neural network (ann) solar forecasting |
| url | https://www.aimspress.com/article/doi/10.3934/geosci.2024035 |
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