This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.
Rishaan Bhatia,
- Student, Department of Electronics & Communication Engineering, Daly College Indore, Madhya Pradesh, India
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As India endeavors to realize its objective of achieving 500 GW of renewable energy capacity by the year 2030, the precise forecasting of solar power generation is rendered increasingly essential. This scholarly article conducts a comprehensive review of the utilization of machine learning (ML) methodologies in the prediction of solar energy across diverse Indian states, underscoring their potential to address the complexities associated with the variability of solar irradiance. Conventional forecasting techniques frequently fall short in capturing the intricate non-linear relationships that exist between meteorological variables and solar energy output. In contrast, ML methodologies, such as artificial neural networks and hybrid models, have demonstrated considerable potential in enhancing the accuracy of predictions. Recent developments indicate that the amalgamation of historical meteorological data with sophisticated algorithms can result in superior forecasting performance. This review accentuates the transformative capacity of machine learning in the optimization of solar energy management and bolsters India’s renewable energy aims by offering insights into effective forecasting strategies. The results highlight the imperative of embracing innovative methodologies to improve the reliability of solar power forecasts, thus enabling a more effective integration of solar energy into the national grid.
Keywords: Solar energy, Prediction, Machine Learning, Renewable energy, India, Indian States, Artificial Intelligence
[This article belongs to Journal of Power Electronics and Power Systems (jopeps)]
Rishaan Bhatia. Renewable Solar Energy Prediction in India : 2025-2030. Journal of Power Electronics and Power Systems. 2024; 14(03):-.
Rishaan Bhatia. Renewable Solar Energy Prediction in India : 2025-2030. Journal of Power Electronics and Power Systems. 2024; 14(03):-. Available from: https://journals.stmjournals.com/jopeps/article=2024/view=0
References
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Journal of Power Electronics and Power Systems
| Volume | 14 |
| Issue | 03 |
| Received | 11/10/2024 |
| Accepted | 18/10/2024 |
| Published | 21/10/2024 |
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