Renewable Solar Energy Prediction in India : 2025-2030

Year : 2024 | Volume : 14 | Issue : 03 | Page : 41 46
    By

    Rishaan Bhatia,

  1. Student, Department of Electronics & Communication Engineering, Daly College Indore, Madhya Pradesh, India

Abstract

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 of 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 ]

How to cite this article:
Rishaan Bhatia. Renewable Solar Energy Prediction in India : 2025-2030. Journal of Power Electronics and Power Systems. 2024; 14(03):41-46.
How to cite this URL:
Rishaan Bhatia. Renewable Solar Energy Prediction in India : 2025-2030. Journal of Power Electronics and Power Systems. 2024; 14(03):41-46. Available from: https://journals.stmjournals.com/jopeps/article=2024/view=178905


References

  1. Singh VP, Ravindra B, Vijay V, Bhatt MS. Forecasting of 5MW solar photovoltaic power plant generation using generalized neural network. 39th Natl Syst Conf (NSC); 2015 Dec 18-20; New Delhi, India. IEEE. p. 1-6. DOI: 10.1109/NATSYS.2015.7489107.
  2. Munawar U, Wang Z. A framework of using machine learning approaches for short-term solar power forecasting. J Electr Eng Technol. 2020;15:561-9. DOI: 10.1007/s42835-020-00346-4.
  3. Rajasundrapandiyanleebanon T, Kumaresan K, Murugan S, Subathra MSP, Sivakumar M. Solar Energy Forecasting Using Machine Learning and Deep Learning Techniques. Arch Comput Methods Eng. 2023;30:3059-79. DOI: 10.1007/s11831-023-09893-1.
  4. Kumar R, Singh P. Solar power forecasting using machine learning techniques: A case study of Uttar Pradesh. Int J Renew Energy Res. 2021;11:1540-50.
  5. Sheoran S, Singh RS, Pasari S, Kulshrestha R. Forecasting of solar irradiances using time series and machine learning models: A case study from India. Appl Solar Energy. 2022;58:137-51. DOI: 10.3103/S0003701X22010170.
  6. Nair S, et al. Predictive analytics for solar energy generation using deep learning: Insights from Indian states. J Clean Prod. 2022;332:129960.
  7. Bhutta MS, Li Y, Abubakar M, Almasoudi FM, Alatawi KSS, Altimania MR, Al-Barashi M. Optimizing solar power efficiency in smart grids using hybrid machine learning models for accurate energy generation prediction. Sci Rep. 2024;14:17101. DOI: 10.1038/s41598-024-68030-5. PubMed: 39048605.
  8. Voyant C, Notton G, Kalogirou S, Nivet ML, Paoli C, Motte F, Fouilloy A. Machine learning methods for solar radiation forecasting: A review. Renew Energy. 2017;105:569-82. DOI: 10.1016/j.renene.2016.12.095.
  9. Arora P, Malik H, Sharma R. Wind speed forecasting model for northern-western region of India using decision tree and multilayer perceptron neural network approach. Interdiscip Environ Rev. 2018;19:13-30. DOI: 10.1504/IER.2018.089766.
  10. Patel S, Parkins JR. Assessing motivations and barriers to renewable energy development: Insights from a survey of municipal decision-makers in Alberta, Canada. Energy Rep. 2023;9:5788-98. DOI: 10.1016/j.egyr.2023.05.027.
  11. Krishnan N, Ravi Kumar KR, S.A. R. Solar radiation forecasting using gradient boosting based ensemble learning model for various climatic zones. Sustain Energy Grids Netw. 2024;38:101312. DOI: 10.1016/j.segan.2024.101312.
  12. Bakhashwain JM. Prediction of global solar radiation using support vector machines. Int J Green Energy. 2016;13:1467-72. DOI: 10.1080/15435075.2014.896256.
  13. Alain K. Chaaban, Najd Alfadl. A comparative study of machine learning approaches for an accurate predictive modeling of solar energy generation. Energy Reports. Volume 12, December 2024, Pages 1293-1302
  14. Yadav HK, Pal Y, Tripathi MM. A novel GA-ANFIS hybrid model for short-term solar PV power forecasting in Indian electricity market. J Inf Optim Sci. 2019;40:377-95. DOI: 10.1080/02522667.2019.1580880.
  15. Choi M, Rachunok B, Nateghi R. Short-term solar irradiance forecasting using convolutional neural networks and cloud imagery. Environ Res Lett. 2021;16:044045. DOI: 10.1088/1748-9326/abe06d.
  16. Borunda M, Ramírez A, Garduno R, Ruíz G, Hernandez S, Jaramillo OA. Photovoltaic power generation forecasting for regional assessment using machine learning. Energies. 2022;15:8895. DOI: 10.3390/en15238895.
  17. Jalali SMJ, Ahmadian S, Nakisa B, Khodayar M, Khosravi A, Nahavandi S, Islam SMS, Shafie-Khah M, Catalão JPS. Solar irradiance forecasting using a novel hybrid deep ensemble reinforcement learning algorithm. Sustain Energy Grids Netw. 2022;32:100903. DOI: 10.1016/j.segan.2022.100903.
  18. Rakholia R, Le Q, Quoc Ho B, Vu K, Simon Carbajo R. Multi-output machine learning model for regional air pollution forecasting in Ho Chi Minh City, Vietnam. Environ Int. 2023;173:107848. DOI: 10.1016/j.envint.2023.107848. PubMed: 36842381.
  19. Paletta Q, Terrén-Serrano G, Nie Y, Li B, Bieker J, Zhang W, Dubus L, Dev S, Feng C. Advances in solar forecasting: Computer vision with deep learning. Adv Appl Energy. 2023;11:100150. DOI: 10.1016/j.adapen.2023.100150.
Regular Issue Subscription Review Article
Volume 14
Issue 03
Received 11/10/2024
Accepted 19/10/2024
Published 25/10/2024
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