STM Journals Peer-reviewed research from Consortium e-Learning Network

Journal of Power Electronics and Power Systems Review Article

Renewable Solar Energy Prediction in India : 2025-2030

  1. Rishaan Bhatia Department of Electronics & Communication Engineering, Daly College Indore

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

References (19)

  1. Singh VP, Ravindra B, Vijay V, Bhatt MS. Forecasting of 5MW solar photovoltaic power plant generation using generalized neural network. 2015 39th National Systems Conference (NSC). 2015:1-6. doi:10.1109/natsys.2015.7489107
  2. Munawar U, Wang Z. A Framework of Using Machine Learning Approaches for Short-Term Solar Power Forecasting. Journal of Electrical Engineering & Technology. 2020;15(2):561-569. 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. Archives of Computational Methods in Engineering. 2023;30(5):3059-3079. 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. Sarita Sheoran, Singh RS, Pasari S, Kulshrestha R. Forecasting of Solar Irradiances using Time Series and Machine Learning Models: A Case Study from India. Applied Solar Energy. 2022;58(1):137-151. 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, et al. Optimizing solar power efficiency in smart grids using hybrid machine learning models for accurate energy generation prediction. Scientific Reports. 2024;14(1). doi:10.1038/s41598-024-68030-5
  8. Voyant C, Notton G, Kalogirou S, Nivet ML, Paoli C, Motte F, et al. Machine learning methods for solar radiation forecasting: A review. Renewable Energy. 2017;105:569-582. 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. Interdisciplinary Environmental Review. 2018;19(1):13. 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 Reports. 2023;9:5788-5798. doi:10.1016/j.egyr.2023.05.027
  11. Krishnan N, Ravi Kumar K, R. SA. Solar radiation forecasting using gradient boosting based ensemble learning model for various climatic zones. Sustainable Energy, Grids and Networks. 2024;38:101312. doi:10.1016/j.segan.2024.101312
  12. Bakhashwain JM. Prediction of global solar radiation using support vector machines. International Journal of Green Energy. 2016;13(14):1467-1472. 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. Journal of Information and Optimization Sciences. 2019;40(2):377-395. 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. Environmental Research Letters. 2021;16(4):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(23):8895. doi:10.3390/en15238895
  17. Jalali SMJ, Ahmadian S, Nakisa B, Khodayar M, Khosravi A, Nahavandi S, et al. Solar irradiance forecasting using a novel hybrid deep ensemble reinforcement learning algorithm. Sustainable Energy, Grids and Networks. 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. Environment International. 2023;173:107848. doi:10.1016/j.envint.2023.107848
  19. Paletta Q, Terrén-Serrano G, Nie Y, Li B, Bieker J, Zhang W, et al. Advances in solar forecasting: Computer vision with deep learning. Advances in Applied Energy. 2023;11:100150. doi:10.1016/j.adapen.2023.100150
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