Renewable Energy Transition in India: Role of Artificial Intelligence in Optimising Renewable Energy Generation and Distribution

Main Article Content

Dr. Sweety Supriya

Abstract

India is prioritising the deployment of renewable energy as a central pillar of its sustainable development policy and climate action plan. This shift towards renewable energy systems presents complex operational constraints arising from the intermittency of renewable energy sources, information asymmetries in forecasting power requirements, and the need for smart and robust energy infrastructure. In this context, this review paper aims to explore the evolving role and potential of Artificial Intelligence (AI) in facilitating sustainable energy transitions. Drawing on interdisciplinary literature, this paper explores the application of AI to data-driven decision-making to enhance renewable energy forecasting and intelligent energy storage management, thereby improving grid stability. Further, by drawing on theoretical and empirical insights, the paper seeks to contribute to the identification of key pathways, limitations, and policy-oriented considerations for shaping the future deployment of AI in sustainable energy production and distribution. The paper finds that recent developments in AI models and machine learning-based technologies, and their deployment in the renewable energy ecosystem, hold great potential for advancing renewable energy generation and distribution.

Downloads

Download data is not yet available.

Article Details

Section

Articles

Author Biography

Dr. Sweety Supriya, Assistant Professor, Department of Electronics, L. S. College, Muzaffarpur (Bihar), India.



How to Cite

[1]
Dr. Sweety Supriya , Tran., “Renewable Energy Transition in India: Role of Artificial Intelligence in Optimising Renewable Energy Generation and Distribution”, IJIES, vol. 13, no. 5, pp. 1–8, May 2026, doi: 10.35940/ijies.D1148.13050526.
Share |

References

PIB (Press Information Bureau), Government of India. (2019). The Cabinet approves measures to promote the hydro power sector. https://www.pib.gov.in/PressReleaseIframePage.aspx?PRID=1567817&reg=3&lang=2

PIB, Government of India. (2023) India is committed to achieving the Net Zero emissions target by 2070.

https://www.pib.gov.in/Pressreleaseshare.aspx?PRID=1961797&reg=3&lang=2

Krishnamurthy, S., Adewuyi, O. B., & Salimon, S. A. (2026). Recent advances in artificial intelligence-based optimization for power system applications: A review of techniques, challenges, and future directions. Renewable and Sustainable Energy Reviews, 226, 116340.

DOI: https://doi.org/10.1016/j.rser.2025.116340

Kumaresh, S. S., Devarapalli, R., García Márquez, F. P., & Acaroğlu, H. (2025). A comprehensive review of optimization, market strategies, and AI applications in energy storage systems. Evolutionary Intelligence, 18(4), 1-23. DOI: https://doi.org/10.1007/s12065-025-01066-2

Cavus, M., Ayan, H., Bell, M., & Dissanayake, D. (2025). Advances in Energy Storage, AI Optimisation, and Cybersecurity for Electric Vehicle Grid Integration. Energies (19961073), 18(17). DOI: https://doi.org/10.3390/en18174599

Mintz, Yoav, and Ronit Brodie (2019). Introduction to artificial intelligence in medicine. Minimally Invasive Therapy & Allied Technologies 28.2 (2019): 73-81. DOI: https://doi.org/10.1080/13645706.2019.1575882

Kaplan, A., & Haenlein, M. (2019). Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Business horizons, 62(1), 15-25. DOI: https://doi.org/10.1016/j.bushor.2018.08.004

Mitchell, M. (2019). Artificial intelligence: A guide for thinking humans. Penguin UK.

Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., ... & Vayena, E. (2018). AI4People—An ethical framework for a good AI society: Opportunities, risks, principles, and recommendations. Minds and machines, 28(4), 689-707.

DOI: https://doi.org/10.1007/s11023-018-9482-5

Brynjolfsson, E., & McAfee, A. N. D. R. E. W. (2017). The business of artificial intelligence. Harvard Business Review, 7(1). https://people.dsv.su.se/~perjons/AI%20and%20Healthcare/The%20Business%20of%20Artificial%20Intelligence.pdf

Liu, P., Lai, Y., & Liu, D. (2024). Artificial intelligence research in organizations: a bibliometric approach.

DOI: https://doi.org/10.1080/23311975.2024.2408439

Kusiak, A. (2018). Smart manufacturing. International journal of production Research, 56(1-2), 508-517.

https://doi.org/10.1080/00207543.2017.1351644

Feng, F., Li, J., Zhang, F., & Sun, J. (2024). The impact of artificial intelligence on green innovation efficiency: Moderating role of dynamic capability. International Review of Economics & Finance, 96, 103649. DOI: https://doi.org/10.1016/j.iref.2024.103649

Bag, S., Pretorius, J. H. C., Gupta, S., & Dwivedi, Y. K. (2021). Role of institutional pressures and resources in the adoption of big-data analytics-powered artificial intelligence, sustainable manufacturing practices, and circular-economy capabilities. Technological Forecasting and Social Change, 163, 120420. approach. Cogent Business & Management, 11(1), 2408439. DOI: https://doi.org/10.1016/j.techfore.2020.120420

Brynjolfsson, E., Rock, D., & Syverson, C. (2021). The productivity J-curve: How intangibles complement general purpose technologies. American Economic Journal: Macroeconomics, 13(1), 333-372. DOI: https://doi.org/10.1257/mac.20180386

Bazionis, I. K., & Georgilakis, P. S. (2021). Review of deterministic and probabilistic wind power forecasting: Models, methods, and future research. Electricity, 2(1), 13-47. DOI: https://doi.org/10.3390/electricity2010002

Vázquez-Canteli, J. R., & Nagy, Z. (2019). Reinforcement learning for demand response: A review of algorithms and modelling techniques. Applied energy, 235, 1072-1089. DOI: https://doi.org/10.1016/j.apenergy.2018.11.002

Divakar, B., Ali, A., Sengupta, D., Sebastiao, B. G., Priyatharsini, G. S., & Babu, B. H. (2025, May). Big data analytics in renewable energy management for intelligent smart grid operation. IEEE. DOI: https://doi.org/10.1109/icSmartGrid66138.2025.11071786

Gupta, R., & Chaturvedi, K. T. (2023). Adaptive energy management of big data analytics in smart grids. Energies, 16(16), 6016.

DOI: https://doi.org/10.3390/en16166016

Choudhary, S. K., & Mondal, A. (2025). Utilization of computer vision and machine learning for solar power prediction. In Computer Vision and Machine Intelligence for Renewable Energy Systems (pp. 67-84). Elsevier. DOI: https://doi.org/10.1016/B978-0-443-28947-7.00004-5

Mucomole, F. V., Silva, C. A. S., & Magaia, L. L. (2025). Parametric Forecast of Solar Energy over Time by Applying Machine Learning Techniques: Systematic Review. Energies, 18(6), 1460. DOI: https://doi.org/10.3390/en18061460

Rajaperumal, T. A., & Christopher Columbus, C. (2025). Enhanced wind power forecasting using machine learning, deep learning models and ensemble integration. Scientific Reports, 15(1), 20572. DOI: https://doi.org/10.1038/s41598-025-05250-3

Oladapo, B. I., Olawumi, M. A., & Omigbodun, F. T. (2024). Machine learning for optimising renewable energy and grid efficiency. Atmosphere, 15(10), 1250. DOI: https://doi.org/10.3390/atmos15101250

Rodríguez-Aburto, C., Montaño-Pisfil, J., Santos-Mejía, C., Morcillo-Valdivia, P., Solís-Farfán, R., Curay-Tribeño, J., ... & Arroyo-Paz, A. (2025). Machine Learning for Photovoltaic Power Forecasting Integrated with Energy Storage Systems: A Scientometric Analysis, Systematic Review, and Meta-Analysis. Energies, 18(23), 6291. DOI: https://doi.org/10.3390/en18236291

Al-Dahidi, S., Hammad, B., Alrbai, M., & Al-Abed, M. (2024). A novel dynamic/adaptive K-nearest neighbor model for the prediction of solar photovoltaic systems’ performance. Results in Engineering, 22, 102141. DOI: https://doi.org/10.1016/j.rineng.2024.102141

Runge, J., & Zmeureanu, R. (2021). A review of deep learning techniques for forecasting energy use in buildings. Energies, 14(3), 608.

DOI: https://doi.org/10.3390/en14030608

Chakraborty, D., Mondal, J., Barua, H. B., & Bhattacharjee, A. (2023). Computational solar energy–Ensemble learning methods for prediction of solar power generation based on meteorological parameters in Eastern India. Renewable energy focus, 44, 277-294.

DOI: https://doi.org/10.1016/j.ref.2023.01.006

Mohan, R., & Pachauri, N. (2025). An ensemble model for the energy consumption prediction of residential buildings. Energy, 314, 134255.

DOI: https://doi.org/10.1016/j.energy.2024.134255

Zemouri, N., Mezaache, H., Zemali, Z., La Foresta, F., Versaci, M., & Angiulli, G. (2025). Hybrid AI-based framework for renewable energy forecasting: one-stage decomposition and sample entropy reconstruction with least-squares regression. Energies, 18(11), 2942.

DOI: https://doi.org/10.3390/en18112942

Tandon, A., Awasthi, A., Pattnayak, K. C., Tandon, A., Choudhury, T., & Kotecha, K. (2025). Machine learning-driven solar irradiance prediction: advancing renewable energy in Rajasthan. Discover Applied Sciences, 7(2), 107. DOI: https://doi.org/10.1007/s42452-025-06490-8

Dewangan, C. L., Singh, S. N., & Chakrabarti, S. (2020). Combining forecasts of day-ahead solar power. Energy, 202, 117743.

DOI: https://doi.org/10.1016/j.energy.2020.117743

Tajjour, S., Chandel, S. S., Malik, H., Márquez, F. P. G., & Alotaibi, M. A. (2025). Daily power generation forecasting for a grid-connected solar power plant using transfer learning technique: S. Tajjour et al. Applied Intelligence, 55(6), 383. DOI: https://doi.org/10.1007/s10489-024-06090-w

Navarro, M. A., Oliva, D., Ramos-Michel, A., & Haro, E. H. (2023). An analysis of the performance of metaheuristic algorithms for the estimation of parameters in solar cell models. Energy Conversion and Management, 276, 116523. DOI: https://doi.org/10.1016/j.enconman.2022.116523

Ngoy, K. R., Lukong, V. T., Yoro, K. O., Makambo, J. B., Chukwuati, N. C., Ibegbulam, C., ... & Jen, T. C. (2025). Lithium-ion batteries and the future of sustainable energy: A comprehensive review. Renewable and Sustainable Energy Reviews, 223, 115971.

DOI: https://doi.org/10.1016/j.rser.2025.115971

Tang, Z., Yang, Y., & Blaabjerg, F. (2021). Power electronics: The enabling technology for renewable energy integration. CSEE Journal of Power and Energy Systems, 8(1), 39-52. DOI: https://doi.org/10.17775/CSEEJPES.2021.02850

Ukoba, K., Olatunji, K. O., Adeoye, E., Jen, T. C., & Madyira, D. M. (2024). Optimizing renewable energy systems through artificial intelligence: Review and prospects. Energy & Environment, 35(7), 3833-3879. DOI: https://doi.org/10.1177/0958305X241256293

Miraftabzadeh, S. M., Longo, M., Di Martino, A., Saldarini, A., & Faranda, R. S. (2024). Exploring the synergy of artificial intelligence in energy storage systems for electric vehicles. Electronics, 13(10), 1973. DOI: https://doi.org/10.3390/electronics13101973

Lanubile, A., Bosoni, P., Pozzato, G., Allam, A., Acquarone, M., & Onori, S. (2024). Domain knowledge-guided machine learning framework for state of health estimation in Lithium-ion batteries. Communications Engineering, 3(1), 168. DOI: https://doi.org/10.1038/s44172-024-00304-2

Wang, X. T., Wang, J. S., Zhang, S. B., Liu, X., Sun, Y. C., & Shang-Guan, Y. P. (2025). Capacity prediction model for lithium-ion batteries based on bi-directional LSTM neural network optimized by adaptive convergence factor gold rush optimizer. Evolutionary Intelligence, 18(2), 1-30. DOI: https://doi.org/10.1007/s12065-024-01013-7

Shahriar, S. M., Bhuiyan, E. A., Nahiduzzaman, M., Ahsan, M., & Haider, J. (2022). State-of-charge estimation for electric vehicle battery management systems using a hybrid recurrent learning approach with explainable artificial intelligence. Energies, 15(21), 8003.

DOI: https://doi.org/10.3390/en15218003

Oloyede, M. O., Akpakwu, G. A., Myburgh, H. C., De Freitas, A., & Kunatsa, T. (2024). A review on state-of-charge estimation methods, energy storage technologies and state-of-the-art simulators: recent developments and challenges. World Electric Vehicle Journal, 15(9), 381. DOI: https://doi.org/10.3390/wevj15090381

Sylvestrin, G. R., Maciel, J. N., Amorim, M. L. M., Carmo, J. P., Afonso, J. A., Lopes, S. F., & Ando Junior, O. H. (2025). State of the art in electric batteries’ state-of-health (soh) estimation with machine learning: A review. Energies, 18(3), 746. DOI: https://doi.org/10.3390/en18030746

Andriani, T., Hudaya, C., & Garniwa, I. (2025, February). Integration of Artificial Intelligence into Battery Energy Storage System Fault Diagnosis: A Review. In International Congress on Information and Communication Technology (pp. 137-154). Singapore: Springer Nature Singapore. DOI: https://doi.org/10.1007/978-981-96-6432-0_12

Supriya, Sweety (2025), Integrating Solar Energy into Power Grids: India's Path to a Sustainable Energy Future; International Journal of Emerging Science and Engineering (IJESE) DOI: https://doi.org/10.35940/ijese.A1050.13100925

Abrahamsen, F. E., Y. Ai, and M. Cheffena (2021). Communication technologies for smart grid: A comprehensive survey. Sensors 21, no. 23 (2021) DOI: https://doi.org/10.3390/s21238087

Anjana, K. R., & Shaji, R. S. (2018). A review of the features and technologies for energy efficiency of the smart grid. International Journal of Energy Research, 42(3), 936-952. DOI: https://doi.org/10.1002/er.3852

Allal, Z., H. N. Noura, O. Salman, and K. Chahine (2024). Leveraging the power of machine learning and data balancing techniques to evaluate stability in smart grids. Engineering Applications of Artificial Intelligence 133 (2024).

DOI: https://doi.org/10.1016/j.engappai.2024.108304

Hassija, V., Chamola, V., Mahapatra, A., Singal, A., Goel, D., Huang, K., ... & Hussain, A. (2024). Interpreting black-box models: a review on explainable artificial intelligence. Cognitive Computation, 16(1), 45-74. DOI: https://doi.org/10.1007/s12559-023-10179-8

CCI (Competition Commission of India) (2025). Market study on Artificial Intelligence and competition. Government of India. https://www.cci.gov.in/images/marketstudie/en/market-study-on-artificial-intelligence-and-competition1759752172.pdf

Sahoo, S., and P. Timmann. (2023) Energy storage technologies for modern power systems: A detailed analysis of functionalities, potentials, and impacts. DOI: https://doi.org/10.1109/ACCESS.2023.3274504

Most read articles by the same author(s)

<< < 1 2 3 4 5 6 7 8 9 10 > >>