Harnessing the Wind: Innovations for a Sustainable Future

Authors

  • Shubham Nanal
  • Ramotar Rajkumar Rajak
  • Shivanshu Mishra
  • Viren Suresh Soni
  • Swaraj Powale
  • Aarya Shaparia

Keywords:

Hydrogen production, Renewable energy, Sustainable energy, Wind energy formatting, Wind power technology

Abstract

This review explores recent advancements in wind energy, focusing on innovation, policy initiatives, and new technologies. It examines how improvements in wind turbine design and learning curve analysis contribute to lowering electricity production costs. The global growth of wind power is discussed, with a special focus on India’s renewable energy strategies and how they compare to other leading nations. The review also looks into the potential of wind energy for hydrogen production, highlighting the role of machine learning in improving efficiency. Additionally, it covers the latest wind energy harvesting technologies and wind farm control strategies, comparing different generator systems for cost-effectiveness and performance. Overall, the findings highlight the importance of continuous technological innovation, supportive government policies, and AI-driven optimization in making wind energy more efficient and sustainable.

References

D. G. V, V. P, K. Asokan, and K. K. Satheesh, “Wind speed forecast using random forest learning method,” Arxiv (Cornell University), Jan. 2022, doi: https://doi.org/10.48550/arxiv.2203.14909.

Y. Li, R. Wang, Y. Li, M. Zhang, and C. Long, “Wind power forecasting considering data privacy protection: A federated deep reinforcement learning approach,” Applied Energy, vol. 329, p. 120291, Jan. 2023, doi: https://doi.org/10.1016/j.apenergy.2022.120291.

D. Lagomarsino-Oneto et al., “Physics informed machine learning for wind speed prediction,” Energy, vol. 268, p. 126628, Apr. 2023, doi: https://doi.org/10.1016/j.energy.2023.126628.

S. Jonas, K. Winter, B. Brodbeck, and A. Meyer, “Bias correction of wind power forecasts with SCADA data and continuous learning,” Arxiv (Cornell University), Feb. 2024, doi: https://doi.org/10.48550/arxiv.2402.13916.

J. Meyers et al., “Wind farm flow control: prospects and challenges,” Wind Energy Science, vol. 7, no. 6, pp. 2271–2306, Nov. 2022, doi: https://doi.org/10.5194/wes-7-2271-2022.

A. Alkesaiberi, F. Harrou, and Y. Sun, “Efficient Wind Power Prediction Using Machine Learning Methods: A Comparative Study,” Energies, vol. 15, no. 7, p. 2327, Mar. 2022, doi: https://doi.org/10.3390/en15072327.

S. Oyucu and A. Aksöz, “Integrating Machine Learning and MLOps for Wind Energy Forecasting: A Comparative Analysis and Optimization Study on Türkiye’s Wind Data,” Applied Sciences, vol. 14, no. 9, pp. 3725–3725, Apr. 2024, doi: https://doi.org/10.3390/app14093725.

V. V. Muhammed, A. Nazar, and S. Maniyath, “Performance Comparison of Machine Learning Algorithms for Wind Energy Forecasting in the Coastal Region of Kerala,” Deleted Journal, vol. 2, no. 12, pp. 2734–2739, Dec. 2024, doi: https://doi.org/10.47392/irjaeh.2024.0378

Z. Liu, H. Guo, Y. Zhang, and Z. Zuo, “A Comprehensive Review of Wind Power Prediction Based on Machine Learning: Models, Applications, and Challenges,” Energies, vol. 18, no. 2, pp. 350–350, Jan. 2025, doi: https://doi.org/10.3390/en18020350

J. Jamii, M. Mansouri, M. Trabelsi, M. F. Mimouni, and W. Shatanawi, “Effective artificial neural network-based wind power generation and load demand forecasting for optimum energy management,” Frontiers in Energy Research, vol. 10, Oct. 2022, doi: https://doi.org/10.3389/fenrg.2022.898413.

S. Park, S. Jung, J. Lee, and J. Hur, “A Short-Term Forecasting of Wind Power Outputs Based on Gradient Boosting Regression Tree Algorithms,” Energies, vol. 16, no. 3, p. 1132, Jan. 2023, doi: https://doi.org/10.3390/en16031132.

S. S. Pattanaik, A. K. Sahoo, and R. Panda, “A Comparative Analysis of KNN and Light GBM Algorithms for Wind Energy Forecasting,” 2023 1st International Conference on Circuits, Power and Intelligent Systems (CCPIS), Sep. 2023, doi: https://doi.org/10.1109/ccpis59145.2023.10291700.

L. Menculini et al., “Comparing Prophet and Deep Learning to ARIMA in Forecasting Wholesale Food Prices,” Forecasting, vol. 3, no. 3, pp. 644–662, Sep. 2021, doi: https://doi.org/10.3390/forecast3030040.

S. T. Ayele and M. B. Ageze, “Adama II wind farm long-term power generation forecasting based on machine learning models,” Scientific African, vol. 21, pp. e01831–e01831, Sep. 2023, doi: https://doi.org/10.1016/j.sciaf.2023.e01831.

A. Javaid et al., “Forecasting Hydrogen Production from Wind Energy in a Suburban Environment Using Machine Learning,” Energies, vol. 15, no. 23, p. 8901, Nov. 2022, doi: https://doi.org/10.3390/en15238901.

Y. Shi, M. Li, J. Wen, Y. Yang, F. Cui, and J. Zeng, “Heat Transfer Efficiency Prediction of Coal-Fired Power Plant Boiler Based on CEEMDAN-NAR Considering Ash Fouling,” Energies, vol. 14, no. 13, p. 4000, Jul. 2021, doi: https://doi.org/10.3390/en14134000.

B. Desalegn, D. Gebeyehu, B. Tamrat, and T. Tadiwose, “Wind energy-harvesting technologies and recent research progresses in wind farm control models,” Frontiers in Energy Research, vol. 11, Feb. 2023, doi: https://doi.org/10.3389/fenrg.2023.1124203.

D. Aguemon , R. Gilles Agbokpanzo,F. Dubas, “Analysis on the Topology and Control of Power Electronics Converters for Wind Energy Conversion Systems,” International Journal of Research and Review (ijrrjournal.com), vol. 8, no. 8, p. 127, 2021, doi: https://doi.org/10.52403/ijrr.20210819.

T. M. Masaud and P. K. Sen, “Modeling and control of doubly fed induction generator for wind power,” North American Power Symposium, pp. 1–8, Aug. 2011, doi: https://doi.org/10.1109/naps.2011.6025122.

M. ortiz, “Teaching ‘Selfish’ Wind Turbines to Share Can Boost Productivity,” Wired, Sep. 15, 2022. Available: https://www.wired.com/story/wind-turbine-efficiency-algorithm/

J. Calma, “Google’s AI weather prediction model is pretty darn good,” The Verge, Dec. 07, 2024. https://www.theverge.com/2024/12/7/24314064/ai-weather-forecast-model-google-deepmind-gencast

WSJ “AI Is Learning to Predict the Weather,” The Wall Street Journal, Jul. 2024. [Online]. Available: https://www.wsj.com/tech/ai/ai-is-learning-to-predict-the-weather-8968fa7e

A. Chaudhary, A. Sharma, A. Kumar, K. Dikshit, and N. Kumar, “Short term wind power forecasting using machine learning techniques,” Journal of Statistics and Management Systems, vol. 23, no. 1, pp. 145–156, Jan. 2020, doi: https://doi.org/10.1080/09720510.2020.1721632.

Published

2025-06-03

How to Cite

Shubham Nanal, Ramotar Rajkumar Rajak, Shivanshu Mishra, Viren Suresh Soni, Swaraj Powale, & Aarya Shaparia. (2025). Harnessing the Wind: Innovations for a Sustainable Future. Journal of Alternative and Renewable Energy Sources, 11(2), 9–15. Retrieved from https://matjournals.net/engineering/index.php/JOARES/article/view/1974

Issue

Section

Articles