MUMBAI, India, Aug. 17 -- Intellectual Property India has published a patent application (202511028825 A) filed by Prof Shafqat Alauddin; Prin. Dr. Ajay M Bhamare; Dr. S. Prince Samuel; Jana Yonathan; Dr. K. Chiranjeevi Sabitha; Dr. R. Uday Kumar; Vasanthi R; Dr. N. Muguntha Manikandan; Muhammed Anees V. V; Dilip Mishra; Dr. T. Hussain; and Dr. B. Karunamoorthy on March 26, 2025, for Optimizing Renewable Energy Management Through Machine Learning-Based Wind Power Prediction.
Inventors include Prof Shafqat Alauddin; Prin. Dr. Ajay M Bhamare; Dr. S. Prince Samuel; Jana Yonathan; Dr. K. Chiranjeevi Sabitha; Dr. R. Uday Kumar; Vasanthi R; Dr. N. Muguntha Manikandan; Muhammed Anees V. V; Dilip Mishra; Dr. T. Hussain; and Dr. B. Karunamoorthy.
The application for the patent was published on August 14, 2026, under issue no. 33/2026.
Abstract: The present invention relates to the optimization of renewable energy management through machine learning-based wind power prediction. Accurate wind power generation forecasting is essential for improving grid efficiency and reliability as the demand for clean energy grows. To enhance short-term wind energy projections, we look at cutting-edge machine learning methods, such as hybrid deep learning models that incorporate meteorological data. By examining univariate time-series data and applying attention mechanisms, our strategy greatly boosts prediction accuracy compared to previous methods. The results show that improved forecasting enhances power system stability overall and makes it easier to allocate energy resources more effectively. The study lays the groundwork for more robust and sustainable energy management techniques by highlighting the significance of utilizing artificial intelligence in the renewable energy sectors. In the end, accurate wind power forecasting can promote the incorporation of renewable energy sources into current networks, aiding in the shift to a more environmentally friendly future. FIG.1
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