Renewable energy sources like wind and solar power play an increasingly vital role in the transition towards sustainable energy systems. Accurate forecasting of renewable energy generation is essential for grid management, energy trading, and resource optimization. This abstract delves into the application of data science and machine learning techniques for renewable energy forecasting. Utilizing historical weather data, energy production records, and environmental factors, data science methodologies are employed to preprocess and analyze this wealth of information. Machine learning algorithms, including neural networks, decision trees, and time series models, are then deployed to make predictions. These models account for complex, nonlinear relationships between variables, enabling more precise and reliable forecasts. The benefits of such an approach are substantial. Improved forecasting accuracy helps grid operators integrate renewable energy seamlessly, reducing reliance on fossil fuels and minimizing greenhouse gas emissions. Moreover, it enhances energy market efficiency, investment decisions, and grid stability. This abstract highlights the critical role of data science and machine learning in harnessing the full potential of renewable energy sources and advancing the transition to a sustainable energy future....
Authors: Dr. Aniruddha D. Shelotkar, Dr. Nilesh S. Pohokar, Dr. Sunil R. Gupta, Ms. Priyanka A. Chorey.