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Dr. Patil Ganesh Sampathrao is an Assistant Professor in the Department of Technology at Shivaji University, Kolhapur, Maharashtra. His contribution focuses on machine learning applications in renewable-energy forecasting and smart-grid management
The swift incorporation of renewable energy sources into contemporary power systems has generated both fresh prospects and considerable obstacles for electricity production, grid stability, energy equilibrium, and demand regulation. Solar and wind energy are intrinsically inconsistent as their production relies on meteorological and environmental factors. Precise predictions and astute grid administration are thus crucial for ensuring dependability and enhancing the use of renewable energy. Machine Learning (ML) offers robust data-centric methodologies for examining past energy generation, meteorological factors, electricity consumption, and grid operational metrics to forecast renewable energy output and facilitate sophisticated power system administration. This chapter elucidates the principles, techniques, and uses of machine learning in predicting renewable energy and managing smart grids. It addresses the forecasting of solar and wind energy, electricity demand prediction, energy price estimation, feature engineering, time-series analysis, and optimisation techniques. The amalgamation of machine learning with the Internet of Things, edge computing, cloud infrastructures, and digital twins is also examined.