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C. H. Mallareddy is an Assistant Professor in Electrical Engineering at N K Orchid College of Engineering and Technology, Solapur, Maharashtra. His contribution focuses on machine learning for 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.