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Afsar Kamal is a Research Assistant at the Centre for Cyber Security and Digital Industry Revolution, National Defence University of Malaysia, Kuala Lumpur, Malaysia. His role is associated with research in a technology-focused academic environment. His contribution to this volume addresses machine learning applications in renewable energy forecasting and management.
The rising incorporation of renewable energy sources into contemporary power systems has generated an escalating demand for precise forecasting, astute energy management, and dependable decision-support frameworks. Solar photovoltaic and wind energy production is intrinsically inconsistent due to fluctuating weather conditions, atmospheric traits, seasonal variations, and geographical influences. Conventional forecasting techniques may find it challenging to accurately represent the nonlinear interactions between climatic factors, past generation data, and power consumption. This chapter explores the principles of machine learning-driven renewable energy prediction and administration, analyses key algorithms and data repositories, and introduces a cohesive framework for merging forecasting with operational decision processes. It additionally investigates assessment metrics, experimental approaches, uncertainty quantification, cybersecurity, interpretability, and nascent research trajectories. The section underscores that dependable management of renewable energy necessitates precise forecasting, strong optimisation, practical validation, and efficient integration with power system operational limitations.