  This study delves into the shift from centralized to decentralized approaches
in the electricity industry, with a particular focus on how machine learning
(ML) advancements play a crucial role in empowering renewable energy sources
and improving grid management. ML models have become increasingly important in
predicting renewable energy generation and consumption, utilizing various
techniques like artificial neural networks, support vector machines, and
decision trees. Furthermore, data preprocessing methods, such as data
splitting, normalization, decomposition, and discretization, are employed to
enhance prediction accuracy.
  The incorporation of big data and ML into smart grids offers several
advantages, including heightened energy efficiency, more effective responses to
demand, and better integration of renewable energy sources. Nevertheless,
challenges like handling large data volumes, ensuring cybersecurity, and
obtaining specialized expertise must be addressed. The research investigates
various ML applications within the realms of solar energy, wind energy, and
electric distribution and storage, illustrating their potential to optimize
energy systems. To sum up, this research demonstrates the evolving landscape of
the electricity sector as it shifts from centralized to decentralized solutions
through the application of ML innovations and distributed decision-making,
ultimately shaping a more efficient and sustainable energy future.
