Electric power networks have undergone considerable changes to meet a range of needs, including environmental compliance, energy conservation, improved grid stability, production performance, and customer service. Microgrid development and optimal operation is a vital step in increasing solar power utilization, improving grid resilience, and expanding the amount of electrical power available in poor countries. Micro grids combine local energy generation and storage, as well as load requirements, and can operate independently or in conjunction with a grid. The idea of a micro grid has been proposed to anticipate the decision in selecting the source to the grids using Machine Learning approach in this article, which outlines a system that delivers day-to-day grid power generation from energy sources (MLTs). We employed four distinct methods to validate the performance of the techniques in this research. In comparison to other methods, we achieved good results with the support vector machine approach.

Renewable Energy, Wind Energy, Smart Grid, K-Nearest Neighborhood, Deep Learning, Optimization.