Deep Analysis on Renewable Energy

The world is moving into a more sustainable energy system more rapidly as the need to combat climate change, lessen reliance on fossil fuels, and provide future secure energy sources, increases.In the new energy landscape, artificial intelligence (AI) and machine learning (ML) have emerged as late but potentially revolutionary forms of technology to deal with renewable energy systems' inherent complex, nonlinear, and dynamic challenges.This research paper presents a wide-ranging literature review for recent advancements of AI/ML in renewable energy systems, including solar, wind, hydro, and hybrids.The analysis discusses how machine learning algorithms improve solar radiation and wind speed forecasting for better integration of variable renewable energy to the grid, and examines intelligent optimization strategies to address the optimal power flow (OPF) in smart grids, particularly with embedded contingency intermittent and distributed energy sources.Additionally, the paper looks at AI strategies for energy storage systems, load forecasting, and performance of net-zero energy buildings that utilize predictive control and adaptive learning.Case studies on renewable systems in extreme environments, including Arctic regions and remote islands, show the real-world usability of AI-enhanced configurations.This study is an analysis of solar energy data using supervised machine learning practices, particularly linear regression models, to examine solar powergenerated patterns and to consider how predictive modeling may help predict solar energy production.The study will start with an examination of current renewable energy technologies and also an analysis of realworld solar datasets.This study only ran linear regression in this phase of the research, however it provides opportunity for future research of more complex algorithms, hybrid processes, etc.This study supports the larger aim of intelligent systems in renewable energy planning, while at the same time recognizes the potential of machine learning to improve energy efficiency and forecasting.