ISI Report

Renewable energy has emerged as a critical component in the global pursuit of sustainable development and carbon neutrality. However, the inherent challenges associated with renewable energy sources—such as intermittency, variability, and storage limitations—necessitate innovative solutions to enhance efficiency and reliability. The integration of Machine Learning (ML) and Artificial Intelligence (AI) has revolutionized the energy sector by optimizing renewable energy generation, forecasting demand, and improving grid stability. Data Science plays a pivotal role in processing vast amounts of energy-related data, enabling accurate predictions and data-driven decision- making. This paper explores how ML, AI, and Data Science contribute to advancements in renewable energy technologies, covering aspects such as predictive maintenance, smart grids, and energy storage optimization. A comprehensive literature review presents key research findings in the domain, demonstrating the application of AI and ML in energy management and predictive modeling. The research methodology section outlines the data-driven approaches used to optimize energy utilization, followed by an in-depth analysis of results obtained from AI-driven models. The study concludes with insights into future research directions, policy implications, and the potential of AI-augmented energy systems in fostering a more resilient and sustainable energy future. Machine Learning (ML) and Artificial Intelligence (AI) play a pivotal role in advancing renewable energy by leveraging data science to optimize energy generation, distribution, and consumption. Through predictive analytics, ML models enhance the efficiency of solar and wind power by forecasting energy output based on weather patterns, historical data, and real-time inputs. AI-driven algorithms improve grid stability by balancing supply and demand, reducing energy wastage, and integrating diverse renewable sources. Additionally, data science enables fault detection, predictive maintenance, and energy storage optimization, ensuring a more reliable and cost-effective renewable energy infrastructure. As AI and ML continue to evolve, their application in renewable energy promises a more sustainable and efficient future.