Rademics Research Institute

Abstract

This book chapter explores the integration of AI and ML techniques in forecasting renewable energy output and optimizing smart grid operations. It delves into the fundamentals of AI and ML, emphasizing their applications in renewable energy systems, such as solar and wind power, to enhance forecasting accuracy and operational efficiency. Various AI-driven models, including supervised learning, deep learning, and reinforcement learning, are examined for their potential in addressing the dynamic and complex nature of energy systems. The chapter also investigates the challenges and opportunities in deploying these advanced techniques for real-time energy forecasting, anomaly detection, and adaptive grid management. By leveraging AI and ML, this work aims to improve decision-making, resource utilization, and sustainability in smart grids. Key concepts covered include energy forecasting, machine learning, deep learning, anomaly detection, smart grids, and reinforcement learning. This comprehensive analysis provides valuable insights for future research and practical applications in energy management.

Introduction

The rapid adoption of renewable energy sources such as solar, wind, and hydroelectric power was reshaping global energy systems [1]. Unlike traditional energy generation methods, renewable sources are intermittent, affected by environmental factors like weather and time of day [2]. This variability introduces significant challenges in predicting energy output and balancing supply with demand in real time [3]. Accurate forecasting of renewable energy was crucial for optimizing energy storage, grid management, and minimizing the reliance on non-renewable backup systems [4]. In this context, AI and ML have emerged as powerful tools capable of addressing these challenges, providing enhanced prediction accuracy and enabling smart, data-driven decisionmaking [5,6]. The application of AI and ML techniques to renewable energy forecasting revolves around leveraging vast amounts of data to generate predictive models that can anticipate energy production with higher precision [7-9]. These models incorporate data from various sources, including weather forecasts, historical energy consumption patterns, and grid performance metrics. By using algorithms that can learn from past data, AI and ML models can provide forecasts that improve over time, becoming more robust as they encounter diverse scenarios [10,11]. The ability of these technologies to learn from both historical data and real-time inputs makes them wellsuited for dynamic environments such as those seen in renewable energy systems [12,13].