AI-Driven Smart Grid Optimization: Reinforcement Learning and IoT for Renewable Energy Management
This paper explores the integration of Artificial Intelligence (AI), specifically Reinforcement Learning (RL), and the Internet of Things (IoT) in optimizing smart grids for efficient renewable energy management. The renewable energy sector faces challenges due to the intermittency of energy sources such as wind and solar, demanding adaptive solutions to ensure grid stability and maximize energy use. In this study, we examine how RL algorithms can optimize grid operations by learning from dynamic, real-time data generated by IoT devices.
Furthermore, we investigate the role of Software Engineering principles in developing scalable, resilient AI-driven grid management systems. Data Migration techniques ensure seamless transfer of energy-related data from legacy systems to modern AI-powered infrastructures, while Data Analytics enhances decision-making by extracting meaningful patterns from vast datasets. The paper presents a hybrid model where IoT sensors monitor real-time grid conditions and send data to a centralized AI system, enabling adaptive control mechanisms. This approach not only reduces energy waste but also enhances grid reliability and resilience. Simulation results demonstrate the effectiveness of this hybrid system compared to traditional grid management strategies, highlighting the impact of AI, IoT, and advanced computing methodologies on modern renewable energy integration.
