Prediction Techniques for Renewable Energy Generation and Load Demand Forecasting

This book offers a detailed look into forecasting methods for renewable energy generation and load demand, making it essential for researchers and students alike.

In Prediction Techniques for Renewable Energy Generation and Load Demand Forecasting, readers are introduced to a comprehensive array of forecasting methods specifically designed for renewable energy sources integrated into existing power grids. This book serves as an essential resource for students and researchers who are navigating the complex landscape of renewable energy and its integration into electrical distribution networks. By offering a detailed exploration of both generation-side and load forecasting methods, it equips readers with the knowledge necessary to understand and apply these techniques effectively.

The book is organized into two main sections: the first focuses on forecasting methods for energy generation, while the second delves into various approaches for load forecasting. It covers a wide range of topics, including artificial intelligence, machine learning, and hybrid techniques, showcasing state-of-the-art methodologies for predicting renewable energy generation and load demands. This thorough examination reflects the advancements in distributed generation systems and the evolving landscape of future microgrids.

Furthermore, Prediction Techniques for Renewable Energy Generation and Load Demand Forecasting combines theoretical foundations with practical applications, featuring algorithms, simulations, and case studies. This multifaceted approach provides invaluable insights that will benefit those engaged in the fields of renewable energy and grid integration, making it a pivotal resource for advancing knowledge and fostering innovation in this critical area of study.