Neural Grids, Gebunden

Klappentext

Master the future of energy by learning how to apply cutting-edge AI to solve the real-world complexity, intermittency, and security challenges of modern power grids and renewable systems.

The convergence of artificial intelligence and power engineering represents one of the most significant developments in the modern energy landscape. Over the past two decades, the rapid expansion of renewable generation, electrification of transportation, distributed energy resources, and digitalization of power grids has fundamentally reshaped how electricity is produced, transmitted, and consumed. These advancements, while enabling sustainability and decarbonization, have introduced heightened levels of uncertainty, intermittency, and operational complexity. Traditional tools based on deterministic models, linear optimization, or rule-based control are increasingly insufficient for managing the nonlinear dynamics and real-time decision-making required in today's energy systems.

This book is an in-depth exploration of how modern artificial intelligence techniques can be systematically applied to the modeling, control, optimization, and forecasting challenges faced by power and energy networks. It covers a wide spectrum of AI methods, including machine learning, deep learning, reinforcement learning, evolutionary computation, and hybrid intelligent systems, focusing on their tailored applications in load forecasting, stability assessment, energy trading, fault detection, demand response, and grid security. Special emphasis is placed on smart grids, renewable energy integration, microgrids, and electric vehicle ecosystems, where conventional analytical methods often fall short due to the complexity and uncertainty of system dynamics.

Balancing conceptual clarity with hands-on implementation, each chapter introduces the underlying AI methodology in a simplified and intuitive way, followed by detailed case studies, mathematical models, and simulation-based demonstrations. The integration of real-world datasets and benchmark problems ensures that readers can directly connect theoretical concepts with applied solutions in contemporary power and energy systems. By combining algorithmic insights with domain-specific energy applications, the book equips readers with the knowledge and skills necessary to design intelligent, adaptive, and sustainable energy systems for the future.