Lefteri H. Tsoukalas

As the global push toward a 'green energy transition' accelerates, this book offers a critical reassessment of the path to Net Zero. Unlike past energy revolutions grounded in physical laws — such as the mastery of fire or the atomic age — today's transition is driven largely by policy mandates, subsidies, and climate alarm. Energy Transitions: The Energy-AI Nexus argues that this policy-first approach risks unintended consequences, including global energy scarcity and economic contraction, especially in the absence of scalable alternatives to fossil fuels. Drawing on deep historical analysis, physical principles like energy density and entropy production, and sobering emissions data, the book reveals that global CO2 emissions have continued to rise — despite more than $10 trillion spent on green initiatives. Instead of pursuing pre-industrial energy paradigms, the book proposes a realistic path to Net Zero: a nuclear energy transition empowered by artificial intelligence. AI is reframed not just as a high-consumption technology, but as a key enabler in managing complex, distributed energy systems. With sharp insights and evidence-based critique, it redefines the debate on climate, energy, and innovation — calling for realism, urgency, and a reorientation of strategy. Energy Transitions is essential reading for policymakers, technologists, energy analysts, and critical thinkers. Blending insights from climate policy, energy engineering, AI systems, and economic modeling, this book challenges conventional climate narratives and offers a blueprint for a more realistic, resilient energy future.

Explore foundational and advanced topics in smart electric power in the context of smart cities In Smart Electric Power: Integrating Power with Information for Smarter Cities, a distinguished trio of researchers delivers a comprehensive framework of smart electric power concepts including aspects of modeling, information processing, simulation, and computation efficiency as applied to power load morphing, management, and power system reliability. The book explores the integration of power systems and information networks using machine intelligence to assist in reliable, continuous, optimal, and efficient power system operation. Functioning as both an introductory textbook and reference handbook, Smart Electric Power provides readers with robust exposure to dynamic, information-driven control methods coupled with machine learning methodologies that facilitate the reliable and automated delivery and management of electricity. Readers will also benefit from the inclusion of: Thorough introductions to smart electric power for smart cities, electrical power systems, and power markets and economicsComprehensive explorations of information networks and digital connectivity, machine intelligence for data processing, and the role of anticipation in implementing smart powerPractical discussions of demand response and load shaping using anticipation, power system stability via information and anticipation, data-driven self-healing power systems, and cybersecurity in smart power networksIn-depth examinations of smart power in modern cities and intelligent city transportation, the Gridlab-D software, and the elements of thermodynamics Perfect for students and scholars studying smart electrical power systems, Smart Electric Power: Integrating Power with Information for Smarter Cities will also earn a place in the libraries of electrical, power, and computer engineers, computer scientists, and economists specializing in power and utilities markets.

* The book provides an self-contained introduction to the various intelligent tools made available by intelligent systems* Presenting a coherent framework for the intelligent management of complex interactive infrastructures* The state of the art is presented with numerous examples that explain fundamentals and present illustrations for further applications

Provides a truly accessible introduction and a fully integrated approach to fuzzy systems and neural networks—the definitive text for students and practicing engineers Researchers are already applying neural networks and fuzzy systems in series, from the use of fuzzy inputs and outputs for neural networks to the employment of individual neural networks to quantify the shape of a fuzzy membership function. But the integration of these two fields into a "neurofuzzy" technology holds even greater potential benefits in reducing computing time and optimizing results. Fuzzy and Neural Approaches in Engineering presents a detailed examination of the fundamentals of fuzzy systems and neural networks and then joins them synergistically—combining the feature extraction and modeling capabilities of the neural network with the representation capabilities of fuzzy systems. Exploring the value of relating genetic algorithms and expert systems to fuzzy and neural technologies, this forward-thinking text highlights an entire range of dynamic possibilities within soft computing. With examples specifically designed to illuminate key concepts and overcome the obstacles of notation and overly mathematical presentations often encountered in other sources, plus tables, figures, and an up-to-date bibliography, this unique work is both an important reference and a practical guide to neural networks and fuzzy systems.