Intelligent Urban Systems: IoT-Driven Energy Management and Smart City Optimization: Renewable Energy Systems and Technologies, Volume 2 (Lecture Notes in Networks and Systems, 2179)

This comprehensive volume delivers cutting-edge solutions for integrating artificial intelligence into renewable energy systems, addressing critical challenges in sustainable energy transition. Readers will discover innovative approaches to optimizing photovoltaic systems, wind energy conversion, smart grids, and hybrid energy storage through advanced machine learning algorithms and intelligent control strategies.

The book presents practical applications spanning MPPT optimization techniques, predictive maintenance using SCADA data, IoT-based monitoring systems, and AI-driven fault detection in power electronics. Contributors explore emerging topics including green hydrogen production, electric vehicle control systems, energy forecasting with deep learning, and autonomous microgrid management. Special emphasis is placed on real-world implementations tested in diverse environmental conditions.

Featuring contributions from leading international researchers and practitioners, this work bridges the gap between academic research and industrial application. Engineers, researchers, and graduate students working in renewable energy, power systems, and artificial intelligence will find invaluable insights for developing efficient, resilient, and sustainable energy solutions that meet urgent climate and energy security challenges.

This comprehensive volume delivers cutting-edge solutions for integrating artificial intelligence into renewable energy systems, addressing critical challenges in sustainable energy transition. Readers will discover innovative approaches to optimizing photovoltaic systems, wind energy conversion, smart grids, and hybrid energy storage through advanced machine learning algorithms and intelligent control strategies.

The book presents practical applications spanning MPPT optimization techniques, predictive maintenance using SCADA data, IoT-based monitoring systems, and AI-driven fault detection in power electronics. Contributors explore emerging topics including green hydrogen production, electric vehicle control systems, energy forecasting with deep learning, and autonomous microgrid management. Special emphasis is placed on real-world implementations tested in diverse environmental conditions.

Featuring contributions from leading international researchers and practitioners, this work bridges the gap between academic research and industrial application. Engineers, researchers, and graduate students working in renewable energy, power systems, and artificial intelligence will find invaluable insights for developing efficient, resilient, and sustainable energy solutions that meet urgent climate and energy security challenges.