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Intelligent Urban Systems: IoT-Driven Energy Management and Smart City Optimization: Volume 1 [Pehme köide]

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  • Formaat: Paperback / softback, kõrgus x laius: 235x155 mm, Approx. 490 p.
  • Sari: Lecture Notes in Networks and Systems
  • Ilmumisaeg: 06-Jun-2026
  • Kirjastus: Springer Nature Switzerland AG
  • ISBN-10: 3032127874
  • ISBN-13: 9783032127877
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Intelligent Urban Systems: IoT-Driven Energy Management and Smart City Optimization: Volume 1Suurem pilt
  • Formaat: Paperback / softback, kõrgus x laius: 235x155 mm, Approx. 490 p.
  • Sari: Lecture Notes in Networks and Systems
  • Ilmumisaeg: 06-Jun-2026
  • Kirjastus: Springer Nature Switzerland AG
  • ISBN-10: 3032127874
  • ISBN-13: 9783032127877
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9783032127877.html
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.
State of Health Estimation of Lithium-Ion Batteries in Electric
Vehicles.- Predictive Maintenance of Wind Turbines Using Machine Learning:
Addressing Fault Detection with SCADA Data.- Photovoltaic and Wind Power
Forecasting Using LSTM Networks with Adaptive Hyperparameter Tuning.-
Short-Term PV Power Forecasting Using LSTM: A Case Study of grid-connected PV
system in Adrar City.- Intra-Hour Solar Irradiance Forecasting Based on
Feature Selection Techniques.- Hourly Global Solar Irradiance Forecasting in
a Desert Region Using a Deep Neural Model with Hybrid Inputs.- Estimating
Power Outputs of Thin Film CIS PV Modules Using Neuronal Approach: A case
Study in Arid Environment.- Artificial Intelligence Applications for Indoor
Thermal Comfort in Residential Buildings: A Scoping Review of Early.- Design
Methods.- AI-Driven Smart Management and Optimization of Green Hydrogen
Production in Renewable Energy Grids Using Bio-Inspired Algorithms and Edge
Computing.- Reinforcement Learning for Energy-Aware Vehicle Routing in
Renewable-Powered Microgrid Systems.- Optimal Power Management and Control of
Islanded Microgrid to Prevent Under-Frequency Load Shedding During Load
Variations.- A New Differential Evolution-based Routing Protocol for
Surveillance Drones in Urban Areas.- AI-Assisted Design and Characterization
of a Novel Cytosine-Based Hybrid Material for Renewable Energy Applications.-
Biomass Diatomite-Supported Ferrihydrite Silicide Hybrid Granule Catalyst
TiO2:Synthesis and Evaluation for Photocatalytic Dye Removal.- Facile sono
chemical synthesis and characterization of cobalt oxide nanoparticles in the
presence of ionic liquid.- Ab Initio Investigation of an Rb-Based
Half-Heusler Alloy for Energy Harvesting Applications.