Driving Affordable and Clean Energy Through AI and Intelligent Systems

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Edited by: Gunjan Gupta, Sandeep Gupta, Tarun Varshney, Tianyi Han

• ISBN: 9798337370415 
• Published: January 2026 
• Format: Hardcover 
• Language: English
• Publisher: IGI Global Scientific Publishing

Description:

The global transition to affordable and clean energy is a defining challenge of our time, demanding solutions that balance sustainability and cost. Artificial intelligence (AI) and intelligent systems are powerful tools in this transformation, enabling advanced data analysis, forecasting, and optimization. By enhancing energy generation, integrating renewable sources, and reducing waste, AI can accelerate the shift toward low-carbon energy systems.

Driving Affordable and Clean Energy Through AI and Intelligent Systems explores how AI-driven and intelligent approaches can support energy sustainability goals across real-world applications.

Key Features:

• Explores AI and intelligent systems applied to clean and affordable energy solutions.
• Connects engineering practice with sustainability goals through optimization, control, and decision support.
• Includes chapter-based contributions spanning renewables, smart grids, energy management, and emerging methods (e.g., reinforcement learning, swarm optimization, quantum computing).

Coverage:

Artificial Intelligence (AI), Clean Energy, Decision Support Systems, Energy Management, Energy Solutions, Intelligent Systems, Machine Learning, Power Systems, Quantum Computing, Renewable Energy, Simulation Designs, Smart Ecosystems, Sustainability Practices, Swarm Optimization

About the Authors:

• Dr. Gunjan Gupta (Cape Peninsula University of Technology, South Africa) — Senior Lecturer with 17+ years of academic experience; research includes IoT/LPWAN, LoRa networks, satellite systems design, and related intelligent systems.
• Sandeep Gupta (Graphic Era University, India) — Associate Professor; research includes AI applications in power systems control, renewable energy, power electronics, and machine learning.
• Tarun Varshney (Sharda University, India) — Editor (affiliation listed in the publication).
• Tianyi Han (Tsinghua University, China) — Editor (affiliation listed in the publication).

Table of Contents:

Note: This book is in development; the table of contents is tentative.

• Chapter 1: Intelligent Design and Optimization of THz Dielectric Resonator Antennas for Sustainable Energy Communication Networks
• Chapter 2: Application of Artificial Intelligence to Wind Energy for the Control of Variable Speed Wind Turbine
• Chapter 3: Renewable Energy Integration in Industrial Operations With Intelligent Systems
• Chapter 4: Intelligent HVAC Control Systems Based on Machine Learning and Deep Learning Models
• Chapter 5: Next-Generation Control for Standalone SPV–WECS–BESS Using Hybrid Optimization
• Chapter 6: Accelerating Clean and Affordable Energy Solutions Through Artificial Intelligence
• Chapter 7: Design of Two-Level Inverter System With USV-PWM for AC Machine Using Swarm Intelligence
• Chapter 8: A Hybrid Q-Learning Decision Support Framework for Human–AI Interactive Energy Management in Industry 5.0
• Chapter 9: Optimal Placement and Sizing of Distributed Generation Using Random Drift Particle Swarm Optimization
• Chapter 10: Optimization and Control of Solar Energy System by Fuzzy Logic for Wireless Sensor Network
• Chapter 11: Quantum Computing-Based State-of-Charge Estimation of Electric Vehicle Battery
• Chapter 12: Optimal Scheduling of Electric Vehicle Charging Using Intelligent Control Strategies

Why buy this book?

• A single, curated reference connecting AI methods with energy sustainability applications.
• Useful for research, teaching, and advanced professional practice in power systems, renewables, and energy management.
• Strong library value: multi-author coverage across modern energy topics and intelligent systems.

Keywords:

Artificial Intelligence, Intelligent Systems, Clean Energy, Renewable Energy, Energy Management, Smart Grids, Power Systems, Machine Learning, Decision Support Systems, Optimization, Reinforcement Learning, Swarm Optimization, Microgrids, Sustainability, Quantum Computing, Electric Vehicles

Target Audience:

Policy makers, Educators, Engineers, Academicians, Graduate students, Energy scientists, Researchers, Energy management professionals

Genre:

Energy, Clean Energy, Renewable Energy, Artificial Intelligence, Engineering, Smart Grids, Sustainability

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