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Submit a ProposalNeural Grids
One Line Description
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.
Description
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.
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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.
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Author / Editor Details
Himanshu Sharma, PhD is the Dean of Research and Development at G.H. Raisoni Skill Tech University, Nagpur, with more than eight years of teaching experience. He has published many books and more than 30 research papers in refereed journals and conferences. His areas of expertise include power electronics, waste management, machine learning, and optimization techniques.
Himanshu Sharma, PhD is the Dean of Research and Development at G.H. Raisoni Skill Tech University, Nagpur, with more than eight years of teaching experience. He has published many books and more than 30 research papers in refereed journals and conferences. His areas of expertise include power electronics, waste management, machine learning, and optimization techniques.
Krishan Arora, PhD is a Professor and Head of the Department of Power Systems in the School of Electronics and Electrical Engineering at Lovely Professional University, with more than seventeen years of experience in academics and research. He has published more than 85 research papers in refereed journals and conferences, eight edited books, ten Indian patents, and a copyright. His research interests include electrical machines, artificial intelligence, load frequency control, and electric vehicles.
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Table of Contents
Preface
1. Introduction to the Neural Grid
Krishan Arora
1.1 Overview of the Neural Grid
1.2 Evolution from Traditional Grid to Neural Grid
1.2.1 Traditional Grid
1.2.2 Smart Grid
1.2.3 Neural Grid
1.3 Structure and Components of a Neural Grid
1.3.1 Intelligent Nodes
1.3.2 Communication Network
1.3.3 AI and Edge Computing
1.4 Major Technologies Making the Neural Grid Possible
1.4.1 Artificial Intelligence and Machine Learning
1.4.2 Internet of Things (IoT)
1.4.3 Digital Twins
1.4.4 Blockchain
1.5 Operation of the Neural Grid
1.5.1 Data Acquisition
1.5.2 Distributed Processing
1.5.3 Collective Learning and Decision-Making
1.5.4 Action Execution
1.6 Applications of Neural Grids
1.6.1 Renewable Energy Integration
1.6.2 Electric Vehicle (EV) Management
1.6.3 Smart Cities
1.6.4 Fault Detection and Self-Healing
1.7 Challenges in Implementing the Neural Grid
1.7.1 Cybersecurity Threats
1.7.2 High Cost of Deployment
1.7.3 Interoperability
1.7.4 Skill Requirements
1.8 Future Trends in Neural Grid Development
1.9 Conclusion
References
2. Role of Internet of Things (IoT) and Artificial Intelligence (AI) in Neural Grid
Jangeri Prem Prajwal and Indu Bala
2.1 Introduction
2.2 Background and Evolution of Neural Grid
2.2.1 Traditional Power Grid Limitations
2.2.2 Birth of the Neural Grid Concept
2.2.3 IoT as the Foundation of Modern Neural Grids
2.2.4 AI as the Intelligence Layer
2.3 Neural Grid Architecture
2.3.1 Physical Power Infrastructure Layer
2.3.2 Communication and Networking Layer
2.3.3 Application and Intelligence Layer
2.4 IoT in Neural Grid
2.4.1 Types of IoT Devices Used in Neural Grids
2.4.2 Communication Requirements for IoT in Neural Grid
2.5 Artificial Intelligence in Neural Grid
2.5.1 Key AI Techniques Used in Neural Grids
2.5.2 AI for Load Forecasting
2.5.3 Fault Detection and Predictive Maintenance
2.5.4 AI in Renewable Energy Integration
2.5.5 Advantages of AI Integration
2.6 Synergy Between AI and IoT in Real-Time Neural Grid Control
2.6.1 Real-Time Operation Framework
2.7 Cybersecurity Challenges in AI-IoT Neural Grids
2.7.1 Common Cyber Threats
2.7.2 Role of AI in Cybersecurity
2.8 Human–AI Collaboration in Grid Operations
2.9 Data Management and Cloud Computing in Neural Grids
2.9.1 The Data Explosion Problem
2.9.2 Cloud Computing Framework
2.9.3 Edge–Cloud Hybrid Approach
2.10 AI in Energy Trading and Dynamic Pricing
2.10.1 AI-Based Energy Market Optimization
2.10.2 Dynamic Pricing Models
2.11 Consumer-Centric Neural Grid Applications
2.11.1 Home Energy Management Systems (HEMS)
2.11.2 AI-Powered Demand Response
2.12 AI-Based Fault Tolerance and Self-Healing Grids
2.12.1 Fault Tolerance Through Multiple Paths and Prediction
2.12.2 Self-Healing Mechanisms
2.12.3 Case Example: Smart Restoration in Southern India
2.13 Integration of Renewable Energy Storage Systems
2.13.1 Role of IoT in Storage Monitoring
2.13.2 AI for Optimizing Storage and Dispatch
2.13.3 Hybrid Energy Storage Systems (HESS)
2.14 Decentralization and Peer-to-Peer (P2P) Energy Trading
2.14.1 How P2P Trading Works
2.14.2 AI and Blockchain Synergy
2.15 Comparative Overview: Traditional vs. Next-Gen Neural Grid
2.16 Ethical and Environmental Implications
2.16.1 Ethical Concerns: Data Privacy and Security
2.16.2 Environmental Impacts of IoT and AI Infrastructure
2.16.3 Sustainable Hardware and E-Waste Management
2.17 Regulatory and Policy Frameworks
2.17.1 Global Policy Initiatives
2.17.2 Need for Unified Standards
2.18 Conclusion and Future Scope
References
3. Electric Mobility Systems Cybersecurity Frames
Ranjit Kumar Bindal, Soumya Bhardwaj, Abhay Sharma and Shubham
3.1 Introduction
3.2 Literature Review
3.2.1 Research Gaps
3.2.2 Distinct Cybersecurity Issues in EMS
3.2.3 Framework Components and Technologies Frameworks and Data Structures
3.3 Methodology
3.3.1 Standardization and Regulatory Environment
3.4 Future Research Directions
3.5 Conclusion and Recommendations
References
4. Load Forecasting and Energy Demand Prediction: Models, Influencing Factors, and Applications Across Multiple Time Horizons
Mobi Mathew
4.1 Introduction
4.2 Necessity of LF and Challenges Associated
4.3 Factors Affecting LF
4.4 Methods of LF
4.4.1 Subjective Methods
4.4.2 Univariate Methods
4.4.3 Multivariate Methods
4.4.4 End-Use and Partial End-Use Methods
4.4.5 Combination and Hybrid Methods
4.5 Categories of LF
4.5.1 Very Short-Term LF (VSTLF)
4.5.2 Short-Term LF (STLF)
4.5.3 Medium-Term LF (MTLF)
4.5.4 Long-Term LF (LTLF)
4.6 LF Models
4.6.1 Statistical-Based Forecasting Models
4.6.2 Artificial Intelligence and Machine Learning (AI/ML) Models
4.6.3 Hybrid Forecasting Models
4.7 Metrics to Evaluate Performance of LF Techniques
4.7.1 Root Mean Square Error (RMSE)
4.7.2 Coefficient of Determination ()2R
4.7.3 Mean Square Error (MSE)
4.7.4 Mean Absolute Error (MAE)
4.7.5 Mean Absolute Percentage Error (MAPE)
4.7.6 Summary of Assessment Metrics
4.8 Applications of LF
4.8.1 Power System Operation and Control
4.8.2 Transmission and Distribution System Planning
4.8.3 Generation Expansion and Resource Planning
4.8.4 Electricity Market Operations and Price Forecasting
4.8.5 Demand Response and Energy Management
4.8.6 Renewable Energy Integration and Grid Flexibility
4.8.7 Electric Mobility and Charging Infrastructure
4.8.8 Smart Grid and Microgrid Management
4.8.9 Policy, Tariff, and Energy Efficiency Planning
4.9 Conclusions
References
5. Artificial Intelligence for Electric Vehicles and Smart Mobility
Akhil Nigam
5.1 Introduction
5.2 Background of AI in Electric Vehicles
5.3 Role of Emerging Trends of AI in Electric Vehicles
5.4 AI-Based Energy Management System
5.5 Performance of AI in Handling Mobility and Transportation Systems
5.5.1 Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) Communication
5.6 Conclusion and Future Scope
References
6. AI-Powered Route Optimization for Smart and Sustainable
Urban Mobility: A Critical Review
Earnest Anand and Sanjeev Kumar
Background of the Study
Integration of AI with Transport Systems
Real-Time and Predictive Routing Model
Internet of Things (IoT): Sensor Integration
Optimization of Routes Based on AI in Urban Mobility
Optimization of Fuel and Energy Efficiency
AI-Based Traffic-Based Indian Smart Mobility Projects
Issues and Flaws of AI-Based Route Optimization
Artificial Intelligence in Climate Resilient Transportation
Conclusion
References
7. AI-Driven Predictive Maintenance for Sustainable Industrial Innovation
Sthitprajna Mishra, Nidhi Sharma and Biswajit Ghosh
7.1 Introduction and Literature Review
7.2 Predictive Maintenance: Concepts and Evolution
7.2.1 Classical Maintenance Paradigms
7.2.2 Condition-Based Maintenance (CBM) Comes into Existence
7.2.3 Predictive Maintenance (PdM)
7.2.4 Evolution from Rule-Based to AI-Driven PdM
7.2.5 Strategic Importance in Sustainable Industry
7.3 AI Techniques for Predictive Maintenance
7.3.1 Machine Learning Approaches
7.3.2 Deep Learning Approaches
7.3.3 Hybrid and Ensemble Models
7.3.4 Urban AI-Based Predictive Maintenance Case Studies
7.3.5 Benefits of AI-Driven Predictive Maintenance
7.3.6 Limitations and Research Gaps
7.3.7 Summary
7.4 Enabling Technologies
7.4.1 Internet of Things (IoT) and Sensor Networks
7.4.2 Digital Twins
7.4.3 Cloud Computing
7.4.4 Edge Computing
7.4.5 Big Data Pipelines
7.4.6 Cybersecurity Considerations
7.4.7 Integration of Enabling Technologies
7.4.8 Summary
7.5 Industrial Applications
7.5.1 Smart Factories and Manufacturing
7.5.2 Automotive Industry
7.5.3 Energy Systems
7.5.4 Infrastructure and Smart Cities
7.5.5 Aerospace and Defense
7.5.6 Healthcare Equipment
7.5.7 Cross-Industry Trends
7.5.8 Summary
7.6 Challenges and Limitations
7.6.1 Data Heterogeneity and Quality Issues
7.6.2 Model Interpretability and Trust
7.6.3 Cybersecurity Risks
7.6.4 High Implementation Costs
7.6.5 Integration with Legacy Systems
7.6.6 Data Privacy and Ethical Concerns
7.6.7 Organizational Resistance and Cultural Barriers
7.6.8 Technical Limitations of AI Models
7.6.9 Regulatory and Compliance Challenges
7.6.10 Environmental and Sustainability Trade-Offs
7.6.11 Summary
7.7 Future Directions
7.7.1 Explainable and Trustworthy AI (XAI)
7.7.2 Federated and Privacy-Preserving Learning
7.7.3 Edge-Cloud Collaboration
7.7.4 Digital Twins and Simulation-Driven Maintenance
7.7.5 Sustainable and Green AI
7.7.6 Autonomous Maintenance and Self-Healing Systems
7.7.7 Standardization and Regulatory Evolution
7.7.8 Workforce Transformation and Skill Development
7.7.9 Cross-Industry Applications and Expansion
7.7.10 Convergence with Industry 5.0
7.7.11 Summary
7.8 Conclusion
References
8. Dynamic Security of Microgrids with Penetration of Electrical Vehicles
Moheid Basher, Amit Kumar Singh and Javed Dhillon
8.1 Introduction
8.2 Literature Review
8.3 Problem Statement and Objectives
8.3.1 Problem Statement
8.3.2 Objectives
8.4 Methodology
8.4.1 System Modeling
8.4.2 EV Load Modeling
8.4.3 Control Modeling
8.5 Results and Discussion
8.5.1 Frequency Deviation Analysis
8.5.2 PID Controller Performance
8.6 Future Work
8.7 Conclusion
Bibliography
9. A Generative Adversarial Network Approach to Adaptive
Wireless Power Transfer and Anomaly Detection
Tanishk Singhal, Manmohan Sharma and Harpreet Singh Bedi
9.1 Introduction
9.2 Objectives
9.3 System Architecture
9.4 Working Principle
9.5 Novelty of the Invention
9.6 Results
9.7 Advantages of the System
9.8 Superiority Over Existing Prior Art
9.9 Conclusion
References
10. Artificial Intelligence Applications in Renewable Energy: Forecasting, Optimization, and Smart Grid Integration
Mobi Mathew
10.1 Introduction
10.2 Renewable Energy
10.2.1 Solar Energy
10.2.2 Wind Energy
10.2.3 Hybrid RE Systems
10.3 AI Tools and Techniques
10.4 Application of AI in Renewable Energy Systems
10.4.1 Applications of AI Techniques in Solar Energy
10.4.2 AI in Solar-Based Microgrids
10.4.3 AI-Driven Enhancements in PV Technology
10.4.4 AI in Wind Energy Optimization
10.5 AI-Enhanced Renewable Energy Generation
10.5.1 AI Applications in Maximizing Efficiency of PV and Wind Turbine Systems
10.6 AI in Renewable Energy Integration
10.6.1 AI-Enabled Management of Intermittent Renewable Energy Resources
10.7 Challenges and Solutions
10.7.1 Data Acquisition and Auditing
10.7.2 Variable Screening and Parameter Calibration in Solar and Wind Energy Systems
10.7.3 Modeling Multiple Faults Simultaneously
10.7.4 Stability and Generalization in Predictive Modeling
10.7.5 Safeguarding Systems Against Data Breaches and Security Threats
10.7.6 Performance and Explainability Issues
10.7.7 Challenges in Energy Storage Systems and Grid Integration
10.8 Conclusions
References
Index
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Preface
1. Introduction to the Neural Grid
Krishan Arora
1.1 Overview of the Neural Grid
1.2 Evolution from Traditional Grid to Neural Grid
1.2.1 Traditional Grid
1.2.2 Smart Grid
1.2.3 Neural Grid
1.3 Structure and Components of a Neural Grid
1.3.1 Intelligent Nodes
1.3.2 Communication Network
1.3.3 AI and Edge Computing
1.4 Major Technologies Making the Neural Grid Possible
1.4.1 Artificial Intelligence and Machine Learning
1.4.2 Internet of Things (IoT)
1.4.3 Digital Twins
1.4.4 Blockchain
1.5 Operation of the Neural Grid
1.5.1 Data Acquisition
1.5.2 Distributed Processing
1.5.3 Collective Learning and Decision-Making
1.5.4 Action Execution
1.6 Applications of Neural Grids
1.6.1 Renewable Energy Integration
1.6.2 Electric Vehicle (EV) Management
1.6.3 Smart Cities
1.6.4 Fault Detection and Self-Healing
1.7 Challenges in Implementing the Neural Grid
1.7.1 Cybersecurity Threats
1.7.2 High Cost of Deployment
1.7.3 Interoperability
1.7.4 Skill Requirements
1.8 Future Trends in Neural Grid Development
1.9 Conclusion
References
2. Role of Internet of Things (IoT) and Artificial Intelligence (AI) in Neural Grid
Jangeri Prem Prajwal and Indu Bala
2.1 Introduction
2.2 Background and Evolution of Neural Grid
2.2.1 Traditional Power Grid Limitations
2.2.2 Birth of the Neural Grid Concept
2.2.3 IoT as the Foundation of Modern Neural Grids
2.2.4 AI as the Intelligence Layer
2.3 Neural Grid Architecture
2.3.1 Physical Power Infrastructure Layer
2.3.2 Communication and Networking Layer
2.3.3 Application and Intelligence Layer
2.4 IoT in Neural Grid
2.4.1 Types of IoT Devices Used in Neural Grids
2.4.2 Communication Requirements for IoT in Neural Grid
2.5 Artificial Intelligence in Neural Grid
2.5.1 Key AI Techniques Used in Neural Grids
2.5.2 AI for Load Forecasting
2.5.3 Fault Detection and Predictive Maintenance
2.5.4 AI in Renewable Energy Integration
2.5.5 Advantages of AI Integration
2.6 Synergy Between AI and IoT in Real-Time Neural Grid Control
2.6.1 Real-Time Operation Framework
2.7 Cybersecurity Challenges in AI-IoT Neural Grids
2.7.1 Common Cyber Threats
2.7.2 Role of AI in Cybersecurity
2.8 Human–AI Collaboration in Grid Operations
2.9 Data Management and Cloud Computing in Neural Grids
2.9.1 The Data Explosion Problem
2.9.2 Cloud Computing Framework
2.9.3 Edge–Cloud Hybrid Approach
2.10 AI in Energy Trading and Dynamic Pricing
2.10.1 AI-Based Energy Market Optimization
2.10.2 Dynamic Pricing Models
2.11 Consumer-Centric Neural Grid Applications
2.11.1 Home Energy Management Systems (HEMS)
2.11.2 AI-Powered Demand Response
2.12 AI-Based Fault Tolerance and Self-Healing Grids
2.12.1 Fault Tolerance Through Multiple Paths and Prediction
2.12.2 Self-Healing Mechanisms
2.12.3 Case Example: Smart Restoration in Southern India
2.13 Integration of Renewable Energy Storage Systems
2.13.1 Role of IoT in Storage Monitoring
2.13.2 AI for Optimizing Storage and Dispatch
2.13.3 Hybrid Energy Storage Systems (HESS)
2.14 Decentralization and Peer-to-Peer (P2P) Energy Trading
2.14.1 How P2P Trading Works
2.14.2 AI and Blockchain Synergy
2.15 Comparative Overview: Traditional vs. Next-Gen Neural Grid
2.16 Ethical and Environmental Implications
2.16.1 Ethical Concerns: Data Privacy and Security
2.16.2 Environmental Impacts of IoT and AI Infrastructure
2.16.3 Sustainable Hardware and E-Waste Management
2.17 Regulatory and Policy Frameworks
2.17.1 Global Policy Initiatives
2.17.2 Need for Unified Standards
2.18 Conclusion and Future Scope
References
3. Electric Mobility Systems Cybersecurity Frames
Ranjit Kumar Bindal, Soumya Bhardwaj, Abhay Sharma and Shubham
3.1 Introduction
3.2 Literature Review
3.2.1 Research Gaps
3.2.2 Distinct Cybersecurity Issues in EMS
3.2.3 Framework Components and Technologies Frameworks and Data Structures
3.3 Methodology
3.3.1 Standardization and Regulatory Environment
3.4 Future Research Directions
3.5 Conclusion and Recommendations
References
4. Load Forecasting and Energy Demand Prediction: Models, Influencing Factors, and Applications Across Multiple Time Horizons
Mobi Mathew
4.1 Introduction
4.2 Necessity of LF and Challenges Associated
4.3 Factors Affecting LF
4.4 Methods of LF
4.4.1 Subjective Methods
4.4.2 Univariate Methods
4.4.3 Multivariate Methods
4.4.4 End-Use and Partial End-Use Methods
4.4.5 Combination and Hybrid Methods
4.5 Categories of LF
4.5.1 Very Short-Term LF (VSTLF)
4.5.2 Short-Term LF (STLF)
4.5.3 Medium-Term LF (MTLF)
4.5.4 Long-Term LF (LTLF)
4.6 LF Models
4.6.1 Statistical-Based Forecasting Models
4.6.2 Artificial Intelligence and Machine Learning (AI/ML) Models
4.6.3 Hybrid Forecasting Models
4.7 Metrics to Evaluate Performance of LF Techniques
4.7.1 Root Mean Square Error (RMSE)
4.7.2 Coefficient of Determination ()2R
4.7.3 Mean Square Error (MSE)
4.7.4 Mean Absolute Error (MAE)
4.7.5 Mean Absolute Percentage Error (MAPE)
4.7.6 Summary of Assessment Metrics
4.8 Applications of LF
4.8.1 Power System Operation and Control
4.8.2 Transmission and Distribution System Planning
4.8.3 Generation Expansion and Resource Planning
4.8.4 Electricity Market Operations and Price Forecasting
4.8.5 Demand Response and Energy Management
4.8.6 Renewable Energy Integration and Grid Flexibility
4.8.7 Electric Mobility and Charging Infrastructure
4.8.8 Smart Grid and Microgrid Management
4.8.9 Policy, Tariff, and Energy Efficiency Planning
4.9 Conclusions
References
5. Artificial Intelligence for Electric Vehicles and Smart Mobility
Akhil Nigam
5.1 Introduction
5.2 Background of AI in Electric Vehicles
5.3 Role of Emerging Trends of AI in Electric Vehicles
5.4 AI-Based Energy Management System
5.5 Performance of AI in Handling Mobility and Transportation Systems
5.5.1 Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) Communication
5.6 Conclusion and Future Scope
References
6. AI-Powered Route Optimization for Smart and Sustainable
Urban Mobility: A Critical Review
Earnest Anand and Sanjeev Kumar
Background of the Study
Integration of AI with Transport Systems
Real-Time and Predictive Routing Model
Internet of Things (IoT): Sensor Integration
Optimization of Routes Based on AI in Urban Mobility
Optimization of Fuel and Energy Efficiency
AI-Based Traffic-Based Indian Smart Mobility Projects
Issues and Flaws of AI-Based Route Optimization
Artificial Intelligence in Climate Resilient Transportation
Conclusion
References
7. AI-Driven Predictive Maintenance for Sustainable Industrial Innovation
Sthitprajna Mishra, Nidhi Sharma and Biswajit Ghosh
7.1 Introduction and Literature Review
7.2 Predictive Maintenance: Concepts and Evolution
7.2.1 Classical Maintenance Paradigms
7.2.2 Condition-Based Maintenance (CBM) Comes into Existence
7.2.3 Predictive Maintenance (PdM)
7.2.4 Evolution from Rule-Based to AI-Driven PdM
7.2.5 Strategic Importance in Sustainable Industry
7.3 AI Techniques for Predictive Maintenance
7.3.1 Machine Learning Approaches
7.3.2 Deep Learning Approaches
7.3.3 Hybrid and Ensemble Models
7.3.4 Urban AI-Based Predictive Maintenance Case Studies
7.3.5 Benefits of AI-Driven Predictive Maintenance
7.3.6 Limitations and Research Gaps
7.3.7 Summary
7.4 Enabling Technologies
7.4.1 Internet of Things (IoT) and Sensor Networks
7.4.2 Digital Twins
7.4.3 Cloud Computing
7.4.4 Edge Computing
7.4.5 Big Data Pipelines
7.4.6 Cybersecurity Considerations
7.4.7 Integration of Enabling Technologies
7.4.8 Summary
7.5 Industrial Applications
7.5.1 Smart Factories and Manufacturing
7.5.2 Automotive Industry
7.5.3 Energy Systems
7.5.4 Infrastructure and Smart Cities
7.5.5 Aerospace and Defense
7.5.6 Healthcare Equipment
7.5.7 Cross-Industry Trends
7.5.8 Summary
7.6 Challenges and Limitations
7.6.1 Data Heterogeneity and Quality Issues
7.6.2 Model Interpretability and Trust
7.6.3 Cybersecurity Risks
7.6.4 High Implementation Costs
7.6.5 Integration with Legacy Systems
7.6.6 Data Privacy and Ethical Concerns
7.6.7 Organizational Resistance and Cultural Barriers
7.6.8 Technical Limitations of AI Models
7.6.9 Regulatory and Compliance Challenges
7.6.10 Environmental and Sustainability Trade-Offs
7.6.11 Summary
7.7 Future Directions
7.7.1 Explainable and Trustworthy AI (XAI)
7.7.2 Federated and Privacy-Preserving Learning
7.7.3 Edge-Cloud Collaboration
7.7.4 Digital Twins and Simulation-Driven Maintenance
7.7.5 Sustainable and Green AI
7.7.6 Autonomous Maintenance and Self-Healing Systems
7.7.7 Standardization and Regulatory Evolution
7.7.8 Workforce Transformation and Skill Development
7.7.9 Cross-Industry Applications and Expansion
7.7.10 Convergence with Industry 5.0
7.7.11 Summary
7.8 Conclusion
References
8. Dynamic Security of Microgrids with Penetration of Electrical Vehicles
Moheid Basher, Amit Kumar Singh and Javed Dhillon
8.1 Introduction
8.2 Literature Review
8.3 Problem Statement and Objectives
8.3.1 Problem Statement
8.3.2 Objectives
8.4 Methodology
8.4.1 System Modeling
8.4.2 EV Load Modeling
8.4.3 Control Modeling
8.5 Results and Discussion
8.5.1 Frequency Deviation Analysis
8.5.2 PID Controller Performance
8.6 Future Work
8.7 Conclusion
Bibliography
9. A Generative Adversarial Network Approach to Adaptive
Wireless Power Transfer and Anomaly Detection
Tanishk Singhal, Manmohan Sharma and Harpreet Singh Bedi
9.1 Introduction
9.2 Objectives
9.3 System Architecture
9.4 Working Principle
9.5 Novelty of the Invention
9.6 Results
9.7 Advantages of the System
9.8 Superiority Over Existing Prior Art
9.9 Conclusion
References
10. Artificial Intelligence Applications in Renewable Energy: Forecasting, Optimization, and Smart Grid Integration
Mobi Mathew
10.1 Introduction
10.2 Renewable Energy
10.2.1 Solar Energy
10.2.2 Wind Energy
10.2.3 Hybrid RE Systems
10.3 AI Tools and Techniques
10.4 Application of AI in Renewable Energy Systems
10.4.1 Applications of AI Techniques in Solar Energy
10.4.2 AI in Solar-Based Microgrids
10.4.3 AI-Driven Enhancements in PV Technology
10.4.4 AI in Wind Energy Optimization
10.5 AI-Enhanced Renewable Energy Generation
10.5.1 AI Applications in Maximizing Efficiency of PV and Wind Turbine Systems
10.6 AI in Renewable Energy Integration
10.6.1 AI-Enabled Management of Intermittent Renewable Energy Resources
10.7 Challenges and Solutions
10.7.1 Data Acquisition and Auditing
10.7.2 Variable Screening and Parameter Calibration in Solar and Wind Energy Systems
10.7.3 Modeling Multiple Faults Simultaneously
10.7.4 Stability and Generalization in Predictive Modeling
10.7.5 Safeguarding Systems Against Data Breaches and Security Threats
10.7.6 Performance and Explainability Issues
10.7.7 Challenges in Energy Storage Systems and Grid Integration
10.8 Conclusions
References
Index
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