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Submit a ProposalArtificial Intelligence for Renewable Energy Systems
 | Edited by Ajay Kumar Vyas, S. Balamurugan, Kamal Kant Hiran, Harsh S. Dhiman
Series: Artificial Intelligence and Soft Computing for Industrial Transformation Copyright: 2022 | Status: Published ISBN: 9781119761693 | Hardcover | 239 pages | 101 illustrations Price: $225 USD  |
One Line Description
Renewable energy systems, including solar, wind, biodiesel, hybrid energy, and other relevant types, have numerous advantages compared to their conventional counterparts. This book presents the application of machine learning and deep learning techniques for renewable energy system modeling, forecasting, and optimization for efficient system design.
Audience
The primary target audience includes research scholars, industry engineers, and graduate students working in renewable energy, electrical engineering, machine learning, information & communication technology.
The primary target audience includes research scholars, industry engineers, and graduate students working in renewable energy, electrical engineering, machine learning, information & communication technology.
Description
Due to the importance of renewable energy in today’s world, this book was designed to enhance the reader’s knowledge based on current developments in the field. For instance, the extraction and selection of machine learning algorithms for renewable energy systems, forecasting of wind and solar radiation are featured in the book. Also highlighted are intelligent data, renewable energy informatics systems based on supervisory control and data acquisition (SCADA); and intelligent condition monitoring of solar and wind energy systems. Moreover, an AI-based system for real-time decision-making for renewable energy systems is presented; and also demonstrated is the prediction of energy consumption in green buildings using machine learning. The chapter authors also provide both experimental and real datasets with great potential in the renewable energy sector, which apply machine learning (ML) and deep learning (DL) algorithms that will be helpful for economic and environmental forecasting of the renewable energy business.
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Due to the importance of renewable energy in today’s world, this book was designed to enhance the reader’s knowledge based on current developments in the field. For instance, the extraction and selection of machine learning algorithms for renewable energy systems, forecasting of wind and solar radiation are featured in the book. Also highlighted are intelligent data, renewable energy informatics systems based on supervisory control and data acquisition (SCADA); and intelligent condition monitoring of solar and wind energy systems. Moreover, an AI-based system for real-time decision-making for renewable energy systems is presented; and also demonstrated is the prediction of energy consumption in green buildings using machine learning. The chapter authors also provide both experimental and real datasets with great potential in the renewable energy sector, which apply machine learning (ML) and deep learning (DL) algorithms that will be helpful for economic and environmental forecasting of the renewable energy business.
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Author / Editor Details
Ajay Kumar Vyas, PhD is an assistant professor at Adani Institute of Infrastructure Engineering, Ahmedabad, India. He has authored several research papers in peer-reviewed international journals and conferences, three books, and two Indian patents.
Ajay Kumar Vyas, PhD is an assistant professor at Adani Institute of Infrastructure Engineering, Ahmedabad, India. He has authored several research papers in peer-reviewed international journals and conferences, three books, and two Indian patents.
S. Balamurugan, PhD SMIEEE, ACM Distinguished Speaker, received his PhD from Anna University, India. He has published 57 books, 300+ international journals/conferences, and 100 patents. He is the Director of the Albert Einstein Engineering and Research Labs. He is also the Vice-Chairman of the Renewable Energy Society of India (RESI). He is serving as a research consultant to many companies, startups, SMEs, and MSMEs. He has received numerous awards for research at national and international levels.
Kamal Kant Hiran, PhD is an assistant professor at the School of Engineering, Sir Padampat Singhania University (SPSU), Udaipur, Rajasthan, India, as well as a research fellow at the Aalborg University, Copenhagen, Denmark. He has published more than 35 scientific research papers in SCI/Scopus/Web of Science and IEEE Transactions Journal, conferences, two Indian patents, one Australian patent granted, and nine books.
Harsh S. Dhiman, PhD is an assistant professor in the Department of Electrical Engineering at Adani Institute of Infrastructure Engineering, Ahmedabad, India. He has published 12 SCI- indexed journal articles and two books, and his research interests include hybrid operation of wind farms, hybrid wind forecasting techniques, and anomaly detection in wind turbines.
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Table of Contents
Preface
1. Analysis of Six-Phase Grid Connected Synchronous Generator in Wind Power Generation
Arif Iqbal and Girish Kumar Singh
1.1 Introduction
1.2 Analytical Modeling of Six-Phase Synchronous Machine
1.2.1 Voltage Equation
1.2.2 Equations of Flux Linkage Per Second
1.3 Linearization of Machine Equations for Stability Analysis
1.4 Dynamic Performance Results
1.5 Stability Analysis Results
1.5.1 Parametric Variation of Stator
1.5.2 Parametric Variation of Field Circuit
1.5.3 Parametric Variation of Damper Winding, Kd
1.5.4 Parametric Variation of Damper Winding, Kq
1.5.5 Magnetizing Reactance Variation Along q-axis
1.5.6 Variation in Load
1.6 Conclusions
References
Appendix
Symbols Meaning
2. Artificial Intelligence as a Tool for Conservation and Efficient Utilization of Renewable Resource
Vinay N., Ajay Sudhir Bale, Subhashish Tiwari and Baby Chithra R.
2.1 Introduction
2.2 AI in Water Energy
2.2.1 Prediction of Groundwater Level
2.2.2 Rainfall Modeling
2.3 AI in Solar Energy
2.3.1 Solar Power Forecasting
2.4 AI in Wind Energy
2.4.1 Wind Monitoring
2.4.2 Wind Forecasting
2.5 AI in Geothermal Energy
2.6 Conclusion
References
3. Artificial Intelligence–Based Energy-Efficient Clustering and Routing in IoT-Assisted Wireless Sensor Network
Nitesh Chouhan
3.1 Introduction
3.2 Related Study
3.3 Clustering in WSN
3.4 Research Methodology
3.4.1 Creating Wireless Sensor–Based IoT Environment
3.4.2 Clustering Approach
3.4.3 AI-Based Energy-Aware Routing Protocol
3.5 Conclusion
References
4. Artificial Intelligence for Modeling and Optimization of the Biogas Production
Narendra Khatri and Kamal Kishore Khatri
4.1 Introduction
4.2 Artificial Neural Network
4.2.1 ANN Architecture
4.2.2 Training Algorithms
4.2.3 Performance Parameters for Analysis of the ANN Model
4.2.4 Application of ANN for Biogas Production Modeling
4.3 Evolutionary Algorithms
4.3.1 Genetic Algorithm
4.3.2 Ant Colony Optimization
4.3.3 Particle Swarm Optimization
4.3.4 Application of Hybrid Models (ANN and Evolutionary Algorithms) for Biogas
Production Modeling
4.4 Conclusion
References
5. Battery State-of-Charge Modeling for Solar PV Array Using Polynomial Regression
Siddhi Vinayak Pandey, Jeet Patel and Harsh S. Dhiman
5.1 Introduction
5.2 Dynamic Battery Modeling
5.2.1 Proposed Methodology
5.3 Results and Discussion
5.4 Conclusion
References
6. Deep Learning Algorithms for Wind Forecasting: An Overview
M. Lydia and G. Edwin Prem Kumar
Nomenclature
6.1 Introduction
6.2 Models for Wind Forecasting
6.2.1 Persistence Model
6.2.2 Point vs. Probabilistic Forecasting
6.2.3 Multi-Objective Forecasting
6.2.4 Wind Power Ramp Forecasting
6.2.5 Interval Forecasting
6.2.6 Multi-Step Forecasting
6.3 The Deep Learning Paradigm
6.3.1 Batch Learning
6.3.2 Sequential Learning
6.3.3 Incremental Learning
6.3.4 Scene Learning
6.3.5 Transfer Learning
6.3.6 Neural Structural Learning
6.3.7 Multi-Task Learning
6.4 Deep Learning Approaches for Wind Forecasting
6.4.1 Deep Neural Network
6.4.2 Long Short-Term Memory
6.4.3 Extreme Learning Machine
6.4.4 Gated Recurrent Units
6.4.5 Autoencoders
6.4.6 Ensemble Models
6.4.7 Other Miscellaneous Models
6.5 Research Challenges
6.6 Conclusion
References
7. Deep Feature Selection for Wind Forecasting-I
C. Ramakrishnan, S. Sridhar, Kusumika Krori Dutta, R. Karthick and C. Janamejaya
7.1 Introduction
7.2 Wind Forecasting System Overview
7.2.1 Classification of Wind Forecasting
7.2.2 Wind Forecasting Methods
7.2.2.1 Physical Method
7.2.2.2 Statistical Method
7.2.2.3 Hybrid Method
7.2.3 Prediction Frameworks
7.2.3.1 Pre-Processing of Data
7.2.3.2 Data Feature Analysis
7.2.3.3 Model Formulation
7.2.3.4 Optimization of Model Structure
7.2.3.5 Performance Evaluation of Model
7.2.3.6 Techniques Based on Methods of Forecasting
7.3 Current Forecasting and Prediction Methods
7.3.1 Time Series Method (TSM)
7.3.2 Persistence Method (PM)
7.3.3 Artificial Intelligence Method
7.3.4 Wavelet Neural Network
7.3.5 Adaptive Neuro-Fuzzy Inference System (ANFIS)
7.3.6 ANFIS Architecture
7.3.7 Support Vector Machine (SVM)
7.3.8 Ensemble Forecasting
7.4 Deep Learning–Based Wind Forecasting
7.4.1 Reducing Dimensionality
7.4.2 Deep Learning Techniques and Their Architectures
7.4.3 Unsupervised Pre-Trained Networks
7.4.4 Convolutional Neural Networks
7.4.5 Recurrent Neural Networks
7.4.6 Analysis of Support Vector Machine and Decision Tree Analysis (With Computation Time)
7.4.7 Tree-Based Techniques
7.5 Case Study
References
8. Deep Feature Selection for Wind Forecasting-II
S. Oswalt Manoj, J.P. Ananth, Balan Dhanka and Maharaja Kamatchi
8.1 Introduction
8.1.1 Contributions of the Work
8.2 Literature Review
8.3 Long Short-Term Memory Networks
8.4 Gated Recurrent Unit
8.5 Bidirectional Long Short-Term Memory Networks
8.6 Results and Discussion
8.7 Conclusion and Future Work
References
9. Data Falsification Detection in AMI: A Secure Perspective Analysis
Vineeth V.V. and S. Sophia
9.1 Introduction
9.2 Advanced Metering Infrastructure
9.3 AMI Attack Scenario
9.4 Data Falsification Attacks
9.5 Data Falsification Detection
9.6 Conclusion
References
10. Forecasting of Electricity Consumption for G20 Members Using Various Machine Learning Techniques
Jaymin Suhagiya, Deep Raval, Siddhi Vinayak Pandey, Jeet Patel, Ayushi Gupta and Akshay Srivastava
10.1 Introduction
10.1.1 Why Electricity Consumption Forecasting Is Required?
10.1.2 History and Advancement in Forecasting of Electricity Consumption
10.1.3 Recurrent Neural Networks
10.1.3.1 Long Short-Term Memory
10.1.3.2 Gated Recurrent Unit
10.1.3.3 Convolutional LSTM
10.1.3.4 Bidirectional Recurrent Neural Networks
10.1.4 Other Regression Techniques
10.2 Dataset Preparation
10.3 Results and Discussions
10.4 Conclusion
Acknowledgement
References
11. Use of Artificial Intelligence (AI) in the Optimization of Production of Biodiesel Energy
Manvinder Singh Pahwa, Manish Dadhich, Jaskaran Singh Saini and Dinesh Kumar Saini
11.1 Introduction
11.2 Indian Perspective of Renewable Biofuels
11.3 Opportunities
11.4 Relevance of Biodiesel in India Context
11.5 Proposed Model
11.6 Conclusion
References
Index
Back to Top
Preface
1. Analysis of Six-Phase Grid Connected Synchronous Generator in Wind Power Generation
Arif Iqbal and Girish Kumar Singh
1.1 Introduction
1.2 Analytical Modeling of Six-Phase Synchronous Machine
1.2.1 Voltage Equation
1.2.2 Equations of Flux Linkage Per Second
1.3 Linearization of Machine Equations for Stability Analysis
1.4 Dynamic Performance Results
1.5 Stability Analysis Results
1.5.1 Parametric Variation of Stator
1.5.2 Parametric Variation of Field Circuit
1.5.3 Parametric Variation of Damper Winding, Kd
1.5.4 Parametric Variation of Damper Winding, Kq
1.5.5 Magnetizing Reactance Variation Along q-axis
1.5.6 Variation in Load
1.6 Conclusions
References
Appendix
Symbols Meaning
2. Artificial Intelligence as a Tool for Conservation and Efficient Utilization of Renewable Resource
Vinay N., Ajay Sudhir Bale, Subhashish Tiwari and Baby Chithra R.
2.1 Introduction
2.2 AI in Water Energy
2.2.1 Prediction of Groundwater Level
2.2.2 Rainfall Modeling
2.3 AI in Solar Energy
2.3.1 Solar Power Forecasting
2.4 AI in Wind Energy
2.4.1 Wind Monitoring
2.4.2 Wind Forecasting
2.5 AI in Geothermal Energy
2.6 Conclusion
References
3. Artificial Intelligence–Based Energy-Efficient Clustering and Routing in IoT-Assisted Wireless Sensor Network
Nitesh Chouhan
3.1 Introduction
3.2 Related Study
3.3 Clustering in WSN
3.4 Research Methodology
3.4.1 Creating Wireless Sensor–Based IoT Environment
3.4.2 Clustering Approach
3.4.3 AI-Based Energy-Aware Routing Protocol
3.5 Conclusion
References
4. Artificial Intelligence for Modeling and Optimization of the Biogas Production
Narendra Khatri and Kamal Kishore Khatri
4.1 Introduction
4.2 Artificial Neural Network
4.2.1 ANN Architecture
4.2.2 Training Algorithms
4.2.3 Performance Parameters for Analysis of the ANN Model
4.2.4 Application of ANN for Biogas Production Modeling
4.3 Evolutionary Algorithms
4.3.1 Genetic Algorithm
4.3.2 Ant Colony Optimization
4.3.3 Particle Swarm Optimization
4.3.4 Application of Hybrid Models (ANN and Evolutionary Algorithms) for Biogas
Production Modeling
4.4 Conclusion
References
5. Battery State-of-Charge Modeling for Solar PV Array Using Polynomial Regression
Siddhi Vinayak Pandey, Jeet Patel and Harsh S. Dhiman
5.1 Introduction
5.2 Dynamic Battery Modeling
5.2.1 Proposed Methodology
5.3 Results and Discussion
5.4 Conclusion
References
6. Deep Learning Algorithms for Wind Forecasting: An Overview
M. Lydia and G. Edwin Prem Kumar
Nomenclature
6.1 Introduction
6.2 Models for Wind Forecasting
6.2.1 Persistence Model
6.2.2 Point vs. Probabilistic Forecasting
6.2.3 Multi-Objective Forecasting
6.2.4 Wind Power Ramp Forecasting
6.2.5 Interval Forecasting
6.2.6 Multi-Step Forecasting
6.3 The Deep Learning Paradigm
6.3.1 Batch Learning
6.3.2 Sequential Learning
6.3.3 Incremental Learning
6.3.4 Scene Learning
6.3.5 Transfer Learning
6.3.6 Neural Structural Learning
6.3.7 Multi-Task Learning
6.4 Deep Learning Approaches for Wind Forecasting
6.4.1 Deep Neural Network
6.4.2 Long Short-Term Memory
6.4.3 Extreme Learning Machine
6.4.4 Gated Recurrent Units
6.4.5 Autoencoders
6.4.6 Ensemble Models
6.4.7 Other Miscellaneous Models
6.5 Research Challenges
6.6 Conclusion
References
7. Deep Feature Selection for Wind Forecasting-I
C. Ramakrishnan, S. Sridhar, Kusumika Krori Dutta, R. Karthick and C. Janamejaya
7.1 Introduction
7.2 Wind Forecasting System Overview
7.2.1 Classification of Wind Forecasting
7.2.2 Wind Forecasting Methods
7.2.2.1 Physical Method
7.2.2.2 Statistical Method
7.2.2.3 Hybrid Method
7.2.3 Prediction Frameworks
7.2.3.1 Pre-Processing of Data
7.2.3.2 Data Feature Analysis
7.2.3.3 Model Formulation
7.2.3.4 Optimization of Model Structure
7.2.3.5 Performance Evaluation of Model
7.2.3.6 Techniques Based on Methods of Forecasting
7.3 Current Forecasting and Prediction Methods
7.3.1 Time Series Method (TSM)
7.3.2 Persistence Method (PM)
7.3.3 Artificial Intelligence Method
7.3.4 Wavelet Neural Network
7.3.5 Adaptive Neuro-Fuzzy Inference System (ANFIS)
7.3.6 ANFIS Architecture
7.3.7 Support Vector Machine (SVM)
7.3.8 Ensemble Forecasting
7.4 Deep Learning–Based Wind Forecasting
7.4.1 Reducing Dimensionality
7.4.2 Deep Learning Techniques and Their Architectures
7.4.3 Unsupervised Pre-Trained Networks
7.4.4 Convolutional Neural Networks
7.4.5 Recurrent Neural Networks
7.4.6 Analysis of Support Vector Machine and Decision Tree Analysis (With Computation Time)
7.4.7 Tree-Based Techniques
7.5 Case Study
References
8. Deep Feature Selection for Wind Forecasting-II
S. Oswalt Manoj, J.P. Ananth, Balan Dhanka and Maharaja Kamatchi
8.1 Introduction
8.1.1 Contributions of the Work
8.2 Literature Review
8.3 Long Short-Term Memory Networks
8.4 Gated Recurrent Unit
8.5 Bidirectional Long Short-Term Memory Networks
8.6 Results and Discussion
8.7 Conclusion and Future Work
References
9. Data Falsification Detection in AMI: A Secure Perspective Analysis
Vineeth V.V. and S. Sophia
9.1 Introduction
9.2 Advanced Metering Infrastructure
9.3 AMI Attack Scenario
9.4 Data Falsification Attacks
9.5 Data Falsification Detection
9.6 Conclusion
References
10. Forecasting of Electricity Consumption for G20 Members Using Various Machine Learning Techniques
Jaymin Suhagiya, Deep Raval, Siddhi Vinayak Pandey, Jeet Patel, Ayushi Gupta and Akshay Srivastava
10.1 Introduction
10.1.1 Why Electricity Consumption Forecasting Is Required?
10.1.2 History and Advancement in Forecasting of Electricity Consumption
10.1.3 Recurrent Neural Networks
10.1.3.1 Long Short-Term Memory
10.1.3.2 Gated Recurrent Unit
10.1.3.3 Convolutional LSTM
10.1.3.4 Bidirectional Recurrent Neural Networks
10.1.4 Other Regression Techniques
10.2 Dataset Preparation
10.3 Results and Discussions
10.4 Conclusion
Acknowledgement
References
11. Use of Artificial Intelligence (AI) in the Optimization of Production of Biodiesel Energy
Manvinder Singh Pahwa, Manish Dadhich, Jaskaran Singh Saini and Dinesh Kumar Saini
11.1 Introduction
11.2 Indian Perspective of Renewable Biofuels
11.3 Opportunities
11.4 Relevance of Biodiesel in India Context
11.5 Proposed Model
11.6 Conclusion
References
Index
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