Artificial Intelligence for Renewable Energy Systems

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.

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.

Audience

The primary target audience includes research scholars, industry engineers, and graduate students working in renewable energy, electrical engineering, machine learning, information & communication technology.

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.

Preface xi

1 Analysis of Six-Phase Grid Connected Synchronous Generator in Wind Power Generation 1
Arif Iqbal and Girish Kumar Singh

1.1 Introduction 2

1.2 Analytical Modeling of Six-Phase Synchronous Machine 4

1.2.1 Voltage Equation 5

1.2.2 Equations of Flux Linkage Per Second 5

1.3 Linearization of Machine Equations for Stability Analysis 10

1.4 Dynamic Performance Results 12

1.5 Stability Analysis Results 15

1.5.1 Parametric Variation of Stator 16

1.5.2 Parametric Variation of Field Circuit 19

1.5.3 Parametric Variation of Damper Winding, K_d 22

1.5.4 Parametric Variation of Damper Winding, K_q 24

1.5.5 Magnetizing Reactance Variation Along q-axis 26

1.5.6 Variation in Load 28

1.6 Conclusions 29

References 30

Appendix 31

Symbols Meaning 32

2 Artificial Intelligence as a Tool for Conservation and Efficient Utilization of Renewable Resource 37
Vinay N., Ajay Sudhir Bale, Subhashish Tiwari and Baby Chithra R.

2.1 Introduction 38

2.2 AI in Water Energy 39

2.2.1 Prediction of Groundwater Level 39

2.2.2 Rainfall Modeling 46

2.3 AI in Solar Energy 47

2.3.1 Solar Power Forecasting 47

2.4 AI in Wind Energy 53

2.4.1 Wind Monitoring 53

2.4.2 Wind Forecasting 54

2.5 AI in Geothermal Energy 55

2.6 Conclusion 60

References 61

3 Artificial Intelligence–Based Energy-Efficient Clustering and Routing in IoT-Assisted Wireless Sensor Network 79
Nitesh Chouhan

3.1 Introduction 80

3.2 Related Study 81

3.3 Clustering in WSN 84

3.4 Research Methodology 85

3.4.1 Creating Wireless Sensor–Based IoT Environment 85

3.4.2 Clustering Approach 86

3.4.3 AI-Based Energy-Aware Routing Protocol 87

3.5 Conclusion 89

References 89

4 Artificial Intelligence for Modeling and Optimization of the Biogas Production 93
Narendra Khatri and Kamal Kishore Khatri

4.1 Introduction 93

4.2 Artificial Neural Network 96

4.2.1 ANN Architecture 96

4.2.2 Training Algorithms 98

4.2.3 Performance Parameters for Analysis of the ANN Model 98

4.2.4 Application of ANN for Biogas Production Modeling 99

4.3 Evolutionary Algorithms 103

4.3.1 Genetic Algorithm 103

4.3.2 Ant Colony Optimization 104

4.3.3 Particle Swarm Optimization 106

4.3.4 Application of Hybrid Models (ANN and Evolutionary Algorithms) for Biogas Production Modeling 106

4.4 Conclusion 107

References 111

5 Battery State-of-Charge Modeling for Solar PV Array Using Polynomial Regression 115
Siddhi Vinayak Pandey, Jeet Patel and Harsh S. Dhiman

5.1 Introduction 115

5.2 Dynamic Battery Modeling 119

5.2.1 Proposed Methodology 120

5.3 Results and Discussion 122

5.4 Conclusion 126

References 127

6 Deep Learning Algorithms for Wind Forecasting: An Overview 129
M. Lydia and G. Edwin Prem Kumar

Nomenclature 129

6.1 Introduction 131

6.2 Models for Wind Forecasting 133

6.2.1 Persistence Model 133

6.2.2 Point vs. Probabilistic Forecasting 133

6.2.3 Multi-Objective Forecasting 134

6.2.4 Wind Power Ramp Forecasting 134

6.2.5 Interval Forecasting 134

6.2.6 Multi-Step Forecasting 134

6.3 The Deep Learning Paradigm 135

6.3.1 Batch Learning 136

6.3.2 Sequential Learning 136

6.3.3 Incremental Learning 136

6.3.4 Scene Learning 136

6.3.5 Transfer Learning 136

6.3.6 Neural Structural Learning 136

6.3.7 Multi-Task Learning 137

6.4 Deep Learning Approaches for Wind Forecasting 137

6.4.1 Deep Neural Network 137

6.4.2 Long Short-Term Memory 138

6.4.3 Extreme Learning Machine 138

6.4.4 Gated Recurrent Units 139

6.4.5 Autoencoders 139

6.4.6 Ensemble Models 139

6.4.7 Other Miscellaneous Models 139

6.5 Research Challenges 139

6.6 Conclusion 141

References 142

7 Deep Feature Selection for Wind Forecasting-I 147
C. Ramakrishnan, S. Sridhar, Kusumika Krori Dutta, R. Karthick and C. Janamejaya

7.1 Introduction 148

7.2 Wind Forecasting System Overview 152

7.2.1 Classification of Wind Forecasting 153

7.2.2 Wind Forecasting Methods 153

7.2.2.1 Physical Method 154

7.2.2.2 Statistical Method 154

7.2.2.3 Hybrid Method 155

7.2.3 Prediction Frameworks 155

7.2.3.1 Pre-Processing of Data 155

7.2.3.2 Data Feature Analysis 156

7.2.3.3 Model Formulation 156

7.2.3.4 Optimization of Model Structure 156

7.2.3.5 Performance Evaluation of Model 157

7.2.3.6 Techniques Based on Methods of Forecasting 157

7.3 Current Forecasting and Prediction Methods 158

7.3.1 Time Series Method (TSM) 159

7.3.2 Persistence Method (PM) 159

7.3.3 Artificial Intelligence Method 160

7.3.4 Wavelet Neural Network 161

7.3.5 Adaptive Neuro-Fuzzy Inference System (ANFIS) 162

7.3.6 ANFIS Architecture 163

7.3.7 Support Vector Machine (SVM) 165

7.3.8 Ensemble Forecasting 166

7.4 Deep Learning–Based Wind Forecasting 166

7.4.1 Reducing Dimensionality 168

7.4.2 Deep Learning Techniques and Their Architectures 169

7.4.3 Unsupervised Pre-Trained Networks 169

7.4.4 Convolutional Neural Networks 170

7.4.5 Recurrent Neural Networks 170

7.4.6 Analysis of Support Vector Machine and Decision Tree Analysis (With Computation Time) 170

7.4.7 Tree-Based Techniques 172

7.5 Case Study 173

References 176

8 Deep Feature Selection for Wind Forecasting-II 181
S. Oswalt Manoj, J.P. Ananth, Balan Dhanka and Maharaja Kamatchi

8.1 Introduction 182

8.1.1 Contributions of the Work 184

8.2 Literature Review 185

8.3 Long Short-Term Memory Networks 186

8.4 Gated Recurrent Unit 190

8.5 Bidirectional Long Short-Term Memory Networks 194

8.6 Results and Discussion 196

8.7 Conclusion and Future Work 197

References 198

9 Data Falsification Detection in AMI: A Secure Perspective Analysis 201
Vineeth V.V. and S. Sophia

9.1 Introduction 201

9.2 Advanced Metering Infrastructure 202

9.3 AMI Attack Scenario 204

9.4 Data Falsification Attacks 205

9.5 Data Falsification Detection 206

9.6 Conclusion 207

References 208

10 Forecasting of Electricity Consumption for G20 Members Using Various Machine Learning Techniques 211
Jaymin Suhagiya, Deep Raval, Siddhi Vinayak Pandey, Jeet Patel, Ayushi Gupta and Akshay Srivastava

10.1 Introduction 211

10.1.1 Why Electricity Consumption Forecasting Is Required? 212

10.1.2 History and Advancement in Forecasting of Electricity Consumption 212

10.1.3 Recurrent Neural Networks 213

10.1.3.1 Long Short-Term Memory 214

10.1.3.2 Gated Recurrent Unit 214

10.1.3.3 Convolutional LSTM 215

10.1.3.4 Bidirectional Recurrent Neural Networks 216

10.1.4 Other Regression Techniques 216

10.2 Dataset Preparation 217

10.3 Results and Discussions 218

10.4 Conclusion 225

Acknowledgement 225

References 225

11 Use of Artificial Intelligence (AI) in the Optimization of Production of Biodiesel Energy 229
Manvinder Singh Pahwa, Manish Dadhich, Jaskaran Singh Saini and Dinesh Kumar Saini

11.1 Introduction 230

11.2 Indian Perspective of Renewable Biofuels 230

11.3 Opportunities 232

11.4 Relevance of Biodiesel in India Context 233

11.5 Proposed Model 234

11.6 Conclusion 236

References 237

Index 239

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