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Artificial Intelligence for Renewable Energy Systems [Kõva köide]

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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.

Preface xi
1 Analysis of Six-Phase Grid Connected Synchronous Generator in Wind Power Generation
1(36)
Arif Iqbal
Girish Kumar Singh
1.1 Introduction
2(2)
1.2 Analytical Modeling of Six-Phase Synchronous Machine
4(6)
1.2.1 Voltage Equation
5(1)
1.2.2 Equations of Flux Linkage Per Second
5(5)
1.3 Linearization of Machine Equations for Stability Analysis
10(2)
1.4 Dynamic Performance Results
12(3)
1.5 Stability Analysis Results
15(14)
1.5.1 Parametric Variation of Stator
16(3)
1.5.2 Parametric Variation of Field Circuit
19(3)
1.5.3 Parametric Variation of Damper Winding, Kd
22(2)
1.5.4 Parametric Variation of Damper Winding, Kq
24(2)
1.5.5 Magnetizing Reactance Variation Along q-axis
26(2)
1.5.6 Variation in Load
28(1)
1.6 Conclusions
29(8)
References
30(1)
Appendix
31(1)
Symbols Meaning
32(5)
2 Artificial Intelligence as a Tool for Conservation and Efficient Utilization of Renewable Resource
37(42)
N. Vinay
Ajay Sudhir Bale
Subhashish Tiwari
R. Baby Chithra
2.1 Introduction
38(1)
2.2 AI in Water Energy
39(8)
2.2.1 Prediction of Groundwater Level
39(7)
2.2.2 Rainfall Modeling
46(1)
2.3 AI in Solar Energy
47(6)
2.3.1 Solar Power Forecasting
47(6)
2.4 AI in Wind Energy
53(2)
2.4.1 Wind Monitoring
53(1)
2.4.2 Wind Forecasting
54(1)
2.5 AI in Geothermal Energy
55(5)
2.6 Conclusion
60(19)
References
61(18)
3 Artificial Intelligence-Based Energy-Efficient Clustering and Routing in IoT-Assisted Wireless Sensor Network
79(14)
Nitesh Chouhan
3.1 Introduction
80(1)
3.2 Related Study
81(3)
3.3 Clustering in WSN
84(1)
3.4 Research Methodology
85(4)
3.4.1 Creating Wireless Sensor-Based IoT Environment
85(1)
3.4.2 Clustering Approach
86(1)
3.4.3 AI-Based Energy-Aware Routing Protocol
87(2)
3.5 Conclusion
89(4)
References
89(4)
4 Artificial Intelligence for Modeling and Optimization of the Biogas Production
93(22)
Narendra Khatri
Kamal Kishore Khatri
4.1 Introduction
93(3)
4.2 Artificial Neural Network
96(7)
4.2.1 ANN Architecture
96(2)
4.2.2 Training Algorithms
98(1)
4.2.3 Performance Parameters for Analysis of the ANN Model
98(1)
4.2.4 Application of ANN for Biogas Production Modeling
99(4)
4.3 Evolutionary Algorithms
103(4)
4.3.1 Genetic Algorithm
103(1)
4.3.2 Ant Colony Optimization
104(2)
4.3.3 Particle Swarm Optimization
106(1)
4.3.4 Application of Hybrid Models (ANN and Evolutionary Algorithms) for Biogas Production Modeling
106(1)
4.4 Conclusion
107(8)
References
111(4)
5 Battery State-of-Charge Modeling for Solar PV Array Using Polynomial Regression
115(14)
Siddhi Vinayak Pandey
Jeet Patel
Harsh S. Dhiman
5.1 Introduction
115(4)
5.2 Dynamic Battery Modeling
119(3)
5.2.1 Proposed Methodology
120(2)
5.3 Results and Discussion
122(4)
5.4 Conclusion
126(3)
References
127(2)
6 Deep Learning Algorithms for Wind Forecasting: An Overview
129(18)
M. Lydia
G. Edwin Prem Kumar
Nomenclature
129(2)
6.1 Introduction
131(2)
6.2 Models for Wind Forecasting
133(2)
6.2.1 Persistence Model
133(1)
6.2.2 Point vs. Probabilistic Forecasting
133(1)
6.2.3 Multi-Objective Forecasting
134(1)
6.2.4 Wind Power Ramp Forecasting
134(1)
6.2.5 Interval Forecasting
134(1)
6.2.6 Multi-Step Forecasting
134(1)
6.3 The Deep Learning Paradigm
135(2)
6.3.1 Batch Learning
136(1)
6.3.2 Sequential Learning
136(1)
6.3.3 Incremental Learning
136(1)
6.3.4 Scene Learning
136(1)
6.3.5 Transfer Learning
136(1)
6.3.6 Neural Structural Learning
136(1)
6.3.7 Multi-Task Learning
137(1)
6.4 Deep Learning Approaches for Wind Forecasting
137(2)
6.4.1 Deep Neural Network
137(1)
6.4.2 Long Short-Term Memory
138(1)
6.4.3 Extreme Learning Machine
138(1)
6.4.4 Gated Recurrent Units
139(1)
6.4.5 Autoencoders
139(1)
6.4.6 Ensemble Models
139(1)
6.4.7 Other Miscellaneous Models
139(1)
6.5 Research Challenges
139(2)
6.6 Conclusion
141(6)
References
142(5)
7 Deep Feature Selection for Wind Forecasting-I
147(34)
C. Ramakrishnan
S. Sridhar
Kusumika Krori Dutta
R. Karthick
C. Janamejaya
7.1 Introduction
148(4)
7.2 Wind Forecasting System Overview
152(6)
7.2.1 Classification of Wind Forecasting
153(1)
7.2.2 Wind Forecasting Methods
153(1)
7.2.2.1 Physical Method
154(1)
7.2.2.2 Statistical Method
154(1)
7.2.2.3 Hybrid Method
155(1)
7.2.3 Prediction Frameworks
155(1)
7.2.3.1 Pre-Processing of Data
155(1)
7.2.3.2 Data Feature Analysis
156(1)
7.2.3.3 Model Formulation
156(1)
7.2.3.4 Optimization of Model Structure
156(1)
7.2.3.5 Performance Evaluation of Model
157(1)
7.2.3.6 Techniques Based on Methods of Forecasting
157(1)
7.3 Current Forecasting and Prediction Methods
158(8)
7.3.1 Time Series Method (TSM)
159(1)
7.3.2 Persistence Method (PM)
159(1)
7.3.3 Artificial Intelligence Method
160(1)
7.3.4 Wavelet Neural Network
161(1)
7.3.5 Adaptive Neuro-Fuzzy Inference System (ANFIS)
162(1)
7.3.6 ANFIS Architecture
163(2)
7.3.7 Support Vector Machine (SVM)
165(1)
7.3.8 Ensemble Forecasting
166(1)
7.4 Deep Learning-Based Wind Forecasting
166(7)
7.4.1 Reducing Dimensionality
168(1)
7.4.2 Deep Learning Techniques and Their Architectures
169(1)
7.4.3 Unsupervised Pre-Trained Networks
169(1)
7.4.4 Convolutional Neural Networks
170(1)
7.4.5 Recurrent Neural Networks
170(1)
7.4.6 Analysis of Support Vector Machine and Decision Tree Analysis (With Computation Time)
170(2)
7.4.7 Tree-Based Techniques
172(1)
7.5 Case Study
173(8)
References
176(5)
8 Deep Feature Selection for Wind Forecasting-II
181(20)
S. Oswalt Manoj
J.P. Ananth
Balan Dhanka
Maharaja Kamatchi
8.1 Introduction
182(3)
8.1.1 Contributions of the Work
184(1)
8.2 Literature Review
185(1)
8.3 Long Short-Term Memory Networks
186(4)
8.4 Gated Recurrent Unit
190(4)
8.5 Bidirectional Long Short-Term Memory Networks
194(2)
8.6 Results and Discussion
196(1)
8.7 Conclusion and Future Work
197(4)
References
198(3)
9 Data Falsification Detection in AMI: A Secure Perspective Analysis
201(10)
V. V. Vineeth
S. Sophia
9.1 Introduction
201(1)
9.2 Advanced Metering Infrastructure
202(2)
9.3 AMI Attack Scenario
204(1)
9.4 Data Falsification Attacks
205(1)
9.5 Data Falsification Detection
206(1)
9.6 Conclusion
207(4)
References
208(3)
10 Forecasting of Electricity Consumption for G20 Members Using Various Machine Learning Techniques
211(18)
Jaymin Suhagiya
Deep Raval
Siddhi Vinayak Pandey
Jeet Patel
Ayushi Gupta
Akshay Srivastava
10.1 Introduction
211(6)
10.1.1 Why Electricity Consumption Forecasting Is Required?
212(1)
10.1.2 History and Advancement in Forecasting of Electricity Consumption
212(1)
10.1.3 Recurrent Neural Networks
213(1)
10.1.3.1 Long Short-Term Memory
214(1)
10.1.3.2 Gated Recurrent Unit
214(1)
10.1.3.3 Convolutional LSTM
215(1)
10.1.3.4 Bidirectional Recurrent Neural Networks
216(1)
10.1.4 Other Regression Techniques
216(1)
10.2 Dataset Preparation
217(1)
10.3 Results and Discussions
218(7)
10.4 Conclusion
225(4)
Acknowledgement
225(1)
References
225(4)
11 Use of Artificial Intelligence (AI) in the Optimization of Production of Biodiesel Energy
229(10)
Manvinder Singh Pahwa
Manish Dadhich
Jaskaran Singh Saini
Dinesh Kumar Saini
11.1 Introduction
230(1)
11.2 Indian Perspective of Renewable Biofuels
230(2)
11.3 Opportunities
232(1)
11.4 Relevance of Biodiesel in India Context
233(1)
11.5 Proposed Model
234(2)
11.6 Conclusion
236(3)
References
237(2)
Index 239
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.