Artificial Intelligence for Renewable Energy and Climate Change

492 Pages·2022·60.804 MB·English

by  Pandian Vasant, Gerhard-Wilhelm Weber, J. Joshua Thomas, José Antonio Marmolejo-Saucedo, Roman Rodriguez-Aguilar

Artificial Intelligence
for Renewable Energy
and Climate Change
Scrivener Publishing
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Beverly, MA 01915-6106
Publishers at Scrivener
Martin Scrivener ([email protected])
Phillip Carmical ([email protected])
Artificial Intelligence
for Renewable Energy
and Climate Change
Edited by
Pandian Vasant
Gerhard-Wilhelm Weber
Joshua Thomas
José Antonio Marmolejo-Saucedo
and
Roman Rodriguez-Aguilar
This edition first published 2022 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA
and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA
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Library of Congress Cataloging-in-Publication Data
ISBN 9781119768999
Cover images: Pixabay.com
Cover design by Russell Richardson
Set in size of 11pt and Minion Pro by Manila Typesetting Company, Makati, Philippines
Printed in the USA
10 9 8 7 6 5 4 3 2 1
Contents
Preface xv
Section I: Renewable Energy 1
1 Artificial Intelligence for Sustainability: Opportunities
and Challenges 3
Amany Alshawi
1.1 Introduction 3
1.2 History of AI for Sustainability and Smart Energy Practices 4
1.3 Energy and Resources Scenarios on the Global Scale 5
1.4 Statistical Basis of AI in Sustainability Practices 6
1.4.1 General Statistics 6
1.4.2 Environmental Stress–Based Statistics 8
1.4.2.1 Climate Change 9
1.4.2.2 Biodiversity 10
1.4.2.3 Deforestation 10
1.4.2.4 Changes in Chemistry of Oceans 10
1.4.2.5 Nitrogen Cycle 10
1.4.2.6 Water Crisis 11
1.4.2.7 Air Pollution 11
1.5 Major Challenges Faced by AI in Sustainability 11
1.5.1 Concentration of Wealth 11
1.5.2 Talent-Related and Business-Related Challenges of AI 12
1.5.3 Dependence on Machine Learning 14
1.5.4 Cybersecurity Risks 15
1.5.5 Carbon Footprint of AI 16
1.5.6 Issues in Performance Measurement 16
1.6 Major Opportunities of AI in Sustainability 17
1.6.1 AI and Water-Related Hazards Management 17
1.6.2 AI and Smart Cities 18
v
vi Contents
1.6.3 AI and Climate Change 21
1.6.4 AI and Environmental Sustainability 23
1.6.5 Impacts of AI in Transportation 24
1.6.6 Opportunities in Disaster Forecasting
and Deforestation Forecasting 25
1.6.7 Opportunities in the Energy Sector 26
1.7 Conclusion and Future Direction 26
References 27
2 Recent Applications of Machine Learning in Solar
Energy Prediction 33
N. Kapilan, R.P. Reddy and Vidhya P.
2.1 Introduction 34
2.2 Solar Energy 34
2.3 AI, ML and DL 36
2.4 Data Preprocessing Techniques 38
2.5 Solar Radiation Estimation 38
2.6 Solar Power Prediction 43
2.7 Challenges and Opportunities 45
2.8 Future Research Directions 46
2.9 Conclusion 46
Acknowledgement 47
References 47
3 Mathematical Analysis on Power Generation – Part I 53
G. Udhaya Sankar, C. Ganesa Moorthy and C.T. Ramasamy
3.1 Introduction 54
3.2 Methodology for Derivations 55
3.3 Energy Discussions 59
3.4 Data Analysis 63
Acknowledgement 67
References 67
Supplementary 69
4 Mathematical Analysis on Power Generation – Part II 87
G. Udhaya Sankar, C. Ganesa Moorthy and C.T. Ramasamy
4.1 Energy Analysis 88
4.2 Power Efficiency Method 89
4.3 Data Analysis 91
Acknowledgement 96
References 97
Supplementary - II 100
Contents vii
5 Sustainable Energy Materials 117
G. Udhaya Sankar
5.1 Introduction 117
5.2 Different Methods 119
5.2.1 Co-Precipitation Method 119
5.2.2 Microwave-Assisted Solvothermal Method 120
5.2.3 Sol-Gel Method 120
5.3 X-R ay Diffraction Analysis 120
5.4 FTIR Analysis 122
5.5 Raman Analysis 124
5.6 UV Analysis 125
5.7 SEM Analysis 127
5.8 Energy Dispersive X-Ray Analysis 127
5.9 Thermoelectric Application 129
5.9.1 Thermal Conductivity 129
5.9.2 Electrical Conductivity 131
5.9.3 Seebeck Coefficient 131
5.9.4 Power Factor 132
5.9.5 Figure of Merit 133
5.10 Limitations and Future Direction 133
5.11 Conclusion 133
Acknowledgement 134
References 134
6 Soft Computing Techniques for Maximum Power Point
Tracking in Wind Energy Harvesting System: A Survey 137
TigiluMitikuDinku, Mukhdeep Singh Manshahia
and Karanvir Singh Chahal
6.1 Introduction 137
6.1.1 Conventional MPPT Control Techniques 138
6.2 Other MPPT Control Methods 142
6.2.1 Proportional Integral Derivative Controllers 142
6.2.2 Fuzzy Logic Controller 144
6.2.2.1 Fuzzy Inference System 150
6.2.2.2 Advantage and Disadvantages of Fuzzy
Logic Controller 151
6.2.3 Artificial Neural Network 151
6.2.3.1 Biological Neural Networks 152
6.2.3.2 Architectures of Artificial Neural
Networks 155
6.2.3.3 Training of Artificial Neural Networks 157
viii Contents
6.2.3.4 Radial Basis Function 158
6.2.4 Neuro-Fuzzy Inference Approach 158
6.2.4.1 Adaptive Neuro-Fuzzy Approach 161
6.2.4.2 Hybrid Training Algorithm 161
6.3 Conclusion 167
References 167
Section II: Climate Change 171
7 The Contribution of AI-Based Approaches
in the Determination of CO Emission Gas Amounts
2
of Vehicles, Determination of CO Emission Rates Yearly
2
of Countries, Air Quality Measurement and Determination
of Smart Electric Grids’ Stability 173
Mesut Toğaçar
7.1 Introduction 174
7.2 Materials 177
7.2.1 Classification of Air Quality Condition
in Gas Concentration Measurement 177
7.2.2 CO Emission of Vehicles 178
2
7.2.3 Countries’ CO Emission Amount 179
2
7.2.4 Stability Level in Electric Grids 179
7.3 Artificial Intelligence Approaches 181
7.3.1 Machine Learning Methods 182
7.3.1.1 Support Vector Machine 183
7.3.1.2 eXtreme Gradient Boosting (XG Boost) 184
7.3.1.3 Gradient Boost 185
7.3.1.4 Decision Tree 186
7.3.1.5 Random Forest 186
7.3.2 Deep Learning Methods 188
7.3.2.1 Convolutional Neural Networks 189
7.3.2.2 Long Short-Term Memory 191
7.3.2.3 Bi-Directional LSTM and CNN 192
7.3.2.4 Recurrent Neural Network 193
7.3.3 Activation Functions 195
7.3.3.1 Rectified Linear Unit 195
7.3.3.2 Softmax Function 196
7.4 Experimental Analysis 196
7.5 Discussion 210
7.6 Conclusion 211
Funding 212