Artificial intelligence for renewable energy and climate change. (2022)

• Record Type: Book
• Further Information: Note: Edited by Pandian Vasant, Gerhard-Wilhelm Weber, J. Joshua Thomas, José Antonio Marmolejo-Saucedo, Roman Rodriguez-Aguilar.
• Editors: Vasant, Pandian; Weber, Gerhard-Wilhelm; Thomas, J. Joshua, 1973-; Marmolejo Saucedo, Jose Antonio; Rodriguez-Aguilar, Roman
• 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 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 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 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 CO2 Emission Gas Amounts of Vehicles, Determination of CO2 Emission Rates Yearly 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 CO2 Emission of Vehicles 178 7.2.3 Countries’ CO2 Emission Amount 179 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 Ethical Approval 212 Conflicts of Interest 212 References 212 8 Performance Analysis and Effects of Dust & Temperature on Solar PV Module System by Using Multivariate Linear Regression Model 217; Sumit Sharma, J. Joshua Thomas and Pandian Vasant 8.1 Introduction 218 8.1.1 Indian Scenario of Renewable Energy 218 8.1.2 Solar Radiation at Earth 220 8.1.3 Solar Photovoltaic Technologies 220 8.1.3.1 Types of SPV Systems 221 8.1.3.2 Types of Solar Photovoltaic Cells 222 8.1.3.3 Effects of Temperature 223 8.1.3.4 Conversion Efficiency 223 8.1.4 Losses in PV Systems 224 8.1.5 Performance of Solar Power Plants 224 8.2 Literature Review 225 8.3 Experimental Setup 228 8.3.1 Selection of Site and Development of Experimental Facilities 229 8.3.2 Methodology 229 8.3.3 Experimental Instrumentation 230 8.3.3.1 Solar Photovoltaic Modules 230 8.3.3.2 PV Grid-Connected Inverter 232 8.3.3.3 Pyranometer 232 8.3.3.4 Digital Thermometer 234 8.3.3.5 Lightning Arrester 235 8.3.3.6 Data Acquisition System 236 8.3.4 Formula Used and Sample Calculations 236 8.3.5 Assumptions and Limitations 237 8.4 Results Discussion 238 8.4.1 Phases of Data Collection 238 8.4.2 Variation in Responses Evaluated During Phase I (From 1 Jan. to 27 Feb.) of Study 238 8.4.2.1 Effect of Dust and Ambient Temperature on Conversion Efficiency 238 8.4.2.2 Capacity Utilization Factor and Performance Ratio 241 8.4.2.3 Evaluation of MLR Model 242 8.4.3 Variation in Responses Evaluated During Phase II (From 1 March to 5 April) 246 8.4.3.1 Influence of Dust and Ambient Temperature on Conversion Efficiency 246 8.4.3.2 Capacity Utilization Factor and Performance Ratio 246</p&
• Edition: 1st
• Publisher Details: Hoboken : Wiley-Scrivener
• Publication Date: 2022
• Extent: 1 online resource
• Subjects: 621.042028563; Renewable energy sources -- Data processing; Artificial intelligence; Climate change mitigation -- Data processing
• Languages: English
• ISBNs: 9781119771500
• Related ISBNs: 9781119768999
• Notes: Note: Description based on CIP data; resource not viewed.
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