Artificial Intelligence for Renewable Energy and Climate Change

Cover
Half-Title Page
Series Page
Title Page
Copyright Page
Contents
Preface
Section I: Renewable Energy
1 Artificial Intelligence for Sustainability: Opportunities and Challenges
1.1 Introduction
1.2 History of AI for Sustainability and Smart Energy Practices
1.3 Energy and Resources Scenarios on the Global Scale
1.4 Statistical Basis of AI in Sustainability Practices
1.4.1 General Statistics
1.4.2 Environmental Stress–Based Statistics
1.4.2.1 Climate Change
1.4.2.2 Biodiversity
1.4.2.3 Deforestation
1.4.2.4 Changes in Chemistry of Oceans
1.4.2.5 Nitrogen Cycle
1.4.2.6 Water Crisis
1.4.2.7 Air Pollution
1.5 Major Challenges Faced by AI in Sustainability
1.5.1 Concentration of Wealth
1.5.2 Talent-Related and Business-Related Challenges of AI
1.5.3 Dependence on Machine Learning
1.5.4 Cybersecurity Risks
1.5.5 Carbon Footprint of AI
1.5.6 Issues in Performance Measurement
1.6 Major Opportunities of AI in Sustainability
1.6.1 AI and Water-Related Hazards Management
1.6.2 AI and Smart Cities
1.6.3 AI and Climate Change
1.6.4 AI and Environmental Sustainability
1.6.5 Impacts of AI in Transportation
1.6.6 Opportunities in Disaster Forecasting and Deforestation Forecasting
1.6.7 Opportunities in the Energy Sector
1.7 Conclusion and Future Direction
References
2 Recent Applications of Machine Learning in Solar Energy Prediction
2.1 Introduction
2.2 Solar Energy
2.3 AI, ML and DL
2.4 Data Preprocessing Techniques
2.5 Solar Radiation Estimation
2.6 Solar Power Prediction
2.7 Challenges and Opportunities
2.8 Future Research Directions
2.9 Conclusion
Acknowledgement
References
3 Mathematical Analysis on Power Generation – Part I
3.1 Introduction
3.2 Methodology for Derivations
3.3 Energy Discussions
3.4 Data Analysis
Acknowledgement
References
Supplementary
4 Mathematical Analysis on Power Generation – Part II
4.1 Energy Analysis
4.2 Power Efficiency Method
4.3 Data Analysis
Acknowledgement
References
Supplementary II
5 Sustainable Energy Materials
5.1 Introduction
5.2 Different Methods
5.2.1 Co-Precipitation Method
5.2.2 Microwave-Assisted Solvothermal Method
5.2.3 Sol-Gel Method
5.3 X-Ray Diffraction Analysis
5.4 FTIR Analysis
5.5 Raman Analysis
5.6 UV Analysis
5.7 SEM Analysis
5.8 Energy Dispersive X-Ray Analysis
5.9 Thermoelectric Application
5.9.1 Thermal Conductivity
5.9.2 Electrical Conductivity
5.9.3 Seebeck Coefficient
5.9.4 Power Factor
5.9.5 Figure of Merit
5.10 Limitations and Future Direction
5.11 Conclusion
Acknowledgement
References
6 Soft Computing Techniques for Maximum Power Point Tracking in Wind Energy Harvesting System: A Survey
6.1 Introduction
6.1.1 Conventional MPPT Control Techniques
6.2 Other MPPT Control Methods
6.2.1 Proportional Integral Derivative Controllers
6.2.2 Fuzzy Logic Controller
6.2.2.1 Fuzzy Inference System
6.2.2.2 Advantages and Disadvantages of Fuzzy Logic Controller
6.2.3 Artificial Neural Network
6.2.3.1 Biological Neural Networks
6.2.3.2 Architectures of Artificial Neural Networks
6.2.3.3 Training of Artificial Neural Networks
6.2.3.4 Radial Basis Function
6.2.4 Neuro-Fuzzy Inference Approach
6.2.4.1 Adaptive Neuro-Fuzzy Approach
6.2.4.2 Hybrid Training Algorithm
6.3 Conclusion
References
Section II: Climate Change
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
7.1 Introduction
7.2 Materials
7.2.1 Classification of Air Quality Condition in Gas Concentration Measurement
7.2.2 CO2 Emission of Vehicles
7.2.3 Countries’ CO2 Emission Amount
7.2.4 Stability Level in Electric Grids
7.3 Artificial Intelligence Approaches
7.3.1 Machine Learning Methods
7.3.1.1 Support Vector Machine
7.3.1.2 eXtreme Gradient Boosting (XG Boost)
Gain = left similarity + right similarity root similarity (7.3) Newguess = initial predicted value + learning rate x output value
7.3.1.3 Gradient Boost
7.3.1.4 Decision Tree
7.3.1.5 Random Forest
7.3.2 Deep Learning Methods
7.3.2.1 Convolutional Neural Networks
7.3.2.2 Long Short-Term Memory
7.3.2.3 Bi-Directional LSTM and CNN
7.3.2.4 Recurrent Neural Network
7.3.3 Activation Functions
7.3.3.1 Rectified Linear Unit
7.3.3.2 Softmax Function
7.4 Experimental Analysis
7.5 Discussion
7.6 Conclusion
Funding
Ethical Approval
Conflicts of Interest
References
8 Performance Analysis and Effects of Dust & Temperature on Solar PV Module System by Using Multivariate Linear Regression Model
8.1 Introduction
8.1.1 Indian Scenario of Renewable Energy
8.1.2 Solar Radiation at Earth
8.1.3 Solar Photovoltaic Technologies
8.1.3.1 Types of SPV Systems
8.1.3.2 Types of Solar Photovoltaic Cells
8.1.3.3 Effects of Temperature
8.1.3.4 Conversion Efficiency
8.1.4 Losses in PV Systems
8.1.5 Performance of Solar Power Plants
8.2 Literature Review
8.3 Experimental Setup
8.3.1 Selection of Site and Development of Experimental Facilities
8.3.2 Methodology
8.3.3 Experimental Instrumentation
8.3.3.1 Solar Photovoltaic Modules
8.3.3.2 PV Grid-Connected Inverter
8.3.3.3 Pyranometer
8.3.3.4 Digital Thermometer
8.3.3.5 Lightning Arrester
8.3.3.6 Data Acquisition System
8.3.4 Formula Used and Sample Calculations
8.3.5 Assumptions and Limitations
8.4 Results Discussion
8.4.1 Phases of Data Collection
8.4.2 Variation in Responses Evaluated During Phase I (From 1 Jan. to 27 Feb.) of Study
8.4.2.1 Effect of Dust and Ambient Temperature on Conversion Efficiency
8.4.2.2 Capacity Utilization Factor and Performance Ratio
8.4.2.3 Evaluation of MLR Model
8.4.3 Variation in Responses Evaluated During Phase II (From 1 March to 5 April)
8.4.3.1 Influence of Dust and Ambient Temperature on Conversion Efficiency
8.4.3.2 Capacity Utilization Factor and Performance Ratio
8.4.3.3 Evaluation of MLR Model
8.4.4 Variation in Responses Evaluated During Phase III (18 May to 25 June)
8.4.4.1 Effect of Dust and Ambient Temperature on Conversion Efficiency
8.4.4.2 Capacity Utilization Factor and Performance Ratio
8.4.4.3 Evaluation of MLR Model
8.4.5 Regression Analysis for the Whole Period
8.4.6 Best Subsets Regression: Conversion Efficiency v/s Exposure Day, Ambient Temperature
8.4.7 Regression Outputs Summary
8.4.8 Comparison Between Measured Efficiency and Predicted Efficiency
8.4.9 Losses Due to Dust Accumulation
8.4.10 Economic Analysis
8.5 Future Research Directions
8.6 Conclusion
References
9 Evaluation of In-House Compact Biogas Plant Thereby Testing FourStroke Single-Cylinder Diesel Engine
9.1 Introduction
9.1.1 Benefits of the Use of Biogas as a Fuel in India
9.1.2 Biogas Generators in India
9.1.3 Biogas
9.1.3.1 Process of Biogas Production
9.2 Literature Review
9.2.1 Wastes and Environment
9.2.2 Economic and Environmental Considerations
9.2.3 Factor Affecting Yield and Production of Biogas
9.2.3.1 The Temperature
9.2.3.2 PH and Buffering Systems
9.2.3.3 C/N Ratio
9.2.3.4 Substrate Type
9.2.3.5 Retention Time
9.2.3.6 Total Solids
9.2.4 Advantages of Anaerobic Digestion to Society
9.2.4.1 Electricity Generation
9.2.4.2 Fertilizer Production
9.2.4.3 Pathogen Reduction
9.3 Methodology
9.3.1 Set Up of Compact Biogas Plant and Equipments
9.3.2 Assembling and Fabrication of Biogas Plant
9.3.3 Design and Technology of Compact Biogas Plant
9.3.4 Gas Quantity and Quality
9.3.5 Calculation of Gas Quantity in Gas Holder
9.4 Analysis of Compact Biogas Plant
9.4.1 Experiment Result
9.4.1.1 Testing on 50 Kg Animal Dung Along With 500 Ltrs Water
9.4.1.2 Testing on Kitchen Waste
9.4.1.3 Testing on Fruits Waste
9.4.2 Comparison of Biogas by Different Substrate
9.4.3 Production of Biogas Per Day at Different Waste
9.4.4 Variation of PH Value
9.4.5 Variation of Average pH Value
9.4.6 Variation of Temperature
9.4.7 Variation of Average Temperature With Respect to No. of Days for Animal Dung, Kitchen Waste, Fruits Waste and Sugar
9.4.8 Variation of Biogas Production W.R.T. Quantity of Kitchen Waste and Fruits Waste
9.5 Analysis of Single-Cylinder Diesel Engine on Dual Fuel
9.5.1 Testing on 4-Stroke Single-Cylinder Diesel Engine
9.5.2 Calculation
9.5.3 Heat Balance Sheet
9.5.4 Testing Result With Dual Fuel (Biogas and Diesel) on 4-Stroke Single-Cylinder Diesel Engine
9.5.5 Calculation
9.5.6 Heat Balance Sheet
9.6 General Comments
9.7 Conclusion
9.8 Future Scope
References
10 Low-Temperature Combustion Technologies for Emission Reduction in Diesel Engines
Abbreviations 10.1 Introduction
10.1.1 Global Scenario of Energy and Emissions
10.1.2 Diesel Engine Emissions
10.1.3 Mitigation of NOx and Particulate Matter
10.1.4 Low-Temperature Combustion Engine Fuels
10.2 Scope of the Current Article
10.3 HCCI Technology
10.3.1 Principle of HCCI
10.3.2 Performance and Emissions with HCCI
10.4 Partially Premixed Compression Ignition (PPCI)
10.5 Exhaust Gas Recirculation (EGR)
10.6 Reactivity Controlled Compression Ignition (RCCI)
10.7 LTC Through Fuel Additives
10.8 Emulsified Fuels (Water-in-Diesel Emulsion Fuel)
10.8.1 Brake Thermal Efficiency (BTE)
10.8.2 Nitrogen Oxide (NOx)
10.8.3 Soot and Particulate Matter (PM)
10.9 Conclusion and Future Scope
Acknowledgement
References
11 Efficiency Optimization of Indoor Air Disinfection by Radiation Exposure for Poultry Breeding Rational for Microclimate Systems Modernization for Livestock Premises
11.1 Introduction
11.2 Materials and Methods
11.3 Results
11.4 Discussion
11.5 Conclusions
References
12 Improving the Efficiency of Photovoltaic Installations for Sustainable Development of the Urban Environment
12.1 Introduction
12.2 Background
12.3 Main Focus of the Chapter
12.4 Solutions and Recommendations
Acknowledgements
References
13 Monitoring System Based MicroController for Biogas Digester
13.1 Introduction
13.2 Related Work
13.3 Methods and Material
13.3.1 Identification of Needs
13.3.2 ADOLMS Software Setup
13.3.3 ADOLMS Sensors
Digital Temperature and Humidity Sensor DHT-22
Analog Pressure Sensor MPX5010DP
Analogue pH Sensor
MQ-4 Gas Sensor
13.3.4 ADOLMS Hardware Architecture
13.4 Results
13.5 Conclusion
Acknowledgements
References
14 Greenhouse Gas Statistics and Methods of Combating Climate Change
Introduction
Methodology
Findings
Conclusion
References
Index
Also of Interest
EULA