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Artificial Intelligence for Renewable Energy and Climate Change [Kietas viršelis]

Edited by (Poznan University of Technology, Poznan, Poland), Edited by (Universidad Panamericana, Mexico), Edited by (KDU Penang University College, Malaysia), Edited by (MERLIN Research Centre of Ton Duc Thang University, HCMC, Vietnam), Edited by (Pan-American University, Mexico)
  • Formatas: Hardback, 496 pages, aukštis x plotis x storis: 10x10x10 mm, weight: 454 g
  • Išleidimo metai: 08-Aug-2022
  • Leidėjas: Wiley-Scrivener
  • ISBN-10: 1119768993
  • ISBN-13: 9781119768999
Kitos knygos pagal šią temą:
Artificial Intelligence for Renewable Energy and Climate ChangeDidesnis paveikslėlis
  • Formatas: Hardback, 496 pages, aukštis x plotis x storis: 10x10x10 mm, weight: 454 g
  • Išleidimo metai: 08-Aug-2022
  • Leidėjas: Wiley-Scrivener
  • ISBN-10: 1119768993
  • ISBN-13: 9781119768999
Kitos knygos pagal šią temą:
Pastovi nuoroda: https://www.kriso.lt/db/9781119768999.html
Raktažodžiai:
ARTIFICIAL INTELLIGENCE FOR RENEWABLE ENERGY AND CLIMATE CHANGE

Written and edited by a global team of experts in the field, this groundbreaking new volume presents the concepts and fundamentals of using artificial intelligence in renewable energy and climate change, while also covering the practical applications that can be utilized across multiple disciplines and industries, for the engineer, the student, and other professionals and scientists.

Renewable energy and climate change are two of the most important and difficult issues facing the world today. The state of the art in these areas is changing rapidly, with new techniques and theories coming online seemingly every day. It is important for scientists, engineers, and other professionals working in these areas to stay abreast of developments, advances, and practical applications, and this volume is an outstanding reference and tool for this purpose.

The paradigm in renewable energy and climate change shifts constantly. In todays international and competitive environment, lean and green practices are important determinants to increase performance. Corresponding production philosophies and techniques help companies diminish lead times and costs of manufacturing, improve delivery on time and quality, and at the same time become more ecological by reducing material use and waste, and by recycling and reusing. Those lean and green activities enhance productivity, lower carbon footprint and improve consumer satisfaction, which in reverse makes firms competitive and sustainable.

This practical, new groundbreaking volume:





Features coverage on a wide range of topics such as classical and nature-inspired optimization and optimal control, hybrid and stochastic systems Is ideally designed for engineers, scientists, industrialist, academicians, researchers, computer and information technologists, sustainable developers, managers, environmentalists, government leaders, research officers, policy makers, business leaders and students Is useful as a practical tool for practitioners in the fields of sustainable and renewable energy sustainability Includes wide coverage of how artificial intelligence can be used to impact the struggle against global warming and climate change
Preface xv
Section I Renewable Energy
1(170)
1 Artificial Intelligence for Sustainability: Opportunities and Challenges
3(30)
Amany Alshawi
1.1 Introduction
3(1)
1.2 History of AI for Sustainability and Smart Energy Practices
4(1)
1.3 Energy and Resources Scenarios on the Global Scale
5(1)
1.4 Statistical Basis of AI in Sustainability Practices
6(5)
1.4.1 General Statistics
6(2)
1.4.2 Environmental Stress-Based Statistics
8(1)
1.4.2.1 Climate Change
9(1)
1.4.2.2 Biodiversity
10(1)
1.4.2.3 Deforestation
10(1)
1.4.2.4 Changes in Chemistry of Oceans
10(1)
1.4.2.5 Nitrogen Cycle
10(1)
1.4.2.6 Water Crisis
11(1)
1.4.2.7 Air Pollution
11(1)
1.5 Major Challenges Faced by AI in Sustainability
11(6)
1.5.1 Concentration of Wealth
11(1)
1.5.2 Talent-Related and Business-Related Challenges of AI
12(2)
1.5.3 Dependence on Machine Learning
14(1)
1.5.4 Cybersecurity Risks
15(1)
1.5.5 Carbon Footprint of AI
16(1)
1.5.6 Issues in Performance Measurement
16(1)
1.6 Major Opportunities of AI in Sustainability
17(9)
1.6.1 AI and Water-Related Hazards Management
17(1)
1.6.2 AI and Smart Cities
18(3)
1.6.3 AI and Climate Change
21(2)
1.6.4 AI and Environmental Sustainability
23(1)
1.6.5 Impacts of AI in Transportation
24(1)
1.6.6 Opportunities in Disaster Forecasting and Deforestation Forecasting
25(1)
1.6.7 Opportunities in the Energy Sector
26(1)
1.7 Conclusion and Future Direction
26(7)
References
27(6)
2 Recent Applications of Machine Learning in Solar Energy Prediction
33(20)
N. Kapilatty
R.P. Reddy
P. Vidhya
2.1 Introduction
34(1)
2.2 Solar Energy
34(2)
2.3 AI, ML and DL
36(2)
2.4 Data Preprocessing Techniques
38(1)
2.5 Solar Radiation Estimation
38(5)
2.6 Solar Power Prediction
43(2)
2.7 Challenges and Opportunities
45(1)
2.8 Future Research Directions
46(1)
2.9 Conclusion
46(7)
Acknowledgement
47(1)
References
47(6)
3 Mathematical Analysis on Power Generation - Part
53(34)
G. Udhaya Sankar
C. Ganesa Moorthy
C.T. Ramasamy
3.1 Introduction
54(1)
3.2 Methodology for Derivations
55(4)
3.3 Energy Discussions
59(4)
3.4 Data Analysis
63(24)
Acknowledgement
67(1)
References
67(2)
Supplementary
69(18)
4 Mathematical Analysis on Power Generation - Part II
87(30)
G. Udhaya Sankar
C. Ganesa Moorthy
C.T. Ramasamy
4.1 Energy Analysis
88(1)
4.2 Power Efficiency Method
89(2)
4.3 Data Analysis
91(26)
Acknowledgement
96(1)
References
97(3)
Supplementary - II
100(17)
5 Sustainable Energy Materials
117(20)
G. Udhaya Sankar
5.1 Introduction
117(2)
5.2 Different Methods
119(1)
5.2.1 Co-Precipitation Method
119(1)
5.2.2 Microwave-Assisted Solvothermal Method
120(1)
5.2.3 Sol-Gel Method
120(1)
5.3 X-Ray Diffraction Analysis
120(2)
5.4 FTIR Analysis
122(2)
5.5 Raman Analysis
124(1)
5.6 UV Analysis
125(2)
5.7 SEM Analysis
127(1)
5.8 Energy Dispersive X-Ray Analysis
127(2)
5.9 Thermoelectric Application
129(4)
5.9.1 Thermal Conductivity
129(2)
5.9.2 Electrical Conductivity
131(1)
5.9.3 Seebeck Coefficient
131(1)
5.9.4 Power Factor
132(1)
5.9.5 Figure of Merit
133(1)
5.10 Limitations and Future Direction
133(1)
5.11 Conclusion
133(4)
Acknowledgement
134(1)
References
134(3)
6 Soft Computing Techniques for Maximum Power Point Tracking in Wind Energy Harvesting System: A Survey
137(34)
Tigilu Mitiku Dinku
Mukhdeep Singh Manshahia
Karanvir Singh Chahal
6.1 Introduction
137(5)
6.1.1 Conventional MPPT Control Techniques
138(4)
6.2 Other MPPT Control Methods
142(25)
6.2.1 Proportional Integral Derivative Controllers
142(2)
6.2.2 Fuzzy Logic Controller
144(6)
6.2.2.1 Fuzzy Inference System
150(1)
6.2.2.2 Advantage and Disadvantages of Fuzzy Logic Controller
151(1)
6.2.3 Artificial Neural Network
151(1)
6.2.3.1 Biological Neural Networks
152(3)
6.2.3.2 Architectures of Artificial Neural Networks
155(2)
6.2.3.3 Training of Artificial Neural Networks
157(1)
6.2.3.4 Radial Basis Function
158(1)
6.2.4 Neuro-Fuzzy Inference Approach
158(3)
6.2.4.1 Adaptive Neuro-Fuzzy Approach
161(1)
6.2.4.2 Hybrid Training Algorithm
161(6)
6.3 Conclusion
167(4)
References
167(4)
Section II Climate Change
171(286)
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(44)
Mesut Togacar
7.1 Introduction
174(3)
7.2 Materials
177(4)
7.2.1 Classification of Air Quality Condition in Gas Concentration Measurement
177(1)
7.2.2 CO2 Emission of Vehicles
178(1)
7.2.3 Countries' Co2 Emission Amount
179(1)
7.2.4 Stability Level in Electric Grids
179(2)
7.3 Artificial Intelligence Approaches
181(15)
7.3.1 Machine Learning Methods
182(1)
7.3.1.1 Support Vector Machine
183(1)
7.3.1.2 Extreme Gradient Boosting (XG Boost)
184(1)
7.3.1.3 Gradient Boost
185(1)
7.3.1.4 Decision Tree
186(1)
7.3.1.5 Random Forest
186(2)
7.3.2 Deep Learning Methods
188(1)
7.3.2.1 Convolutional Neural Networks
189(2)
7.3.2.2 Long Short-Term Memory
191(1)
7.3.2.3 Bi-Directional LSTM and CNN
192(1)
7.3.2.4 Recurrent Neural Network
193(2)
7.3.3 Activation Functions
195(1)
7.3.3.1 Rectified Linear Unit
195(1)
7.3.3.2 Softmax Function
196(1)
7.4 Experimental Analysis
196(14)
7.5 Discussion
210(1)
7.6 Conclusion
211(6)
Funding
212(1)
Ethical Approval
212(1)
Conflicts of Interest
212(1)
References
212(5)
8 Performance Analysis and Effects of Dust & Temperature on Solar PV Module System by Using Multivariate Linear Regression Model
217(60)
Sumit Sharma
J. Joshua Thomas
Pandian Vasant
8.1 Introduction
218(7)
8.1.1 Indian Scenario of Renewable Energy
218(2)
8.1.2 Solar Radiation at Earth
220(1)
8.1.3 Solar Photovoltaic Technologies
220(1)
8.1.3.1 Types of SPV Systems
221(1)
8.1.3.2 Types of Solar Photovoltaic Cells
222(1)
8.1.3.3 Effects of Temperature
223(1)
8.1.3.4 Conversion Efficiency
223(1)
8.1.4 Losses in PV Systems
224(1)
8.1.5 Performance of Solar Power Plants
224(1)
8.2 Literature Review
225(3)
8.3 Experimental Setup
228(10)
8.3.1 Selection of Site and Development of Experimental Facilities
229(1)
8.3.2 Methodology
229(1)
8.3.3 Experimental Instrumentation
230(1)
8.3.3.1 Solar Photovoltaic Modules
230(2)
8.3.3.2 PV Grid-Connected Inverter
232(1)
8.3.3.3 Pyranometer
232(2)
8.3.3.4 Digital Thermometer
234(1)
8.3.3.5 Lightning Arrester
235(1)
8.3.3.6 Data Acquisition System
236(1)
8.3.4 Formula Used and Sample Calculations
236(1)
8.3.5 Assumptions and Limitations
237(1)
8.4 Results Discussion
238(33)
8.4.1 Phases-of Data Collection
238(1)
8.4.2 Variation in Responses Evaluated During Phase I (From 1 Jan. to 27 Feb.) of Study
238(1)
8.4.2.1 Effect of Dust and Ambient Temperature on Conversion Efficiency
238(3)
8.4.2.2 Capacity Utilization Factor and Performance Ratio
241(1)
8.4.2.3 Evaluation of MLR Model
242(4)
8.4.3 Variation in Responses Evaluated During Phase II (From 1 March to 5 April)
246(1)
8.4.3.1 Influence of Dust and Ambient Temperature on Conversion Efficiency
246(1)
8.4.3.2 Capacity Utilization Factor and Performance Ratio
246(1)
8.4.3.3 Evaluation of MLR Model
246(6)
8.4.4 Variation in Responses Evaluated During Phase III (18 May to 25 June)
252(1)
8.4.4.1 Effect of Dust and Ambient Temperature on Conversion Efficiency
252(3)
8.4.4.2 Capacity Utilization Factor and Performance Ratio
255(1)
8.4.4.3 Evaluation of MLR Model
256(2)
8.4.5 Regression Analysis for the Whole Period
258(9)
8.4.6 Best Subsets Regression: Conversion Efficiency v/s Exposure Day, Ambient Temperature
267(1)
8.4.7 Regression Outputs Summary
268(1)
8.4.8 Comparison Between Measured Efficiency and Predicted Efficiency
268(2)
8.4.9 Losses Due to Dust Accumulation
270(1)
8.4.10 Economic Analysis
270(1)
8.5 Future Research Directions
271(1)
8.6 Conclusion
271(6)
References
272(5)
9 Evaluation of In-House Compact Biogas Plant Thereby Testing Four-Stroke Single-Cylinder Diesel Engine
277(68)
Pradeep Kumar Meena
Sumit Sharma
Amit Pal
Samsher
9.1 Introduction
278(3)
9.1.1 Benefits of the Use of Biogas as a Fuel in India
278(1)
9.1.2 Biogas Generators in India
279(1)
9.1.3 Biogas
279(1)
9.1.3.1 Process of Biogas Production
280(1)
9.2 Literature Review
281(9)
9.2.1 Wastes and Environment
281(2)
9.2.2 Economic and Environmental Considerations
283(2)
9.2.3 Factor Affecting Yield and Production of Biogas
285(1)
9.2.3.1 The Temperature
285(2)
9.2.3.2 PH and Buffering Systems
287(1)
9.2.3.3 C/N Ratio
287(2)
9.2.3.4 Substrate Type
289(1)
9.2.3.5 Retention Time
289(1)
9.2.3.6 Total Solids
289(1)
9.2.4 Advantages of Anaerobic Digestion to Society
290(1)
9.2.4.1 Electricity Generation
290(1)
9.2.4.2 Fertilizer Production
290(1)
9.2.4.3 Pathogen Reduction
290(1)
9.3 Methodology
290(9)
9.3.1 Set Up of Compact Biogas Plant and Equipments
290(2)
9.3.2 Assembling and Fabrication of Biogas Plant
292(2)
9.3.3 Design and Technology of Compact Biogas Plant
294(1)
9.3.4 Gas Quantity and Quality
295(1)
9.3.5 Calculation of Gas Quantity in Gas Holder
295(4)
9.4 Analysis of Compact Biogas Plant
299(14)
9.4.1 Experiment Result
299(1)
9.4.1.1 Testing on 50 Kg Animal Dung Along With 500 Ltrs Water
299(1)
9.4.1.2 Testing on Kitchen Waste
300(2)
9.4.1.3 Testing on Fruits Waste
302(2)
9.4.2 Comparison of Biogas by Different Substrate
304(1)
9.4.3 Production of Biogas Per Day at Different Waste
304(3)
9.4.4 Variation of PH Value
307(1)
9.4.5 Variation of Average pH Value
307(1)
9.4.6 Variation of Temperature
308(1)
9.4.7 Variation of Average Temperature With Respect to No. of Days for Animal Dung, Kitchen Waste, Fruits Waste and Sugar
309(2)
9.4.8 Variation of Biogas Production W.R.T. Quantity of Kitchen Waste and Fruits Waste
311(2)
9.5 Analysis of Single-Cylinder Diesel Engine on Dual Fuel
313(23)
9.5.1 Testing on 4-Stroke Single-Cylinder Diesel Engine
313(3)
9.5.2 Calculation
316(6)
9.5.3 Heat Balance Sheet
322(4)
9.5.4 Testing Result With Dual Fuel (Biogas and Diesel) on 4-Stroke Single-Cylinder Diesel Engine
326(4)
9.5.5 Calculation
330(5)
9.5.6 Heat Balance Sheet
335(1)
9.6 General Comments
336(3)
9.7 Conclusion
339(1)
9.8 Future Scope
340(5)
References
340(5)
10 Low-Temperature Combustion Technologies for Emission Reduction in Diesel Engines
345(26)
Amit Jhalani
Sumit Sharma
Pushpendra Kumar Sharma
Digambar Singh
Abbreviations
346(1)
10.1 Introduction
346(5)
10.1.1 Global Scenario of Energy and Emissions
347(1)
10.1.2 Diesel Engine Emissions
348(2)
10.1.3 Mitigation ofNOx and Particulate Matter
350(1)
10.1.4 Low-Temperature Combustion Engine Fuels
350(1)
10.2 Scope of the Current Article
351(1)
10.3 HCCI Technology
352(2)
10.3.1 Principle of HCCI
353(1)
10.3.2 Performance and Emissions with HCCI
354(1)
10.4 Partially Premixed Compression Ignition (PPCI)
354(1)
10.5 Exhaust Gas Recirculation (EGR)
355(1)
10.6 Reactivity Controlled Compression Ignition (RCCI)
356(1)
10.7 LTC Through Fuel Additives
357(1)
10.8 Emulsified Fuels (Water-in-Diesel Emulsion Fuel)
358(3)
10.8.1 Brake Thermal Efficiency (BTE)
359(1)
10.8.2 Nitrogen Oxide (NOx)
359(1)
10.8.3 Soot and Particulate Matter (PM)
360(1)
10.9 Conclusion and Future Scope
361(10)
Acknowledgement
361(1)
References
361(10)
11 Efficiency Optimization of Indoor Air Disinfection by Radiation Exposure for Poultry Breeding Rational for Microclimate Systems Modernization for Livestock Premises
371(18)
Dovlatov Igor Mamedjarevich
Yurochka Sergey Sergeevich
11.1 Introduction
372(2)
11.2 Materials and Methods
374(5)
11.3 Results
379(3)
11.4 Discussion
382(3)
11.5 Conclusions
385(4)
References
386(3)
12 Improving the Efficiency of Photovoltaic Installations for Sustainable Development of the Urban Environment
389(34)
Pavel Kuznetsov
Leonid Yuferev
Dmitry Voronin
12.1 Introduction
390(2)
12.2 Background
392(10)
12.3 Main Focus of the
Chapter
402(15)
12.4 Solutions and Recommendations
417(6)
Acknowledgements
417(1)
References
418(5)
13 Monitoring System Based Micro-Controller for Biogas Digester
423(12)
Ahmed Abdelouareth
Mohamed Tamali
13.1 Introduction
423(1)
13.2 Related Work
424(1)
13.3 Methods and Material
425(5)
13.3.1 Identification of Needs
425(1)
13.3.2 ADOLMS Software Setup
425(1)
13.3.3 ADOLMS Sensors
426(2)
13.3.4 ADOLMS Hardware Architecture
428(2)
13.4 Results
430(2)
13.5 Conclusion
432(3)
Acknowledgements
433(1)
References
433(2)
14 Greenhouse Gas Statistics and Methods of Combating Climate Change
435(22)
Tatyana G. Krotova
Introduction
435(19)
Methodology
436(1)
Findings
436(18)
Conclusion
454(1)
References
455(2)
About the Editors 457(2)
Index 459
Pandian Vasant, PhD, is Editor-in-Chief of the International Journal of Energy Optimization and Engineering and senior research associate at MERLIN Research Centre of Ton Duc Thang University, HCMC, Vietnam. He has 31 years of teaching experience and has co-authored over 300 publications, including research articles in journals, conference proceedings, presentations and book chapters. He has also been a guest editor for various scientific and technical journals.

Gerhard-Wilhelm Weber, PhD, is a professor at Poznan University of Technology, Poznan, Poland. He received his PhD in mathematics, and economics / business administration, from RWTH Aachen. He held professorships by proxy at University of Cologne, and TU Chemnitz, Germany.

J. Joshua Thomas, PhD, has been a senior lecturer at KDU Penang University College, Malaysia since 2008. He obtained his PhD in intelligent systems techniques in 2015 from University Sains Malaysia, Penang, and is an editorial board member for the International Journal of Energy Optimization and Engineering. He has also published more than 30 papers in leading international conference proceedings and peer reviewed journals.

Jose A. Marmolejo Saucedo, PhD, is a professor at Pan-American University, Mexico. He received his PhD in operations research at the National Autonomous University of Mexico and has co-authored numerous research articles in scientific and scholarly journals, conference proceedings, presentations, books, and book chapters.

Roman Rodriguez-Aguilar, PhD, is a professor in the School of Economic and Business Sciences of the Universidad Panamericana in Mexico. He received his PhD at the School of Economics at the National Polytechnic Institute, Mexico and has co-authored multiple research articles in scientific and scholarly journals, conference proceedings, presentations, and book chapters.