Muutke küpsiste eelistusi

E-raamat: Sustainable Developments by Artificial Intelligence and Machine Learning for Renewable Energies

Edited by , Edited by (Associate Professor, Department of Computer Science and Engineering of Ambedkar Institute of Advanced Communication Technologies and Research, ), Edited by , Edited by (Research and Development Engineer, UJVN Ltd. (A Govt. of Uttarakhand Enterprises), India), Edited by
  • Formaat: EPUB+DRM
  • Ilmumisaeg: 18-Mar-2022
  • Kirjastus: Elsevier - Health Sciences Division
  • Keel: eng
  • ISBN-13: 9780323914284
Teised raamatud teemal:
  • Formaat - EPUB+DRM
  • Hind: 173,35 €*
  • * hind on lõplik, st. muud allahindlused enam ei rakendu
  • Lisa ostukorvi
  • Lisa soovinimekirja
  • See e-raamat on mõeldud ainult isiklikuks kasutamiseks. E-raamatuid ei saa tagastada.
Sustainable Developments by Artificial Intelligence and Machine Learning for Renewable EnergiesSuurem pilt
  • Formaat: EPUB+DRM
  • Ilmumisaeg: 18-Mar-2022
  • Kirjastus: Elsevier - Health Sciences Division
  • Keel: eng
  • ISBN-13: 9780323914284
Teised raamatud teemal:

DRM piirangud

  • Kopeerimine (copy/paste):

    ei ole lubatud

  • Printimine:

    ei ole lubatud

  • Kasutamine:

    Digitaalõiguste kaitse (DRM)
    Kirjastus on väljastanud selle e-raamatu krüpteeritud kujul, mis tähendab, et selle lugemiseks peate installeerima spetsiaalse tarkvara. Samuti peate looma endale  Adobe ID Rohkem infot siin. E-raamatut saab lugeda 1 kasutaja ning alla laadida kuni 6'de seadmesse (kõik autoriseeritud sama Adobe ID-ga).

    Vajalik tarkvara
    Mobiilsetes seadmetes (telefon või tahvelarvuti) lugemiseks peate installeerima selle tasuta rakenduse: PocketBook Reader (iOS / Android)

    PC või Mac seadmes lugemiseks peate installima Adobe Digital Editionsi (Seeon tasuta rakendus spetsiaalselt e-raamatute lugemiseks. Seda ei tohi segamini ajada Adober Reader'iga, mis tõenäoliselt on juba teie arvutisse installeeritud )

    Seda e-raamatut ei saa lugeda Amazon Kindle's. 

Sustainable Developments by Artificial Intelligence and Machine Learning for Renewable Energies analyzes the changes in this energy generation shift, including issues of grid stability with variability in renewable energy vs. traditional baseload energy generation. Providing solutions to current critical environmental, economic and social issues, this book comprises various complex nonlinear interactions among different parameters to drive the integration of renewable energy into the grid. It considers how artificial intelligence and machine learning techniques are being developed to produce more reliable energy generation to optimize system performance and provide sustainable development.

As the use of artificial intelligence to revolutionize the energy market and harness the potential of renewable energy is essential, this reference provides practical guidance on the application of renewable energy with AI, along with machine learning techniques and capabilities in design, modeling and for forecasting performance predictions for the optimization of renewable energy systems. It is targeted at researchers, academicians and industry professionals working in the field of renewable energy, AI, machine learning, grid Stability and energy generation.

  • Covers the best-performing methods and approaches for designing renewable energy systems with AI integration in a real-time environment
  • Gives advanced techniques for monitoring current technologies and how to efficiently utilize the energy grid spectrum
  • Addresses the advanced field of renewable generation, from research, impact and idea development of new applications
Contributors xv
About the editors xix
Preface xxiii
1 Application of alternative clean energy
Adarsh Gaurav
Sujeet Kesharvani
Sakshi Sarathe
Gaurav Dwivedi
Gaurav Saini
Anuj Kumar
Kamaraj Nithyanandhan
1.1 Introduction
1(1)
1.2 Solar energy
2(4)
1.2.1 Photovoltaic systems
2(1)
1.2.2 Solar thermal energy systems
2(1)
1.2.3 Solar water heating (SWH) systems
2(1)
1.2.4 Solar cooker
3(1)
1.2.5 Solar water pumps
4(1)
1.2.6 Solar space heating
4(2)
1.3 Geothermal energy
6(2)
1.3.1 Geothermal power generation
6(1)
1.3.2 Direct uses of geothermal energy
7(1)
1.4 Wind energy
8(2)
1.4.1 Horizontal Axis wind turbine
8(1)
1.4.2 Vertical axis wind turbine
8(1)
1.4.3 Wind turbine applications
9(1)
1.5 Biomass energy
10(4)
1.5.1 Method of biomass energy extraction
10(1)
1.5.2 Gasification
11(1)
1.5.3 Anaerobic digestion
11(1)
1.5.4 Biofuels
12(1)
1.5.5 Bioethanol production
12(1)
1.5.6 Biodiesel
13(1)
1.6 Ocean and tidal energy
14(1)
1.6.1 Wave energy
14(1)
1.6.2 OTEC
15(1)
1.6.3 TIC
15(1)
1.7 Small, micro, and mini hydro plants
15(1)
1.8 Case study
16(1)
1.9 Conclusion
17(4)
References
17(4)
2 Optimization of hybrid energy generation
Poonam B. Dhabai
Neeraj Tiwari
2.1 Introduction
21(2)
2.2 RES data and uncertainty statistical analysis
23(4)
2.2.1 Wind source analysis
24(1)
2.2.2 Solar source analysis
25(2)
2.3 Test case modifications and solution methodology
27(7)
2.3.1 Test case modifications
27(1)
2.3.2 Configuration of cases
28(4)
2.3.3 Solution methodology
32(1)
2.3.4 Sensitivity factors
33(1)
2.3.5 Locational marginal pricing (LMP)
34(1)
2.3.6 Reliability parameters
34(1)
2.4 Results
34(7)
2.4.1 Impact of probabilistic nature and location of RES on sensitivity factors
35(3)
2.4.2 Impact of probabilistic nature and location of RES on LMP
38(1)
2.4.3 Impact of the probabilistic nature and location of RES on TTC and TRM
39(2)
2.5 Discussion and conclusion, future scope
41(8)
2.5.1 Discussion
41(2)
2.5.2 Conclusion
43(1)
2.5.3 Future scope
44(1)
Acknowledgment
44(1)
References
44(5)
3 IoET-SG: Integrating internet of energy things with smart grid
M. Shahidul Islam
Md. Mehedi Islam
Sabbir Ahmed
Md. Sazzadur Rahman
Krishna Kumar
M. Shamim Kaiser
3.1 Introduction
49(1)
3.2 Traditional grid
50(1)
3.3 Smart grid
51(1)
3.4 Internet of energy things (loET)
51(4)
3.5 IoET-SG system
55(2)
3.6 Research challenges and future guidelines
57(3)
3.7 Conclusion
60(3)
References
60(3)
4 Evolution of high efficiency passivated emitter and rear contact (PERC) solar cells
Sourav Sadhukhan
Shiladitya Acharya
Tamalika Panda
Nabin Chandra Mandal
Sukanta Bose
Anupam Nandi
Courab Das
Santanu Maity
Susanta Chakraborty
Partha Chaudhuri
Hiranmay Saha
4.1 Introduction
63(3)
4.2 Photon absorption and optical generation
66(3)
4.3 Loss mechanisms in PERC solar cells
69(10)
4.3.1 Optical losses
70(1)
4.3.2 Electrical losses
70(9)
4.4 Carrier transport equations
79(4)
4.4.1 Solar cell parameters
80(3)
4.5 PERC technology
83(15)
4.5.1 PERC process flow
85(1)
4.5.2 Surface passivation
85(7)
4.5.3 LBSF and rear local contact
92(1)
4.5.4 Rear polishing
93(1)
4.5.5 PERC performance
93(2)
4.5.6 Improvements of PERC solar cells
95(1)
4.5.7 Further improvements
96(1)
4.5.8 Bifacial PERC
97(1)
4.6 Fabrication of PERC solar cells
98(13)
4.6.1 Saw damage removal, texturization, and cleaning
99(2)
4.6.2 Diffusion and oxidation
101(2)
4.6.3 Reactive ion etching
103(1)
4.6.4 Plasma-enhanced chemical vapor deposition (PECVD)
104(2)
4.6.5 Atomic layer deposition (ALD)
106(1)
4.6.6 Laser ablation
107(2)
4.6.7 Metallization
109(2)
4.7 Characterization equipment
111(10)
4.7.1 Scanning electron microscopy (SEM)
111(1)
4.7.2 Four point probe measurement
111(1)
4.7.3 Thickness profilometer
111(3)
4.7.4 I-Vand C-Vmeasurement
114(1)
4.7.5 X-ray photo electron spectroscopy (XPS)
114(1)
4.7.6 Lifetime and Suns-Voc measurement
115(1)
4.7.7 Reflectance and external quantum efficiency (EQE) measurement
116(2)
4.7.8 Current-voltage (I-V) measurement
118(3)
4.8 Conclusion
121(10)
References
121(10)
5 Online-based approach for frequency control of microgrid using biologically inspired intelligent controller
Bhola Jha
Manoj Kumar Panda
Yatindra Kumar
5.1 Introduction
131(2)
5.2 Test system description
133(4)
5.2.1 Photovoltaic model
133(2)
5.2.2 Wind energy
135(1)
5.2.3 Diesel engine generator (DEC) model
136(1)
5.2.4 Fuel cell, BESS, and FESS
136(1)
5.3 Fuzzy logic controller
137(3)
5.4 Particle swarm optimization (PSO)
140(2)
5.5 Gray wolf optimization (GWO)
142(3)
5.6 Results analysis
145(1)
5.7 Conclusion
145(4)
References
146(3)
6 Optimal allocation of renewable energy sources in electrical distribution systems based on technical and economic indices
Mohamed Zellagui
Samir Settoul
Heba Ahmed Hassan
6.1 Introduction
149(3)
6.1.1 Motivation
149(1)
6.1.2 Literature review
150(1)
6.1.3 Contribution and chapter organization
151(1)
6.2 Problem formulation
152(2)
6.2.1 Multiobjective function
152(1)
6.2.2 Equality constraints
153(1)
6.2.3 Inequality constraints of distribution line
153(1)
6.2.4 Inequality constraints of DG units
154(1)
6.3 Cosine adapted whale optimization algorithm (CAWOA)
154(1)
6.4 Results and discussion
155(21)
6.4.1 Test systems
155(3)
6.4.2 Analysis of optimal results
158(8)
6.4.3 Comparison results
166(5)
6.4.4 Impact of DG on branch currents
171(1)
6.4.5 Impact of loadability variation on EDS
172(4)
6.5 Conclusions
176(12)
References
182(6)
7 Optimization of renewable energy sources using emerging computational techniques
Aman Kumar
Krishna Kumar
Nishant Raj Kapoor
7.1 Introduction
188(2)
7.2 Sources of renewable energy
190(16)
7.2.1 Bioenergy (BE)
193(1)
7.2.2 Geothermal energy (GE)
193(4)
7.2.3 Hydropower energy (HPE)
197(3)
7.2.4 Hydrogen energy (HE)
200(3)
7.2.5 Solar energy (SE)
203(3)
7.2.6 Wind energy (WE)
206(1)
7.2.7 Ocean energy (OE)
206(1)
7.3 Artificial intelligence (Al)
206(15)
7.3.1 Artificial intelligence in bioenergy
213(1)
7.3.2 Artificial intelligence in geothermal energy
214(1)
7.3.3 Artificial intelligence in hydro energy
215(6)
7.3.4 Artificial intelligence in hydrogen energy
221(1)
7.3.5 Artificial intelligence in solar energy
221(1)
7.3.6 Artificial intelligence in wind energy
221(1)
7.3.7 Artificial intelligence in ocean energy
221(1)
7.4 Conclusion
221(16)
References
229(8)
8 Advanced renewable dispatch with machine learning-based hybrid demand-side controller: The state of the art and a novel approach
Yuekuan Zhou
8.1 Introduction
237(1)
8.2 Building energy demand forecasting with machine learning
238(4)
8.2.1 Predictions on cooling/heating/electrical loads
239(1)
8.2.2 Machine learning modeling techniques
239(3)
8.3 Flexible demand-side management strategies
242(8)
8.3.1 Smart appliances
247(1)
8.3.2 HVAC systems
247(1)
8.3.3 Plug-in loads and storages
248(2)
8.4 Machine learning-based advanced controllers
250(7)
Acknowledgment
252(1)
References
252(5)
9 A machine learning-based design approach on PCMs-PV systems with multilevel scenario uncertainty
Yuekuan Zhou
9.1 Introduction
257(2)
9.2 Overview on PCMs-PV systems and operations
259(4)
9.2.1 Passive PCMs-PV systems
259(2)
9.2.2 Active PCMs-PV systems
261(1)
9.2.3 Combined passive/active PCMs-PV systems
262(1)
9.3 Mechanism for machine learning on performance prediction of nonlinear systems
263(1)
9.4 Application of machine learning in PCMs-PV systems
264(4)
9.4.1 Surrogate model for performance prediction
264(1)
9.4.2 System optimization
265(2)
9.4.3 Robust optimization with multilevel scenario uncertainty
267(1)
9.5 Challenges and outlooks
268(5)
9.5.1 Uncertainty quantification and probability density function
268(1)
9.5.2 Stochastic sampling size and uncertainty-based optimization function
268(2)
9.5.3 Hybrid learning and advanced optimization algorithms
270(1)
9.5.4 Multicriteria decision-marking for trade-off solutions
270(1)
Acknowledgment
270(1)
References
270(3)
10 Agent-based peer-to-peer energy trading between prosumers and consumers with cost-benefit business models
Yuekuan Zhou
Jia Liu
10.1 Introduction
273(1)
10.2 Agent-based peer-to-peer energy trading with dynamic internal pricing
274(8)
10.2.1 P2P energy trading modes with different energy forms
274(2)
10.2.2 Mechanisms and mathematical models for dynamic internal pricing
276(6)
10.3 Blockchain and machine learning technologies in P2P energy trading
282(2)
10.3.1 Blockchain in P2P energy trading
282(1)
10.3.2 Machine learning technologies in P2P energy trading
283(1)
10.4 Electricity market and techno-economic incentives for P2P energy market
284(2)
10.4.1 Decentralized electricity market design
285(1)
10.4.2 Techno-economic incentives
285(1)
10.5 Challenges and outlook
286(5)
Acknowledgment
286(1)
References
286(5)
11 Machine learning-based hybrid demand-side controller for renewable energy management
Padmanabhan Sanjeevikumar
Tina Samavat
Morteza Azimi Nasab
Mohammad Zand
Mohammad Khoobani
11.1 Introduction
291(4)
11.1.1 Renewable and hybrid energy system
293(1)
11.1.2 Demand-side management
294(1)
11.2 Machine learning at a glance
295(9)
11.2.1 Machine learning meets model-based control
296(1)
11.2.2 The application of machine learning in hybrid demand-side controllers
297(2)
11.2.3 Support vector machine
299(3)
11.2.4 K-means clustering
302(1)
11.2.5 Extreme learning machine
303(1)
11.2.6 Linear regression
303(1)
11.2.7 Partial least squares
304(1)
11.2.8 Challenges and future research direction
304(1)
11.3 Conclusion
304(1)
References
305(4)
12 Prediction of energy generation target of hydropower plants using artificial neural networks
Krishna Kumar
Gaurav Saini
Narendra Kumar
M. Shamim Kaiser
Ramani Kannan
Rachna Shah
12.1 Introduction
309(1)
12.2 Artificial neural network (ANN)
310(3)
12.3 Performance measurement parameters
313(1)
12.4 Modeling and analysis
314(5)
12.5 Conclusion
319(2)
References
319(2)
13 Response surface methodology-based optimization of parameters for biodiesel production
Pijush Dutta
Bittab Biswas
Biplab Pal
Madhurima Majumder
Amit Kumar Das
13.1 Introduction
321(3)
13.2 Problem formulation
324(1)
13.3 Mathematical model of biodiesel production
324(4)
13.3.1 Optimization of the mathematical model
326(1)
13.3.2 Proposed methodology
327(1)
13.3.3 Basic elephant swarm water search algorithm (ESWSA)
327(1)
13.4 Methodology
328(1)
13.5 Reaction conditions by RSM
329(1)
13.6 Surface plot by different combinations in RSM model
329(2)
13.7 Conclusion
331(5)
References
336(5)
14 Reservoir simulation model for the design of irrigation projects
Siva Ramakrishna Madeti
Gaurav Saini
Krishna Kumar
14.1 Introduction
341(2)
14.2 System description
343(1)
14.3 Cost-benefit functions
343(2)
14.4 Methodology
345(5)
14.4.1 Linear programming model (LP model)
345(4)
14.4.2 Reservoir simulation
349(1)
14.5 Simulation computations
350(2)
14.6 Results and discussion
352(1)
14.7 Response of Harabhangi irrigation project
353(2)
14.7.1 Support for the use of simulation
353(2)
14.8 Conclusion
355(4)
References
357(2)
15 Effect of hydrofoils on the starting torque characteristics of the Darrieus hydrokinetic turbine
Gaurav Saini
Anuj Kumar
R.P. Saini
15.1 Introduction
359(3)
15.2 Investigated parameters for the Darrieus hydrokinetic turbine
362(1)
15.3 Numerical simulation analysis
362(4)
15.3.1 Turbine model development
363(1)
15.3.2 Grid generation
364(1)
15.3.3 Boundary conditions and turbulence modeling
365(1)
15.4 Results and discussion
366(8)
15.4.1 Performance characteristics
366(4)
15.4.2 Flow contours
370(4)
15.5 Conclusions
374(3)
References
374(3)
Index 377
Dr. Krishna Kumar received his BE degree in Electronics and Communication Engineering from Govind Ballabh Pant Engineering College, Pauri Garhwal, Uttarakhand, India, MTech degree in Digital Systems from Motilal Nehru NIT, Allahabad, India, in 2006 and 2012, respectively, and PhD degree in the Department of Hydro and Renewable Energy at the Indian Institute of Technology Roorkee, India, in 2023.

He is currently working as an Assistant Engineer at UJVN Ltd. (a State Government PSU of Uttarakhand) since January 2013. Before joining UJVNL, he worked as an Assistant Professor at BTKIT, Dwarahat (a Government of Uttarakhand Institution). He has published numerous research papers in international journals and conferences, including IEEE, Elsevier, Springer, MDPI, Hindawi, and Wiley. He has also edited and written books for Taylor & Francis, Elsevier, Springer, River Press, and Wiley. His current research interests include IoT, AI, and renewable energy.

Dr. Ram Shringar Rao received his Ph.D. (Computer Science and Technology) from School of Computer and Systems Sciences, Jawaharlal Nehru University, New Delhi. He has worked as an Associate Professor in the Department of Computer Science, Indira Gandhi National Tribal and is currently Associate Professor in the Department of Computer Science and Engineering of Ambedkar Institute of Advanced Communication Technologies and Research, Delhi, India. He has more than 18 years of teaching, administrative and research experience. Dr. Rao has worked administrative works in the capacities of HOO (Head of Office, AIACTR), Member Academic Council (IGNTU), Chief Warden, Coordinator University Cultural Cell, Coordinator University Computer Center, HoD of Computer Sc. and Engg., Proctor, Warden, Member of BOS and Nodal Officer of Technical Education Quality Improvement Programme (TEQIP) etc. Dr. Omprakash Kaiwartya is a Senior Lecturer and Course Leader for MSc Engineering at the School of Science & Technology, Nottingham Trent University (NTU). He was a Research Associate at the Department of Computer and Information Science at Northumbria University, UK, and involved in the gLINK, European Union project. Prior to this, he was a Post-Doctoral Fellow in the Faculty of Computing, University of Technology (UTM), Malaysia. He has authored/co-authored over 100 international Journal articles, Conference Proceedings, Book Chapters, and books. Dr. Omprakashs research focuses on IoT centric smart environment for diverse domain areas including Transport, Healthcare, and Industrial Production. His recent scientific contributions are in Internet of Connected Vehicles (IoV), E-Mobility, Electronic Vehicles Charging Management (EV), Internet of Healthcare Things (IoHT), Smart use case implementation of Sensor Networks, and Next Generation Wireless Communication Technologies (6G and Beyond). Dr. M. Shamim Kaiser is currently working as a Professor at the Institute of Information Technology of Jahangirnagar University, Savar, Dhaka-1342, Bangladesh. He received his Bachelor's and Master's degrees in Applied Physics Electronics and Communication Engineering from the University of Dhaka, Bangladesh in 2002 and 2004 respectively, and the Ph. D. degree in Telecommunication Engineering from the Asian Institute of Technology (AIT) Pathumthani, Thailand, in 2010. His current research interests include Data Analytics, Machine Learning, Wireless Network & Signal processing, Cognitive Radio Network, Big data and Cyber Security, Renewable Energy. He has authored more than 100 papers in different peer-reviewed journals and conferences and his google citation is more than 1020. Sanjeevikumar Padmanaban is a Full Professor in Electrical Power Engineering with the Department of Electrical Engineering, Information Technology, and Cybernetics of the University of South-Eastern Norway, Norway. He has over a decade of academic and teaching experience, including Associate/Assistant Professorships at the University of Johannesburg, South Africa (2016-2018), Aalborg University, Denmark (2018-2021) and the CTIF Global Capsule Laboratory at Aarhus University, Denmark (2021-present). Prof. Padmanaban received a lifetime achievement award from Marquis Whos Who - USA 2017 for contributing to power electronics and renewable energy research, and was listed among the worlds top 2% of scientists by Stanford University, USA in 2019.
Lisainfo e-raamatute kohta Lisainfo e-raamatute kohta
Püsilink: https://www.kriso.ee/db/97803239142846e.html
Märksõnad: