08448nM2.01200024      h001 BV050599600\x1e002a20260206\x1e004 20260215\x1e020 9781394300
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51 m|||||||\x1e060 \x1faText\x1fbtxt\x1e061 \x1faComputermedien\x1fbc\x1e062 \x1faOnline-Ressource\x1fbcr\x1e0
70 DE-706\x1e076 RDA-Aufnahme\x1e078eZDB-35-WIC\x1e080 01\x1e100 Dethlefs, Nina\x1e331 Renewabl
e Energy Transition with Artificial Intelligence\x1e335 Challenge-Driven Solutions\x1e
403 1st ed.\x1e419 \x1faNewark\x1fbJohn Wiley & Sons, Incorporated\x1fc2026\x1e425a2026\x1e433 1 o
nline resource (275 pages)\x1e521 \x1faCover -- Title Page -- Copyright -- Contents --
 Preface -- List of Contributors -- Chapter 1: AI for Renewables: Addressing Ope
rational, Engineering, and Socioeconomic Adoption Challenges -- 1.1 Introduction
 -- 1.2 Opportunities and Challenges -- 1.3 Current High-priority Areas -- 1.3.1
 Explainability and Trust in AI for Renewables -- 1.3.2 Model Transferability an
d Generalization -- 1.3.3 Grounding AI Models to Domain-specific Operational and
 Engineering Knowledge -- 1.4 Nascent Areas in the AI and Renewables Domain -- 1
.5 Conclusion -- Bibliography -- Chapter 2: Techno-economic Analysis for Offshor
e Renewable Energy Technologies Incorporating a Holistic O& -- M Model -- 2.1
 Challenge -- 2.2 Case Study -- 2.2.1 Before State-of-the-art -- 2.2.2 Methodolo
gy -- 2.2.3 Results -- 2.3 Discussion -- 2.4 Conclusion and Future Work -- Bibli
ography -- Chapter 3: Making the Most of Data in Offshore Wind Energy: From Popu
lation to Physics-informed Modeling -- 3.1 Introduction -- 3.2 Autoregressive Ga
ussian Processes -- 3.3 Population Modeling of Wind Farm Wake Effects -- 3.3.1 A
 Switching GP-SPARX Model -- 3.3.2 A Case Study of a Simulated Wind Farm -- 3.3.
3 Results -- 3.3.4 Discussion -- 3.4 Physics-informed Machine Learning for Wave 
Loading Prediction -- 3.4.1 Monopile Wave Tank Experiment -- 3.4.2 Model Structu
re -- 3.4.3 Results -- 3.5 Conclusions -- Acknowledgments -- Bibliography -- Cha
pter 4: Leveraging the Power of Informal Networks in Renewables -- 4.1 Challenge
 -- 4.2 Case Study -- 4.2.1 Before SOA: What Was the State-of-the-art/Accepted S
olution in the Past? -- 4.2.2 Influencing and Educating Informal Networks -- 4.2
.3 Methodology -- 4.2.4 Enabling Continuous Improvement Through ML and AI -- 4.2
.5 Next Steps -- 4.2.6 Results -- 4.2.7 After: What Is the Accepted Solution Now
? -- 4.3 Discussion -- 4.4 Conclusion and Future Work\x1e521 \x1fa4.4.1 Challenges and
 Opportunities -- Acknowledgments -- Bibliography -- Chapter 5: Relevance of AI 
in Addressing Barriers to Rooftop Solar Photovoltaic Adoption in Building Projec
ts in Nigeria -- 5.1 Introduction -- 5.2 Literature Review -- 5.2.1 Overview of 
Rooftop Solar Photovoltaic Systems -- 5.2.2 Reluctance to Adopt Sustainable Ener
gy Solutions in Nigeria -- 5.2.3 Barriers to Rooftop Solar Photovoltaic Adoption
 in Building Projects -- 5.2.4 Artificial Intelligence Solutions to Overcome Bar
riers to Rooftop Solar Photovoltaic Adoption -- 5.3 Research Methods -- 5.4 Resu
lts and Findings -- 5.4.1 Background of the Respondents -- 5.4.2 Background Info
rmation of the Respondents -- 5.4.3 Barriers to the Adoption of Rooftop Solar Ph
otovoltaics and the Preferred AI-Solution -- 5.4.4 Mean Score (MIS) Analysis -- 
5.4.5 Standard Deviation (S.D.) Analysis -- 5.4.6 Mann-Whitney Test Analysis -- 
5.4.7 Exploratory Factor Analysis -- 5.4.8 Discussion and Implications of Findin
gs -- 5.4.9 Mean Score -- 5.4.10 Exploratory Factor Analysis -- 5.4.11 Mann-Whit
ney U Test -- 5.4.12 Harnessing the Power of AI to Overcome Barriers to Rooftop 
Solar Photovoltaic Adoption -- 5.5 Conclusion and Perspectives -- Bibliography -
- Chapter 6: Predicting Comfort: AI-driven HVAC for Intelligent Energy Managemen
t -- 6.1 Challenge -- 6.2 Case Study -- 6.2.1 Dataset Overview -- 6.2.2 Methodol
ogy -- 6.2.3 Results -- 6.2.4 After -- 6.3 Discussion -- 6.4 Conclusion and Futu
re Work -- Bibliography -- Chapter 7: Leveraging Generative AI-Driven Digital Tw
ins for Renewable Energy Systems -- 7.1 Data and Communication Barriers -- 7.2 C
ase Study: The NorthWind Project -- 7.2.1 Conventional Practices: Tackling Data 
Scarcity -- 7.2.2 Conventional Practices: Reliability of Critical Communication 
Infrastructure -- 7.2.3 Methodology: Generative AI-driven Digital Twin Framework
\x1e521 \x1fa7.2.4 Results and Impact -- 7.3 Discussion -- 7.4 Conclusion and Future W
ork -- Bibliography -- Chapter 8: Vision Transformer-based O& -- M Model for 
Condition Monitoring of Solar Panels -- 8.1 Challenges -- 8.1.1 Practical Issues
 -- 8.1.2 Computer Vision Approaches in Photovoltaic -- 8.1.3 Using Large Langua
ge Models for Interpreting Vision Transformer Results -- 8.2 Case Study -- 8.2.1
 Model Description -- 8.2.2 Vision Transformer Applied to Photovoltaic Cells -- 
8.2.3 Vision Transformer Comparison with Other Models -- 8.2.4 Attention Map Ima
ges -- 8.2.5 Confusion Matrices -- 8.2.6 Model Fine-tuning Process -- 8.3 Future
 Work -- 8.4 Conclusion -- Bibliography -- Chapter 9: Artificial Intelligence Ap
plications: Case Studies from Challenging Domains -- 9.1 Introduction -- 9.2 Urb
an Traffic Control -- 9.3 Textile Sorting -- 9.4 Power Distribution Networks -- 
9.5 Discussion and Conclusion -- Bibliography -- Chapter 10: Blockchain-enabled 
Digital Twins for Advancing Sustainable Reverse Logistics in Renewable Energy Sy
stems -- 10.1 Introduction -- 10.1.1 Reverse Logistics Importance in Renewable E
nergy -- 10.1.2 Blockchain-enabled Digital Twins -- 10.1.3 Technical Architectur
e of BEDT -- 10.1.4 Chapter Objectives -- 10.2 Digital Twins and Blockchain in R
everse Logistics -- 10.2.1 Blockchain and Transparency and Traceability -- 10.2.
2 Synergy of DTs with Blockchain -- 10.2.3 Additional Considerations for DTs and
 Blockchain -- 10.3 Role of AI and IoT in Reverse Logistics Optimization -- 10.3
.1 Integration of AI into BEDT -- 10.3.2 IoT-enabled Data Collection -- 10.4 Cas
e Studies and Applications -- 10.4.1 Industry Case Studies -- 10.4.2 Real-world 
Application to Renewable Energy -- 10.5 Smoothing the Transition to Renewable En
ergy -- 10.5.1 BEDT's Role in Transitioning to Renewable Energy\x1e521 \x1fa10.5.2 Con
tributions to the Circular Economy and Sustainability -- 10.6 Conclusion and Way
 Forward -- Bibliography -- Chapter 11: Pathways to AI Adoption in Offshore Wind
 Energy Operations and Maintenance -- 11.1 Introduction -- 11.1.1 Current Applic
ations of AI in OSW O& -- M -- 11.1.2 Defining Stakeholders -- 11.2 Technical
 Challenges and Solutions -- 11.2.1 Setup and Running Costs of AI -- 11.2.2 O&am
p -- M Costs -- 11.2.3 Environmental Factors -- 11.2.4 Deploying AI in the Field
 -- 11.2.5 Dependability/Trustworthiness -- 11.2.6 Human-AI Interaction -- 11.2.
7 Cybersecurity -- 11.2.8 Data Availability -- 11.2.9 Collaboration Between Acad
emia and Industry -- 11.3 Communication and Opinion -- 11.3.1 AI Winters -- 11.3
.2 Search Trends -- 11.3.3 Gartner Hype Cycle -- 11.3.4 Conflicting Findings and
 Definitions -- 11.3.5 Overstated Benefits of Novel Methods -- 11.3.6 Recommenda
tions -- 11.4 Conclusion -- Bibliography -- Chapter 12: Incremental Drift-aware 
Learning in Renewable Energy Systems -- 12.1 Introduction -- 12.1.1 Context of P
dM and Renewable Energy System Data -- 12.1.2 Challenges in PdM for Renewable En
ergy Systems -- 12.1.3 Incremental Drift-aware Learning for PdM -- 12.2 Fundamen
tals of Incremental Learning -- 12.2.1 Definition and Significance -- 12.2.2 Key
 Approaches in Incremental Learning -- 12.2.3 Incremental Learning Periods in MA
TLAB -- 12.2.4 Challenges in Incremental Learning -- 12.3 Concept Drift and Its 
Effect -- 12.3.1 What is Concept Drift? -- 12.3.2 Impact of Concept Drift in Ren
ewable Energy -- 12.4 Drift-aware Learning: Approaches and Techniques -- 12.4.1 
How to Detect Concept Drift and MATLAB Software Solutions -- 12.5 Case Study: De
tection of Drift Using MATLAB Software Solutions -- 12.6 Conclusion -- Acknowled
gment -- Bibliography -- Bindex -- EULA\x1e540aISBN 9781394300068 Online\x1e655e\x1fuhttp
s://www.bib-bvb.de/E-Book/noch_nicht_verfuegbar.html\x1fxVerlag\x1fzlizenzpflichtig\x1f3V
olltext\x1e656e\x1fuhttps://www.bib-bvb.de/E-Book/noch_nicht_verfuegbar.html\x1flUBY01\x1fpZ
DB-35-WIC\x1fqUBY_PDA_WIC_Kauf25\x1fxVerlag\x1f3Volltext\x1e750bExplores harnessing AI to ov
ercome strategic and operational challenges in renewable energy transition The u
rgent need to decarbonize global energy systems has propelled renewable energy i
nto a position of unprecedented importance, yet this shift presents major techni
cal, economic, and policy challenges\x1eLOWaUBY01\x1e\x1d