Renewable Energy Transition with Artificial Intelligence

NINA DETHLEFS is Professor of Computer Science (Artificial Intelligence) at Loughborough University, where she leads the Language and Data Research Group and contributes to UK-based doctoral training in offshore wind energy. Her research lies at the intersection of AI, natural language processing, and sustainability, with a focus on developing ethical, interpretable, and data-efficient methods to address climate resilience and renewable energy challenges. She has published widely on applying AI to environmental and energy domains. JOYJIT CHATTERJEE is Lead Data Scientist at EPAM Systems, UK, and an invited visiting academic at Loughborough and Hull universities. His expertise bridges academic and industrial applications of AI in sustainability, manufacturing, and energy. His work has been featured in global outlets such as Forbes and the World Economic Forum, and he frequently engages with Fortune 500 leaders, European Commission projects, and international energy agencies on the future of AI-enabled renewables.

• Preface ixList of Contributors xi1 AI for Renewables: Addressing Operational, Engineering, and Socioeconomic Adoption Challenges 11.1 Introduction 11.2 Opportunities and Challenges 41.3 Current High-priority Areas 51.3.1 Explainability and Trust in AI for Renewables 51.3.2 Model Transferability and Generalization 71.3.3 Grounding AI Models to Domain-specific Operational and Engineering Knowledge 91.4 Nascent Areas in the AI and Renewables Domain 111.5 Conclusion 11Bibliography 122 Techno-economic Analysis for Offshore Renewable Energy Technologies Incorporating a Holistic O&M Model 152.1 Challenge 152.2 Case Study 172.2.1 Before State-of-the-art 172.2.2 Methodology 182.2.3 Results 222.3 Discussion 232.4 Conclusion and Future Work 25Bibliography 263 Making the Most of Data in Offshore Wind Energy: From Population to Physics-informed Modeling 293.1 Introduction 293.2 Autoregressive Gaussian Processes 313.3 Population Modeling of Wind Farm Wake Effects 313.3.1 A Switching GP-SPARX Model 333.3.2 A Case Study of a Simulated Wind Farm 343.3.3 Results 353.3.4 Discussion 373.4 Physics-informed Machine Learning for Wave Loading Prediction 373.4.1 Monopile Wave Tank Experiment 393.4.2 Model Structure 413.4.3 Results 423.5 Conclusions 44Acknowledgments 44Bibliography 454 Leveraging the Power of Informal Networks in Renewables 494.1 Challenge 494.2 Case Study 514.2.1 Before SOA: What Was the State-of-the-art/Accepted Solution in the Past? 524.2.2 Influencing and Educating Informal Networks 554.2.3 Methodology 554.2.4 Enabling Continuous Improvement Through ML and AI 594.2.5 Next Steps 604.2.6 Results 624.2.7 After: What Is the Accepted Solution Now? 644.3 Discussion 664.4 Conclusion and Future Work 684.4.1 Challenges and Opportunities 69Acknowledgments 72Bibliography 725 Relevance of AI in Addressing Barriers to Rooftop Solar Photovoltaic Adoption in Building Projects in Nigeria 735.1 Introduction 735.2 Literature Review 755.2.1 Overview of Rooftop Solar Photovoltaic Systems 755.2.2 Reluctance to Adopt Sustainable Energy Solutions in Nigeria 755.2.3 Barriers to Rooftop Solar Photovoltaic Adoption in Building Projects 775.2.4 Artificial Intelligence Solutions to Overcome Barriers to Rooftop Solar Photovoltaic Adoption 785.3 Research Methods 805.4 Results and Findings 825.4.1 Background of the Respondents 825.4.2 Background Information of the Respondents 835.4.3 Barriers to the Adoption of Rooftop Solar Photovoltaics and the Preferred AI-Solution 835.4.4 Mean Score (MIS) Analysis 835.4.5 Standard Deviation (S.D.) Analysis 855.4.6 Mann–Whitney Test Analysis 865.4.7 Exploratory Factor Analysis 865.4.8 Discussion and Implications of Findings 885.4.9 Mean Score 885.4.10 Exploratory Factor Analysis 895.4.11 Mann–Whitney U Test 905.4.12 Harnessing the Power of AI to Overcome Barriers to Rooftop Solar Photovoltaic Adoption 915.5 Conclusion and Perspectives 92Bibliography 926 Predicting Comfort: AI-driven HVAC for Intelligent Energy Management 976.1 Challenge 976.2 Case Study 1016.2.1 Dataset Overview 1026.2.2 Methodology 1066.2.3 Results 1076.2.4 After 1086.3 Discussion 1096.4 Conclusion and Future Work 110Bibliography 1107 Leveraging Generative AI-Driven Digital Twins for Renewable Energy Systems 1137.1 Data and Communication Barriers 1137.2 Case Study: The NorthWind Project 1167.2.1 Conventional Practices: Tackling Data Scarcity 1177.2.2 Conventional Practices: Reliability of Critical Communication Infrastructure 1187.2.3 Methodology: Generative AI-driven Digital Twin Framework 1207.2.4 Results and Impact 1287.3 Discussion 1317.4 Conclusion and Future Work 133Bibliography 1358 Vision Transformer-based O&M Model for Condition Monitoring of Solar Panels 1398.1 Challenges 1398.1.1 Practical Issues 1398.1.2 Computer Vision Approaches in Photovoltaic 1408.1.3 Using Large Language Models for Interpreting Vision Transformer Results 1438.2 Case Study 1438.2.1 Model Description 1448.2.2 Vision Transformer Applied to Photovoltaic Cells 1468.2.3 Vision Transformer Comparison with Other Models 1478.2.4 Attention Map Images 1478.2.5 Confusion Matrices 1508.2.6 Model Fine-tuning Process 1508.3 Future Work 1538.4 Conclusion 153Bibliography 1559 Artificial Intelligence Applications: Case Studies from Challenging Domains 1579.1 Introduction 1579.2 Urban Traffic Control 1589.3 Textile Sorting 1619.4 Power Distribution Networks 1629.5 Discussion and Conclusion 164Bibliography 16510 Blockchain-enabled Digital Twins for Advancing Sustainable Reverse Logistics in Renewable Energy Systems 17110.1 Introduction 17110.1.1 Reverse Logistics Importance in Renewable Energy 17210.1.2 Blockchain-enabled Digital Twins 17310.1.3 Technical Architecture of BEDT 17410.1.4 Chapter Objectives 17610.2 Digital Twins and Blockchain in Reverse Logistics 17610.2.1 Blockchain and Transparency and Traceability 17710.2.2 Synergy of DTs with Blockchain 17810.2.3 Additional Considerations for DTs and Blockchain 17910.3 Role of AI and IoT in Reverse Logistics Optimization 17910.3.1 Integration of AI into BEDT 18010.3.2 IoT-enabled Data Collection 18110.4 Case Studies and Applications 18210.4.1 Industry Case Studies 18210.4.2 Real-world Application to Renewable Energy 18610.5 Smoothing the Transition to Renewable Energy 18710.5.1 BEDT’s Role in Transitioning to Renewable Energy 18710.5.2 Contributions to the Circular Economy and Sustainability 18810.6 Conclusion and Way Forward 189Bibliography 19011 Pathways to AI Adoption in Offshore Wind Energy Operations and Maintenance 19711.1 Introduction 19711.1.1 Current Applications of AI in OSW O&M 19811.1.2 Defining Stakeholders 20011.2 Technical Challenges and Solutions 20111.2.1 Setup and Running Costs of AI 20111.2.2 O&M Costs 20411.2.3 Environmental Factors 20711.2.4 Deploying AI in the Field 20911.2.5 Dependability/Trustworthiness 21011.2.6 Human–AI Interaction 21211.2.7 Cybersecurity 21311.2.8 Data Availability 21411.2.9 Collaboration Between Academia and Industry 21511.3 Communication and Opinion 21611.3.1 AI Winters 21611.3.2 Search Trends 21611.3.3 Gartner Hype Cycle 21711.3.4 Conflicting Findings and Definitions 21911.3.5 Overstated Benefits of Novel Methods 21911.3.6 Recommendations 21911.4 Conclusion 220Bibliography 22012 Incremental Drift-aware Learning in Renewable Energy Systems 22712.1 Introduction 22712.1.1 Context of PdM and Renewable Energy System Data 22712.1.2 Challenges in PdM for Renewable Energy Systems 22712.1.3 Incremental Drift-aware Learning for PdM 22812.2 Fundamentals of Incremental Learning 22812.2.1 Definition and Significance 22812.2.2 Key Approaches in Incremental Learning 23012.2.3 Incremental Learning Periods in MATLAB 23212.2.4 Challenges in Incremental Learning 23312.3 Concept Drift and Its Effect 23312.3.1 What is Concept Drift? 23312.3.2 Impact of Concept Drift in Renewable Energy 23512.4 Drift-aware Learning: Approaches and Techniques 23512.4.1 How to Detect Concept Drift and MATLAB Software Solutions 23512.5 Case Study: Detection of Drift Using MATLAB Software Solutions 23712.6 Conclusion 249Acknowledgment 250Bibliography 250Index 251