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eBook Statistical Relational AI for PV Multi-Timescale Uncertainty Modeling
Theory, Case Analysis, and Engineering Practice EPUB
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Wiley-IEEE Press 2026.10.12.
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Preface xii

Acknowledgments  xiii

1. Statistical Relational AI for PV Multi-Timescale Uncertainty Modeling Theory: A Comprehensive Survey and Analysis 8

Xueqian Fu, Qiaoyu Ma, Na Lu, Chunyu Zhang, Chen Zhang

1.1 Introduction to Statistical Relational AI 9

1.2 Statistical Relational AI in High-Resolution Reconstruction of PV Data 26

1.3 Statistical Relational AI in PV Scenario Generation 45

1.4 Statistical Relational AI in Representative PV Scenario Extraction 63

1.5 Statistical Relational AI in Day-Ahead PV Forecasting 71

1.6 Statistical Relational AI in Intraday PV Forecasting 87

1.7 Future Directions 96

References 103

2. Online Monitoring of Smart PV Meters Based on Decision Tree Models 111

Nange Li, Xueqian Fu

2.1 Introduction 112

2.2 Problem Formulation and Data Description 123

2.3 Physical Analysis of Three-Phase Electrical Characteristics in PV Scenarios 132

2.4 Hybrid Feature Construction Method for Three-Phase Meter Anomaly Detection 141

2.5 Construction of an Anomaly Detection Model Based on Gradient Boosting Decision Trees 154

2.6 Experimental Design and Result Analysis 165

2.7 Discussion and Extension for Photovoltaic Applications 178

References 185

3. High-Resolution Reconstruction of Photovoltaic Data via Fourier Diffusion Models 187

Qiaoyu Ma, Yihan Jiang, Xueqian Fu

3.1 Overview 188

3.2 Problem Formulation 195

3.3 Methodology 225

3.4 Case Study 248

3.5 Discussion 257

References 263

4. Scenario Stochastic Generation via Trend-Decomposition Enhanced Diffusion Models 267

Fuhao Chang, Yanan Cui, Xueqian Fu

4.1 Introduction 268

4.2 Methodology 282

4.3 Comprehensive Evaluation System 305

4.4 Experiments 307

4.5 Conclusion 322

References 325

5. Representative Photovoltaic Scenario Extraction Using Graph Clustering Modelsg 329

Na Lu, Yuxi Liu, Chunxin Hu, Shuangyu Yin, Xueqian Fu

5.1 Introduction 330

5.2 Methodology 341

5.3 Case Study 358

5.4 Conclusion 390

References 395

6. Implicit Category Projection-Enhanced Multimodal Seq2Seq for Ensuring Accurate Day-Ahead PV Forecasting under Transitional Weather 398

Xiaolong Zhao, Xiangrong Zeng, LinfengYang, Xueqian Fu

6.1 Introduction 397

6.2 Methodology 399

6.3 Case Study 442

6.4 Summary 461

References 466

7. Unified Fourier Graph-Based Spatiotemporal Learning for Multi-Site Ultra-Short-Term Photovoltaic Power Forecasting 470

Chunyu Zhang, Xueqian Fu

7.1 Introduction 471

7.2 Problem Formulation 474

7.3 Fundamental Approaches for Spatiotemporal Dependency Modeling

 477

7.4 Proposed Model 505

7.5 Experiments 517

7.6 Computational Efficiency Analysis 530

7.7 Discussion 533

7.8 Conclusion 536

References 537

8. Engineering Practice for Power Planning and Electricity Markets  540

Xueqian Fu, Xiao Guo, Xiao Lv, Huiyan Wang, Nange Li, Zhaoyang Han

8.1 Introduction 541

8.2 Engineering Practice of Stochastic Planning for Distribution Networs Considering Multidimensional Correlations and Dimensionality Reduction 542

8.3 Mechanism Design of Day-ahead Market Considering Carbon Trading and Photovoltaic Uncertainty 575

8.4 Conclusion 601

References 601

저자 소개

Xueqian Fu, PhD, is an Associate Professor with the College of Information and Electrical Engineering at the China Agricultural University. He has been recognized as one of the Stanford/Elsevier Top 2% Scientists (Career-long Impact) in the field of energy in both the 2024 and 2025 rankings. He is a IEEE Senior Member and currently serves as the Vice President of the IEEE Smart Village China Committee. He received his B.S. and M.S. degrees from North China Electric Power University in 2008 and 2011, respectively, and his Ph.D. degree from South China University of Technology in 2015. He was a Postdoctoral Researcher at Tsinghua University from 2015 to 2017. He serves as the Deputy Editor-in-Chief of Information Processing in Agriculture and is the founding chair of the IEEE International Symposium on the Application of Artificial Intelligence in Electrical Engineering (AAIEE).

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