Statistical Relational Artificial Intelligence in Photovoltaic Power Uncertainty Analysis
- 1st Edition - June 2, 2025
- Author: Xueqian Fu
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 3 4 0 4 1 - 3
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 3 4 0 4 2 - 0
Statistical Relational Artificial Intelligence in Photovoltaic Power Uncertainty Analysis addresses uncertainty issues in photovoltaic power generation while also supporting the co… Read more
Statistical Relational Artificial Intelligence in Photovoltaic Power Uncertainty Analysis addresses uncertainty issues in photovoltaic power generation while also supporting the collaborative enhancement of understanding and applying theory and methods through the integration of models, cases, and code. The book employs StaRAI to address uncertainty analysis and modeling issues at different time scales in photovoltaic power generation, including photovoltaic power prediction, probabilistic power flow, stochastic planning, and more. Chapters cover uncertainty of PV power generation from short to long time scales, including day-ahead scheduling (24 hours in advance), intraday scheduling (minute to hour rolling), and grid planning (15 years).
Other sections study the impact of photovoltaic uncertainty on the power grid, offering the most classic cases of probabilistic load flow and PV stochastic planning.
The theoretical content of this book is not only systematic but supplemented with concrete examples and MATLAB/Python codes. Its contents will be of interest to all those working on photovoltaic planning, power generation, power plants, and applications of AI, including researchers, advanced students, faculty engineers, R&D, and designers.
Other sections study the impact of photovoltaic uncertainty on the power grid, offering the most classic cases of probabilistic load flow and PV stochastic planning.
The theoretical content of this book is not only systematic but supplemented with concrete examples and MATLAB/Python codes. Its contents will be of interest to all those working on photovoltaic planning, power generation, power plants, and applications of AI, including researchers, advanced students, faculty engineers, R&D, and designers.
- Explores how Statistical Relational Artificial Intelligence (StaRAI) can be applied to photovoltaic power prediction, maintenance, and planning
- Provides a theoretical framework supported by schematic diagrams, real examples, and code
- Discusses the potential for groundbreaking AI applications in PV, future opportunities, and ethical and societal impacts
Researchers, advanced students, and faculty in photovoltaics, solar energy, renewable energy, electronics, and power systems
1. Review of PV Uncertainty Models
2. LSTM-based Day-Ahead Photovoltaic Power Prediction
3. Transformer-based Intra-Day Photovoltaic Power Prediction
4. Unsupervised Learning-based Annual Photovoltaic Power Scene Reduction
5. Adversarial Network-based Annual Photovoltaic Power Simulation
6. Photovoltaic Power Generation Meteorological Information Mining and Forecasting
7. Statistical Machine Learning-based Probabilistic Power Flow in PV-integrated Grid
8. Statistical Machine Learning-based Stochastic Planning for Photovoltaics
9. Photovoltaics and Artificial Intelligence Applications – Future Predictions and Summary
2. LSTM-based Day-Ahead Photovoltaic Power Prediction
3. Transformer-based Intra-Day Photovoltaic Power Prediction
4. Unsupervised Learning-based Annual Photovoltaic Power Scene Reduction
5. Adversarial Network-based Annual Photovoltaic Power Simulation
6. Photovoltaic Power Generation Meteorological Information Mining and Forecasting
7. Statistical Machine Learning-based Probabilistic Power Flow in PV-integrated Grid
8. Statistical Machine Learning-based Stochastic Planning for Photovoltaics
9. Photovoltaics and Artificial Intelligence Applications – Future Predictions and Summary
- Edition: 1
- Published: June 2, 2025
- Language: English
XF
Xueqian Fu
Xueqian Fu is an Associate Professor at China Agricultural University (CAU), a Senior Member of IEEE, and Vice Chairman of IEEE Smart Village-CWG, IEEE Young Professionals. He is a one of the World’s Top 2% Scientists 2023 and has been recognized as 'Outstanding Talent' and 'Young Star B’ by China Agricultural University. Dr. Xu received his Ph.D. degree from South China University of Technology in 2015 and was a Post-Doctoral Researcher at Tsinghua University from 2015 to 2017. His current research interests include Statistical Machine Learning, Agricultural Energy Internet, and PV system integration. He serves as Deputy Editor-in-Chief for Information Processing in Agriculture and as Associate Editor for IET Renewable Power Generation, Artificial Intelligence and Applications, Protection and Control of Modern Power Systems and the Journal of Data Science and Intelligent Systems. He also serves as a youth editor for Clean Energy Science and Technology and Lead Guest Editor role for International Transactions on Electrical Energy Systems.
Affiliations and expertise
Associate Professor, China Agricultural University (CAU), ChinaRead Statistical Relational Artificial Intelligence in Photovoltaic Power Uncertainty Analysis on ScienceDirect