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.
Table of Contents
1. Review of PV Uncertainty Models2. 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
Authors
Xueqian Fu Associate Professor, China Agricultural University (CAU), China.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.
