Statistical Relational Artificial Intelligence in Photovoltaic Power Uncertainty Analysis

Auteur

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.

Texte du rabat

Statistical Relational Artificial Intelligence in Photovoltaic Power Uncertainty Analysis addresses uncertainty issues in photovoltaic power generation and supports the collaborative enhancement of understanding and application of 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 2, 3, 4, and 5 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). Chapters 6, 7, and 8 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. This is 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.