{"product_id":"9781394439119","title":"Statistical Relational AI for PV Multi-Timescale Uncertainty Modeling Theory, Case Analysis, and Engineering Practice","description":"\u003ch1\u003eStatistical Relational AI for PV Multi-Timescale Uncertainty Modeling\u003c\/h1\u003e\u003ch2\u003eTheory, Case Analysis, and Engineering Practice\u003c\/h2\u003e\u003ch3\u003eXueqian Fu\u003c\/h3\u003e\u003cdiv\u003e\u003cb\u003eTechnology \u0026amp; Engineering \/ Power Resources \/ General\u003c\/b\u003e\u003c\/div\u003e\u003cbr\u003e\u003cdiv\u003e\n\u003cp\u003e\u003cb\u003eA unified framework for photovoltaic multi-timescale uncertainty modeling\u003c\/b\u003e \u003c\/p\u003e\n\u003cp\u003eResearch on photovoltaic uncertainty remains fragmented: physical models lack interpretability, deep learning sacrifices generalizability, and no end-to-end solutions exist for real grid scenarios. \u003ci\u003eStatistical Relational AI for PV Multi-Timescale Uncertainty Modeling: Theory, Case Analysis, and Engineering Practice\u003c\/i\u003e delivers a unified framework integrating real-world PV power data with complete workflows for grid planning, operation, and uncertainty-aware decision-making. \u003c\/p\u003e\n\u003cp\u003eThe book systematically addresses how weather conditions, seasonal patterns, and time-of-day effects drive generation variability across multiple time scales. Case studies drawn from operational PV plants and real power system environments demonstrate a complete workflow from problem formulation through solution development. Practical datasets, executable code, and engineering examples show how proposed approaches translate into implementable solutions. \u003c\/p\u003e\n\u003cp\u003eReaders will also find: \u003c\/p\u003e\n\u003cul\u003e \u003cli\u003eConcrete implementation guidance for statistical relational AI methods applied to data organization, pattern discovery, and supporting analytical tasks\u003c\/li\u003e \u003cli\u003eProbabilistic techniques for quantifying PV output variability for stochastic optimization and electricity market operations\u003c\/li\u003e \u003cli\u003eA complete end-to-end technical pipeline spanning data acquisition, preprocessing, modeling, forecasting, and engineering deployment\u003c\/li\u003e \u003cli\u003eA structured perspective on future development trajectories for AI-driven photovoltaic uncertainty research and applications\u003c\/li\u003e \u003cli\u003eSolutions designed specifically for real PV grid scenarios rather than idealized or purely simulated environments\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eDesigned for university faculty, academic researchers, power-system engineers, and graduate students, this book provides structured methodologies and reproducible tools for modeling PV uncertainty across time scales. Grid planners and renewable energy technology practitioners will also find directly applicable workflows for operational decision-making.\u003c\/p\u003e\n\u003c\/div\u003e\u003cdiv\u003e  \u003cp\u003e\u003cb\u003eXueqian Fu, PhD, \u003c\/b\u003eis 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). \u003c\/p\u003e\n\u003c\/div\u003e\u003cbr\u003e\u003ctable\u003e\n\u003ctr\u003e\n\u003ctd\u003ePublication Date: \u003c\/td\u003e\n\u003ctd\u003e03 December 2026\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePublisher: \u003c\/td\u003e\n\u003ctd\u003eWiley\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eImprint: \u003c\/td\u003e\n\u003ctd\u003eWiley-IEEE Press\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eISBN-13: \u003c\/td\u003e\n\u003ctd\u003e9781394439119\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eFormat: \u003c\/td\u003e\n\u003ctd\u003eHardback\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePage Count: \u003c\/td\u003e\n\u003ctd\u003e688\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":50462797856908,"sku":"9781394439119","price":135.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0710\/9545\/1788\/files\/9781394439119.jpg?v=1780617624","url":"https:\/\/fh90cf-fv.myshopify.com\/products\/9781394439119","provider":"Late Knight Books and Services, LLC","version":"1.0","type":"link"}