Statistical Relational AI for PV Multi-Timescale Uncertainty Modeling: Theory, Case Analysis, and Engineering Practice

A unified framework for photovoltaic multi-timescale uncertainty modeling Research on photovoltaic uncertainty remains fragmented: physical models lack interpretability, deep learning sacrifices generalizability, and no end-to-end solutions exist for real grid scenarios.Statistical Relational AI for PV Multi-Timescale Uncertainty Modeling: Theory, Case Analysis, and Engineering Practice delivers a unified framework integrating real-world PV power data with complete workflows for grid planning, operation, and uncertainty-aware decision-making.The 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 solutio