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Full Description
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.
Contents
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
