2018/02/02 by Yize Chen, Pan Li, Chen, Yize +3 · 8 citations
Computer Science · Mathematics · #Artificial intelligence #Artificial neural network #Bayesian inference #Bayesian network #Bayesian probability #Computational Physics and Python Applications #Computer science #Data mining #FOS: Computer and information sciences #FOS: Mathematics #Generative Adversarial Networks and Image Synthesis #Generative grammar #Image Processing and 3D Reconstruction #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine learning #Optimization and Control (math.OC) #Representation (politics) #Variable-order Bayesian network #cs.LG #math.OC #stat.ML
paper · pdf · doi:10.48550/arxiv.1802.00868
published in arXiv (Cornell University) (Cornell University) · Paper accepted to Annual Conference on Information Sciences and Systems (CISS)
arxiv created 2018/02/02 · openalex publication_date 2018/02/02 · arxiv updated 2018/02/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
We present a method to generate renewable scenarios using Bayesian probabilities by implementing the Bayesian generative adversarial network~(Bayesian GAN), which is a variant of generative adversarial networks based on two interconnected deep neural networks. By using a Bayesian formulation, generators can be constructed and trained to produce scenarios that capture different salient modes in the data, allowing for better diversity and more accurate representation of the underlying physical process. Compared to conventional statistical models that are often hard to scale or sample from, this method is model-free and can generate samples extremely efficiently. For validation, we use wind and solar times-series data from NREL integration data sets to train the Bayesian GAN. We demonstrate that proposed method is able to generate clusters of wind scenarios with different variance and mean value, and is able to distinguish and generate wind and solar scenarios simultaneously even if the historical data are intentionally mixed.