{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/bayesian-renewables-scenario-generation-via","title":"Bayesian Renewables Scenario Generation via Deep Generative Networks","arxiv_id":"1802.00868","date":"2018-02-02","proceeding":null,"authors":["Yize Chen","Pan Li","Baosen Zhang"],"abstract":"We present a method to generate renewable scenarios using Bayesian\nprobabilities by implementing the Bayesian generative adversarial\nnetwork~(Bayesian GAN), which is a variant of generative adversarial networks\nbased on two interconnected deep neural networks. By using a Bayesian\nformulation, generators can be constructed and trained to produce scenarios\nthat capture different salient modes in the data, allowing for better diversity\nand more accurate representation of the underlying physical process. Compared\nto conventional statistical models that are often hard to scale or sample from,\nthis method is model-free and can generate samples extremely efficiently. For\nvalidation, we use wind and solar times-series data from NREL integration data\nsets to train the Bayesian GAN. We demonstrate that proposed method is able to\ngenerate clusters of wind scenarios with different variance and mean value, and\nis able to distinguish and generate wind and solar scenarios simultaneously\neven if the historical data are intentionally mixed.","url_abs":"http://arxiv.org/abs/1802.00868v1","url_pdf":"http://arxiv.org/pdf/1802.00868v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"bayesian-renewables-scenario-generation-via","repo_url":"https://github.com/chennnnnyize/BayesianRenewablesGAN","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"diversity","task_name":"Diversity"},{"task_slug":null,"task_name":"Generative Adversarial Network"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}