{"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/model-free-renewable-scenario-generation","title":"Model-Free Renewable Scenario Generation Using Generative Adversarial Networks","arxiv_id":"1707.09676","date":"2017-07-30","proceeding":null,"authors":["Yize Chen","Yishen Wang","Daniel Kirschen","Baosen Zhang"],"abstract":"Scenario generation is an important step in the operation and planning of\npower systems with high renewable penetrations. In this work, we proposed a\ndata-driven approach for scenario generation using generative adversarial\nnetworks, which is based on two interconnected deep neural networks. Compared\nwith existing methods based on probabilistic models that are often hard to\nscale or sample from, our method is data-driven, and captures renewable energy\nproduction patterns in both temporal and spatial dimensions for a large number\nof correlated resources. For validation, we use wind and solar times-series\ndata from NREL integration data sets. We demonstrate that the proposed method\nis able to generate realistic wind and photovoltaic power profiles with full\ndiversity of behaviors. We also illustrate how to generate scenarios based on\ndifferent conditions of interest by using labeled data during training. For\nexample, scenarios can be conditioned on weather events~(e.g. high wind day) or\ntime of the year~(e,g. solar generation for a day in July). Because of the\nfeedforward nature of the neural networks, scenarios can be generated extremely\nefficiently without sophisticated sampling techniques.","url_abs":"http://arxiv.org/abs/1707.09676v2","url_pdf":"http://arxiv.org/pdf/1707.09676v2.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":"model-free-renewable-scenario-generation","repo_url":"https://github.com/chennnnnyize/Renewables_Scenario_Gen_GAN","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}},{"paper_slug":"model-free-renewable-scenario-generation","repo_url":"https://github.com/chwneo7/VAE-code","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"diversity","task_name":"Diversity"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1707.09676","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}