{"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/deep-forecast-deep-learning-based-spatio","title":"Deep Forecast: Deep Learning-based Spatio-Temporal Forecasting","arxiv_id":"1707.08110","date":"2017-07-24","proceeding":null,"authors":["Amir Ghaderi","Borhan M. Sanandaji","Faezeh Ghaderi"],"abstract":"The paper presents a spatio-temporal wind speed forecasting algorithm using\nDeep Learning (DL)and in particular, Recurrent Neural Networks(RNNs). Motivated\nby recent advances in renewable energy integration and smart grids, we apply\nour proposed algorithm for wind speed forecasting. Renewable energy resources\n(wind and solar)are random in nature and, thus, their integration is\nfacilitated with accurate short-term forecasts. In our proposed framework, we\nmodel the spatiotemporal information by a graph whose nodes are data generating\nentities and its edges basically model how these nodes are interacting with\neach other. One of the main contributions of our work is the fact that we\nobtain forecasts of all nodes of the graph at the same time based on one\nframework. Results of a case study on recorded time series data from a\ncollection of wind mills in the north-east of the U.S. show that the proposed\nDL-based forecasting algorithm significantly improves the short-term forecasts\ncompared to a set of widely-used benchmarks models.","url_abs":"http://arxiv.org/abs/1707.08110v1","url_pdf":"http://arxiv.org/pdf/1707.08110v1.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":"deep-forecast-deep-learning-based-spatio","repo_url":"https://github.com/amirstar/Deep-Forecast","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"deep-forecast-deep-learning-based-spatio","repo_url":"https://github.com/oshapio/Localized-CNNs-for-Geospatial-Wind-Forecasting","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"spatio-temporal-forecasting","task_name":"Spatio-Temporal Forecasting"},{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series","task_name":"Time Series Analysis"}],"methods":[{"method_slug":"speed","method_name":"SPEED"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1707.08110","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}