{"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/sdwpf-a-dataset-for-spatial-dynamic-wind","title":"SDWPF: A Dataset for Spatial Dynamic Wind Power Forecasting Challenge at KDD Cup 2022","arxiv_id":"2208.04360","date":"2022-08-08","proceeding":null,"authors":["Jingbo Zhou","Xinjiang Lu","Yixiong Xiao","Jiantao Su","Junfu Lyu","Yanjun Ma","Dejing Dou"],"abstract":"The variability of wind power supply can present substantial challenges to incorporating wind power into a grid system. Thus, Wind Power Forecasting (WPF) has been widely recognized as one of the most critical issues in wind power integration and operation. There has been an explosion of studies on wind power forecasting problems in the past decades. Nevertheless, how to well handle the WPF problem is still challenging, since high prediction accuracy is always demanded to ensure grid stability and security of supply. We present a unique Spatial Dynamic Wind Power Forecasting dataset: SDWPF, which includes the spatial distribution of wind turbines, as well as the dynamic context factors. Whereas, most of the existing datasets have only a small number of wind turbines without knowing the locations and context information of wind turbines at a fine-grained time scale. By contrast, SDWPF provides the wind power data of 134 wind turbines from a wind farm over half a year with their relative positions and internal statuses. We use this dataset to launch the Baidu KDD Cup 2022 to examine the limit of current WPF solutions. The dataset is released at https://aistudio.baidu.com/aistudio/competition/detail/152/0/datasets.","url_abs":"https://arxiv.org/abs/2208.04360v2","url_pdf":"https://arxiv.org/pdf/2208.04360v2.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":"sdwpf-a-dataset-for-spatial-dynamic-wind","repo_url":"https://github.com/climate-change-automl/climate-change-automl","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"sdwpf-a-dataset-for-spatial-dynamic-wind","repo_url":"https://github.com/jseaj/2dxformer","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[{"slug":"sdwpf","name":"SDWPF","full_name":"A Dataset for Spatial Dynamic Wind Power Forecasting  Challenge at KDD Cup 2022"},{"slug":"sdwpf-a-dataset-for-spatial-dynamic-wind","name":"SDWPF: A Dataset for Spatial Dynamic Wind Power Forecasting over a Large Turbine Array.","full_name":"SDWPF: A Dataset for Spatial Dynamic Wind Power Forecasting over a Large Turbine Array."}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2208.04360","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2208.04360"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/jseaj/2dxformer","reach":null},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/climate-change-automl/climate-change-automl","reach":{"status":"ok","spdx":"Apache-2.0"}}],"summary":{"ran_draft_wrong":2},"by_repo_kind":{"listed":{"samples":2,"ran":2,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"c9c18a8a3248d43b","entry":"loss_fn","repo":"jseaj/2dxformer","repo_kind":"listed","path":"run_model.py","file_url":"https://github.com/jseaj/2dxformer/blob/HEAD/run_model.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"c9c18a8a3248d43b"}},{"code_sha256_prefix":"f06b9dde37d1edca","entry":"split_data_by_ratio","repo":"jseaj/2dxformer","repo_kind":"listed","path":"lib/dataloader.py","file_url":"https://github.com/jseaj/2dxformer/blob/HEAD/lib/dataloader.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"f06b9dde37d1edca"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}