A multi-scale time-series dataset with benchmark for machine learning in decarbonized energy grids
Measurement(s) temperature • wind speed • solar zeinth angle • dew point • irradiance • voltage • current Technology Type(s) weather station • power grid model-based simulation Factor Type(s) load power • renewable generation power • disturbance location, type, and duration
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2022-06-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-022-01455-7 |
| _version_ | 1811334356210286592 |
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| author | Xiangtian Zheng Nan Xu Loc Trinh Dongqi Wu Tong Huang S. Sivaranjani Yan Liu Le Xie |
| author_facet | Xiangtian Zheng Nan Xu Loc Trinh Dongqi Wu Tong Huang S. Sivaranjani Yan Liu Le Xie |
| author_sort | Xiangtian Zheng |
| collection | DOAJ |
| description | Measurement(s) temperature • wind speed • solar zeinth angle • dew point • irradiance • voltage • current Technology Type(s) weather station • power grid model-based simulation Factor Type(s) load power • renewable generation power • disturbance location, type, and duration |
| first_indexed | 2024-04-13T17:06:42Z |
| format | Article |
| id | doaj.art-f6d88833689d4d10b2fc7f5470e4a866 |
| institution | Directory Open Access Journal |
| issn | 2052-4463 |
| language | English |
| last_indexed | 2024-04-13T17:06:42Z |
| publishDate | 2022-06-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj.art-f6d88833689d4d10b2fc7f5470e4a8662022-12-22T02:38:27ZengNature PortfolioScientific Data2052-44632022-06-019111810.1038/s41597-022-01455-7A multi-scale time-series dataset with benchmark for machine learning in decarbonized energy gridsXiangtian Zheng0Nan Xu1Loc Trinh2Dongqi Wu3Tong Huang4S. Sivaranjani5Yan Liu6Le Xie7Texas A&M University, Department of Electrical and Computer EngineeringUniversity of Southern California, Computer Science DepartmentUniversity of Southern California, Computer Science DepartmentTexas A&M University, Department of Electrical and Computer EngineeringMassachusetts Institute of Technology, Laboratory for Information and Decision SystemsPurdue University, School of Industrial EngineeringUniversity of Southern California, Computer Science DepartmentTexas A&M University, Department of Electrical and Computer EngineeringMeasurement(s) temperature • wind speed • solar zeinth angle • dew point • irradiance • voltage • current Technology Type(s) weather station • power grid model-based simulation Factor Type(s) load power • renewable generation power • disturbance location, type, and durationhttps://doi.org/10.1038/s41597-022-01455-7 |
| spellingShingle | Xiangtian Zheng Nan Xu Loc Trinh Dongqi Wu Tong Huang S. Sivaranjani Yan Liu Le Xie A multi-scale time-series dataset with benchmark for machine learning in decarbonized energy grids Scientific Data |
| title | A multi-scale time-series dataset with benchmark for machine learning in decarbonized energy grids |
| title_full | A multi-scale time-series dataset with benchmark for machine learning in decarbonized energy grids |
| title_fullStr | A multi-scale time-series dataset with benchmark for machine learning in decarbonized energy grids |
| title_full_unstemmed | A multi-scale time-series dataset with benchmark for machine learning in decarbonized energy grids |
| title_short | A multi-scale time-series dataset with benchmark for machine learning in decarbonized energy grids |
| title_sort | multi scale time series dataset with benchmark for machine learning in decarbonized energy grids |
| url | https://doi.org/10.1038/s41597-022-01455-7 |
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