G. Kariniotakis, S. Camal, F. Sossan, B. Nouri, J. Lezaca, M. Lange, B. Alonzo, Q. Libois, P. Pinson, R. Bessa, C. Gonçalves
{"title":"下一代可再生能源预测的数据科学- Smart4RES项目的重点结果","authors":"G. Kariniotakis, S. Camal, F. Sossan, B. Nouri, J. Lezaca, M. Lange, B. Alonzo, Q. Libois, P. Pinson, R. Bessa, C. Gonçalves","doi":"10.1049/icp.2021.2499","DOIUrl":null,"url":null,"abstract":"—Smart4RES is a European Horizon2020 project de-veloping next generation solutions for renewable energy forecast- ing. This paper presents highlight results obtained during the first year of the project. Data science is used throughout the proposed solutions in order to process the large amount of heterogeneous data available to forecasters, and derive model-free approaches of forecasting and decision-aid tasks. This paper presents a series of solutions addressing relevant for Photovoltaics (PV) and storage applications. High-resolution Numerical Weather Predictions and regional solar irradiance forecasting provide detailed information on local weather conditions and their variability. PV power forecasting benefits from such new data sources, but also the pro- posed collaborative data exchange. Finally, data-driven methods simplify decision-making for trading in short-term markets and for grid management.","PeriodicalId":186086,"journal":{"name":"11th Solar & Storage Power System Integration Workshop (SIW 2021)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Data Science for Next Generation Renewable Energy Forecasting - Highlight Results from the Smart4RES Project\",\"authors\":\"G. Kariniotakis, S. Camal, F. Sossan, B. Nouri, J. Lezaca, M. Lange, B. Alonzo, Q. Libois, P. Pinson, R. Bessa, C. Gonçalves\",\"doi\":\"10.1049/icp.2021.2499\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"—Smart4RES is a European Horizon2020 project de-veloping next generation solutions for renewable energy forecast- ing. This paper presents highlight results obtained during the first year of the project. Data science is used throughout the proposed solutions in order to process the large amount of heterogeneous data available to forecasters, and derive model-free approaches of forecasting and decision-aid tasks. This paper presents a series of solutions addressing relevant for Photovoltaics (PV) and storage applications. High-resolution Numerical Weather Predictions and regional solar irradiance forecasting provide detailed information on local weather conditions and their variability. PV power forecasting benefits from such new data sources, but also the pro- posed collaborative data exchange. Finally, data-driven methods simplify decision-making for trading in short-term markets and for grid management.\",\"PeriodicalId\":186086,\"journal\":{\"name\":\"11th Solar & Storage Power System Integration Workshop (SIW 2021)\",\"volume\":\"2 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"11th Solar & Storage Power System Integration Workshop (SIW 2021)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1049/icp.2021.2499\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"11th Solar & Storage Power System Integration Workshop (SIW 2021)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1049/icp.2021.2499","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Data Science for Next Generation Renewable Energy Forecasting - Highlight Results from the Smart4RES Project
—Smart4RES is a European Horizon2020 project de-veloping next generation solutions for renewable energy forecast- ing. This paper presents highlight results obtained during the first year of the project. Data science is used throughout the proposed solutions in order to process the large amount of heterogeneous data available to forecasters, and derive model-free approaches of forecasting and decision-aid tasks. This paper presents a series of solutions addressing relevant for Photovoltaics (PV) and storage applications. High-resolution Numerical Weather Predictions and regional solar irradiance forecasting provide detailed information on local weather conditions and their variability. PV power forecasting benefits from such new data sources, but also the pro- posed collaborative data exchange. Finally, data-driven methods simplify decision-making for trading in short-term markets and for grid management.