{"title":"基于决策树的测量相关预测方法估算全球再分析数据集的峰值阵风","authors":"Serkan Kartal, Sukanta Basu, Simon J. Watson","doi":"10.5194/wes-8-1533-2023","DOIUrl":null,"url":null,"abstract":"Abstract. Peak wind gust (Wp) is a crucial meteorological variable for wind farm planning and operations. However, for many wind farm sites, there is a dearth of on-site measurements of Wp. In this paper, we propose a machine-learning approach (called INTRIGUE, decIsioN-TRee-based wInd GUst Estimation) that utilizes numerous inputs from a public-domain reanalysis dataset and, in turn, generates multi-year, site-specific Wp series. Through a systematic feature importance study, we also identify the most relevant meteorological variables for Wp estimation. The INTRIGUE approach outperforms the baseline predictions for all wind gust conditions. However, the performance of this proposed approach and the baselines for extreme conditions (i.e., Wp>20 m s−1) is less satisfactory.","PeriodicalId":46540,"journal":{"name":"Wind Energy Science","volume":null,"pages":null},"PeriodicalIF":3.6000,"publicationDate":"2023-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A decision-tree-based measure–correlate–predict approach for peak wind gust estimation from a global reanalysis dataset\",\"authors\":\"Serkan Kartal, Sukanta Basu, Simon J. Watson\",\"doi\":\"10.5194/wes-8-1533-2023\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract. Peak wind gust (Wp) is a crucial meteorological variable for wind farm planning and operations. However, for many wind farm sites, there is a dearth of on-site measurements of Wp. In this paper, we propose a machine-learning approach (called INTRIGUE, decIsioN-TRee-based wInd GUst Estimation) that utilizes numerous inputs from a public-domain reanalysis dataset and, in turn, generates multi-year, site-specific Wp series. Through a systematic feature importance study, we also identify the most relevant meteorological variables for Wp estimation. The INTRIGUE approach outperforms the baseline predictions for all wind gust conditions. However, the performance of this proposed approach and the baselines for extreme conditions (i.e., Wp>20 m s−1) is less satisfactory.\",\"PeriodicalId\":46540,\"journal\":{\"name\":\"Wind Energy Science\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.6000,\"publicationDate\":\"2023-10-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Wind Energy Science\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.5194/wes-8-1533-2023\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"GREEN & SUSTAINABLE SCIENCE & TECHNOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Wind Energy Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5194/wes-8-1533-2023","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"GREEN & SUSTAINABLE SCIENCE & TECHNOLOGY","Score":null,"Total":0}
引用次数: 0
摘要
摘要峰值阵风(Wp)是风电场规划和运行的重要气象变量。然而,对于许多风力发电场,缺乏Wp的现场测量。在本文中,我们提出了一种机器学习方法(称为“阴谋”,基于决策树的阵风估计),该方法利用来自公共领域再分析数据集的大量输入,进而生成多年的、特定地点的Wp系列。通过系统的特征重要性研究,我们还确定了与Wp估算最相关的气象变量。阴谋方法优于所有阵风条件的基线预测。然而,该方法的性能和极端条件(即Wp>20 m s−1)的基线不太令人满意。
A decision-tree-based measure–correlate–predict approach for peak wind gust estimation from a global reanalysis dataset
Abstract. Peak wind gust (Wp) is a crucial meteorological variable for wind farm planning and operations. However, for many wind farm sites, there is a dearth of on-site measurements of Wp. In this paper, we propose a machine-learning approach (called INTRIGUE, decIsioN-TRee-based wInd GUst Estimation) that utilizes numerous inputs from a public-domain reanalysis dataset and, in turn, generates multi-year, site-specific Wp series. Through a systematic feature importance study, we also identify the most relevant meteorological variables for Wp estimation. The INTRIGUE approach outperforms the baseline predictions for all wind gust conditions. However, the performance of this proposed approach and the baselines for extreme conditions (i.e., Wp>20 m s−1) is less satisfactory.