Completion of wind turbine data sets for wind integration studies applying random forests and k-nearest neighbors

Becker Raik; Thraen Daniela

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Completion of wind turbine data sets for wind integration studies applying random forests and k-nearest neighbors

机译：完善风力涡轮机数据集，以进行应用随机森林和k近邻的风能集成研究

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The importance of wind power as a renewable and cost-efficient power generation technology is growing globally. The impact of wind power on the existing power system, land use, and others over time has been widely studied. Such wind integration studies, especially when they are designed as retrospective bottom-up studies, rely on detailed wind turbine data, including the geographic locations, hub height, and dates of commission. Given the frequency of gaps present in these data sets, basic concepts have been developed to cope with missing data points. In this paper, multiple advanced algorithms were compared with respect to their ability to complete such data sets. One focus was on the selection of predictor variables to analyze the impact of different completion techniques depending on the specific gaps in the data set. A sample application using a German data set indicated that random forests are particularly well suited to the problem at hand.

机译：风能作为一种可再生且具有成本效益的发电技术的重要性在全球范围内日益提高。随着时间的流逝，风力对现有电力系统，土地使用及其他方面的影响已得到广泛研究。此类风集成研究，尤其是当其设计为追溯自下而上的研究时，依赖于详细的风力涡轮机数据，包括地理位置，轮毂高度和投产日期。考虑到这些数据集中出现差距的频率，已经开发出基本概念来应对缺失的数据点。在本文中，比较了多种高级算法完成这些数据集的能力。一个重点是选择预测变量，以根据数据集中的特定差距分析不同完成技术的影响。使用德国数据集的示例应用程序表明，随机森林特别适合当前的问题。

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来源
《Applied Energy》 |2017年第15期|252-262|共11页
作者
Becker Raik; Thraen Daniela;
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作者单位

Helmholtz Ctr Environm Res GmbH UFZ, Dept Bioenergy, Permoserstr 15, D-04318 Leipzig, Germany;

Helmholtz Ctr Environm Res GmbH UFZ, Dept Bioenergy, Permoserstr 15, D-04318 Leipzig, Germany|DBFZ Deutsch Biomasseforschungszentrum gGmbH, Bioenergy Syst Dept, Torgauer Str 116, D-04347 Leipzig, Germany;

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收录信息美国《科学引文索引》(SCI);美国《工程索引》(EI);
原文格式 PDF
正文语种 eng
中图分类
关键词
Wind energy; Wind turbine data; Machine learning; Random forests; Wind power integration;

机译：风能;风轮机数据;机器学习;随机森林;风电集成;

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1. Pipeline for Annual Averaged Wind Power Output Generation Prediction of Wind Turbines Based on Large Wind Speed Data Sets and Power Curve Data [J] . Benjamin Wacker, Jan Chr. Schlüter MethodsX . 2021,第a期

机译：基于大型风速数据集和功率曲线数据的风力涡轮机的年平均风力输出产生预测
2. Switching strategy of the low wind speed wind turbine based on realtime wind process prediction for the integration of wind power and EVs [J] . Wang Han, Yan Jie, Han Shuang, Renewable energy . 2020,第Sepa期

机译：低风速风力涡轮机的切换策略基于实时风流程预测，对风电和EVS集成
3. Increasing the operational capability of a horizontal axis wind turbine by its integration with a vertical axis wind turbine [J] . Govind Bala Applied Energy . 2017,第AUGa1期

机译：通过与垂直轴风力发电机集成来提高水平轴风力发电机的运行能力
4. Prediction of wind turbine blades icing based on MBK-SMOTE and random forest in imbalanced data set [C] . Yangming Ge, Dong Yue, Lei Chen IEEE Conference on Energy Internet and Energy System Integration . 2017

机译：不平衡数据集中基于MBK-SMOTE和随机森林的风电叶片结冰预测
5. Randomized and Evolutionary Approaches to Dataset Characterization, Feature Weighting, and Sampling in K-Nearest Neighbors [D] . Basak, Suryoday. 2020

机译：基于数据集特征的随机和进化方法，具有在k离邻居中的采样和抽样
6. Wind speed reductions by large-scale wind turbine deployments lower turbine efficiencies and set low generation limits [O] . Lee M. Miller, Axel Kleidon 2017

机译：大规模风力涡轮机部署降低了风速，降低了涡轮机效率并设定了较低的发电极限
7. Study of Learning Data Selection Method with K-nearest Neighbor and VAR Model in Wind Speed Prediction using Wind Speed Time-series Data of Multiple Sites by Support Vector Regression [O] . Aimi YUYAMA, Junya YAMASAKI, Daisuke MIZUNO, 2017

机译：基于多网站的风速预测与k最近邻和VAR模型的学习数据选择方法研究了传染媒介回归的风速时间序列数据
8. Advancements in Wind Integration Study Data Modeling: The Wind Integration National Dataset (WIND) Toolkit. [R] . Draxl, C., Hodge, B. M., Orwig, K., 2013

机译：风综合研究数据建模的进展：风综合国家数据集（WIND）工具包。

Completion of wind turbine data sets for wind integration studies applying random forests and k-nearest neighbors

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