Short-Horizon Prediction of Wind Power: A Data-Driven Approach

被引：96

作者：

Kusiak, Andrew ^{[1
]}

Zhang, Zijun ^{[1
]}

机构：

[1] Univ Iowa, Intelligent Syst Lab, Dept Mech & Ind Engn, Iowa City, IA 52242 USA

来源：

IEEE TRANSACTIONS ON ENERGY CONVERSION | 2010年 / 25卷 / 04期

关键词：

Data mining; evolutionary strategy (ES) algorithm; exponential smoothing; neural networks (NNs); power prediction; time-series model; wind speed prediction; NONLINEAR CONTROL; SPEED PREDICTION; GENERATION; TURBINE;

D O I：

10.1109/TEC.2010.2043436

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

This paper discusses short-horizon prediction of wind speed and power using wind turbine data collected at 10 s intervals. A time-series model approach to examine wind behavior is studied. Both exponential smoothing and data-driven models are developed for wind prediction. Power prediction models are established, which are based on the most effective wind prediction model. Comparative analysis of the power predicting models is discussed. Computational results demonstrate performance advantages provided by the data-driven approach. All computations reported in the paper are based on the data collected at a large wind farm.

引用

页码：1112 / 1122

页数：11

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