Estimation of wind speed: A data-driven approach

被引：41

作者：

Kusiak, Andrew ^{[1
]}

Li, Wenyan ^{[1
]}

机构：

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

来源：

JOURNAL OF WIND ENGINEERING AND INDUSTRIAL AERODYNAMICS | 2010年 / 98卷 / 10-11期

关键词：

Data mining; Pearson's correlation coefficient; Wind turbine; Wind energy; Wind speed prediction; NEURAL-NETWORKS; PREDICTION; ENERGY; TURBINES; GENERATION;

D O I：

10.1016/j.jweia.2010.04.010

中图分类号：

TU [建筑科学];

学科分类号：

0813 ;

摘要：

A method for prediction of wind speed at a selected location based on the data collected at neighborhood locations is presented. The affinity of wind speeds measured at different locations is defined by Pearson's correlation coefficient. Five turbines with similar wind conditions are selected among 30 wind turbines for in-depth analysis. The wind data from these turbines are used to predict wind speed at a selected location. A neural network ensemble is used to predict the value of wind speed at the turbine of interest. The models have been tested and the computational results are discussed. The results demonstrate that a higher Pearson's correlation coefficient between the wind speeds measured at different turbines has produced better prediction accuracy for the same training and test scenario. (C) 2010 Elsevier Ltd. All rights reserved.

引用

页码：559 / 567

页数：9

共 28 条

[21] A new strategy for wind speed forecasting using artificial intelligent methods [J].

Monfared, Mohammad ;

Rastegar, Hasan ;

Kojabadi, Hossein Madadi .

RENEWABLE ENERGY, 2009, 34 (03) :845-848

[22] Sensorless output maximization control for variable-speed wind generation system using IPMSG [J].

Morimoto, S ;

Nakayama, H ;

Sanada, M ;

Takeda, Y .

IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS, 2005, 41 (01) :60-67

[23] Pitch-controlled variable-speed wind turbine generation [J].

Muljadi, E ;

Butterfield, CP .

IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS, 2001, 37 (01) :240-246

[24] Predictive control of wind turbines in small power systems at high turbulent wind speeds [J].

Nanayakkara, N ;

Nakamura, M ;

Hatazaki, H .

CONTROL ENGINEERING PRACTICE, 1997, 5 (08) :1063-1069

[25]

RIAHY G, 1996, P SOL 96 C DARW AUST

[26] Short term wind speed forecasting for wind turbine applications using linear prediction method [J].

Riahy, G. H. ;

Abedi, M. .

RENEWABLE ENERGY, 2008, 33 (01) :35-41

[27] Accurate short-term wind speed prediction by exploiting diversity in input data using banks of artificial neural networks [J].

Salcedo-Sanz, Sancho ;

Perez-Bellido, Angel M. ;

Ortiz-Garcia, Emilio G. ;

Portilla-Figueras, Antonio ;

Prieto, Luis ;

Correoso, Francisco .

NEUROCOMPUTING, 2009, 72 (4-6) :1336-1341

[28] APPLICATION OF WIND-SPEED FORECASTING TO THE INTEGRATION OF WIND ENERGY INTO A LARGE-SCALE POWER-SYSTEM [J].

WATSON, SJ ;

LANDBERG, L ;

HALLIDAY, JA .

IEE PROCEEDINGS-GENERATION TRANSMISSION AND DISTRIBUTION, 1994, 141 (04) :357-362

← 1 2 3 →