Prediction of wind speed for the estimation of wind turbine power output from site climatological data using artificial neural network

被引：10

作者：

Ayodele T.R. ^{[1
]}

Ogunjuyigbe A.S.O. ^{[1
]}

机构：

[1] Power Energy Machine and Drive Research Group, Department of Electrical and Electronic Engineering, Faculty of Technology, University of Ibadan, Ibadan

来源：

Ayodele, T.R. (tr.ayodele@ui.edu.ng) | 2017年 / Taylor and Francis Ltd.卷 / 38期

关键词：

climatological variables; neural network; prediction; South Africa; wind speed;

D O I：

10.1080/01430750.2015.1023845

中图分类号：

学科分类号：

摘要：

In this paper, the wind speeds of Noupoort in the Western Cape region of South Africa are forecasted from the site climatological data using feed forward artificial neural network (ANN) with the back propagation training method. Different architectural designs are tested with different combinations of climatological data to obtain the most suitable ANN for predicting the wind speed of the site. The predicted wind speeds are compared with the actual measured wind speeds and the results are evaluated using mean absolute error (MAE), mean absolute percentage error (MAPE), root mean square error (RMSE) and correlation coefficient (R). Some of the key results show that combination of temperature, wind direction and time of the day (TEM + WD + T) could effectively predict wind speed of Noupoort. The forecasted wind speed shows a strong agreement with the measured wind speed with R, RMSE, MAPE and MAE of 0.96, 0.56, 6.64% and 0.44, respectively. © 2015 Taylor & Francis.

引用

页码：29 / 36

页数：7

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