Short-Term Wind Speed Forecasting of Lelystad Wind Farm by using ANN Algorithms

被引：0

作者：

Sahay, Kishan Bhushan ^{[1
]}

Srivastava, Shwetank ^{[2
]}

机构：

[1] Madan Mohan Malaviya Univ Technol, Dept Elect Engn, Gorakhpur, Uttar Pradesh, India

[2] Madan Mohan Malaviya Univ Technol, Dept Civil Engn, Gorakhpur, Uttar Pradesh, India

来源：

2018 6TH INTERNATIONAL ELECTRICAL ENGINEERING CONGRESS (IEECON) | 2018年

关键词：

Mean absolute percentage error (MAPE); neural network (NN); power system; short-term wind speed forecasting (STWSF);

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

The installation of wind energy based electricity systems is growing at a very fast pace all over the world because of the increased urge of using renewable energy resources and environmental concerns regarding electricity generation. Forecasting wind speed is found to be critical for wind energy systems since it greatly influences its large-scale integration. The intermittent nature of wind speed leads to further problems in its large-scale integration in the power systems. Wind speed forecasting is essential to operate wind energy based power systems in an efficient and secure way. In this paper, different ANN algorithms have been applied to forecast short-term wind speed of Lelystad Wind Farm, Nederland using MATLAB R14a. The data used in the forecasting are hourly historical data of the wind direction & wind speed. The simulation results have shown accurate one hour ahead forecasts with small error in wind speed forecasting.

引用

页数：4

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