Ultra-short-term Wind Speed Prediction Model for Wind Farms Based on Spatiotemporal Neural Network

被引：0

作者：

Fan H. ^{[1
,2
]}

Zhang X. ^{[1
,2
]}

Mei S. ^{[1
,2
]}

Yang Z. ^{[3
]}

机构：

[1] Department of Electrical Engineering, Tsinghua University, Beijing

[2] State Key Laboratory of Power System and Generation Equipment, Tsinghua University, Beijing

[3] Department of Electronic Engineering, Tsinghua University, Beijing

来源：

Dianli Xitong Zidonghua/Automation of Electric Power Systems | 2021年 / 45卷 / 01期

关键词：

Convolutional neural network; Renewable energy consumption; Spatiotemporal correlation; Wind farm; Wind speed prediction;

D O I：

10.7500/AEPS20190831001

中图分类号：

学科分类号：

摘要：

With the large-scale integration of wind farms, improving the prediction accuracy of wind speed in wind farms is of great significance to promote the consumption of renewable energy. Traditional prediction methods are usually based on the historical wind speed of a single altitude in the wind farm. When the prediction horizon reaches about three or four hours, the prediction error becomes relatively large. Wind speed and direction data at different altitudes contain the spatiotemporal correlation and the numerical weather prediction data reflects the influence of atmospheric motion around the wind farm on the variation of wind speed. In this paper, wind speed and direction data at different altitudes and numerical weather prediction data are introduced at the input data level. In order to fully exploit the rules of data, a new spatiotemporal neural network (STNN) is proposed. The deep convolutional network and the bidirectional gated recurrent unit are used to extract the spatiotemporal features of historical wind speed, wind direction and numerical weather prediction, respectively. The fused features are used to predict the wind speed. Finally, the actual measurement data of a wind farm in northeast China is used to verify the effectiveness of the algorithm. © 2021 Automation of Electric Power Systems Press.

引用

页码：28 / 35

页数：7

共 26 条

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