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

1.Department of Electrical Engineering, Tsinghua University, Beijing 100084, China;2.State Key Laboratory of Power System and Generation Equipment, Tsinghua University, Beijing 100084, China;3.Department of Electronic Engineering, Tsinghua University, Beijing 100084, China

Abstract:

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.

Keywords:

Foundation:

This work is supported by National Key R&D Program of China (No. 2018YFB0904200) and State Grid Corporation of China (No. SGLNDKOOKJJS1800266).

Get Citation
[1]FAN Hang, ZHANG Xuemin, MEI Shengwei, et al. Ultra-short-term Wind Speed Prediction Model for Wind Farms Based on Spatiotemporal Neural Network[J]. Automation of Electric Power Systems,2021,45(1):28-35. DOI:10.7500/AEPS20190831001
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History
  • Received:August 31,2019
  • Revised:November 16,2019
  • Adopted:
  • Online: January 05,2021
  • Published: