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Spatio-temporal graph cross-correlation auto-encoding network for wind power prediction
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AI
DOI：10.1007/s13042-022-01688-3.png)
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Abstract
En 中文
The rate of wind energy shows a sharp increase in recent years. Due to the instability, it is necessary to accurately predict the value of wind power over a period of time. Wind turbine cluster power prediction is an emerging form of wind power prediction in recent years, which can simultaneously predict the wind power value in a region. Recently, many researchers have utilized convolutional neural networks to process spatio-temporal representation constructed by multi-turbines' data. However, the research to date has tended to focus on local spatial scale rather than global spatio-temporal scale. The wind has instability, drastic changes, and wake effects, but local features cannot cover the affected area in the entire wind propagation process. To address this problem, this paper proposes a Spatio-temporal Graph Cross-correlation Auto-encoding Network (STGCAN) for wind power prediction, which can extract global wind power features and improve the problem that convolution and its derived methods only extract local features. The STGCAN performs hierarchical feature extraction on wind turbine clusters through the hierarchical structure of the shallow spatio-temporal feature layer, the feature cohesion layer, as well as the spatio-temporal graph cross-correlation auto-encoding layer altogether. It can extract both the local and global features of wind power. Experiments show that the mean square error of wind power prediction of the new approach is on average reduced by 20.87% over all the baseline methods.
Keywords:
Wind power prediction
Spatio-temporal graphs
Attention mechanism
Feature extraction
Deep learning
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