.png){kind=icon}
返回
Spatio-temporal graph cross-correlation auto-encoding network for wind power prediction
.png){kind=icon}
PRE
AI
DOI：10.1007/s13042-022-01688-3.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
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.
Keyword:
Wind power prediction
Spatio-temporal graphs
Attention mechanism
Feature extraction
Deep learning
期刊
IF:
2.7
论文数:
3.2K
被引数:
5.6K
机构
引用论文
Spatio-Temporal Graph Deep Neural Network for Short-Term Wind Speed Forecasting用于短期风速预测的时空图深度神经网络
PREAI
分享收藏Augmented Convolutional Network for Wind Power Prediction: A New Recurrent Architecture Design With Spatial-Temporal Image Inputs用于风电功率预测的增强卷积网络: 具有时空图像输入的新型循环体系结构设计
PREAI
分享收藏A novel spatio-temporal wind power forecasting framework based on multi-output support vector machine and optimization strategy基于多输出支持向量机和优化策略的风电功率时空预测框架
PREAI
分享收藏Probabilistic wind power forecasting using selective ensemble of finite mixture Gaussian process regression models基于有限混合高斯过程回归模型选择性集成的风电功率概率预测.png)
RENEWABLE ENERGY
RENEWABLE ENERGYIF9.1
PREAI
分享收藏
Completion of wind turbine data sets for wind integration studies applying random forests and k-nearest neighborsAPPLIED ENERGY
APPLIED ENERGYIF11
PREAI
分享收藏
查看更多内容