Journal of Computer Applications ›› 2023, Vol. 43 ›› Issue (11): 3616-3624.DOI: 10.11772/j.issn.1001-9081.2022111749
Special Issue: 前沿与综合应用
• Frontier and comprehensive applications • Previous Articles Next Articles
Bolu LI, Li WU, Xiaoying WANG(), Jianqiang HUANG, Tengfei CAO
Received:
2022-11-24
Revised:
2023-03-10
Accepted:
2023-03-17
Online:
2023-04-03
Published:
2023-11-10
Contact:
Xiaoying WANG
About author:
LI Bolu, born in 1997, M. S. candidate. His research interests include artificial intelligence, spatio-temporal meteorological prediction.Supported by:
通讯作者:
王晓英
作者简介:
李博录(1997—),男,甘肃天水人,硕士研究生,主要研究方向:人工智能、时空气象预测基金资助:
CLC Number:
Bolu LI, Li WU, Xiaoying WANG, Jianqiang HUANG, Tengfei CAO. Multi-site wind speed prediction based on graph dynamic attention network[J]. Journal of Computer Applications, 2023, 43(11): 3616-3624.
李博录, 吴利, 王晓英, 黄建强, 曹腾飞. 基于图动态注意力网络的多站点风速预测[J]. 《计算机应用》唯一官方网站, 2023, 43(11): 3616-3624.
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URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2022111749
网络模块 | 模块层数 | |||||
---|---|---|---|---|---|---|
1 | 2 | 3 | 4 | 5 | 6 | |
n_Decoder | 1.38 | 1.37 | 1.23 | 1.40 | 1.55 | 1.57 |
n_Encoder | 1.51 | 1.48 | 1.23 | 3.48 | 2.80 | 1.66 |
n_Layer | 1.46 | 1.30 | 1.23 | 1.42 | 1.34 | 1.70 |
网络模块 | 注意力头数 | |||||
1 | 2 | 4 | 8 | 16 | 32 | |
n_Head | 1.43 | 1.33 | 1.34 | 1.23 | 1.31 | 1.37 |
Tab. 1 RMSE under different conditions
网络模块 | 模块层数 | |||||
---|---|---|---|---|---|---|
1 | 2 | 3 | 4 | 5 | 6 | |
n_Decoder | 1.38 | 1.37 | 1.23 | 1.40 | 1.55 | 1.57 |
n_Encoder | 1.51 | 1.48 | 1.23 | 3.48 | 2.80 | 1.66 |
n_Layer | 1.46 | 1.30 | 1.23 | 1.42 | 1.34 | 1.70 |
网络模块 | 注意力头数 | |||||
1 | 2 | 4 | 8 | 16 | 32 | |
n_Head | 1.43 | 1.33 | 1.34 | 1.23 | 1.31 | 1.37 |
时长/h | 模型 | RMSE | MAE | 时长/h | 模型 | RMSE | MAE | 时长/h | 模型 | RMSE | MAE |
---|---|---|---|---|---|---|---|---|---|---|---|
1 | ConvLSTM | 1.73 | 1.36 | 6 | ConvLSTM | 2.18 | 1.71 | 12 | ConvLSTM | 2.97 | 2.31 |
STDN | 1.70 | 1.31 | STDN | 2.12 | 1.70 | STDN | 2.34 | 1.76 | |||
GMAN | 1.74 | 1.38 | GMAN | 2.08 | 1.57 | GMAN | 2.29 | 1.75 | |||
STGCN | 1.50 | 1.16 | STGCN | 2.13 | 1.70 | STGCN | 2.95 | 2.30 | |||
Xgboost | 1.36 | 1.04 | Xgboost | 1.93 | 1.48 | Xgboost | 2.19 | 1.72 | |||
DSAN | 1.28 | 0.95 | DSAN | 2.07 | 1.52 | DSAN | 2.49 | 1.97 | |||
Graph⁃DSAN | 1.23 | 0.89 | Graph⁃DSAN | 1.85 | 1.43 | Graph⁃DSAN | 2.13 | 1.71 |
Tab. 2 Comparison of multi-step wind speed prediction results of different models
时长/h | 模型 | RMSE | MAE | 时长/h | 模型 | RMSE | MAE | 时长/h | 模型 | RMSE | MAE |
---|---|---|---|---|---|---|---|---|---|---|---|
1 | ConvLSTM | 1.73 | 1.36 | 6 | ConvLSTM | 2.18 | 1.71 | 12 | ConvLSTM | 2.97 | 2.31 |
STDN | 1.70 | 1.31 | STDN | 2.12 | 1.70 | STDN | 2.34 | 1.76 | |||
GMAN | 1.74 | 1.38 | GMAN | 2.08 | 1.57 | GMAN | 2.29 | 1.75 | |||
STGCN | 1.50 | 1.16 | STGCN | 2.13 | 1.70 | STGCN | 2.95 | 2.30 | |||
Xgboost | 1.36 | 1.04 | Xgboost | 1.93 | 1.48 | Xgboost | 2.19 | 1.72 | |||
DSAN | 1.28 | 0.95 | DSAN | 2.07 | 1.52 | DSAN | 2.49 | 1.97 | |||
Graph⁃DSAN | 1.23 | 0.89 | Graph⁃DSAN | 1.85 | 1.43 | Graph⁃DSAN | 2.13 | 1.71 |
方法 | 预测时长为1 h | 预测时长为12 h | ||
---|---|---|---|---|
RMSE | MAE | RMSE | MAE | |
DSAN-NS | 1.31 | 0.98 | 2.42 | 1.92 |
DSAN-SS | 1.41 | 1.06 | 2.60 | 2.00 |
DSAN-NE | 1.48 | 1.10 | 2.45 | 1.97 |
DSAN-ND | 1.42 | 1.07 | 2.74 | 2.15 |
Graph⁃DSAN | 1.23 | 0.89 | 2.13 | 1.71 |
Tab. 3 Influence of different modules on model
方法 | 预测时长为1 h | 预测时长为12 h | ||
---|---|---|---|---|
RMSE | MAE | RMSE | MAE | |
DSAN-NS | 1.31 | 0.98 | 2.42 | 1.92 |
DSAN-SS | 1.41 | 1.06 | 2.60 | 2.00 |
DSAN-NE | 1.48 | 1.10 | 2.45 | 1.97 |
DSAN-ND | 1.42 | 1.07 | 2.74 | 2.15 |
Graph⁃DSAN | 1.23 | 0.89 | 2.13 | 1.71 |
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