Wind Power Group Prediction Model Based on Multi-Task Learning

被引:4
|
作者
Wang, Da [1 ]
Yang, Mao [1 ]
Zhang, Wei [1 ]
机构
[1] Northeast Elect Power Univ, Minist Educ, Key Lab Modern Power Syst Simulat & Control & Rene, Jilin 132012, Peoples R China
关键词
multi-task learning; wind farms; wind power cluster; MMoE; wind power prediction; NETWORK; SPEED;
D O I
10.3390/electronics12173683
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Large-scale wind power grid connection increases the uncertainty of the power system, which reduces the economy and security of power system operations. Wind power prediction technology provides the wind power sequence for a period of time in the future, which provides key technical support for the reasonable development of the power generation plan and the arrangement of spare capacity. For large-scale wind farm groups, we propose a cluster model of wind power prediction based on multi-task learning, which can directly output the power prediction results of multiple wind farms. Firstly, the spatial and temporal feature matrix is constructed based on the meteorological forecast data provided by eight wind farms, and the dimensionality of each attribute is reduced by the principal component analysis algorithm to form the spatial fusion feature set. Then, a network structure with bidirectional gated cycle units is constructed, and a multi-output network structure is designed based on the Multi-gate Mixture-of-Experts (MMoE) framework to design the wind power group prediction model. Finally, the data provided by eight wind farms in Jilin, China, was used for experimental analysis, and the predicted average normalized root mean square error is 0.1754, meaning the prediction precision meets the scheduling requirement, which verifies the validity of the wind power prediction model.
引用
收藏
页数:14
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