电气传动2025，Vol.55Issue(10)：64-72,9.DOI:10.19457/j.1001-2095.dqcd25954
结合数据清洗及并联时空神经网络的风电机组功率预测
A Wind Turbine Power Prediction Method Combining Data Cleaning and Parallel Spatio-temporal Neural Network
高革命 1叶漫红 1刘亚楠 1徐鸿琪 2陈凡2
作者信息
- 1. 中国电建集团江西省电力设计院有限公司,江西 南昌 330096
- 2. 南京工程学院电力工程学院,江苏 南京 211167
- 折叠
摘要
Abstract
Aiming at the problems of difficult identification of stacked outliers and insufficient extraction of raw data features in the data-driven ultra-short-term power prediction of wind turbines,a prediction method combining data cleaning and parallel spatio-temporal neural network was proposed.First,a combined data cleaning method was proposed to clean the wind turbine power data;then,a parallel spatio-temporal neural network was proposed to extract the temporal features of the power and meteorological data of the target wind turbine,and the spatial features of the power data of similar wind turbines for the fusion prediction,respectively.In addition,a prediction interval accuracy indicator was defined to reflect the accuracy of prediction results under different error intervals,avoiding the drawback of traditional error indicators that obscure large local prediction errors.The analysis results indicate that the proposed method can effectively identify anomalous data and improve the ultra-short-term power prediction accuracy of wind turbines.关键词
风电机组功率/深度学习/数据清洗/特征融合/评价指标Key words
wind turbine power/deep learning/data cleaning/feature fusion/evaluation index分类
信息技术与安全科学引用本文复制引用
高革命,叶漫红,刘亚楠,徐鸿琪,陈凡..结合数据清洗及并联时空神经网络的风电机组功率预测[J].电气传动,2025,55(10):64-72,9.
