现代电力2025，Vol.42Issue(5)：928-936,9.DOI:10.19725/j.cnki.1007-2322.2023.0246
基于时空混合注意力机制的超短时风电功率预测方法
An Ultra-short Term Wind Power Prediction Method Based on Spatio-temporal Hybrid Attention Mechanism
摘要
Abstract
In response to the challenges posed by poor forecasting accuracy of wind power and inadequate meteorological sensors in certain wind farms,a wind power prediction model algorithm independent of meteorological data is proposed.Firstly,the sliding window based method is utilized to split the data and construct the time sequence data.The one-dimensional convolution network and long short-term memory network(LSTM)are subsequently employed to extract spatial and time dimension features from time series data with long term dependence.Finally,a spatio-temporal hybrid attention mechanism is employed to fuse features and predict power.Through the case analysis of three-year data from a wind farm in eastern China,the practicability of the above prediction model is proved and the results are more accurate than those of convolutional neural network-long short-term memory(CNN-LSTM)model,CNN model and LSTM model.Both validity and practicability of the proposed prediction method are proved,providing substantial support for reliability analysis of power prediction in actual scenarios.关键词
卷积神经网络/风电功率/功率预测/长短期记忆网络/时间序列Key words
deep convolutional neural network/wind power/power prediction/long short-term memory network/time series分类
信息技术与安全科学引用本文复制引用
黄贤明,郝雨辰,霍雪松,柴赟,彭程..基于时空混合注意力机制的超短时风电功率预测方法[J].现代电力,2025,42(5):928-936,9.基金项目
国网江苏省电力有限公司科技项目(J2021065).Project Supported by the Science and Technology Project of State Grid Jiangsu Electric Power Co.,Ltd.(J2021065). （J2021065）
