发电技术2024，Vol.45Issue(2)：323-330,8.DOI:10.12096/j.2096-4528.pgt.22038
一种改进组合神经网络的超短期风速预测方法研究
Research on an Ultra-Short-Term Wind Speed Prediction Method Based on Improved Combined Neural Networks
摘要
Abstract
Ultra-short-term wind speed prediction is the key to ensure the implementation effect of wind turbine pitch angle feedforward control,and has an important impact on improving the environmental adaptability of wind turbines.In order to improve the prediction accuracy,an ultra-short-term wind speed prediction method based on an improved combined neural networks was proposed.In this method,BP neural network and long short-term memory(LSTM)neural network,which are suitable for time series prediction and have strong nonlinear learning ability,are selected for weighted combination to eliminate the large error that may exist in a single neural network.At the same time,to improve the combination effect,the differential evolution(DE)algorithm was used to optimize the combination weight.The method was applied to the ultra-short-term wind speed prediction of a wind farm.Compared with the results of single neural network prediction and equal weight combined neural networks prediction,the effectiveness of the proposed method in improving the prediction accuracy was verified.关键词
风力发电/超短期风速预测/BP神经网络/长短期记忆(LSTM)神经网络/差分进化(DE)算法Key words
wind power/ultra-short-term wind speed prediction/BP neural network/long short-term memory(LSTM)neural network/differential evolution(DE)algorithm分类
能源科技引用本文复制引用
邵宜祥,刘剑,胡丽萍,过亮,方渊,李睿..一种改进组合神经网络的超短期风速预测方法研究[J].发电技术,2024,45(2):323-330,8.基金项目
国家电网公司科技项目(524608140152). Project Supported by Science and Technology Project of SGCC(524608140152). （524608140152）
