现代电力2024，Vol.41Issue(4)：631-641,11.DOI:10.19725/j.cnki.1007-2322.2022.0337
基于密度聚类模态分解的卷积神经网络和长短期记忆网络短期风电功率预测
CEEMDAN-CNN-LSTM Short-term Wind Power Prediction Based on Density Clustering
崔明勇 1董文韬 1卢志刚1
作者信息
- 1. 电力电子节能与传动控制河北省重点实验室(燕山大学),河北省秦皇岛市 066004
- 折叠
摘要
Abstract
In recent years,with the introduction of"double carbon"strategic goal of achieving carbon peak and carbon neutralization,wind power generation has pivotal component of renewable energy power generation.Aiming to improve the ac-curacy of short-term wind power prediction,a short-term wind power prediction method was proposed based on density clus-tering and complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)and the combination of con-volutional neural network and long short-term memory net-work(CNN-LSTM).The wind power and weather characterist-ics were initially classified into different categories of data sets using density clustering.Through the adaptive noise complete integration empirical mode decomposition algorithm,various types of data were decomposed in frequency domain to obtain subsequence components.On this basis,the selected sub-sequence components and weather characteristics were input in-to the prediction model of convolutional neural network and long-term and short-term memory network.Finally,different prediction results were superimposed to obtain the final out-comes.Through clustering,decomposition and feature selec-tion techniques,the accuracy of short-term wind power predic-tion was effectively improved.关键词
风电功率预测/密度聚类/自适应噪声完备集成经验模态分解/卷积神经网络/长短期记忆网络Key words
wind power prediction/density clustering/complete ensemble empirical mode decomposition with adapt-ive noise/convolutional neural network/long short-term memory network分类
信息技术与安全科学引用本文复制引用
崔明勇,董文韬,卢志刚..基于密度聚类模态分解的卷积神经网络和长短期记忆网络短期风电功率预测[J].现代电力,2024,41(4):631-641,11.
