综合智慧能源2026，Vol.48Issue(1)：13-22,10.DOI:10.3969/j.issn.2097-0706.2026.01.002
基于CEEMDAN-DBO-VMD-TCN-BiGRU的短期风电功率预测
Short-term wind power prediction based on CEEMDAN-DBO-VMD-TCN-BiGRU
摘要
Abstract
Improving the accuracy of wind power prediction is crucial for ensuring safe and stable operation of the power grid.However,wind power exhibits high randomness and volatility,and traditional prediction methods have limitations in feature extraction and modeling capabilities.Therefore,CEEMDAN-DBO-VMD-TCN-BiGRU,a short-term wind power prediction model integrating complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN),dung beetle optimizer(DBO)algorithm,variational mode decomposition(VMD),temporal convolutional network(TCN),and bidirectional gated recurrent unit(BiGRU)was proposed.CEEMDAN was used to decompose the original wind power data,extracting intrinsic mode functions(IMFs)to capture key features of the time series.The IMFs were divided into high-frequency,medium-frequency,and low-frequency components using sample entropy and K-means clustering.The high-frequency components were selected for secondary decomposition using DBO-optimized VMD to improve feature extraction effectiveness and reduce computational complexity.All components were normalized and then input into the TCN-BiGRU combined model for prediction.The prediction results of each component were superimposed and denormalized to obtain the final prediction value.Experimental results showed that compared with benchmark models,the proposed model had the best prediction accuracy,verifying its effectiveness,stability,and application potential.关键词
风电功率预测/完全自适应噪声集合经验模态分解/蜣螂优化算法/变分模态分解/样本熵/K-means聚类/时间卷积网络/双向门控循环单元分类
能源科技引用本文复制引用
陈旭东,卞礼杰,马刚,陈浩,詹孝升,彭乐瑶..基于CEEMDAN-DBO-VMD-TCN-BiGRU的短期风电功率预测[J].综合智慧能源,2026,48(1):13-22,10.基金项目
江苏省碳达峰碳中和科技创新专项资金项目(BE2022003)Technological Innovation Special Fund Project for Carbon Peaking and Carbon Neutrality in Jiangsu Province(BE2022003) （BE2022003）
