摘要
作为一种清洁的可再生能源,风能在缓解日益严重的能源危机方面充当着重要作用。然而,风速的波动性和随机性给电力系统的稳定运行带来了严峻的挑战。针对该问题,提出一种基于自适应噪声完备集合经验模态分解(complete ensemble empirical mode decomposition with adaptive noise,CEEMDAN)与霜冰优化算法(rime optimization algorithm,RIME)-卷积神经网络(convolutional neural network,CNN)-双向长短期记忆网络(bidirectional long short-term memory network,BiLSTM)-注意力机制(attention mechanism,AM)的短期风速预测组合模型CEEMDAN-RIME-CNN-BiLSTM-AM。首先,对初始风速序列采用CEEMDAN算法,得到一系列较平稳的子模态,以降低风速序列的波动性;然后,采用RIME霜冰优化算法优化CNN超参数,建立CNN-RIME模型,对风速数据进行自适应提取和挖掘;接着,采用BiLSTM-AM模型对处理后的数据进行预测;最后,将各子序列的预测结果叠加,得到最终预测结果。以某地实际风速数据集进行对比试验,该模型在单步与多步预测中均展现出良好的预测性能,可以为制定调度计划提供参考,以最大程度地提高能源利用率和供电。
Serving as a clean and renewable energy source,wind energy plays a significant role in mitigating the increasingly severe energy crisis.However,the fluctuation and randomness of wind speed pose severe challenges to the stable operation of power systems.To address this issue,a combined short-term wind speed forecasting model named CEEMDAN-RIME-CNN-BiLSTM-AM was proposed,which was based on complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN),rime optimization algorithm(RIME),convolutional neural network(CNN),bidirectional long short-term memory network(BiLSTM),and attention mechanism(AM).Initially,the CEEMDAN algorithm was applied to the original wind speed series to obtain a series of relatively stable submodes,thereby reducing the volatility of the wind speed series.Subsequently,the CNN hyperparameters were optimized using the RIME algorithm to establish the CNN-RIME model for adaptive extraction and mining of wind speed data.Then,the BiLSTM-AM model was employed to forecast the processed data.Finally,the forecasting results of each sub-series were superimposed to obtain the final forecasting result.A comparative experiment was conducted using an actual wind speed dataset from a certain location.The proposed model demonstrates good forecasting performance in both single-step and multi-step forecasting,providing a reference for scheduling plans to maximize energy utilization and power supply.
作者
朱婷
颜七笙
ZHU Ting;YAN Qi-sheng(College of Economic and Management,East China University of Technology,Nanchang 330000,China;College of Science,East China University of Technology,Nanchang 330013,China)
出处
《科学技术与工程》
北大核心
2025年第20期8514-8525,共12页
Science Technology and Engineering
基金
国家自然科学基金(71961001)。
作者简介
第一作者:朱婷(2001-),女,汉族,安徽阜阳人,硕士研究生。研究方向:智能计算及应用。E-mail:1668986880@qq.com;通信作者:颜七笙(1976-),男,汉族,江西临川人,博士,教授。研究方向:智能计算及应用。E-mail:yanqs93@126.com。
