2022年 02期

基于深度时间卷积神经网络的风电功率预测

Wind Power Forecasting Based on Deep Temporal Convolutional Networks


摘要(Abstract):

为了提高风力发电预测的准确性,依据某近海地区风电场出力数据,提出基于深度时间卷积网络的风电功率组合预测模型;利用自适应集成经验模态分解对风电功率序列进行特征提取,得到若干本征模态分量,通过排列熵相关理论计算各模态分量的复杂度,根据复杂度进行序列重构,并输入至改进余弦退火算法优化的深度时间卷积网络中进行风电功率分析与预测。结果表明,该模型与其他模型相比具有较好的预测效果,能够有效提高超短期风电功率预测精度。

关键词(KeyWords): 风电功率预测;深度时间卷积网络;自适应集成经验模态分解;排列熵;改进余弦退火

基金项目(Foundation): 国家自然科学基金项目(12072205);; 天津市科技计划项目(19YFZGQY0040);; 山东省自然科学基金项目(ZR202102240076);; 石家庄市科技计划项目(209060561A)

作者(Author): 刘晗,王硕禾,张嘉姗,常宇健,张国驹

DOI: 10.13349/j.cnki.jdxbn.20211207.001

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