基于多元模态分解与多目标算法优化的深度集成学习模型的超短期风电功率预测
摘要：针对风电功率预测问题，提出了一种基于多元变分模态分解(multivariate variational mode decomposition, MVMD)、多目标纵横交叉优化(multi-objective crisscross optimization, MOCSO)算法和Blending集成学习的超短期风电功率预测。在数据处理阶段，为了保持各序列间的同步相关性以及分解后得到本征模态函数(intrinsic mode functions, IMF) 分量个数和分量频率相匹配，使用MVMD对多通道原始数据进行同步分解。针对单一机器学习模型导致预测的全面性不足，且存在精度和鲁棒性低的问题，提出基于MOCSO算法动态加权的Blending集成学习模型。通过对递归神经网络、卷积神经网络、长短期记忆网络的预测结果进行动态加权集成，并通过MOCSO优化调整权重，以提高模型的预测准确性与稳定性。实验结果表明，所提预测模型不仅有效，且显著优于其他预测模型。
Abstract:To address the issue of ultra-short-term wind power prediction, a novel prediction model is proposed based on multivariate variational mode decomposition (MVMD), multi-objective crisscross optimization (MOCSO) algorithm and blending ensemble learning. In the data processing stage, to maintain synchronization correlation and ensure matching of intrinsic mode fuctions (IMFs) number and frequency, the MVMD method is used to decompose the multi-channel original data synchronously. Considering the insufficient comprehensiveness, inaccuracy, and low robustness of the single machine learning model, a blending ensemble learning model is proposed to combine multiple deep learning networks using MOCSO dynamic weighting. The prediction results of RNN, CNN and LSTM are dynamically weighted, integrated, and then optimized by MOCSO to improve the prediction accuracy and stability. Experimental results show that the proposed model is not only effective, but also significantly superior to other prediction models.
标题：基于多元模态分解与多目标算法优化的深度集成学习模型的超短期风电功率预测
英文标题：Ultra-short-term Wind Power Prediction Based on Deep Ensemble Learning Model Using Multivariate Mode Decomposition and Multi-objective Optimization
作者：朱梓彬, 孟安波, 欧祖宏, 王陈恩, 张铮, 陈黍, 梁濡铎,
关键词：风电功率预测, 多元变分模态分解, 多目标纵横交叉优化, Blending集成学习,
发表日期：2024-06-10
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