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
摘要：近年来，随着碳达峰和碳中和“双碳”战略目标的提出，风力发电已成为可再生能源发电的关键部分。为提高风电功率短期预测的准确度，提出基于密度聚类与自适应噪声完备集成经验模态分解（complete ensemble empirical mode decomposition with adaptive noise, CEEMDAN）和卷积神经网络与长短期记忆网络结合的短期风电功率预测方法。首先，利用密度聚类将风电功率与天气特征分成不同类别的数据集，通过自适应噪声完备集成经验模态分解算法将不同类别的数据进行频域分解得到子序列分量。以此为基础，将不同的子序列分量与天气特征进行特征选择，输入到卷积神经网络与长短期记忆网络的预测模型。最后，将不同的预测结果进行叠加得到最终的预测结果。整个预测过程通过聚类、分解和特征选择，有效提高了短期风电功率预测的准确度。
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 accuracy of short-term wind power prediction, a short-term wind power prediction method was proposed based on density clustering and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and the combination of convolutional neural network and long short-term memory network (CNN-LSTM). The wind power and weather characteristics 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 subsequence components and weather characteristics were input into 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 outcomes. Through clustering, decomposition and feature selection techniques, the accuracy of short-term wind power prediction was effectively improved.
标题：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
英文标题：CEEMDAN-CNN-LSTM Short-term Wind Power Prediction Based on Density Clustering
