基于变分模态分解和时间卷积网络的发电侧碳排放预测模型

Carbon emission forecast model for generation side based on variational modal decomposition and time convolution network

中文关键词: 碳排放预测  时序性  变分模态分解  时间卷积网络  发电侧

英文关键词: carbon emission forecast  characteristic of temporal sequence  variational modal decomposition  temporal convolutional network  generation side

中文摘要:

      发电企业是碳排放的重要来源之一,精细化发电侧碳排放量预测对我国碳排放政策的制定具有积极意义。在此背景下,为了准确反映发电侧碳排放的变化趋势,针对发电侧碳排放存在不规律性、非线性和时序性的特征,提出一种基于变分模态分解（variational modal decomposition,VMD）和时间卷积网络（temporal convolutional network,TCN）的碳排放量预测模型。首先,利用VMD对碳排放量时序数据进行平稳化预处理,将原始碳排放数据拆分成若干个模态分量数据,降低数据序列的不规律性和非线性；其次,考虑到已有机器学习算法在网络训练过程中的性能退化问题,基于TCN对各模态分量分别进行预测,实现碳排放时序数据利用效率的最大化。最后,重构预测结果,获取最终的碳排放预测值。结果表明,相比于传统4种预测模型,方案通过创新性的将VMD模型与TCN相结合,有效提高了预测模型的效果和准确率。

英文摘要:

      Power generation enterprises are one of the important sources of carbon emission, and the refinement of carbon emission forecasts on the power generation side is of positive significance to the formulation of China’s carbon emission policy. In this context, a carbon emission forecast model based on variational modal decomposition（VMD）and temporal convolutional network（TCN）is proposed for the characteristics of irregularity, nonlinearity and temporal sequence of carbon emission on the power generation side. First, VMD is used to smooth the preprocessing of the carbon emission time series data, splitting the raw carbon emission data into several modal components to reduce irregularities and nonlinearities in the data series. Second, considering the performance degradation of existing machine learning algorithms during the network training process, each modal component is predicted separately based on TCN to maximize efficiency in the use of carbon emission time seriesdata. Finally, the forecast results are reconstructed to obtain the final forecast values of carbon emissions. The results show that compared with the traditional four forecast models, the method effectively improves the effectiveness and accuracy of the forecast model by innovatively combining VMD model and TCN.