摘要： 针对传统电力负荷预测方法存在精度不高、负荷数据噪声大等问题，提出一种基于改进变分模态分解（VMD）、卷积神经网络（CNN）和形变长短时记忆网络（Mogrifier LSTM）的多因素电力负荷预测方法。首先，运用麻雀搜索算法（SSA）对变分模态分解进行优化，得到最佳效果的分解子序列，有效减轻了负荷数据噪声对预测精度的影响；其次，分析各因素对负荷预测的影响机理，利用皮尔逊（Pearson）相关系数推导各影响因素与负荷之间的相关性，剔除冗余特征，大大降低了模型失准的发生概率；最后，采用CNN提取特征向量，将分解后的负荷数据及温度、湿度等特征数据输入到CNN-Mogrifier LSTM深度网络模型中，通过CNN-Mogrifier LSTM深度网络模型对特征数据进行多维分析，使短期负荷预测精度得到提高。算例分析结果表明，本文所提出的多因素电力负荷预测模型具有较好的适配性和预测效果。与文中次优VMD-CNN-Mogrifier LSTM模型相比，本文所提模型在两份所用真实数据集上的预测精度分别提升0.5和2.4个百分点，这为短期电力负荷预测提供了一种可行的解决思路。

Abstract: Aiming at the problems of low accuracy and large noise of load data in traditional power load forecasting methods, this paper proposed a multi-factor power load forecasting method based on improved variational modal decomposition (VMD), convolutional neural network (CNN) and deformed length short-term memory network (Mogrifier LSTM). Firstly, it utilized the Sparrow Search Algorithm (SSA) to optimize the variational mode decomposition, and obtained the decomposition subsequence with the best effect, which effectively reduces the influence of load data noise on the prediction accuracy. Secondly, it analyzed the influence mechanism of each factor on load prediction, derived the correlation between each influencing factor and load by using Pearson's correlation coefficient, and eliminated redundancy features, which greatly reduces the probability of model inaccuracy. Finally, it used CNN to extract feature vectors, The decomposed load data and feature data such as temperature and humidity are fed into the CNN-Mogrifier LSTM deep network model.and input the decomposed load data and feature data such as temperature and humidity into the CNN-Mogrifier LSTM deep network model, and analyzed the feature data in multiple dimensions through the CNN-Mogrifier LSTM deep network model, so that improved the short-term load prediction accuracy. The results show that the multi-factor power load prediction model proposed in this paper has good adaptability and prediction effect. Compared with the sub-optimal VMD-CNN-Mogrifier LSTM model, the prediction accuracy of the proposed model on the two real datasets is improved by 0.5 and 2.4 percentage points, respectively, which provides a feasible solution for short-term power load forecasting.