摘要：

针对传统电力负荷预测方法存在精度较低、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)和形变长短期记忆(Mogrifier LSTM)网络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, 大幅降低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, 提高短期负荷预测精度。算例分析结果表明, 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, 与次优VMD-CNN-Mogrifier LSTM模型相比, 在两份所用真实数据集上的预测精度分别提升0.5和2.4百分点, 为短期电力负荷预测提供一种可行的解决思路。

关键词: 负荷预测, 麻雀搜索算法, 变分模态分解, 长短期记忆网络, 相关分析

Abstract:

To address the problems of low accuracy and large noise of load data in traditional power load forecasting methods, this study proposes 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). First, the Sparrow Search Algorithm(SSA) is used to optimize the VMD and obtain the decomposition subsequence with the best effect, which effectively reduces the influence of load data noise on the prediction accuracy. Second, the influence mechanism of each factor on load prediction is analyzed, the correlation between each influencing factor and load is derived using the Pearson's correlation coefficient, and redundant features are removed, which greatly reduces the probability of model inaccuracy. Finally, a CNN is employed 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. The feature data are analyzed in multiple dimensions in this model to improve the short-term load prediction accuracy. The results show that the multi-factor power load prediction model proposed in this study has good adaptability and prediction effects. Compared with the suboptimal VMD-CNN-Mogrifier LSTM model, the prediction accuracy of the proposed model on 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.

Key words: load forecasting, Sparrow Search Algorithm(SSA), variational mode decomposition, Long Short-Term Memory(LSTM) network, correlation analysis