基于多尺度特征增强DHTCN的电力系统短期负荷预测研究

为充分挖掘蕴含在电力负荷数据中的多尺度时序信息，提升短期电力负荷预测精度，提出了一种多尺度特征增强的改进时间卷积神经网络(improved temporal convolutional network with multi-scale feature enhancement, ECA-MS-DHTCN)模型。首先，使用4种不同尺寸卷积核的因果卷积提取负荷数据特征，并在特征提取层中嵌入高效通道注意力(efficient channel attention network, ECA)模块实现不降维的局部跨通道交互，得到带有通道注意力的多尺度负荷特征。然后，利用双混合扩张卷积层改进基本时间卷积神经网络(temporal convolutional network, TCN)残差块结构，克服TCN模型中扩张卷积结构存在的信息不连续及远距离信息不相关问题，兼顾负荷特征浅层细节及深层联系。最后，将ECA优化的多尺度特征提取层与改进TCN模型结合搭建ECA-MS-DHTCN负荷预测框架，完成短期负荷预测任务。经实际电网负荷数据仿真，结果表明所提出的ECA-MS-DHTCN模型可以在保持较快训练速度的同时有效地提高预测精度。

Short-term load forecasting of a power system based on multi-scale feature enhanced DHTCN

To fully mine the multi-scale time series information contained in power load data and improve short-term power load prediction accuracy, an improved temporal convolutional neural network with multi-scale feature enhancement (ECA-MS-DHTCN) model is proposed. First, the load data features are extracted using causal convolutions with four convolution kernels of different sizes, and an efficient channel attention (ECA) module is embedded in the feature extraction layer to achieve local cross-channel interactions without dimensional reduction. It obtains multi-scale loading features with channel attention. Then, the basic TCN residual block structure is improved using double-hybrid dilated convolutional layers to overcome the problems of information omission and long-distance information irrelevance in the dilated convolution structure of the TCN model. It also takes into account the shallow details and deep connections of the load characteristics. Finally, the ECA-MS-DHTCN load forecasting framework is built by combining the ECA-optimized multi-scale feature extraction module with the improved TCN model to complete the short-term load forecasting task. Through the simulation of actual power grid load data, the results show that the ECA-MS-DHTCN model proposed can effectively improve prediction accuracy while maintaining fast training.