Since their inception, convolutional neural networks (CNNs) have been shown to have powerful feature extraction and learning capabilities, and the creation of deep residual networks (DRNs) was a milestone in the development of CNNs. However, residual networks mostly use convolution structures, which are widely applied to image recognition and classification problems. Therefore, when facing a load forecasting problem that involves nonlinear regression, will a DRN using a convolution structure still achieve great results? To answer this question, we present a network based on a DRN with a convolution structure to carry out short-term load forecasting, and we mainly focus on the effects of DRNs with different depths, widths and block structures for dealing with nonlinear regression problems. Through multiple sets of controlled experiments, we obtain the best network architecture and the corresponding hyperparameters for short-term load forecasting. The experimental results demonstrate that the model has higher prediction accuracy than existing models, and the DRN with a convolution structure can handle load forecasting while still achieving state-of-the-art results.

自成立以来，卷积神经网络（CNN）已被证明具有强大的特征提取和学习能力，而深度残差网络（DRN）的创建是CNN发展的一个里程碑。但是，残差网络大多使用卷积结构，广泛应用于图像识别和分类问题。因此，当面对涉及非线性回归的负荷预测问题时，使用卷积结构的DRN是否还会取得很好的结果？为了回答这个问题，我们提出了一种基于DRN的具有卷积结构的网络来进行短期负荷预测，我们主要关注具有不同深度，宽度和块结构的DRN在处理非线性回归问题上的效果。通过多套受控实验，我们获得了用于短期负载预测的最佳网络体系结构和相应的超参数。实验结果表明，该模型比现有模型具有更高的预测精度，并且具有卷积结构的DRN可以处理负载预测，同时仍能获得最新的结果。