This article proposed a new electricity price interval forecasting method based on a novel Residual Neural Network (ResNet) for the electricity price interval forecasting. The significant outcome of the ResNet model was that the model performs excellently on normal and spike price interval forecasting in accuracy and reliability point of view. The proposed ResNet was consisting of two network layers. The first neural network layers were probabilistic normal, high, and spike prices prediction part. The Lower and Upper Bound Estimation (LUBE) formulates the price interval forecasting from the output of the second neural network layers. The LUBE methods included Quantile Regression and Mean and Variance estimation. The proposed forecasting models were demonstrated with the GEFCom2014 dataset. The dataset is consisting of 15 tasks for electricity prices forecasting. The results of proposed ResNet models compared with GEFCom2014's benchmarks, Quantile Regression Average, and Multilayer Perceptron Network approaches. The performances of forecasting models are evaluated in terms of accuracy and reliability metrics by Pinball Loss Function and Coverage Width‐based Criterion (CWC), respectively. Also, this article shows the effect of an increase in confidence level, which could generate lower CWC values and represent high reliability's satisfaction.

中文翻译：

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用残差神经网络预测电价区间

本文提出了一种基于新型残差神经网络（ResNet）的电价区间预测方法。ResNet模型的重要结果是，从准确性和可靠性的角度来看，该模型在正常价格和峰值价格区间的预测中均表现出色。建议的ResNet由两个网络层组成。第一个神经网络层是概率正常，高和峰值价格预测部分。上下限估计（LUBE）根据第二个神经网络层的输出来制定价格区间预测。LUBE方法包括分位数回归和均值和方差估计。GEFCom2014数据集演示了建议的预测模型。该数据集由15个电价预测任务组成。拟议的ResNet模型的结果与GEFCom2014的基准，分位数回归平均值和多层感知器网络方法进行了比较。分别通过弹球损失函数和基于覆盖宽度的标准（CWC）根据准确性和可靠性指标评估预测模型的性能。此外，本文还显示了置信度增加的影响，这可能会产生较低的CWC值并代表较高的可靠性。分别通过弹球损失函数和基于覆盖宽度的标准（CWC）根据准确性和可靠性指标评估预测模型的性能。此外，本文还显示了置信度增加的影响，这可能会产生较低的CWC值并代表较高的可靠性。分别通过弹球损失函数和基于覆盖宽度的标准（CWC）根据准确性和可靠性指标评估预测模型的性能。此外，本文还显示了置信度增加的影响，这可能会产生较低的CWC值并代表较高的可靠性。