This article proposes a deep neural network (DNN) framework for multivariate deterministic power forecasting in the context of the high penetration of variable and uncertain renewable energy sources. The deep learning model is organized based on the 1-D convolutional neural network to lessen the computational burden, typical of recurrent neural network based models, and combines WaveNet and EfficientNet to improve the forecasting accuracy. Motivated by the inefficiency that all the models conduct the same tasks in the popular ensemble approach, we also designed a feedforward error learning DNN, which computes the error of the basic model separately. We further incorporated embedded and filter methods for feature selection to enhance the model visibility and the utility of the framework. Comprehensive studies on the public load and PV datasets demonstrate that the proposed framework outperforms the conventional methods in applicability, computational efficiency, and forecasting accuracy.

中文翻译：

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用于多元确定性功率预测的前馈误差学习深度神经网络


本文提出了一种在可变和不确定的可再生能源高渗透率的背景下进行多元确定性电力预测的深度神经网络（DNN）框架。深度学习模型基于一维卷积神经网络进行组织，以减轻典型的基于循环神经网络的模型的计算负担，并结合 WaveNet 和 EfficientNet 以提高预测精度。由于流行的集成方法中所有模型执行相同任务的效率低下，我们还设计了前馈误差学习 DNN，它分别计算基本模型的误差。我们进一步结合了嵌入和过滤方法进行特征选择，以增强模型的可见性和框架的实用性。对公共负荷和光伏数据集的综合研究表明，所提出的框架在适用性、计算效率和预测精度方面优于传统方法。