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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用于多变量确定性功率预测的前馈误差学习深度神经网络

本文在可变和不确定的可再生能源的高渗透率背景下，提出了一种用于多变量确定性功率预测的深度神经网络 (DNN) 框架。深度学习模型是基于一维卷积神经网络组织的，以减轻基于循环神经网络的模型的典型计算负担，并结合 WaveNet 和 EfficientNet 来提高预测精度。由于所有模型在流行的集成方法中执行相同任务的效率低下，我们还设计了一个前馈误差学习 DNN，它分别计算基本模型的误差。我们进一步结合了用于特征选择的嵌入式和过滤方法，以增强模型的可见性和框架的实用性。