Feedforward Error Learning Deep Neural Networks for Multivariate Deterministic Power Forecasting

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.

Automated summary

This article introduces a deep neural network framework for multivariate deterministic power forecasting in the context of high penetration of variable and uncertain renewable energy sources. The framework utilizes a 1-D convolutional neural network, WaveNet, EfficientNet, and error learning DNN to improve accuracy, efficiency, and model visibility.