Abstract
This paper presents a deep residual network for improving time-series forecasting models, indispensable to reliable and economical power grid operations, especially with high shares of renewable energy sources. Motivated by the potential performance degradation due to the overfitting of the prevailing stacked bidirectional long short-term memory (Bi-LSTM) layers associated with its linear stacking, we propose a concatenated residual learning by connecting the multi-level residual network (MRN) and DenseNet. This method further integrates long and short Bi-LSTM networks, ReLU, and SeLU for its activating function. Rigorous studies present superior prediction accuracy and parameter efficiency for the widely used temperature dataset as well as the actual wind power dataset. The peak value forecasting and generalization capability, along with the credible confidence range, demonstrate that the proposed model offers essential features of a time-series forecasting, enabling a general forecasting framework in grid operations. The source code of this paper can be found in https://github.com/MinseungKo/DRNet.git.
Highlights
T HE increasing concerns about sustainable environment and energy systems have led to the widespread growthManuscript received May 13, 2020; revised September 3, 2020 and November 4, 2020; accepted December 2, 2020
deep concatenated residual networks (DRNets) integrate the key concepts of DenseNet and multi-level residual network and further incorporate several new improvements, including activation functions and fused structure with short and long bidirectional long short-term memory (Bi-long short-term memory (LSTM))
This paper proposed the deep learning model for 1-h ahead wind power forecasting (WPF), where the basic layer is composed of Bi-LSTM layer
Summary
T HE increasing concerns about sustainable environment and energy systems have led to the widespread growth. ANN-based shallow models for WPF or wind speed forecasting (WSF) are proposed with higher accuracy than physical or conventional statistical methods [28], [29]. The LSTM based WSF models outperform the ANN or ARIMA based models as demonstrated in [17], [36] These previous studies mainly focused on obtaining the diverse information from various LSTM networks with few LSTM cells and the benefits from the deep learning were not fully exploited. DRNets integrate the key concepts of DenseNet and multi-level residual network and further incorporate several new improvements, including activation functions and fused structure with short and long Bi-LSTMs. The adequate constitution of RNN layers is firstly investigated when DRNets are employed.
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