Dec 7, 2022 · A model for short-term power load forecasting of residual multiscale-RNN (RM-RNN) was proposed in this study.
Dec 7, 2022 · A model for short-term power load forecasting of residual multiscale-RNN (RM-RNN) was proposed in this study.
In this paper, we propose a neural network framework based on a modified deep residual network (DRN) and a long short-term memory (LSTM) recurrent neural ...
We present in this paper a model for forecasting short-term power loads based on deep residual networks. The proposed model is able to integrate domain ...
Oct 16, 2023 · To improve the accuracy of short-term load forecasting, this paper proposes a novel multi-scale ensemble method and multi-scale ensemble neural ...
May 30, 2018 · Abstract—We present in this paper a model for forecasting short-term electric load based on deep residual networks. The proposed model is able ...
Missing: recurrent | Show results with:recurrent
Apr 15, 2024 · The present study proposes a residual network (ResNet) - long short-term memory (LSTM) with an attention neural network model founded on a dual signal ...
Recurrent neural networks (RNNs) are the most popular deep learning models for short-term load forecasting.
Short-term load forecasting is an important prerequisite for smart grid controls. The current methods are mainly based on the convolution neural network ...
Missing: recurrent | Show results with:recurrent
Jul 9, 2024 · This paper presents a straight forward application of Layer Recurrent Neural Network (LRNN) to predict the load of a large distribution network.