scholarly journals Long-Short-Term Memory Network Based Hybrid Model for Short-Term Electrical Load Forecasting

Information ◽  
2018 ◽  
Vol 9 (7) ◽  
pp. 165 ◽  
Author(s):  
Liwen Xu ◽  
Chengdong Li ◽  
Xiuying Xie ◽  
Guiqing Zhang
Keyword(s):  
Hybrid Model ◽  
Short Term Memory ◽  
Load Forecasting ◽  
Electrical Load ◽  
Short Term ◽  
Term Memory ◽  
Memory Network ◽  
Electrical Load Forecasting ◽  
Long Short Term Memory

scholarly journals An Ultra-Short-Term Electrical Load Forecasting Method Based on Temperature-Factor-Weight and LSTM Model

Energies ◽  
2020 ◽  
Vol 13 (18) ◽  
pp. 4875
Author(s):  
Dengyong Zhang ◽  
Haixin Tong ◽  
Feng Li ◽  
Lingyun Xiang ◽  
Xiangling Ding
Keyword(s):  
Short Term Memory ◽  
Load Forecasting ◽  
Temperature Factor ◽  
Memory Model ◽  
Electrical Load ◽  
Short Term ◽  
Term Memory ◽  
Forecasting Method ◽  
Electrical Load Forecasting ◽  
Long Short Term Memory

Ultra-short-term electrical load forecasting is an important guarantee for the safety and efficiency of energy system operation. Temperature is also an important factor affecting the changes in electric load. However, in different cases, the impact of temperature on load forecasting will vary greatly, and sometimes even lead to the decrease of forecasting accuracy. This often brings great difficulties to researchers’ work. In order to make more scientific use of temperature factor for ultra-short-term electrical load forecasting, especially to avoid the negative influence of temperature on load forecasting, in this paper we propose an ultra-short-term electrical load forecasting method based on temperature factor weight and long short-term memory model. The proposed method evaluates the importance of the current prediction task’s temperature based on the change magnitude of the recent load and the correlation between temperature and load, and therefore the negative impacts of the temperature model can be avoided. The mean absolute percentage error of proposed method is decreased by 1.24%, 1.86%, and 6.21% compared with traditional long short-term memory model, back-propagation neural network, and gray model on average, respectively. The experimental results demonstrate that this method has obvious advantages in prediction accuracy and generalization ability.

scholarly journals Classification of Microblog Users’ Sentiments Based on BERT-BiLSTM-CBAM

CONVERTER ◽  
2021 ◽  
pp. 281-287
Author(s):  
Juan Chen, Ruyun Chen, Di Yu
Keyword(s):  
Hybrid Model ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Feature Vectors ◽  
Text Information ◽  
Memory Network ◽  
Long Short Term Memory ◽  
New Feature

This study proposes a method for more accurate classification of microblog users' sentiments based on the BERT-BiLSTM-CBAM hybrid model. First, the text information is pre-trained by the bidirectional encoder representation from transformers (BERT) model to get feature vectors. Then, the feature vectors are spliced and recombined using bidirectional long-short-term memory network (BiLSTM) and CBAM mechanism to obtain new feature vectors. Finally, these new feature vectors are input to the full connection layer and then processed by the softmax function to obtain the sentiment category of the text. The experiment conducted on the sample dataset demonstrates that the model proposed in this study yielded accurate and dependable result in classifying microblog texts in the sample dataset. The model based on BERT-BiLSTM-CBAM algorithm is more efficient than the traditional depth model in processing microblog contents

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