scholarly journals Energy management optimisation using a combined Long Short-Term Memory recurrent neural network – Particle Swarm Optimisation model

2021 ◽  
pp. 129246
Author(s):  
Zhengnan Cao ◽  
Xiaoqing Han ◽  
William Lyons ◽  
Fergal O'Rourke
Keyword(s):  
Neural Network ◽  
Energy Management ◽  
Recurrent Neural Network ◽  
Short Term Memory ◽  
Particle Swarm ◽  
Particle Swarm Optimisation ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Optimisation Model

A new methodology for kick detection during petroleum drilling using long short-term memory recurrent neural network

2020 ◽  
Vol 142 ◽  
pp. 126-137 ◽  
Author(s):  
Augustine Osarogiagbon ◽  
Somadina Muojeke ◽  
Ramachandran Venkatesan ◽  
Faisal Khan ◽  
Paul Gillard
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Petroleum Drilling

scholarly journals The Daily Container Volumes Prediction of Storage Yard in Port with Long Short-Term Memory Recurrent Neural Network

2019 ◽  
Vol 2019 ◽  
pp. 1-11
Author(s):  
Yinping Gao ◽  
Daofang Chang ◽  
Ting Fang ◽  
Yiqun Fan
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Bp Neural Network ◽  
Short Term Memory ◽  
Arima Model ◽  
Training Set ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Box Plot

The effective forecast of container volumes can provide decision support for port scheduling and operating. In this work, by deep learning the historical dataset, the long short-term memory (LSTM) recurrent neural network (RNN) is used to predict daily volumes of containers which will enter the storage yard. The raw dataset of daily container volumes in a certain port is chosen as the training set and preprocessed with box plot. Then the LSTM model is established with Python and Tensorflow framework. The comparison between LSTM and other prediction methods like ARIMA model and BP neural network is also provided in this study, and the prediction gap of LSTM is lower than other methods. It is promising that the proposed LSTM is helpful to predict the daily volumes of containers.

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