Research on Network Traffic Prediction Based on Long Short-Term Memory Neural Network

2018 ◽  
Author(s):  
Haipeng Lu ◽  
Fan Yang
Keyword(s):  
Neural Network ◽  
Network Traffic ◽  
Short Term Memory ◽  
Traffic Prediction ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

A method for constructing a network traffic classifier based on deep learning

2021 ◽  
Author(s):  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Network Traffic ◽  
Short Term Memory ◽  
Short Term ◽  
Raw Data ◽  
Term Memory ◽  
High Classification Accuracy ◽  
Data Flows ◽  
Long Short Term Memory

An overview and analysis of methods for constructing of network traffic classifiers is given and the advantage of deep learning methods is shown. Based on a comparative analysis, the methods of deep learning with a teacher is selected. A method based on the use of a multilayer neural network of long short-term memory (LSTM) is considered. The structure of a deep network, at the input of which raw data flows fed, divided into sessions is created. Based on the selected classes of applications, it is experimentally proven that the developed neural network of long short-term memory, to the input of which raw data is supplied, allows to obtain a high classification accuracy. Keywords network traffic; deep learning; long short-term memory neural network; raw data

scholarly journals Electricity Consumption Forecasting Based on a Bidirectional Long-Short-Term Memory Artificial Neural Network

2020 ◽  
Vol 13 (1) ◽  
pp. 104
Author(s):  
Dana-Mihaela Petroșanu ◽  
Alexandru Pîrjan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Short Term Memory ◽  
Electricity Consumption ◽  
Short Term ◽  
Term Memory ◽  
Storage Room ◽  
Long Short Term Memory ◽  
Commercial Center ◽  
Business And Management

The accurate forecasting of the hourly month-ahead electricity consumption represents a very important aspect for non-household electricity consumers and system operators, and at the same time represents a key factor in what regards energy efficiency and achieving sustainable economic, business, and management operations. In this context, we have devised, developed, and validated within the paper an hourly month ahead electricity consumption forecasting method. This method is based on a bidirectional long-short-term memory (BiLSTM) artificial neural network (ANN) enhanced with a multiple simultaneously decreasing delays approach coupled with function fitting neural networks (FITNETs). The developed method targets the hourly month-ahead total electricity consumption at the level of a commercial center-type consumer and for the hourly month ahead consumption of its refrigerator storage room. The developed approach offers excellent forecasting results, highlighted by the validation stage’s results along with the registered performance metrics, namely 0.0495 for the root mean square error (RMSE) performance metric for the total hourly month-ahead electricity consumption and 0.0284 for the refrigerator storage room. We aimed for and managed to attain an hourly month-ahead consumed electricity prediction without experiencing a significant drop in the forecasting accuracy that usually tends to occur after the first two weeks, therefore achieving a reliable method that satisfies the contractor’s needs, being able to enhance his/her activity from the economic, business, and management perspectives. Even if the devised, developed, and validated forecasting solution for the hourly consumption targets a commercial center-type consumer, based on its accuracy, this solution can also represent a useful tool for other non-household electricity consumers due to its generalization capability.

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