scholarly journals TSN Switch Queue Length Prediction Based on an Improved LSTM Network

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Xin Wang ◽  
Zhijun Shang ◽  
Changqing Xia ◽  
Shijie Cui ◽  
Shuai Shao
Keyword(s):  
Feature Selection ◽  
Prediction Model ◽  
Influencing Factors ◽  
Network Topology ◽  
Queue Length ◽  
Prediction Accuracy ◽  
Network Throughput ◽  
Lstm Network ◽  
Dynamic Route ◽  
Route Scheduling

With the high-speed development of network technology, time-sensitive networks (TSNs) are experiencing a phase of significant traffic growth. At the same time, they have to ensure that highly critical time-sensitive information can be transmitted in a timely and accurate manner. In the future, TSNs will have to further improve network throughput to meet the increasing traffic demand based on the guaranteed transmission delay. Therefore, an efficient route scheduling scheme is necessary to achieve network load balance and improve network throughput. A time-sensitive software-defined network (TSSDN) can address the highly distributed industrial Internet network infrastructure, which cannot be accomplished by traditional industrial communication technologies, and it can achieve distributed intelligent dynamic route scheduling of the network through global network monitoring. The prerequisite for intelligent dynamic scheduling is that the queue length of future switches can be accurately predicted so that dynamic route planning for flow can be performed based on the prediction results. To address the queue length prediction problem, we propose a TSN switch queue length prediction model based on the TSSDN architecture. The prediction process has three steps: network topology dimension reduction, feature selection, and training prediction. The principal component analysis (PCA) algorithm is used to reduce the dimensionality of the network topology to eliminate unnecessary redundancy and overlap of relevant information. Feature selection requires comprehensive consideration of the influencing factors that affect the switch queue length, such as time and network topology. The training prediction is performed with the help of our enhanced long short-term memory (LSTM) network. The input-output structure of the network is changed based on the extracted features to improve the prediction accuracy, thus predicting the network congestion caused by bursty traffic. Finally, the results of the simulation demonstrate that our proposed TSN switch queue length prediction model based on the improved LSTM network algorithm doubles the prediction accuracy compared to the original model because it considers more influencing factors as features in the neural network for training and learning.

Outburst prediction and influencing factors analysis based on Boruta-Apriori and BO-SVM algorithms

2021 ◽  
pp. 1-18
Author(s):  
Zhang Zixian ◽  
Liu Xuning ◽  
Li Zhixiang ◽  
Hu Hongqiang
Keyword(s):  
Feature Selection ◽  
Prediction Model ◽  
Influencing Factors ◽  
Association Rules ◽  
Coal And Gas Outburst ◽  
Feature Subset ◽  
Gas Outburst ◽  
Proposed Model ◽  
Optimal Feature Subset ◽  
Feature Dimension

The influencing factors of coal and gas outburst are complex, now the accuracy and efficiency of outburst prediction and are not high, in order to obtain the effective features from influencing factors and realize the accurate and fast dynamic prediction of coal and gas outburst, this article proposes an outburst prediction model based on the coupling of feature selection and intelligent optimization classifier. Firstly, in view of the redundancy and irrelevance of the influencing factors of coal and gas outburst, we use Boruta feature selection method obtain the optimal feature subset from influencing factors of coal and gas outburst. Secondly, based on Apriori association rules mining method, the internal association relationship between coal and gas outburst influencing factors is mined, and the strong association rules existing in the influencing factors and samples that affect the classification of coal and gas outburst are extracted. Finally, svm is used to classify coal and gas outbursts based on the above obtained optimal feature subset and sample data, and Bayesian optimization algorithm is used to optimize the kernel parameters of svm, and the coal and gas outburst pattern recognition prediction model is established, which is compared with the existing coal and gas outbursts prediction model in literatures. Compared with the method of feature selection and association rules mining alone, the proposed model achieves the highest prediction accuracy of 93% when the feature dimension is 3, which is higher than that of Apriori association rules and Boruta feature selection, and the classification accuracy is significantly improved, However, the feature dimension decreased significantly; The results show that the proposed model is better than other prediction models, which further verifies the accuracy and applicability of the coupling prediction model, and has high stability and robustness.

scholarly journals Failure Prediction Model Using Iterative Feature Selection for Industrial Internet of Things

Symmetry ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 454 ◽  
Author(s):  
Jung-Hyok Kwon ◽  
Eui-Jik Kim
Keyword(s):  
Feature Selection ◽  
Internet Of Things ◽  
Prediction Model ◽  
Prediction Accuracy ◽  
Model Building ◽  
Failure Prediction ◽  
Support Vector ◽  
Industrial Internet Of Things ◽  
Industrial Internet ◽  
New Feature

This paper presents a failure prediction model using iterative feature selection, which aims to accurately predict the failure occurrences in industrial Internet of Things (IIoT) environments. In general, vast amounts of data are collected from various sensors in an IIoT environment, and they are analyzed to prevent failures by predicting their occurrence. However, the collected data may include data irrelevant to failures and thereby decrease the prediction accuracy. To address this problem, we propose a failure prediction model using iterative feature selection. To build the model, the relevancy between each feature (i.e., each sensor) and the failure was analyzed using the random forest algorithm, to obtain the importance of the features. Then, feature selection and model building were conducted iteratively. In each iteration, a new feature was selected considering the importance and added to the selected feature set. The failure prediction model was built for each iteration via the support vector machine (SVM). Finally, the failure prediction model having the highest prediction accuracy was selected. The experimental implementation was conducted using open-source R. The results showed that the proposed failure prediction model achieved high prediction accuracy.

scholarly journals Short-Term Forecasting of Photovoltaic Power Generation Based on Feature Selection and Bias Compensation–LSTM Network

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3086
Author(s):  
Cai Tao ◽  
Junjie Lu ◽  
Jianxun Lang ◽  
Xiaosheng Peng ◽  
Kai Cheng ◽  
...  
Keyword(s):  
Power Generation ◽  
Feature Selection ◽  
Hybrid Model ◽  
Prediction Error ◽  
Prediction Accuracy ◽  
Short Term Memory ◽  
Feature Selection Method ◽  
Short Term ◽  
Lstm Network ◽  
Bias Compensation

In this paper, a hybrid model that considers both accuracy and efficiency is proposed to predict photovoltaic (PV) power generation. To achieve this, improved forward feature selection is applied to obtain the optimal feature set, which aims to remove redundant information and obtain related features, resulting in a significant improvement in forecasting accuracy and efficiency. The prediction error is irregularly distributed. Thus, a bias compensation–long short-term memory (BC–LSTM) network is proposed to minimize the prediction error. The experimental results show that the new feature selection method can improve the prediction accuracy by 0.6% and the calculation efficiency by 20% compared to using feature importance identification based on LightGBM. The BC–LSTM network can improve accuracy by 0.3% using about twice the time compared with the LSTM network, and the hybrid model can further improve prediction accuracy and efficiency based on the BC–LSTM network.

scholarly journals An Improved Power System Transient Stability Prediction Model Based on mRMR Feature Selection and WTA Ensemble Learning

2020 ◽  
Vol 10 (7) ◽  
pp. 2255
Author(s):  
Jun Liu ◽  
Huiwen Sun ◽  
Yitong Li ◽  
Wanliang Fang ◽  
Shuanbao Niu
Keyword(s):  
Feature Selection ◽  
Power Systems ◽  
Power System ◽  
Prediction Model ◽  
Ensemble Learning ◽  
Prediction Accuracy ◽  
Transient Stability ◽  
Stability Assessment ◽  
Stability Prediction ◽  
Electromagnetic Power

Fast online transient stability assessment (TSA) is very important to maintain the stable operation of power systems. However, the existing transient stability assessment methods suffer the drawbacks of unsatisfactory prediction accuracy, difficult applicability, or a heavy computational burden. In light of this, an improved high accuracy power system transient stability prediction model is proposed, based on min-redundancy and max-relevance (mRMR) feature selection and winner take all (WTA) ensemble learning. Firstly, the contributions of four different series of raw sampled data from all of the three-time stages, namely the pre-fault, during-fault and post-fault, to transient stability are compared. The new feature of generator electromagnetic power is introduced and compared with three conventional types of input features, through a support vector machine (SVM) classifier. Furthermore, the two types of most contributive input features are obtained by the mRMR feature selection method. Finally, the prediction results of the electromagnetic power of generators and the voltage amplitude of buses are combined using the WTA ensemble learning method, and an improved transient stability prediction model with higher accuracy for unstable samples is obtained, whose overall prediction accuracy would not decrease either. The real-time data collected by wide area monitoring systems (WAMS) can be fed into this model for fast online transient stability prediction; the results can also provide a basis for the future emergency control decision-making of power systems.

scholarly journals Research on Railway Freight Volume Prediction Based on Neural Network

2020 ◽  
Vol 143 ◽  
pp. 01050 ◽  
Author(s):  
Can Yang ◽  
Xuemei Li
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Influencing Factors ◽  
Prediction Accuracy ◽  
Transportation Network ◽  
Market Demand ◽  
Investment Planning ◽  
Important Indicator ◽  
Railway Freight ◽  
Freight Volume

Railway freight volume is an important part of the total social freight volume and an important indicator of the national economy. Scientific prediction of railway freight volume can provide decision support for the formulation of China's railway policy and railway investment planning, and is of great significance for adjusting transportation structure and building an efficient transportation network. In order to improve the prediction accuracy, this paper constructs a combined prediction model based on GRA-GABP. The model uses grey correlation analysis to screen out the key influencing factors of railway freight volume, and optimizes the weight and threshold of BP neural network based on genetic algorithm to improve the prediction accuracy. This paper comprehensively considers the influencing factors of macroeconomics, market demand, logistics competition and railway supply. The historical data of railway freight transport from 1978 to 2018 is selected for case analysis. The results show that the prediction accuracy of the GRA-GA-BP based combination prediction model is significantly improved and can be used as an effective tool for railway freight volume forecasting.

scholarly journals Development of an Improved Model to Predict Building Thermal Energy Consumption by Utilizing Feature Selection

Energies ◽  
2019 ◽  
Vol 12 (21) ◽  
pp. 4187 ◽  
Author(s):  
Jihoon Jang ◽  
Joosang Lee ◽  
Eunjo Son ◽  
Kyungyong Park ◽  
Gahee Kim ◽  
...  
Keyword(s):  
Feature Selection ◽  
Energy Consumption ◽  
Prediction Model ◽  
Thermal Energy ◽  
Prediction Accuracy ◽  
Selection Process ◽  
Ghg Emissions ◽  
Efficient Operation ◽  
Artificial Neural Network Ann ◽  
Improved Model

Humans spend approximately 90% of the daytime in buildings, and greenhouse gases (GHGs) emitted by buildings account for approximately 20% of total GHG emissions. As the energy consumed during building operation from a building life-cycle perspective amounts to approximately 70–90% of the total energy, it is essential to accurately predict the energy consumption of buildings for their efficient operation. This study aims to optimize a model for predicting the thermal energy consumption of buildings by (i) first extracting major variables through feature selection and deriving significant variables in addition to the collected data and (ii) predicting the thermal energy consumption using a machine learning model. Feature selection using random forest was performed, and 11 out of 17 available data were selected. The accuracy of the prediction model was significantly improved when the hour of day variable was added. The prediction model was constructed using an artificial neural network (ANN), and the improvement in the prediction accuracy was analyzed by comparing different cases of variable combinations. The ANN prediction accuracy was improved by 15% using the feature selection process compared to when all data were used as input data, and 25% coefficient of variation of the root mean square error (CVRMSE) accuracy was achieved.

scholarly journals An Air Quality Prediction Model Based on a Noise Reduction Self-Coding Deep Network

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Jianxian Cai ◽  
Xun Dai ◽  
Li Hong ◽  
Zhitao Gao ◽  
Zhongchao Qiu
Keyword(s):  
Time Series ◽  
Air Quality ◽  
Prediction Model ◽  
Noise Reduction ◽  
Prediction Accuracy ◽  
Air Pollutant ◽  
Monitoring Data ◽  
Series Data ◽  
Deep Network ◽  
Lstm Network

Aiming at remedying the problem of low prediction accuracy of existing air pollutant prediction models, a denoising autoencoder deep network (DAEDN) model that is based on long short-term memory (LSTM) networks was designed. This model created a noise reduction autoencoder with an LSTM network to extract the inherent air quality characteristics of original monitoring data and to implement noise reduction processing on monitoring data to improve the accuracy of air quality predictions. The LSTM network structure in the DAEDN model was designed as bidirectional LSTM (Bi-LSTM) to solve the problem of a lag in the unidirectional LSTM prediction results and thereby to further improve the prediction accuracy of the prediction model. Using air pollutant time series data, the DAEDN model was trained using hourly PM2.5 concentration data collected in Beijing over 5 years. The experimental results show that the DAEDN model can extract more stable features from the noisy input after training was completed. The models were evaluated using RMSE and MAE, and the results show that the indexes are 15.504 and 6.789; compared with unidirectional LSTM, it is reduced by 7.33% and 5.87%, respectively. In addition, the new prediction model essentially considered the time series properties of the prediction of the concentration of spatial pollutants and the fully integrated environmental big data, such as air quality monitoring, meteorological monitoring, and forecasting.

scholarly journals A Carbon Price Prediction Model Based on the Secondary Decomposition Algorithm and Influencing Factors

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1328
Author(s):  
Jianguo Zhou ◽  
Shiguo Wang
Keyword(s):  
Prediction Model ◽  
Extreme Learning Machine ◽  
Influencing Factors ◽  
Search Algorithm ◽  
Carbon Price ◽  
Carbon Trading ◽  
Price Prediction ◽  
Model Based ◽  
Mode Decomposition ◽  
Learning Machine

Carbon emission reduction is now a global issue, and the prediction of carbon trading market prices is an important means of reducing emissions. This paper innovatively proposes a second decomposition carbon price prediction model based on the nuclear extreme learning machine optimized by the Sparrow search algorithm and considers the structural and nonstructural influencing factors in the model. Firstly, empirical mode decomposition (EMD) is used to decompose the carbon price data and variational mode decomposition (VMD) is used to decompose Intrinsic Mode Function 1 (IMF1), and the decomposition of carbon prices is used as part of the input of the prediction model. Then, a maximum correlation minimum redundancy algorithm (mRMR) is used to preprocess the structural and nonstructural factors as another part of the input of the prediction model. After the Sparrow search algorithm (SSA) optimizes the relevant parameters of Extreme Learning Machine with Kernel (KELM), the model is used for prediction. Finally, in the empirical study, this paper selects two typical carbon trading markets in China for analysis. In the Guangdong and Hubei markets, the EMD-VMD-SSA-KELM model is superior to other models. It shows that this model has good robustness and validity.

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