scholarly journals Hourly Heat Load Prediction Model Based on Temporal Convolutional Neural Network

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 16726-16741 ◽  
Author(s):  
Jiancai Song ◽  
Guixiang Xue ◽  
Xuhua Pan ◽  
Yunpeng Ma ◽  
Han Li
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Convolutional Neural Network ◽  
Heat Load ◽  
Load Prediction ◽  
Model Based

scholarly journals Application of Improved Naive Bayesian-CNN Classification Algorithm in Sandstorm Prediction in Inner Mongolia

2019 ◽  
Vol 2019 ◽  
pp. 1-13
Author(s):  
Li Tiancheng ◽  
Ren Qing-dao-er-ji ◽  
Qiu Ying
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Convolutional Neural Network ◽  
Inner Mongolia ◽  
Prediction Method ◽  
Classification Algorithm ◽  
Bayesian Algorithm ◽  
Naive Bayesian ◽  
Model Based ◽  
Naïve Bayesian

Hazards of sandstorm are increasingly recognized and valued by the general public, scientific researchers, and even government decision-making bodies. This paper proposed an efficient sandstorm prediction method that considered both the effect of atmospheric movement and ground factors on sandstorm occurrence, called improved naive Bayesian-CNN classification algorithm (INB-CNN classification algorithm). Firstly, we established a sandstorm prediction model based on the convolutional neural network algorithm, which considered atmospheric movement factors. Convolutional neural network (CNN) is a deep neural network with convolution structure, which can automatically learn features from massive data. Then, we established a sandstorm prediction model based on the Naive Bayesian algorithm, which considered ground factors. Finally, we established a sandstorm prediction model based on the improved naive Bayesian-CNN classification algorithm. Experimental results showed that the prediction accuracy of the sandstorm prediction model based on INB-CNN classification algorithm is higher than that of others and the model can better reflect the law of sandstorm occurrence. This paper used two algorithms, naive Bayesian algorithm and CNN algorithm, to identify and diagnose the strength of sandstorm in Inner Mongolia and found that combining the two algorithms, INB-CNN classification algorithm had the greatest success in predicting the occurrence of sandstorms.

CVaR Prediction Model of the Investment Portfolio Based on the Convolutional Neural Network Facilitates the Risk Management of the Financial Market

2022 ◽  
Vol 30 (7) ◽  
pp. 0-0
Keyword(s):  
Neural Network ◽  
Risk Management ◽  
Prediction Model ◽  
Convolutional Neural Network ◽  
Financial Market ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
Investment Portfolio ◽  
Model Based ◽  
Swarm Algorithm

In summary, firstly, a method for establishing a portfolio model is proposed based on the risk management theory of the financial market. Then, a prediction model for CVaR is established based on the convolutional neural network, and the improved particle swarm algorithm is employed to solve the model. The actual data analysis is implemented to prove the feasibility of CVaR prediction model based on deep learning and particle swarm optimization algorithm in financial market risk management. The test results show that the investment portfolio CVaR prediction model based on the convolutional neural network can obtain the optimal solution in the 18th generation at the fastest after using the improved particle swarm algorithm, which is more effective than the traditional algorithm. The CVaR prediction model of the investment portfolio based on the convolutional neural network facilitates the risk management of the financial market.

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