Hybrid wind speed forecasting model based on multivariate data secondary decomposition approach and deep learning algorithm with attention mechanism

2021 ◽  
Author(s):  
Shuai Zhang ◽  
Yong Chen ◽  
Jiuhong Xiao ◽  
Wenyu Zhang ◽  
Ruijun Feng
Keyword(s):  
Deep Learning ◽  
Wind Speed ◽  
Learning Algorithm ◽  
Multivariate Data ◽  
Attention Mechanism ◽  
Forecasting Model ◽  
Decomposition Approach ◽  
Wind Speed Forecasting ◽  
Model Based ◽  
Deep Learning Algorithm

scholarly journals Ultra‐short‐term irradiance forecasting model based on ground‐based cloud image and deep learning algorithm

2021 ◽  
Author(s):  
Zhao Zhen ◽  
Xuemin Zhang ◽  
Shengwei Mei ◽  
Xiqiang Chang ◽  
Hua Chai ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Forecasting Model ◽  
Short Term ◽  
Model Based ◽  
Deep Learning Algorithm

scholarly journals Application of Model-Based Deep Learning Algorithm in Fault Diagnosis of Coal Mills

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Yifan Jian ◽  
Xianguo Qing ◽  
Yang Zhao ◽  
Liang He ◽  
Xiao Qi
Keyword(s):  
Deep Learning ◽  
Fault Diagnosis ◽  
Learning Algorithm ◽  
Mechanism Model ◽  
Model Based ◽  
Deep Learning Algorithm ◽  
Stacked Autoencoders ◽  
Different Types ◽  
Operation Status ◽  
Types Of Faults

The coal mill is one of the important auxiliary engines in the coal-fired power station. Its operation status is directly related to the safe and steady operation of the units. In this paper, a model-based deep learning algorithm for fault diagnosis is proposed to effectively detect the operation state of coal mills. Based on the system mechanism model of coal mills, massive fault data are obtained by analyzing and simulating the different types of faults. Then, stacked autoencoders (SAEs) are established by combining the said data with the deep learning algorithm. The SAE model is trained by the fault data, which provide it with the learning and identification capability of the characteristics of faults. According to the simulation results, the accuracy of fault diagnosis of coal mills based on SAE is high at 98.97%. Finally, the proposed SAEs can well detect the fault in coal mills and generate the warnings in advance.

Sign in / Sign up

Export Citation Format

Share Document