Title
Research on Gray Prediction of Heated Surface Combining Empirical Mode Decomposition and Long Short-term Memory Network
Abstract
Aiming at ash and slag of boiler heated surface will reduce the heat transfer efficiency and security. This paper adopts clearness factor as the indicator to monitor healthy condition of the boiler heated surface, and put forward a model combining empirical mode decomposition (EMD) and long short-term memory (LSTM) model to predict boiler ash accumulation in the future. EMD can decompose the time series into a series of frequency-stable intrinsic mode functions. In addition, the special gate structure inside LSTM makes it possible to mine the long-term dependencies in the time series. The combination of the two increases the prediction accuracy of the time series. It is verified by simulation software that the model has a satisfactory accuracy in predicting healthy condition of the boiler heated surface, and the feasibility and effectiveness of the model are verified.
Year
DOI
Venue
2020
10.1109/ICARCV50220.2020.9305412
2020 16th International Conference on Control, Automation, Robotics and Vision (ICARCV)
Keywords
DocType
ISSN
empirical mode decomposition,long short-term memory network,boiler heated surface,heat transfer efficiency,system security,healthy condition,time series,frequency-stable intrinsic mode functions,gray prediction,predict boiler ash accumulation,long-term dependencies,prediction accuracy,simulation software
Conference
2474-2953
ISBN
Citations 
PageRank 
978-1-7281-7710-6
0
0.34
References 
Authors
0
5
Name
Order
Citations
PageRank
Mengwei Li100.34
Yuanhao Shi200.34
Fangshu Cui300.34
Jie Wen400.34
Jian Chao Zeng513.73