Deep learning for quality prediction of nonlinear dynamic processes with variable attention‐based long short‐term memory network

2019 ◽  
Vol 98 (6) ◽  
pp. 1377-1389 ◽  
Author(s):  
Xiaofeng Yuan ◽  
Lin Li ◽  
Yalin Wang ◽  
Chunhua Yang ◽  
Weihua Gui
Keyword(s):  
Deep Learning ◽  
Nonlinear Dynamic ◽  
Short Term Memory ◽  
Dynamic Processes ◽  
Quality Prediction ◽  
Short Term ◽  
Term Memory ◽  
Memory Network ◽  
Long Short Term Memory ◽  
Nonlinear Dynamic Processes

scholarly journals Deep Learning Based on Multi-Decomposition for Short-Term Load Forecasting

Energies ◽  
2018 ◽  
Vol 11 (12) ◽  
pp. 3433 ◽  
Author(s):  
Seon Kim ◽  
Gyul Lee ◽  
Gu-Young Kwon ◽  
Do-In Kim ◽  
Yong-June Shin
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Load Forecasting ◽  
Small Scale ◽  
Intrinsic Mode Functions ◽  
Short Term ◽  
Term Memory ◽  
Load Profile ◽  
Memory Network ◽  
Long Short Term Memory

Load forecasting is a key issue for efficient real-time energy management in smart grids. To control the load using demand side management accurately, load forecasting should be predicted in the short term. With the advent of advanced measuring infrastructure, it is possible to measure energy consumption at sampling rates up to every 5 min and analyze the load profile of small-scale energy groups, such as individual buildings. This paper presents applications of deep learning using feature decomposition for improving the accuracy of load forecasting. The load profile is decomposed into a weekly load profile and then decomposed into intrinsic mode functions by variational mode decomposition to capture periodic features. Then, a long short-term memory network model is trained by three-dimensional input data with three-step regularization. Finally, the prediction results of all intrinsic mode functions are combined with advanced measuring infrastructure measured in the previous steps to determine an aggregated output for load forecasting. The results are validated by applications to real-world data from smart buildings, and the performance of the proposed approach is assessed by comparing the predicted results with those of conventional methods, nonlinear autoregressive networks with exogenous inputs, and long short-term memory network-based feature decomposition.

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