Journal of System Simulation
Abstract
Abstract: Aiming at the traditional energy consumption modeling methods of discrete manufacturing system being difficult to adapt to the complexity and variability of working conditions, an online dynamic energy consumption modeling method based on real-time data is proposed. The energy consumption affecting factors are determined by analyzing the operation mechanism of the discrete manufacturing system and equipment. An online sequential extreme learning machine algorithm that can dynamically adjust the number of hidden layer nodes is proposed to construct the energy consumption model. The real-time data can update the model quickly. Bernstein's inequality is introduced to improve the model data screening ability. The simulation experiment and the comparison show that the method has better regression accuracy, smaller prediction error and shorter modeling time, and can be applied to the dynamic modeling and online prediction scenarios of energy consumption of discrete manufacturing systems.
Recommended Citation
Chen, Wei; Wang, Yan; and Ji, Zhicheng
(2023)
"Dynamics Modeling and Online Prediction of Energy Consumption of Discrete Manufacturing System,"
Journal of System Simulation: Vol. 35:
Iss.
4, Article 7.
DOI: 10.16182/j.issn1004731x.joss.21-1304
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol35/iss4/7
First Page
760
Revised Date
2022-02-27
DOI Link
https://doi.org/10.16182/j.issn1004731x.joss.21-1304
Last Page
772
CLC
TP391.9
Recommended Citation
Wei Chen, Yan Wang, Zhicheng Ji. Dynamics Modeling and Online Prediction of Energy Consumption of Discrete Manufacturing System[J]. Journal of System Simulation, 2023, 35(4): 760-772.
DOI
10.16182/j.issn1004731x.joss.21-1304
Included in
Artificial Intelligence and Robotics Commons, Computer Engineering Commons, Numerical Analysis and Scientific Computing Commons, Operations Research, Systems Engineering and Industrial Engineering Commons, Systems Science Commons