Vol.41, No.3, 2022, pp.1191-1205, doi:10.32604/csse.2022.021239
System Dynamics Forecasting on Taiwan Power Supply Chain
  • Zhiqiu Yu1,*, Shuo-Yan Chou1, Phan Nguyen Ky Phuc2, Tiffany Hui-Kuang Yu3
1 Department of Industrial Management, National Taiwan University of Science and Technology, Taipei, 10607, Taiwan
2 Industrial Engineering and Management Department, International University, Ho Chi Minh City, 70000, Vietnam
3 Department of Public Finance, Feng Chia University, Taichung, Taiwan
* Corresponding Author: Zhiqiu Yu. Email:
(This article belongs to this Special Issue: Impact of Industry 4.0 on Supply Chain Management and Optimization)
Received 28 June 2021; Accepted 12 August 2021; Issue published 10 November 2021
This research aims to study the sustainability of Taiwan power supply chain based on system dynamics forecasting. The paper tries to investigate electricity shortage effects not only on the industrial side, but also from the standpoint of society. In our model, different forecasting methods such as linear regression, time series analysis, and gray forecasting are also considered to predict the parameters. Further tests such as the structure, dimension, historical fit, and sensitivity of the model are also conducted in this paper. Through analysis forecasting result, we believe that the demand for electricity in Taiwan will continue to increase to a certain level for a period of time in the future. This phenomenon is closely related to Taiwan’s economic development, especially industrial development. We also point out that electricity prices in Taiwan do not match with high industrial demand, and that prices are still slightly low. Finally, the future growth trend of Taiwan’s electricity demand has not changed, and ensuring adequate supply to meet electricity demand to prevent potential power shortages will pose some difficulty.
System dynamics; taiwan power supply chain; electricity demand
Cite This Article
Yu, Z., Chou, S., Nguyen, P., Yu, T. H. (2022). System Dynamics Forecasting on Taiwan Power Supply Chain. Computer Systems Science and Engineering, 41(3), 1191–1205.
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