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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. [ABSTRACT FROM AUTHOR]

Fuzzy time-series models have been widely applied due to their ability to handle nonlinear data directly and because no rigid assumptions for the data are needed. In addition, many such models have been shown to provide better forecasting results than their conventional counterparts. However, since most of these models require complicated matrix computations, this paper proposes the adoption of a multivariate heuristic function that can be integrated with univariate fuzzy time-series models into multivariate models. Such a multivariate heuristic function can easily be extended and integrated with various univariate models. Furthermore, the integrated model can handle multiple variables to improve forecasting results and, at the same time, avoid complicated computations due to the inclusion of multiple variables. [ABSTRACT FROM AUTHOR]

Forecasting has been considered important in a service industry. Many techniques have been applied to improve forecasting results. This study intends to apply a neural network based fuzzy time series model to forecast the international tourist numbers arriving in Taiwan. Neural network is good at handling nonlinear data. On the other hand, the fuzzy time series models have been applied to time series problems in various domains and have been shown to outperform some conventional models. The tourist numbers arriving in Taiwan show a nonlinear characteristic with a structural break. And the application of the neural network based fuzzy time series model is expected to outperform some other models in forecasting these tourist numbers. [ABSTRACT FROM AUTHOR]