Browsing by Author "Chang, Chi-Ya"

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Deploying Arima and Artificial Neural Networks Models to Predict Energy Consumption in Taiwan
(2013-12-01)
Chuang, Feng-Kuang
;
Hung, Chih-Young
;
Chang, Chi-Ya
;
Kuo, Kuo-Cheng
;
科技管理研究所
With the aim of predicting Taiwan's energy consumption for the short term (1 year), the medium term (3 years), the medium-long term (5 years), and the long term (10 years), this study applies autoregressive integrated moving average (ARIMA) models and artificial neural networks (ANNs) models and the mean absolute percentage error (MAPE) approach is employed to measure prediction accuracy. Based on data extracted from over the period 1965-2010, the results indicate that the single variable ARIMA models illustrate superior performance than that of ANNs1. As to multivariable models, ANNs8 model including variables of energy consumption and exports show the most accurate prediction in short term and medium-long term, while ANNs6 model comprising energy consumption, GDP, and exports has the highest accuracy for medium term prediction. Meanwhile, ANNs5 model consisting of energy consumption and population shows the best accuracy for the long term prediction. Overall, it may conclude that exports and population are two essential variables to predict Taiwan's energy consumption for the short, medium, medium-long, and long term periods. The results support the assumption that parsimonious set of variables incorporated in research models may not sacrifice prediction accuracy. This concludes the contributions of this study.
DEPLOYING ARIMA AND ARTIFICIAL NEURAL NETWORKS MODELS TO PREDICT ENERGY CONSUMPTION IN TAIWAN
(2013-04-01)
Chuang, Feng-Kuang
;
Hung, Chih-Young
;
Chang, Chi-Ya
;
Kuo, Kuo-Cheng
;
科技管理研究所
ENERGY CONSUMPTION FORECASTING IN TAIWAN BASED ON ARIMA AND ARTIFICIAL NEURAL NETWORKS MODELS
(2011)
Feng-Kuang, Chuang
;
Chih-Young, Hung
;
Kuo, Kuo-Cheng
;
Chang, Chi-Ya
;
科技管理研究所
This study compares the prediction performance of Taiwan's energy consumption based on a autoregressive integrated moving average (ARIMA) model and artificial neural networks (ANNs) models for forecasting the short term (I year), the medium term (3 years), the medium-long term (5 years), and the long term (10 years) over the period 1965-2010. On the average of the four time periods, the results indicate that the single-variable ARIMA model shows superior performance than that of ANN1. As to multi-variable-models, the prediction accuracy of different models has advantages in the different time periods. ANN4 model including variables of energy consumption and export shows the most accurate prediction in short term and medium-long term, while ANN6 model including energy consumption, GDP and export has the highest accuracy for medium term prediction. Meanwhile, ANN3 model including energy consumption and population has the best accuracy for the long term prediction. Overall, on the average of the four different time periods of ARIMA model and ANNs models, ANN3 proves the most accurate prediction in comparison to the others. This concludes the contributions of this study.