AI SearchElectricity demand forecast for Taiwan Republic of China
Call Number: AIT Thesis no.ET-84-9 Material type:
TextSeries: Asian Institute of Technology. Thesis ; no. ET-84-9Publication details: Bangkok : Asian Institute of Technology, 1984Description: 110 pSubject(s): Online resources: Dissertation note: Thesis (M.Eng.) - Asian Institute of Technology, 1984 Summary: This study presents three different approaches to electricity demand forecasting and applied them to the case of Taiwan up to the year 1992. The econometric models are estimated for each of eight economic sectors separately, using yearly data from 1970 to 1981 for each sectors. The price of electricity, the price of natural gas and value added in each sector are important factors determining the electricity demand. Long and short term elasticities are estimated and discussed. The model forecasts electricity demand by different scenarios. A probability forecast is also made to include the stochastic nature of the different scenarios in the considerations. The Box-Jenkins ARIMA and bivariate transfer function models are also used to compare the performance of these methods. It is concluded that the bivariate transfer function model could be a very good approach to the aggregate electricity demand forecast. The forecasting results show that electricity demand will continue to grow. However, the rates of growth will be considerably lower than those observed in the 1960s and 1970s.
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22-AIT Thesis (Replacement)
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Asian Institute of Technology Library Archives | AIT Thesis no.ET-84-9 (Browse shelf(Opens below)) | Available | 30050120353221 |
A thesis submitted in partial fulfillment of the requirements for the degree of Master of Engineering, School of Environment, Resources & Development
Thesis (M.Eng.) - Asian Institute of Technology, 1984
This study presents three different approaches to electricity demand forecasting and applied them to the case of Taiwan up to the year 1992. The econometric models are estimated for each of eight economic sectors separately, using yearly data from 1970 to 1981 for each sectors. The price of electricity, the price of natural gas and value added in each sector are important factors determining the electricity demand. Long and short term elasticities are estimated and discussed. The model forecasts electricity demand by different scenarios. A probability forecast is also made to include the stochastic nature of the different scenarios in the considerations. The Box-Jenkins ARIMA and bivariate transfer function models are also used to compare the performance of these methods. It is concluded that the bivariate transfer function model could be a very good approach to the aggregate electricity demand forecast. The forecasting results show that electricity demand will continue to grow. However, the rates of growth will be considerably lower than those observed in the 1960s and 1970s.
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