AI SearchElectricity demand forecasting : a case study in Vietnam
Call Number: AIT Thesis no.ET-06-9 Material type:
SeriesSeries: Asian Institute of Technology. Thesis ; no. ET-06-9Publication details: Pathum Thani, Thailand : Asian Institute of Technology, 2006Description: 111 p. : illSubject(s): Online resources: Dissertation note: Thesis (M.Eng.) - Asian Institute of Technology, 2006 Summary: This study deals with analysis and forecasting of electricity demand including load and energy forecasting. Load forecasting is done using the artificial neural network method (ANN) for the short term. This applies to the South of Vietnam for hourly and daily (for weekdays and weekends) forecast but not for holidays, not for holidays. The variables used in this method are load and temperature. For the long tern peak load forecast, the author uses regression method for case of Vietnam. The variables used in this case are peak load, GDP, population, average price of electricity and price of fuel oil. Energy forecasting is carried out by applying the Box Cox model as a non-linear regression method for four sectors of the North and the South of Vietnam. The variables of each model includes GDP, own price, cross price, and demand of the previous year. To see the validity of each applied method, the author compares the method to another one. After that she analyses which method is the best for each case such as neural network with time series method for short tern, ANN with regression for peak load, Box-Cox with double log, semi log and linear. The period of forecasting is from 2005 to 2025, and the data is from 1990 to 2004. The results include high case, low case and base case besides the author performs sensitivity analysis of the different models
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A thesis proposal submitted in partial fulfillment of the requirements for the degree of Master of Engineering, School of Environment, Resources and Development
Thesis (M.Eng.) - Asian Institute of Technology, 2006
This study deals with analysis and forecasting of electricity demand including load and energy forecasting. Load forecasting is done using the artificial neural network method (ANN) for the short term. This applies to the South of Vietnam for hourly and daily (for weekdays and weekends) forecast but not for holidays, not for holidays. The variables used in this method are load and temperature. For the long tern peak load forecast, the author uses regression method for case of Vietnam. The variables used in this case are peak load, GDP, population, average price of electricity and price of fuel oil. Energy forecasting is carried out by applying the Box Cox model as a non-linear regression method for four sectors of the North and the South of Vietnam. The variables of each model includes GDP, own price, cross price, and demand of the previous year. To see the validity of each applied method, the author compares the method to another one. After that she analyses which method is the best for each case such as neural network with time series method for short tern, ANN with regression for peak load, Box-Cox with double log, semi log and linear. The period of forecasting is from 2005 to 2025, and the data is from 1990 to 2004. The results include high case, low case and base case besides the author performs sensitivity analysis of the different models
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