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Statistical Modeling for Forecasting Maximum Electricity Demand in Korea
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 Title & Authors
Statistical Modeling for Forecasting Maximum Electricity Demand in Korea
Yoon, Sang-Hoo; Lee, Young-Saeng; Park, Jeong-Soo;
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 Abstract
It is necessary to forecast the amount of the maximum electricity demand for stabilizing the flow of electricity. The time series data was collected from the Korea Energy Research between January 2000 and December 2006. The data showed that they had a strong linear trend and seasonal change. Winters seasonal model, ARMA model were used to examine it. Root mean squared prediction error and mean absolute percentage prediction error were a criteria to select the best model. In addition, a nonstationary generalized extreme value distribution with explanatory variables was fitted to forecast the maximum electricity.
 Keywords
RMSE;Winters seasonal model;ARMA model;additional explanatory variable;generalized extreme value distribution;
 Language
Korean
 Cited by
1.
시계열모형에 의한 전력판매량 예측,손영숙;

응용통계연구, 2014. vol.27. 3, pp.419-430 crossref(new window)
2.
A Study on the Comparison of Electricity Forecasting Models: Korea and China,;;

Communications for Statistical Applications and Methods, 2015. vol.22. 6, pp.675-683 crossref(new window)
1.
Prediction of Electricity Sales by Time Series Modelling, Korean Journal of Applied Statistics, 2014, 27, 3, 419  crossref(new windwow)
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