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Short-Term Load Forecasting Using Multiple Time-Series Model Including Dummy Variables  

이경훈 (한양대 대학원 전기공학과)
김진오 (한양대 전기공학과)
Publication Information
The Transactions of the Korean Institute of Electrical Engineers A / v.52, no.8, 2003 , pp. 450-456 More about this Journal
Abstract
This paper proposes a multiple time-series model with dummy variables for one-hour ahead load forecasting. We used 11 dummy variables that were classified by day characteristics such as day of the week, holiday, and special holiday. Also, model specification and selection of input variables including dummy variables were made by test statistics such as AIC(Akaike Information Criterion) and t-test statistics of each coefficient. OLS (Ordinary Least Squares) method was used for estimation and forecasting. We found out that model specifications for each hour are not identical usually at 30％ of optimal significance level, and dummy variables reduce the forecasting error if they are classified properly. The proposed model has much more accurate estimates in forecasting with less MAPE (Mean Absolute Percentage Error).
Keywords
short-term load forecasting; dummy variable; multiple time-series model; AIC(Akaike Information Criterion); t-value;
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Times Cited By KSCI : 1  (Citation Analysis)
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