| Title |
A Correction Technique of Missing Load Data Based on ARIMA Model |
| Authors |
박종배 ; 이찬주 ; 이재용 ; 신중린 ; 이창호 |
| Keywords |
Time Series Analysis ; ARIMA(Autoregressive Integrated Moving Average) ; Load Forecast ; PCHIP(Piecewise Cubic Interporation) ; Branch and Bound |
| Abstract |
Traditionally, electrical power systems had the vertically-integrated industry structures based on the economics of scale. However power systems have been recently reformed to increase the energy efficiency of the power system. According to these trends, Korean power industry has been partially restructured, and the competitive generation market was opened in 2001. In competitive electric markets, correct demand data are one of the most important issue to maintain the flexible electric markets as well as the reliable power systems. However, the measuring load data can have the uncertainty because of mechanical trouble, communication jamming, and other things. To obtain the reliable load data, an efficient evaluation technique to adust the missing load data is needed. This paper analyzes the load pattern of historical real data and then the turned ARIMA (Autoregressive Integrated Moving Average) model, PCHIP(Piecewise Cubic Interporation) and Branch & Bound method are applied to seek the missing parameters. The proposed method is tested under a variety of conditions and tested with historical measured data from the Korea Energy Management Corporation (KEMCO). |