| Title |
Development of a Weekly Load Forecasting Expert System |
| Authors |
황갑주(Hwang, Kap-Ju) ; 김광호(Kim, Kwang-Ho) ; 김성학(Kim, Sung-Hak) |
| Keywords |
Weekly Load Forecasting ; Expert System ; Exponedtial Smoothing ; Multiple Regression ; Neural Network ; Rule based System ; Relative Coefficients |
| Abstract |
This paper describes the Weekly Load Forecasting Expert System(Named WLoFy) which was developed and implemented for Korea Electric Power Corporation(KEPCO). WLoFy was designed to provide user oriented features with a graphical user interface to improve the user interaction. The various forecasting models such as exponential smoothing, multiple regression, artificial nerual networks, rult-based model, and relative coefficient model also have been included in WLofy to increase the forecasting accuracy. The simulation based on historical data shows that the weekly forecasting results form WLoFy is an improvement when compared to the results from the conventional methods. Especially the forecasting accuracy on special days has been improved remakably. |