| Title |
Special - Days Load Handling Method using Neural Networks and Regression Models |
| Authors |
Hee-Seong Koh ; Se-Hun Lee ; Sik-Chung Lee |
| Abstract |
In case of power demand forecasting, the most important problems are to deal with the load of special-days. Accordingly, this paper presents the method that forecasting long(the Lunar New Year, the Full Moon Festival) and short(the Planting Trees Day, the Memorial Day, etc.) special-days peak load using neural networks and regression models. Long and short special-days peak load forecast by neural networks models uses pattern conversion ratio and four-order orthogonal polynomials regression models. There are using that special-days peak load data during ten years(1985～1994). In the result of special-days peak load forecasting, forecasting % error shows good results as about 1～2[%] both neural networks models and four-order orthogonal polynomials regression models. Besides, from the result of analysis of adjusted coefficient of determination and F-test, the significance of the are convinced four-order orthogonal polynomials regression models. When the neural networks models are compared with the four-order orthogonal polynomials regression models at a view of the results of special-days peak load forecasting, the neural networks models which uses pattern conversion ratio are more effective on forecasting long special-days peak load. On the other hand, in case of forecasting short special-days peak load, both are valid. |