| Title |
Daily Peak Electric Load Forecasting Using Neural Network and Fuzzy System |
| Authors |
방영근(Young-Keun Bang) ; 김재현(Jae-Hyoun Kim) ; 이철희(Chul-Heui Lee) |
| DOI |
http://doi.org/10.5370/KIEE.2018.67.1.96 |
| Keywords |
Peak load forecasting ; Preprocessing by interpolation ; Fuzzy Inference system ; Neural Network ; Rough set |
| Abstract |
For efficient operating strategy of electric power system, forecasting of daily peak electric load is an important but difficult problem. Therefore a daily peak electric load forecasting system using a neural network and fuzzy system is presented in this paper. First, original peak load data is interpolated in order to overcome the shortage of data for effective prediction. Next, the prediction of peak load using these interpolated data as input is performed in parallel by a neural network predictor and a fuzzy predictor. The neural network predictor shows better performance at drastic change of peak load, while the fuzzy predictor yields better prediction results in gradual changes. Finally, the superior one of two predictors is selected by the rules based on rough sets at every prediction time. To verify the effectiveness of the proposed method, the computer simulation is performed on peak load data in 2015 provided by KPX. |