| Title |
A System Marginal Price Forecasting Method Based on an Artificial Neural Network Using Time and Day Information |
| Authors |
이정규(Lee Jeong-Kyu) ; 신중린(Shin Joong-Rin) ; 박종배(Park Jong-Bae) |
| Keywords |
역전파 신경회로망 ; 계통한계가격 ; 가격예측 ; |
| Abstract |
This paper presents a forecasting technique of the short-term marginal price (SMP) using an Artificial Neural Network (ANN). The SW forecasting is a very important element in an electricity market for the optimal biddings of market participants as well as for market stabilization of regulatory bodies. Input data are organized in two different approaches, time-axis and day-axis approaches, and the resulting patterns are used to train the ANN. Performances of the two approaches are compared and the better estimate is selected by a composition rule to forecast the SMP. By combining the two approaches, the proposed composition technique reflects the characteristics of hourly, daily and seasonal variations, as well as the condition of sudden changes in the spot market, and thus improves the accuracy of forecasting. The proposed method is applied to the historical real-world data from the Korea Power Exchange (KPX) to verify the effectiveness of the technique. |