A System Marginal Price Forecasing Method Based on an Artificial Neural Network using Time and Day Information
| Authors: | Park Jong-Bae, Konkuk University, Korea, Republic of
Lee Jeong-Kyu, Konkuk University, Korea, Republic of
Shin Joong-Rin, Konkuk University, Korea, Republic of
Lee Kwang Y., Penn state University, United States |
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| Topic: | 9.1 Economic & Business Systems |
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| Session: | Economic and Business Systems |
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| Keywords: | Electric power systems, Forecasts, Neural networks, Backpropagation. |
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Abstract
This paper presents a forecasting technique of the short-term system marginal price (SMP) using an Artificial Neural Network (ANN). 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.
