A system marginal price forecasting based on an artificial neural network adapted with rough set theory

This paper presents a forecasting technique of the short-term system marginal price (SMP) using an Artificial Neural Network (ANN) adapted with Rough Set theory. The SNIP 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 is grouped into similar pattern using rough set theory, and the resulting patterns are used to train the ANN. In training of ANN, it is more efficient because some patterns are combined into one pattern. After training with the combined patterns adapted with rough set, the SMP is forecasted using the ANN. The proposed method is applied to the historical real-world data from the Korea Power Exchange (KPX) to verify the effectiveness of the technique.