• electricity price forecasting;
  • neural network;
  • data mining;
  • rough sets theory;
  • PJM electricity market;
  • IESO market


Electricity price forecasting is an essential task for market participants in deregulated electricity market. This paper proposes an approach for next-day peak electricity price forecasting, since it is important for risk management and bidding strategy. In the proposed method, neural network (NN) is employed as the forecasting method, and its learning data is selected by using rough sets. Moreover, the creating method of learning data based on temperature fluctuation is also proposed for generation of new learning data in order to efficiently learn. This method is examined by using the data of Pennsylvania-New Jersey-Maryland (PJM) electricity market and The independent electricity system operator (IESO) market. From the simulation results, it is observed that the proposed method is useful for next-day peak electricity price forecasting. Copyright © 2009 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.