Next-Day Peak Electricity Price Forecasting Using NN Based on Rough Sets Theory

作者：Senjyu Tomonobu^*; Toyama Hirofumi; Areekul Phatchakorn; Chakraborty Shantanu; Yona Atsushi; Urasaki Naomitsu; Funabashi Toshihisa

来源：IEEJ Transactions on Electrical and Electronic Engineering, 2009, 4(5): 618-624.

DOI：10.1002/tee.454

• 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.

• 出版日期2009-9