Classification of day-ahead prices in Asia´s first liberalized electricity market using GRNN

In electricity market the price sequence fluctuates frequently, periodically and stochastically, consequently price spikes often appear and it impacts the accuracy of price forecasting. The electricity price classification method is as an alternative to numerical electricity price forecasting due to high forecasting errors in various approaches. This paper proposes a day-ahead electricity price classification that could be realized using generalized regression neural networks (GRNN). These electricity price classifications are important because all market participants do not know the exact value of future prices in their decision making process. In this paper, classification of electricity market prices with respect to pre specified electricity price threshold are used. The simulation results show that the proposed method provides a better and efficient method for day-ahead deregulated electricity market of national electricity market of Singapore (NEMS), i.e. Asia´s first liberalized electricity market.