The electricity supply industry is being restructured worldwide into a competitive market structure in which electricity is produced by generators, transmitted by transmission companies, and distributed by suppliers according to new trading agreements. In this market, system marginal price (SMP) plays a very important role. Obviously, an accurate prediction would benefit all market participants involved. The SMP profile is a typical time series and, to some extent, similar to the load profile. In this study, an SMP forecasting model is developed based on load demand and supply as well as past SMP data. The proposed forecasting model is compared with NN method and wavelet combined with NN scheme. Due to the different life style during weekdays and weekend, we distinguish comparisons between weekdays and weekends in summer, autumn and winter. For weekend forecasting, the NN method exhibits better forecasting performance than other methods. During weekdays, the proposed SMP forecasting method shows the best forecasting performance among other methods.

A new forecasting scheme is presented of short-term system marginal price (SMP) using past data on power supply and demand as well as past cost data. The forecasting of SMP is one of the most significant factors in an electricity market in which power is produced by generators, transmitted by transmission companies, and distributed by suppliers according to new trading agreements. The accurate forecasting of SMP can significantly influence the profit of market participants. In this paper, a new methodology for day-ahead SMP forecasting is proposed using the law of supply and demand on power. The salient feature of the proposed approach is that it exhibits excellent predicting performance in short-term forecasting.