Short-Term System Marginal Price Forecasting Using System-Type Neural Network Architecture

Neural networks have been applied in various new ways to the problem of short-term load and electricity price forecasting for power systems. Virtually all of these methods are based on using statistical patterns, which are perceived between the yearly load and system marginal price (SMP) histories of the system to predict the forecasted year's power demand and SMP. The SMP 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. The proposed method introduces a system type neural network architecture to perform electricity price forecasting. Specifically, the proposed approach begins with the premise that the electricity price for a given year can be given a structure which can then be related to the structure of the reference year, in such a way that a transformation can be found from the reference year's structure to the forecasting year's structure. The transformation depends upon how parameters, which influenced the SMP but can not be measured, move from the reference year to the forecasting year