A forecasting system of electric price using the refined Back propagation Neural Network

Abstract

This paper proposed a forecasting system of electric price for participants to quickly and accurately predict the electric price for avoiding the risk due to the electricity price volatility. Based on the Back-propagation Neural Network(BPN) and Orthogonal Experimental Design(OED), a Refined BPN (RBPN) is constructed in the searching process. The data cluster, including Locational Marginal Price(LMP), system load, temperature, line-flow, are first collected and embedded in the Excel Database. In order to get a better solution, the OED is used to automatically regulate the parameters during the RBPN training process. Linking the RBPN and Excel database, the RBPN retrieved the input data from Excel Database to perform and analyze the efficiency and accuracy of the predicting system until the forecasting system is convergent. Simulation results will provide the participants to obtain the maximal profits and raise its ability of market's competition in a price volatility environment.