A temporal Input based day-ahead price forecasting in Asia's first liberalized electricity market using GRNN

Abstract

This paper proposes a day-ahead electricity price forecasting that could be realized using generalized regression neural network (GRNN) with temporal input. In this work application of GRNN model were applied to national electricity market of Singapore (NEMS), i.e. Asia's first liberalized electricity market. The individual price of year 2006 is very volatile with a very wide range. Therefore, accurate forecasting models are required for Singapore electricity market company (EMC) to maximize their profits and for consumers to maximize their utilities. Hence the year 2006 has been taken for forecasting the uniform Singapore electricity price (USEP). The mean absolute percentage error (MAPE) results show that the proposed GRNN model possess better forecasting abilities than the other ANN models without temporal input and its performance was least affected by the volatility.