  Short-term electricity price forecasting has become important for demand side
management and power generation scheduling. Especially as the electricity
market becomes more competitive, a more accurate price prediction than the
day-ahead locational marginal price (DALMP) published by the independent system
operator (ISO) will benefit participants in the market by increasing profit or
improving load demand scheduling. Hence, the main idea of this paper is to use
autoregressive integrated moving average (ARIMA) models to obtain a better LMP
prediction than the DALMP by utilizing the published DALMP, historical
real-time LMP (RTLMP) and other useful information. First, a set of seasonal
ARIMA (SARIMA) models utilizing the DALMP and historical RTLMP are developed
and compared with autoregressive moving average (ARMA) models that use the
differences between DALMP and RTLMP on their forecasting capability. A
generalized autoregressive conditional heteroskedasticity (GARCH) model is
implemented to further improve the forecasting by accounting for the price
volatility. The models are trained and evaluated using real market data in the
Midcontinent Independent System Operator (MISO) region. The evaluation results
indicate that the ARMAX-GARCH model, where an exogenous time series indicates
weekend days, improves the short-term electricity price prediction accuracy and
outperforms the other proposed ARIMA models
