Modeling special-day effects for forecasting intraday electricity demand

We propose and apply a novel approach for modeling special-day effects to predict electricity demand in Korea. Notably, we model special-day effects on an hourly rather than a daily basis. Hourly specified predictor variables are implemented in the regression model with a seasonal autoregressive moving average (SARMA) type error structure in order to efficiently reflect the special-day effects. The interaction terms between the hour-of-day effects and the hourly based special-day effects are also included to capture the unique intraday patterns of special days more accurately. The multiplicative SARMA mechanism is employed in order to identify the double seasonal cycles, namely, the intraday effect and the intraweek effect. The forecast results of the suggested model are evaluated by comparing them with those of various benchmark models for the following year. The empirical results indicate that the suggested model outperforms the benchmark models for both special- and non-special day predictions.