  By significant improvements in modern electrical systems, planning for unit
commitment and power dispatching of them are two big concerns between the
researchers. Short-term load forecasting plays a significant role in planning
and dispatching them. In recent years, numerous works have been done on
Short-term load forecasting. Having an accurate model for predicting the load
can be beneficial for optimizing the electrical sources and protecting energy.
Several models such as Artificial Intelligence and Statistics model have been
used to improve the accuracy of load forecasting. Among the statistics models,
time series models show a great performance. In this paper, an Autoregressive
integrated moving average (SARIMA) - generalized autoregressive conditional
heteroskedasticity (GARCH) model as a powerful tool for modeling the
conditional mean and volatility of time series with the T-student Distribution
is used to forecast electric load in short period of time. The attained model
is compared with the ARIMA model with Normal Distribution. Finally, the
effectiveness of the proposed approach is validated by applying real electric
load data from the Electric Reliability Council of Texas (ERCOT). KEYWORDS:
Electricity load, Forecasting, Econometrics Time Series Forecasting, SARIMA
