Forecasting day and night electricity demand variances using data analytics

Electricity generation planning requires accurate forecasting of electricity demand for efficient management of existing capacity and optimization of the decisions on the additional capacity. Short-term demand predictions such as hourly, daytime and nighttime, day-ahead become important factors for the electricity planning. The hourly electricity of Daytime and Nighttime data is considered to analyze the patterns of variances using Data analytics techniques SARIMA and GARCH models to fit the data and for forecasting of Day and Night time electricity demand. GARCH model gives better forecast accuracy when the conditional heteroskedasticity is present.