Temperature Effect on Short-Term Electricity Demand Forecasting

Short-term forecasting of electricity demand is a crucial tool for understanding the dynamic nature of demand. Energy utilities can schedule generation and other tasks like demand side control more quickly because to this expertise. A lot of things affect how much electricity is used, such as the effects of temperature, social activities, holidays, the workplace, and so on. If erroneous forecasting methods and data are used, the power system operators could be duped and undergo major adjustments. A thorough examination of how temperature affects consumption patterns on weekdays, weekends, and during various seasons is included. Using demand and temperature data from the previous day for the full year, short-term demand forecasting is done for different days of the calendar year. RNN is the model that this study suggests using to forecast demand with high accuracy and higher MAPE.