Comparison of Different Forecasting Models Used for short term load Forecasting

Short term load forecasting is very helpful for a system operator. The accuracy of the forecasted demand depends on the forecasting model and the pattern of data to be forecasted. In this paper, three models named autoregressive integrated moving average (ARIMA), exponential smoothing (ETS) and artificial neural network (ANN) are used to forecast the hourly load variation of GRIDCO, West Bengal, Sikkim and Eastern Region. It is also shown how accuracy of forecasting models changes with the average load of the area and load variation on different days of the week.