Deseasonalisation in electricity load forecasting - Nor, Maria Elena; Rusiman, Mohd Saifullah; Sufahani, Suliadi Firdaus; Abdullah, Mohd Asrul Affendi; Bataraja, Sathwinee (Science Publishing Corporation, 2017)
Nowadays, there is an increasing demand for electricity however overproduction of electricity lead to wastage. Therefore, electricity load forecasting plays a crucial role in operation, planning and maintenance of power system. There were many ways that have been employed towards electricity load forecasting. The present study was designed to study the effect of deseasonalizing the electricity load data in forecast performance and to compare the methods of Exponential Smoothing and Box-Jenkins in electricity load forecasting. The daily seasonality in electricity load data was removed and the forecast methods were employed on both the seasonal data and non-seasonal data. Holt Winters method and Seasonal-Autoregressive Integrated Moving Average (SARIMA) methods were used on the seasonal data. Meanwhile, Simple and Double Exponential Smoothing methods as well as Autoregressive Integrated Moving Average (ARIMA) methods were used on the non-seasonal data. Previous studies employed similar approach in electricity load forecasting for neural network method. This paper focused on the traditional time series forecasting method. The forecast accuracy measures used for this research were mean absolute error (MAE) and mean absolute percentage error (MAPE). The results revealed that both Exponential Smoothing method and Box-Jenkins method produced better forecast for deseasonalised data. Besides, the study proved that Box-Jenkins method was better in forecasting electricity load data for both seasonal and non-seasonal data.