Abstract
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. This study was designed to investigate the effect of deseasonalisation on electricity load data 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. The error measurement that were used to assess the forecast performance 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.
Highlights
Electricity supply systems regard load demand as a sensitive factor as the demand has to be balanced with the supply as to avoid overproduction of load or interruption in electricity load supply
There were various studies conducted in modeling the seasonality that exist in electricity load forecasting
The aim of this research is to examine the impact of deseasonalisation data on electricity load forecasting using traditional methods that is exponential smoothing method and Box-Jenkins method
Summary
Electricity supply systems regard load demand as a sensitive factor as the demand has to be balanced with the supply as to avoid overproduction of load or interruption in electricity load supply. Seasonal adjustments were made before employing forecasting method on a time series data. This approach had been very useful in predicting the future of a data. Deseasonalisation was regarded as a useful method in forecasting time series data. The aim of this research is to examine the impact of deseasonalisation data on electricity load forecasting using traditional methods that is exponential smoothing method and Box-Jenkins method. These two methods have been used widely in time series forecasting in many areas of study. As example Box Jenkins approach were applied in forecasting of gold price [6], import of palm oil products [7], tourism demand forecasting [8], [9] and [10]
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