The Development of a Hybrid Model for Forecasting Time Series Data of Monthly Household Electrical Distribution Units of People in the Northeast, Thailand
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Abstract
The objective of this research was to develop a suitable model for forecasting time series data of monthly household electrical distribution units of people in the Northeast, Thailand. Data were collected from the Provincial Electricity Authority from January 2009 – May 2019, a total of 127 values, consisting of 3 data sets, namely EGAT 1, EGAT 2, and EGAT 3. For the development of the model, the researcher applied empirical mode decomposition to reduce Fast Oscillation of the data before being forecasted by two univariate time series models, including Holt –Winters Exponential Smoothing Method and Seasonal Auto-regressive integrated moving average. After that, a hybrid forecasting model was created using combined forecasting method. In addition, the forecasting efficiency was compared by 5 performance measurement criteria, 1) namely Mean Absolute Error, 2) Root Mean Square Error, 3) Mean Absolute Percentage Error, 4) Median Absolute Percentage Error and 5) Symmetric Mean Absolute Percentage Error. The findings indicated that the forecasting efficiency of the developed model was better than two univariate time series models in all criteria. Therefore, it can be concluded that the hybrid model is the suitable model for forecasting time series data of monthly household electrical distribution units of people in the Northeast, Thailand.
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References
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