Predicting Thailand Electricity Load Demand with Modified Fourier Series and Seasonal-Trend Decomposition Methods Using LOESS Transformation

Main Article Content

Nikorn Saengngam
Unchalee Tonggumnead


Accurate long-term and midterm electricity load forecasting play an essential role in electric power system planning. Drawing on the seasonal-trend forecasting capacity of Fourier series and LOESS transformation, this paper applies modified Fourier series transformation (MFST) and modified seasonal-trend decomposition using LOESS transformation (MSTLT) to electricity load forecasting and compares the performance of two alternative models: the ARIMA(p,d,q) SARIMA(P,D,Q) model and the support vector regression (SVR) model. The data comprise monthly electricity consumption volumes between 2002 and 2019. The data between 2002 and 2018 are utilized to construct the forecasting model, while those in 2019 are employed to test the accuracy of the predicted values. The results confirm the validity of the proposed model in terms of forecasting accuracy and interpretability.

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How to Cite
Saengngam, N., & Tonggumnead, U. (2023). Predicting Thailand Electricity Load Demand with Modified Fourier Series and Seasonal-Trend Decomposition Methods Using LOESS Transformation. Applied Science and Engineering Progress, 16(2), 5720.
Research Articles


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