Forecasting Electricity Consumption Under the Responsibility of the Provincial Electricity Authority (PEA): A Sectoral Approach - Kumjinda, Saran; Santipolvut, Sumalee; Thamma-Apiroam, Rewat (Thai Journal of East Asian Studies, 2019)
The Forecasting electricity consumption under the responsibility of the Provincial Electricity Authority (PEA) for seven years from 2017 to 2023 is examined by exploring all five sectors, namely residential, commercial, industrial, agricultural, and non-profit, and comparing the effectiveness of each forecast model. The four models under study are the Holt-Winters’ two parameters linear exponential smoothing non-seasonal (Holt-Winters’ non-seasonal), Autoregressive Integrated Moving Average (ARIMA), Artificial Neural Network (ANN), and Support Vector Machine-Regression (SVM-R). The Mean Absolute Percentage Error (MAPE) was used to determine the most suitable and effective model. MAPE was the first to be considered, and the comparative results of the lowest MAPE values indicate that the Holt-Winters’ non-seasonal model was the most suitable for forecasting electricity demand in the residential, industrial, and non-profit sectors, with ARIMA(3,1,0) and ANN being most appropriate for the commercial and agricultural sectors. Total electricity demand from 2017 to 2023 for five sectors is projected to continuously increase. The annual electricity capacity of the PEA shows a continuous decrease when compared to total electricity demand from 2017 to 2023. The distributed volume was 369.86, 335.35, 292.18, 270.93, 246.03, 223.60, and 207.42 million kWh respectively, due to the continuous decrease in electricity sales significantly impacting on the management of electricity production and distribution. Consequently, in the future, there may be a shortfall in electricity production and distribution under the responsibility of the PEA.