Forecasting the Total Electricity Demand in the City of Malaybalay: Application of Seasonal Autoregressive Integrated Moving Average Model

Power outages remain a significant challenge in Malaybalay as it aims to become a highly urbanized city. Scheduled and unscheduled brownouts exacerbate economic losses across almost all sectors of the economy. This study attempts to examine the trends of the total electricity demand by type of consumer, namely residential, commercial, and industrial, utilizing the 2012 to 2025 monthly data from Bukidnon Second Electric Cooperative (BUSECO). It employed generalized least squares to estimate the demand function for total electricity demand and a seasonal autoregressive integrated moving-average model to forecast electricity demand for 2025 to 2031. The results of the study indicate that a percentage increase in the number of consumers leads to a 1.25 percent increase in total electricity demand, ceteris paribus. Moreover, the SARIMA (1, 1, 1) (1, 0, 1, 12) model has been statistically identified as the best predictive model based on Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) for total electricity demand. Also, the SARIMA (1, 1, 1) (1, 1, 1, 12) model was identified for the residential demand. The SARIMA (0, 1, 1) (0, 1, 1, 12) model for commercial demand, and the SARIMA (1, 1, 1) (1, 0, 1, 12) model for industrial demand. SARIMA model estimates show that total electricity demand could reach 25,859,611.8 kWh kilowatt hours (kWh) in the year 2031, while residential demand is estimated to reach 15,886,455.11 kilowatt hours (kWh), the commercial demand is 6,707,288.70 kilowatt hours (kWh), and industrial demand could reach 16,878,389.31 kilowatt hours (kWh) for the same year. Results highlight the importance of anticipatory planning to ensure a stable electricity supply and promote sustainable growth and development in the city.

Forecasting, generalized least squares, total electricity demand, seasonal autoregressive integrated moving average

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