Using Time Series Analysis to Forecast Electricity Consumption in Al-Musayyib City Using the SARIMAX Model: A Theoretical and Applied Study - AL- mamoory., Oday Merzah Hamzah (Universitas Muhammadiyah Sidoarjo, 2026)
General Background: Electricity consumption forecasting is important for energy planning and power grid stability in growing urban areas. Specific Background: Al-Musayyib City faces increasing electricity demand caused by population growth, temperature variation, and rising subscriber numbers. Knowledge Gap: Previous studies often excluded multiple explanatory variables from forecasting models, reducing prediction accuracy. Aims: This study develops a SARIMAX model to forecast electricity consumption using population, temperature, and subscriber data. Results: Monthly data from 2010–2024 were analyzed using statistical methods and the Augmented Dickey–Fuller test. The SARIMAX model achieved accurate forecasting results with RMSE = 120, MAE = 95, and MAPE = 2.5%. Population growth, temperature changes, and subscriber increases were found to raise electricity demand. Novelty: The study integrates climatic and demographic variables within a SARIMAX framework for electricity forecasting in Al-Musayyib City. Implications: The model provides a reliable tool for energy planning, reducing losses, and improving grid management in similar cities. Highlights: • SARIMAX achieved low forecasting error with MAPE reaching 2.5%• Seasonal demand patterns were strongly associated with temperature variation• Subscriber growth and demographic expansion increased monthly power demand Keywords: SARIMAX, Electricity Consumption Forecasting, Time Series Analysis, Energy Planning, Seasonal Demand Prediction