Akademik Veri Yönetim Sistemi
Applied Information Systems and Technologies in the Digital Society (AISTDS 2025 ), Kyyiv, Ukrayna, 01 Ekim 2025, cilt.4133, ss.169-181, (Tam Metin Bildiri)
This study examines the forecasting of national electricity demand (nat_demand) using a dataset of 48,046 hourly records, incorporating electricity demand, weather variables, and calendar-related features across multiple locations. The Seasonal AutoRegressive Integrated Moving Average (SARIMA) model was applied to capture both linear and seasonal patterns in the time series, with performance assessed via RMSE, MAPE, and diagnostic analyses. The findings reveal that SARIMA effectively modeled the seasonal behavior of electricity demand, achieving an RMSE of 173 and a MAPE of 12%. The study emphasizes the critical role of accurate forecasting in managing electricity demand and mitigating the risks of system failures, providing valuable insights into the suitability of SARIMA for enhancing grid reliability. However, challenges such as incorporating real-time failure data and handling sudden nonlinear shifts persist, highlighting the need for future enhancements—such as hybrid approaches–to further improve forecasting accuracy and support robust failure management in energy systems.