Optimizing Electricity Demand Forecasting Using ARIMA, SARIMA, and GRU with Weather and Calendar Variables
TL;DR
The results of the experiment demonstrate that the GRU model performs better than ARIMA and SARIMA models especially for longer forecasting horizons due to its capability to learn nonlinear relationships and long-term temporal dependencies.
Abstract
Electricity demand forecasting is crucial for effective grid operation, planning, and decision-making. This study presents a comparison between classical time series models—AutoRegressive Integrated Moving Average (ARIMA) and Seasonal AutoRegressive Integrated Moving Average (SARIMA)—and a deep learning–based method, Gated Recurrent Units (GRU), for forecasting hourly electricity demand. The models are tested on a real-world dataset, enriched with weather and calendar variables to capture temporal and exogenous effects on electricity consumption. To ensure a fair and reproducible comparison, all models are trained and evaluated in a common experimental framework, including a well-defined chronological train-test split and rolling-origin (walk-forward) validation strategy. The forecasting performance is evaluated for short-term (24 h) and medium-term (168 h) horizons using standard error metrics, namely, root mean squared error, mean absolute error, and Mean Absolute Percentage Error (MAPE). The results of the experiment demonstrate that the GRU model performs better than ARIMA and SARIMA models especially for longer forecasting horizons due to its capability to learn nonlinear relationships and long-term temporal dependencies. The GRU approach gives better forecasting accuracy in the case of complex demand dynamics, but linear seasonal patterns can still be modeled by classical statistical models. The aim of this study is not to directly detect or predict system failures, nor does it depend on explicit fault or outage data. Its main contribution is instead in improving the accuracy of electricity demand forecasting, which can indirectly assist preventive grid operation and planning by reducing the uncertainty in expected load profiles. Received: 7 August 2025 | Revised: 20 March 2026 | Accepted: 23 June 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in Kaggle at https://www.kaggle.com/datasets/saurabhshahane/electricity-load-forecasting. Author Contribution Statement Emrah Aslan: Conceptualization, Methodology, Software, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration. Yıldırım Özüpak: Conceptualization, Methodology, Software, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision. Feyyaz Alpsalaz: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision. Hasan Uzel: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision.