• A Darts-based machine learning framework is applied for electricity demand forecasting. • Comparative analysis of ML models- TSMixer, TiDE, Random Forest, and FB Prophet using real SCADA data from Tetouan, Morocco. • Min Max, Max Absolute, and Standard scaling preprocessing techniques are used, and Performance is assessed using MAE, RMSE, and MAPE. • TiDE and TSMixer demonstrated better forecasting accuracy and stability. • The Darts framework enhances forecasting accuracy and lowers RMSE and MAE by up to 60 Accurate electricity demand forecasting is essential for reliable power system operation, load balancing, and energy management in rapidly urbanizing cities, where modern smart grids exhibit rapid load variability. This study presents a comprehensive comparative analysis of electricity demand forecasting models using the Darts time-series framework for Tetouan, Morocco, based on SCADA data collected at 10-minute intervals across three distribution zones in 2017. Weather variables and historical load features are incorporated to capture nonlinear consumption patterns. The proposed methodology incorporates consistent preprocessing, feature engineering, and strictly chronological train–test splitting to avoid temporal leakage. Six models, Linear Regression, Random Forest, XGBoost, Prophet, TiDE, and TSMixer, are evaluated at both 10-minute and hourly intervals under a consistent preprocessing pipeline (Min-Max, Max Absolute, and Standard scaling). Model performance is assessed using standard error metrics, including MAE, RMSE, and MAPE. Results show that TSMixer and TiDE excel in fine-grained, short-term forecasting, with TiDE achieving a MAPE of 1.92% for combined 10-minute forecasts. While Random Forest provides a strong balance between accuracy and computational efficiency at hourly resolution with a MAPE of 1.04% and an MAE of 224.2. Compared with prior Tetouan-focused studies, the proposed framework shows substantial error reductions under identical data resolution. This work contributes a transparent benchmarking framework and actionable insights for short-term electricity demand forecasting in smart grid applications.
Biswas et al. (Sun,) studied this question.