Machine Learning-Based Short-Term Electricity Forecasting within an Operational and Governance Framework

Short-term electricity demand forecasting is essential for operational planning in electricity systems, particularly in cooling-dominated regions. While prior studies have largely focussed on improving predictive accuracy, less attention has been given to how forecasting models can be structured and evaluated for operational decision support. This study proposes a structured framework that connects high-frequency data inputs, interpretable feature engineering and machine learning-based short-term forecasting with downstream decision and governance considerations. The forecasting component of the proposed framework is empirically evaluated using high-resolution electricity load and weather data from the Tetouan (Morocco) dataset, incorporating temporal, lagged and climate-derived features. The proposed framework, instantiated through boosting-based and other regression methods in its forecasting layer, demonstrates reliable short-term predictive performance and provides a foundation for operational planning and governance in smart energy systems. The framework is intended to be transferable to smart-meter-based electricity systems in hot and arid regions.