A Hybrid Mathematical Framework for Synthetic Energy Data Generation in Morocco Smart Grids

The development of accurate energy forecasting models for smart grids is strongly dependent on the availability of high-quality and high-resolution datasets. However, in many developing regions, particularly in Morocco, such datasets remain scarce, incomplete, or non-public. This limitation significantly constrains the application of advanced machine learning and deep learning techniques for energy demand prediction and grid optimization. This paper proposes a novel hybrid synthetic energy data generation framework specifically designed for the Moroccan context. The proposed approach integrates temporal dynamics, climatic influences, and socio-cultural behavior into a unified mathematical model for hourly electricity consumption simulation. In particular, the model explicitly incorporates seasonal variations, temperature-dependent effects, and behavioral shifts induced by socio-cultural events, including Ramadan and weekend patterns.