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 ...

Complex network models  Green networking  Grid Computing 

This paper presents a novel framework for optimizing Carbon Release (CR) through an AI-driven approach to Fossil Fuel Intake (FFI) management. We propose a new training methodology for AI models to enhance their effectiveness in reducing FFI by learning from historical energy consumption trends and emissions data. By attributing specific quantities to key optimization components such as combustion efficiency, renewable integration, and AI impact, we derive a detailed matrix that quantifies their...

Machine Learning  Information Management  Artificial Intelligence  Grid Computing  Algebra