Native Artificial Intelligence Deployment in IoSGT Systems: A Holistic Approach

Smart Grids (SG) aim to integrate Artificial Intelligence (AI) to optimize energy production, enhance transmission efficiency, and improve consumption management. However, integrating AI into the edge-cloud presents challenges such as data processing constraints, communication latency, and security risks. This work proposes the IAIoSGT architecture, which integrates data processing from the edge to the cloud, combining energy consumption forecasting with the classification of electronic devices to identify consumption patterns. The approach was evaluated in two testbads: one for device classification, using algorithms such as KNN, SVM, MLP, NB, and DT, and another for consumption prediction, comparing the Naive and LSTM algorithms. The high accuracy achieved in both tasks demonstrates the effectiveness of the proposed architecture in enhancing AI-driven energy management in SG.