Implantação de inteligência artificial nativa em sistemas IoSGT: uma abordagem holística

Abstract

The growing energy demand sharpens the quest for technological modernizations capable of meeting imminent needs, as well as increasing concern about mitigating the environmental impacts associated with this escalation. The state of the art in sg provides evidence of the use of IA techniques in IoSGT use cases, aiming to revolutionize the way energy is produced, transmitted, and consumed. Indeed, IA is presumed to offer unprecedented levels of disruption in the electrical sector through intelligent control methods that can unlock new value streams for consumers, while supporting a highly accurate, reliable, and resilient system. However, much research is still needed in this area, such as the positioning of IA based instances along the edge-cloud continuum, types of techniques and algorithms for each use case, efficient use of predictive analytics capable of forecasting future demands, fault detection, consumption patterns, and anomalies in the electrical grid that allow for proactive measures to enhance grid reliability, among many others. This research proposal aims to use energy consumption forecasting as a tool to optimize resource use, avoid waste, and contribute to environmental preservation, as well as to classify electronic devices to understand consumption patterns. This approach will be implemented through a holistic architecture called IAIoSGT (Artificial Intelligence native in IoSGT). IAIoSGT is designed to accelerate the adoption of IA and ML techniques in an approach that efficiently integrates data processing from the edge to the cloud. The evaluation of the IAIoSGT architecture, including its compliance, performance, and implementation feasibility, was conducted in two distinct testbeds, addressing both physical devices and Machine Learning algorithms. The first testbed focused on classifying and identifying electronic devices connected to the same electrical network, exploring ML algorithms such as KNN, SVM, MLP, NB, and DT; the second test focused on energy consumption prediction comparing the Naive and LSTM algorithms. These tests are essential to validate the effectiveness and robustness of the proposed approach, thus contributing to the advancement of the state of the art in IA applied to SG.