Banca de QUALIFICAÇÃO: PAULO EUGENIO DA COSTA FILHO

Uma banca de QUALIFICAÇÃO de MESTRADO foi cadastrada pelo programa.
STUDENT : PAULO EUGENIO DA COSTA FILHO
DATE: 19/05/2023
TIME: 09:00
LOCAL: meet.google.com/vab-fptv-xbn
TITLE:

Native Artificial Intelligence Deployment in IoSGT Systems: A Holistic Approach

KEY WORDS:

Edge cloud computing, Smart grid, ioT, ML, AI.

PAGES: 65
BIG AREA: Ciências Exatas e da Terra
AREA: Ciência da Computação
SUBÁREA: Sistemas de Computação
SPECIALTY: Arquitetura de Sistemas de Computação
SUMMARY:

The growing energy demand sharpens the search for technological modernizations capable of meeting imminent needs, as well as increasing concerns about mitigating the environmental impacts that come with this escalation.

The state of the art in Smart Grids refers to evidence of the use of AI techni-
ques in IoSGT use cases, aiming to revolutionize the way energy is produced,
transmitted, and consumed. In fact, AI is expected to offer unprecedented
levels of disruption in the electric sector, through intelligent control methods
that can unlock new value streams for consumers, while allowing support for
a highly assertive, reliable, and resilient system. However, much research is
still needed in this area, such as the positioning of AI-based instances along
the edge-cloud continuum, types of techniques and algorithms for each use
case, efficient use of predictive analytics capable of predicting future demands,
detecting failures and anomalies in the power grid that allow for the adoption
of proactive measures and improving network reliability, among many others.
This research proposal seeks to address some of the problems described above
through a holistic architecture called IAIoSGT (Native Artificial Intelligence
in IoSGT). IAIoSGT is designed with the assumption of accelerating the use
of AI techniques in an approach based on the edge-cloud continuum. The
compliance assessment of the IAIoSGT architecture, as well as its behavior
and feasibility of use, was carried out on a test bench with real technologies at
both the level of physical devices and Machine Learning algorithms, including
KNN, SVM, MLP, NB, and DT. The use case considered in the evaluation is
the classification and identification of electro-electronic devices connected to
the same power grid.

COMMITTEE MEMBERS:
Presidente - 1694485 - MARCIO EDUARDO KREUTZ
Interno - 1699087 - AUGUSTO JOSE VENANCIO NETO
Externo ao Programa - 2143852 - EDUARDO NOGUEIRA CUNHA - UFRNExterno à Instituição - DENIS LIMA DO ROSÁRIO - UFPA