Inteligência Computacional e Sistemas de Potência

Código:

PDEEC0034

Sigla:

CIPS

Áreas Científicas
Classificação	Área Científica
OFICIAL	Engenharia Eletrotécnica e de Computadores

Ocorrência: 2019/2020 - 1S

Ativa?	Sim
Unidade Responsável:	Departamento de Engenharia Eletrotécnica e de Computadores
Curso/CE Responsável:	Programa Doutoral em Engenharia Electrotécnica e de Computadores

Ciclos de Estudo/Cursos

Sigla	Nº de Estudantes	Plano de Estudos	Anos Curriculares	Créditos UCN	Créditos ECTS	Horas de Contacto	Horas Totais
PDEEC	2	Plano de estudos oficial	1	-	7,5	70	202,5

Língua de trabalho

Inglês

Objetivos

This course aims at making students familiar with a number of tools pertaining to the domain of computatinal intelligence, which will be useful in dealing with power and energy system models in their research activity, in outher courses and in their profesional life.

The students having taken this course shall be able to develop models under the compytational intelligence paradigm, to programm algorithms and to discuss their results in terms of accuracy, effort and credibility.

Resultados de aprendizagem e competências

As a result of the course, the student will be better prepared to use Computational Intelligence methods.

Modo de trabalho

Presencial

Programa

The course syllabus includes:
- Evolutionary Computing: history, basic concepts; self adaptive models; convergence; theoretical foundations. Variants: genetic algorithms, evolutionary programming/evolution strategies.
- Particle Swarm Optimization: mouvement equation, convergence; convergence control, constriction.
- Evolutionary Particle Swarms: self-adaptive recombination.
- Cross-entropy optimization.
- Application of evolutionary swarm and cross-entropy algorithms to power system problems and discussion of the results. Application in reliability: population-based methods.
- Concept of mappers. Criticism of Minimum Square Error (MSE) as a training cost function. Introduction to Information Theoretic Learning (ITL). Training mappers under an Entropy-related MEE cost criterium. Correntropy and MCC criterium. Application of mappers in power system problems.
- Autoencoders and deep networks. Data compression and feature reduction. Interpretation in the information flow context. Unsupervised training using Infornation Theoretic concepts.
- Clustering using ITL concepts. Renyi Entropy and Cauchy-Schwarz divergence. Kulback-Leibler divergence. Mean Shift algorithms and Information Theoretic Mean Shift.  
- Polynomial networks: introduction to GDHM (Group Data Handling Method). Construction/training algorithm. Application of GDHM in power system problems.

Bibliografia Obrigatória

Vladimiro Miranda; REDESIGNING MODELS WITH INFORMATION THEORETIC LEARNING CONCEPTS: A SHORT OVERVIEW, 2018 (Text supplied by the lecturer)

Métodos de ensino e atividades de aprendizagem

Lectures and assignments.

Palavras Chave

Ciências Físicas > Matemática > Algoritmos
Ciências Tecnológicas > Engenharia > Engenharia electrotécnica
Ciências Tecnológicas > Engenharia > Engenharia de sistemas

Tipo de avaliação

Avaliação distribuída com exame final

Componentes de Avaliação

Designação	Peso (%)
Apresentação/discussão de um trabalho científico	20,00
Exame	50,00
Trabalho prático ou de projeto	30,00
Total:	100,00

Componentes de Ocupação

Designação	Tempo (Horas)
Apresentação/discussão de um trabalho científico	10,00
Elaboração de projeto	40,00
Frequência das aulas	50,00
Total:	100,00

Obtenção de frequência

In order to have his/her attendance of the course recognized, the student must complete all assignments.

Fórmula de cálculo da classificação final

In order to obtain a passing mark, the student must complete all the assignments with a positive evaluation and have a minimum of 8/20 in the final exam.
To compute the final mark, the exam will entre with a weight of 50% and the set of assignments with a weight of 50%.

Avaliação especial (TE, DA, ...)

No exceptions allowed to the general evaluation method.

Melhoria de classificação

In order to improve the evaluation results, the student may be submitted to a second exam and may request a period to improve one of the assignments.