Forecasting Techniques

Code:

PDEEC0087

Acronym:

TP

Keywords
Classification	Keyword
OFICIAL	Electrical and Computer Engineering

Instance: 2023/2024 - 2S

Active?	Yes
Responsible unit:	Department of Electrical and Computer Engineering
Course/CS Responsible:	Doctoral Program in Electrical and Computer Engineering

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
PDEEC	2	Syllabus	1	-	6	42	162

Teaching language

Suitable for English-speaking students

Objectives

Knowledge on different forecasting techniques and on the application specificity of forecasting electricity consumption, electricity markets prices and energy production.

Learning outcomes and competences

Competences on building forecasting model based on temporal series analysis. Ability to implement forecasting models based on neural networks. Competences on the performance evaluation of forecasting models. Knowledge and practice on available computational applications for building forecasting models.

Working method

B-learning

Pre-requirements (prior knowledge) and co-requirements (common knowledge)

Fundamental concepts on electrical power systems.

Program

Forecasting techniques: Regression models. Time series analysis. Forecasting based on computational intelligence techniques. Load forecasting for short, medium and lon term. Wind and solar forecasting. Energy market prices forecasting.

Mandatory literature

Makridakis, Spyros; Forecasting

Teaching methods and learning activities

Theory classes supported by multimedia. Practical classes based on the analysis of typical examples and development of field works.

The practical work will be carried out on a data set with production and consumption series of an electrical power system. This set of data will be used to exercise the practical application of the knowledge of the various themes discussed in the theoretical. All practical work carried out throughout the classes, applied to the data set provided at the beginning of the semester, will be presented in the form of a report. There will also be a forecast competition for the various components (consumption, production, price), the result of this contest will be used as an evaluation element together with the report.

Software

Matlab
Excel
SPSS 17.0

keywords

Physical sciences > Mathematics > Computational mathematics > Computational models
Physical sciences > Computer science > Systems design > Neural networks
Technological sciences > Technology > Electrical technology
Technological sciences > Engineering > Electrical engineering

Evaluation Type

Distributed evaluation without final exam

Assessment Components

Designation	Weight (%)
Teste	25,00
Trabalho prático ou de projeto	75,00
Total:	100,00

Amount of time allocated to each course unit

Designation	Time (hours)
Estudo autónomo	30,00
Frequência das aulas	40,00
Trabalho de campo	50,00
Trabalho escrito	20,00
Total:	140,00

Eligibility for exams

According to faculty regulation.

Calculation formula of final grade

25%Te + 25% TC + 50% TP

Te - test. The components TC (field work) and TP (practical work) intend to represent the students' performance in the practical classes and in the reports related to the practical works proposed.

The forecasts should be delivered individually.

Examinations or Special Assignments

Not appliable

Internship work/project

Not appliable

Special assessment (TE, DA, ...)

Same rules.

Classification improvement

The improvement of classification is only possible with the execution of a new work in the following academic year.