ENE909 Data Analytics and Digitization
- ECTS Credits:
- 2.5
- Responsible department:
- Faculty of Engineering and Science
- Course Leader:
- Joao Gouveia Aparicio Bento Leal
- Lecture Semester:
- Autumn
- Teaching language:
- English
- Duration:
- 1 term
The course is connected to the following study programs
- Offshore Wind Energy
Teaching language
EnglishRecommended prerequisites
Previous working experience in offshore industries and energy sector.
Course contents
Introduction to Python programming - Importing relevant SCADA data - Pre-processing data (inspect, clean, working with timestamps, imputation techniques) - Plot relevant data and display basic statistics - Data mining (outlier detection and basic association rules)
Learning outcomes
After the successful completion of the course, the students should:
- be able to import and display data from SCADA systems.
- be able to pre-process large datasets containing different types of variables.
- be able to analyse the performance data from the turbines in the offshore wind farm and use it for the efficient management of offshore wind projects.
- be able to perform basic data mining for wind energy analytics.
Teaching methods
Online lectures and exercises (Python programming). Estimated workload for the average student is approximately 70 hours.
Evaluation
A digital evaluation will be organized in accordance with the quality system for education, chapter 4.1.
Offered as Single Standing Module
Yes, if there are places available.
Admission Requirement if given as Single Standing Module
Admission requirements for the course are the same as for the Study programme on Offshore Wind
Assessment methods and criteria
Assessment of the group project assignment leading to Pass/Fail. These assignments can be undertaken in groups with a maximum of 4 members. The group as a whole is graded. Further information on the assessment criterion will be given in Canvas at the beginning of the semester.