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Data Analytics in Electrical Energy SystemsLaajuus (5 cr)

Code: YE00BG81

Credits

5 op

Teaching language

  • Finnish

Responsible person

  • Mikko Pääkkönen

Learning objectives

The aim is to learn the devices used in measuring electrical networks, measuring network structure and architecture. Students will learn how different data-analysis are done for the collected data and how artificial intelligense and machine learning is applied.

Content

- Measurement systems of electrical networks.
- Datacollecting and handling systems
- The analysing technologies
- Applying machine learning to the collected data
- Applying Artificial Intelligense with the data-analysis results

Materials

Learning matrerial is in the Moodle.

Qualifications

Electrical engineering studies at bachelor UAS-level.

Assessment criteria, satisfactory (1)

1: The student masters the basic concepts of the course.
2: The student knows approximately half of the course content, and can
solve simple problems related to the course content.

Assessment methods: Online assignments, exercises and
presentations

Assessment criteria, good (3)

solve simple problems related to the course content.
3: The student can solve moderately challenging problems related to the course content
related problems and masters the course concepts.
4: The student knows the main features of the material studied in the course.

Assessment methods: Online assignments, exercises and
presentations

Assessment criteria, excellent (5)

5: The student knows the material taught in the course excellently
level.
Assessment methods: Online assignments, exercises and
presentations

Enrollment

01.12.2024 - 15.01.2025

Timing

10.03.2025 - 04.05.2025

Credits

5 op

Mode of delivery

Contact teaching

Teaching languages
  • Finnish
Degree programmes
  • Master's Degree Programme in Advanced Electrical Energy Systems
Teachers
  • Mika Vanhanen
  • Janne Karppanen
  • Mikko Pääkkönen
Student groups
  • EJS24SY
    Advanced Electrical Energy Systems (Master's Degree)

Teaching methods

Online lectures and tutoring (ZOOM), total 26 h

Working in a Moodle environment
- Self-study material (videos and learning paths) 10 h
- Practical work

The course consists of two areas, metering systems for the electricity network and data analytics for measurements. The data analytics part includes getting to know and using the cloud service.

Alternative implementation methods

Self-motivated working in a Moodle environment
- Practical work

Student workload

Student workload 135 h, of which online lectures and tutoring 26 h, self-study material 10 h. The rest of the time is devoted to internalizing the topic and doing the practical work.

Practical training and working life cooperation

Potential guest lecturers from the industry.

Qualifications

Electrical engineering studies at bachelor UAS-level.

Materials

Learning matrerial is in the Moodle.
Some of the learning material is in English.

Further information

The assessment is carried out on an approved/failed scale. A more detailed set of criteria for the approved grade is described in the course Moodle.

The learning environment of the course in Moodle will open no later than week 10.

Assessment criteria, satisfactory (1)

1: The student masters the basic concepts of the course.
2: The student knows approximately half of the course content, and can
solve simple problems related to the course content.

Assessment methods: Online assignments, exercises and
presentations

Assessment criteria, good (3)

solve simple problems related to the course content.
3: The student can solve moderately challenging problems related to the course content
related problems and masters the course concepts.
4: The student knows the main features of the material studied in the course.

Assessment methods: Online assignments, exercises and
presentations

Assessment criteria, excellent (5)

5: The student knows the material taught in the course excellently
level.
Assessment methods: Online assignments, exercises and
presentations

Enrollment

01.12.2023 - 25.04.2024

Timing

11.03.2024 - 30.04.2024

Credits

5 op

Mode of delivery

Contact teaching

Teaching languages
  • Finnish
Seats

0 - 75

Teachers
  • Janne Karppanen
  • Janne Koponen
  • Mikko Pääkkönen
Student groups
  • EJS23SY
    Advanced Electrical Energy Systems (Master's Degree)

Teaching methods

Online lectures and tutoring (ZOOM), total 26 h

Working in a Moodle environment
- Self-study material (videos and learning paths) 10 h
- Practical work

The course consists of two areas, metering systems for the electricity network and data analytics for measurements. The data analytics part includes getting to know and using the cloud service.

Alternative implementation methods

Self-motivated working in a Moodle environment
- Practical work

Student workload

Student workload 135 h, of which online lectures and tutoring 26 h, self-study material 10 h. The rest of the time is devoted to internalizing the topic and doing the practical work.

Practical training and working life cooperation

Potential guest lecturers from the industry.

Qualifications

Electrical engineering studies at bachelor UAS-level.

Materials

Learning matrerial is in the Moodle.
Some of the learning material is in English.

Further information

The assessment is carried out on an approved/failed scale. A more detailed set of criteria for the approved grade is described in the course Moodle.

The learning environment of the course in Moodle will open no later than week 10.

Assessment criteria, satisfactory (1)

1: The student masters the basic concepts of the course.
2: The student knows approximately half of the course content, and can
solve simple problems related to the course content.

Assessment methods: Online assignments, exercises and
presentations

Assessment criteria, good (3)

solve simple problems related to the course content.
3: The student can solve moderately challenging problems related to the course content
related problems and masters the course concepts.
4: The student knows the main features of the material studied in the course.

Assessment methods: Online assignments, exercises and
presentations

Assessment criteria, excellent (5)

5: The student knows the material taught in the course excellently
level.
Assessment methods: Online assignments, exercises and
presentations