Modeling and Optimisation of Energy Systems
| Structure Type: | Study unit |
|---|---|
| Code: | ET00BN46 |
| Curriculum: | KT 2026 |
| Level: | Bachelor of Engineering |
| Credits: | 5 cr |
| Responsible Teacher: | Satpute, Shekhar |
| Language of Instruction: | English |
Learning Outcomes
This is an advanced-level course targeted towards students in their final year of engineering studies. This course explores the application of data science techniques to model and optimise modern energy systems. Students will learn to analyse real-world energy data, build predictive models, and apply optimisation techniques to enhance system performance and efficiency. The course emphasises the integration of diverse energy systems.
Student's Workload
Total workload: 135 hours
- Lectures, simulation sessions and instructor-led activities: 70 h
- Independent study, reporting, and preparation: 65 h
Includes 1 hour of student self-assessment within contact time
Prerequisites / Recommended Optional Courses
Students must have basic knowledge of
- energy production, renewables, and energy markets.
- Fundamentals of Python programming and a good command of Excel.
Contents
By the end of the course, students will be able to:
1. Analyse real-world energy data sets and extract meaningful insights.
2. Develop and apply data-driven models for renewable energy systems.
3. Analyse the role of grid stability measures, for example: energy storage, reserve markets, and decentralised production.
4. Apply modern tools to optimise energy generation, storage, and utilisation.
Mode of Delivery / Planned Learning Activities and Teaching Methods
Lectures, data collection, hands-on experiments, reporting, and simulation exercises.
Assessment Criteria
Assessment Criteria
- Grade 1 (Pass): Student can safely carry out basic data acquisition, analysis and report findings.
- Grade 2 (Satisfactory): Student has gained some knowledge and understanding of system-level energy dynamics.
- Grade 3 (Good): Student demonstrates consistent performance, good data interpretation skills, and good reporting.
- Grade 4 (Very Good): Student is able to analyse and apply the knowledge to solve practical problems.
- Grade 5 (Excellent): Student performs all tasks independently, shows initiative, and delivers analytical and well-documented results.
Assessment Methods
Individual assignments and group tasks. Data acquisition and interpretation assignments, and final project report and presentation of results.