Module Database Search
| MODULE DESCRIPTOR | |||
|---|---|---|---|
| Module Title | |||
| Artificial Intelligence and Machine Learning for Renewable Energy Systems | |||
| Reference | EN4200 | Version | 1 |
| Created | October 2023 | SCQF Level | SCQF 10 |
| Approved | February 2024 | SCQF Points | 15 |
| Amended | ECTS Points | 7.5 | |
| Aims of Module | |||
|---|---|---|---|
| To provide students with the ability to evaluate and apply Artificial Intelligence and Machine Learning methods, tools and techniques in renewable energy systems. | |||
| Learning Outcomes for Module | |
|---|---|
| On completion of this module, students are expected to be able to: | |
| 1 | Examine the use of Artificial Intelligence and Machine Learning techniques in renewable energy applications. |
| 2 | Critique the different Artificial Intelligence and Machine Learning algorithms used in renewable energy applications. |
| 3 | Develop Artificial Intelligence and Machine Learning solutions for renewable energy systems. |
| Indicative Module Content |
|---|
| Fundamentals of logic, reasoning, Artificial Intelligence and Machine Learning. Supervised and unsupervised learning including neural nets, support vector machines, decision trees, probabilistic learning, instance-based learning,metric learning and clustering algorithms. Real-World Applications targeting renewable energy systems. |
| Module Delivery |
|---|
| This module will be delivered by means of lectures, tutorials and self-guided study, integrated with computer-based applications. |
| Indicative Student Workload | Full Time | Part Time |
|---|---|---|
| Contact Hours | 35 | 35 |
| Non-Contact Hours | 115 | 115 |
| Placement/Work-Based Learning Experience [Notional] Hours | N/A | N/A |
| TOTAL | 150 | 150 |
| Actual Placement hours for professional, statutory or regulatory body |
| ASSESSMENT PLAN | |||||
|---|---|---|---|---|---|
| If a major/minor model is used and box is ticked, % weightings below are indicative only. | |||||
| Component 1 | |||||
| Type: | Coursework | Weighting: | 100% | Outcomes Assessed: | 1, 2, 3 |
| Description: | The coursework consists of a written technical report to examine, critique, design and implement Artificial Intelligence and Machine Learning solutions for renewable energy systems. | ||||
| MODULE PERFORMANCE DESCRIPTOR | |
|---|---|
| Explanatory Text | |
| Component 1 comprises 100% of module grade. To pass the module, a D grade is required. | |
| Module Grade | Minimum Requirements to achieve Module Grade: |
| A | A |
| B | B |
| C | C |
| D | D |
| E | E |
| F | F |
| NS | Non-submission of work by published deadline or non-attendance for examination |
| Module Requirements | |
|---|---|
| Prerequisites for Module | EN3201 |
| Corequisites for module | None. |
| Precluded Modules | None. |
| INDICATIVE BIBLIOGRAPHY | |
|---|---|
| 1 | C. Tong,Introduction to Materials for Advanced Energy Systems, Springer, 2018, ISBN 978-3-319-98002-7 (eBook) |
| 2 | E. L. Wolf, Physics and Technology of Sustainable Energy, Oxford Graduate Texts (Oxford, 2018; online edn, Oxford Academic). |
| 3 | S. Rogers and M. Girolami, A first course in Machine Learning, CRC Press, 2011 |
| 4 | M. P. Deisenroth, A. A. Faisal and C. S. Ong, Mathematics for Machine Learning, Cambridge University Press, 2020 |
| 5 | W. ERTEL, 2011, Introduction to Artificial Intelligence,Springer |
| 6 | S. Russell and P. Norvig. Artificial Intelligence: A Modern Approach. |