Programplaner og emneplaner - Student
PENG9620 Smart cities for a Sustainable Energy Future - From Design to Practice Course description
- Course name in Norwegian
- Smart cities for a Sustainable Energy Future - From Design to Practice
- Study programme
-
PhD Programme in Engineering Science
- Weight
- 5.0 ECTS
- Year of study
- 2025/2026
- Programme description
- Course history
-
Introduction
Knowledge
Upon successful completion of the course, the student should:
- have specialized knowledge on the specific areas of a master thesis
- understand scientific writing as a process of both constructing and communicating meaning.
- be able to explain the main stages of the writing process
- understand the role and methods of peer learning and peer review, particularly the "summarize, evaluate, suggest" structure for commenting
- understand the role of revision in writing
Skills
Upon successful completion of the course, the student:
- can clearly define and limit a problem area
- can connect his/her own project to relevant literature
- can plan and carry out limited research or development projects
- can identify types and scopes of results which are required to ensure the claims and conclusions are scientifically valid
- can reflect on the decisions made and their consequences for the project
- can effectively articulate scientific problems through writing
- can give and receive peer-feedback
- can effectively revise writing
General competence
Upon successful completion of the course, the student:
- can apply knowledge and skills in new areas and carry out advanced projects
- can carry out comprehensive independent study
- can contribute to the innovation of their field
- can discuss their work in an the context of interdisciplinary engineering and ethics
- can apply their research and writing knowledge and skills in other contexts
- can independently conceptualize, delineate, and execute other academic writing processes that result in effective discipline-appropriate texts.
Recommended preliminary courses
Bachelor's or master's degree in engineering or science.
Required preliminary courses
Følgende arbeidskrav må være godkjent for å fremstille seg til avsluttende vurdering:
- deltakelse i gruppearbeid med ferdighetsøvelser (1)
- Ett individuelt fag-/refleksjonsnotat leveres i Canvas, omfang 2 sider (1)
- deltakelse i gruppepresentasjon av individuell video (1)
- deltakelse i gruppearbeid (2)
Learning outcomes
Knowledge
On successful completion of the course, the student:
- is at the forefront of knowledge about smart energy systems, both at the system level and at the specific component/application level.
- understands what different technologies can be used at what level in energy generation, transmission, distribution and consumption networks.
- knows about communication technologies and their performance limits for enabling energy intelligence in smart energy systems.
Skills
On successful completion of the course, the student can:
- solve resource optimisation problems for the energy information network.
- apply optimisation techniques and machine learning-based approaches for residential demand response management and vehicle-to-grid.
General competence
On successful completion of the course, the student can:
- communicate and collaborate with experts from other disciplines on larger interdisciplinary and multidisciplinary research projects.
- Recognise and assess a project's potential and value
- participate in debates and communicate results through recognised international channels, such as academic conferences.
- can construct and develop relevant models and discuss the model's validity.
- Disseminate knowledge to broader audiences
Content
The course is divided into three modules.
The first module covers lectures on economic interactions for the energy market, focusing mainly on applications such as demand response management (DRM), and vehicle-to-grid (V2G), etc.
The second module consists of lectures on current and emerging approaches such as machine learning and blockchain for energy intelligence and network security.
The third module will be a seminar which will include a hands-on session on tools such as optimisation and machine learning for solving specific problems in future energy information networks, and will conclude with a project assignment to be submitted by a given deadline.
Teaching and learning methods
All aids are permitted.
Course requirements
Grade Scale A-F.
The written thesis must be awarded a grade of A-E (preliminary grade) in order for a student to take the oral exam. The final grade is set after the oral exam. The grade can be adjusted up or down by one grade based on the oral exam.
Assessment
Two external examiners will be used for the assessment.
Permitted exam materials and equipment
En sensor
Grading scale
Pass or fail.
Examiners
One examiner. External examiner is used periodically.