PENG9560 Topics in Artificial Intelligence and Machine Learning Emneplan

This course covers contemporary topics in smart energy systems such as smart power grid, smart buildings, vehicle-to-grid (V2G) and communication technologies for and network security in smart energy systems, including emerging approaches towards energy intelligence such as machine learning and blockchain.

The course will be offered once a year, provided 5 or more students sign up for the course. If less than 5 students sign up for a course, the course will be cancelled for that year

A project assignment and an oral presentation.

Both exams must be passed in order to pass the course.

The oral exam cannot be appealed.

None.

All aids are permitted.

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

Module 1 and 2 will take the form of a series of lectures. Module 3 will be a combination of hands-on sessions along with the project assignment.

Practical training

The students will solve specific problems using optimisation or machine learning techniques. The students will submit a brief report with results for the problem in the assignment, also describing the process they used for solving the assignment, including the code.

None.

Students who complete the course are expected to have the following learning outcomes, defined in terms of knowledge, skills and general competence:

Knowledge:

On successful completion of the course, the student:

• is at the forefront of knowledge within the topic of his/her engineering project.
• has a profound understanding of the state-of-the-art and the latest developments in the professional engineering field relevant to his/her doctoral thesis.
• can apply the theories, methods and processes in scholarly projects as well as in professional engineering projects specific to his/her field of engineering.

Skills:

On successful completion of the course, the student can:

• apply theoretical knowledge, scientific methods and simulation tools suitable for solving complex civil engineering problems.
• deal with complex professional issues with an academic approach and reflect critically on established knowledge and practice within the research field of the project.
• plan and conduct scholarly work within the topic of his/her the engineering project.
• analyse existing theories, methods and standardised solutions on practical and theoretical engineering problems.

General competence:

On successful completion of the course, the student can:

• apply his/her knowledge and skills to carrying out advanced tasks and projects.
• communicate issues, analyses and solutions to both specialists and non-specialists.
• assess the need for, and initiate innovation in his/her field of expertise.

Seminars, project assignment (scholarly work), scientific report and oral presentation.

None.