PENG9200 Scientific Research Methods and Data Analysis in Engineering Science Course description

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

Master's degree in engineering science or related fields. Students are expected to have a basic understanding of the various phases of planning and implementing a research project and the academic writing process, including literature reviews, and analysing and reporting data

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.

The students will acquire a broad and concrete theoretical and practical perspective on the production and consumption of empirical research across the fields that compose engineering science. applied mathematics and physics in the broad sense, engineering and technology. They will be familiarised with how practical problems from these areas are translated into research questions and with how research problems can be defined in order to answer those research questions. The students will be introduced to a spectrum of quantitative, qualitative and mixed-method approaches, building on their exising expert knowledge, and learn how quantitative and qualitative analytical methods can provide insight into contemporary research issues in engineering science.

The results for the project assignment, process description, and the code will be assessed by the course leader. The exam can be appealed.

All aids are permitted.

Pass or fail.

One examiner. External examiner is used periodically.

Bachelor's or master's degree in engineering or science.

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.