ØAMET2000 Matematikk II 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

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.

None.

Studenten skal etter å ha fullført emnet ha følgende totale læringsutbytte definert i kunnskap, ferdigheter og generell kompetanse:

Kunnskap

Studenten har

• elementære kunnskaper i lineæralgebra og differensiallikninger
• kunnskaper i matematisk analyse utover Matematikk 1

Ferdigheter

Studenten kan

• regne med vektorer, matriser og determinanter
• løse systemer av lineære likninger med eliminasjonsmetoder og med Cramers formler
• finne inversmatriser
• finne inverse funksjoner
• beregne grenser og bruke L-Hopitals regel
• standard integrasjonsmetoder
• løse enkle separable differensiallikninger
• løse enkle førsteordens lineære differensiallikninger
• undersøke homogenitet for funksjoner av flere variable
• implisittderivere og differensiere ikkelineære systemer av likninger

Generell kompetanse

Studenten kan

• lese mer avansert matematisk formulert faglitteratur og har fått trening i logisk og analytisk tenkning

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 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.