Information Technology and Energy Storage
Course, Master's level, 1DT107
Spring 2024 Spring 2024, Uppsala, 33%, On-campus, English
- Location
- Uppsala
- Pace of study
- 33%
- Teaching form
- On-campus
- Instructional time
- Daytime
- Study period
- 15 January 2024–17 March 2024
- Language of instruction
- English
- Entry requirements
-
120 credits including 90 credits in computer science, chemistry, mathematics, and technology. Proficiency in English equivalent to the Swedish upper secondary course English 6.
- Selection
-
Higher education credits in science and engineering (maximum 240 credits)
- Fees
-
If you are not a citizen of a European Union (EU) or European Economic Area (EEA) country, or Switzerland, you are required to pay application and tuition fees.
- Application fee: SEK 900
- First tuition fee instalment: SEK 12,083
- Total tuition fee: SEK 12,083
- Application deadline
- 16 October 2023
- Application code
- UU-61201
Admitted or on the waiting list?
- Registration period
- 15 December 2023–22 January 2024
- Information on registration
Spring 2024 Spring 2024, Uppsala, 33%, On-campus, English For exchange students
- Location
- Uppsala
- Pace of study
- 33%
- Teaching form
- On-campus
- Instructional time
- Daytime
- Study period
- 15 January 2024–17 March 2024
- Language of instruction
- English
- Entry requirements
-
120 credits including 90 credits in computer science, chemistry, mathematics, and technology. Proficiency in English equivalent to the Swedish upper secondary course English 6.
Admitted or on the waiting list?
- Registration period
- 15 December 2023–22 January 2024
- Information on registration
About the course
Introduction to recent digitalisation concepts of technological importance: Internet of Things, wireless communication systems, and its interrelation with energy storage. Introduction to machine learning and artificial intelligence: its terminology, an overview of basic algorithms and literature study on its use in modelling energy storage. Use of established tools and algorithms for machine learning in modelling energy storage.
To pass, you must be able to:
- discuss the energy profile for Internet of Things applications, wireless systems, and other emerging technologies
- explain and motivate use cases in which artificial intelligence tools can be used in the field of chemical energy storage
- explain and compare basic machine learning methods in the context of modelling energy storage
- use machine learning techniques and software to model energy storage.
Reading list
No reading list found.