An article about our coordination meeting in March 2019 was published in the CPUT Bulletin.
The fifth project meeting took place in the great city of Cape Town from March 11 – 12, 2019. Further, we carried out the second block course from March 13 – 15, 2019, in which the new and adapted modules at CPUT were presented and discussed. Furthermore, updates from all other partners especially regarding the courses were presented and discussed. We thank all the participants from the different partner institutions for taking part in the block course and providing us with valuable feedback. We had the unique opportunity to see the lab and its new equipment at CPUT during the lab representation.
Special thanks goes to our partners from CPUT for hosting and organizing this great events.
An article about the new lab equipment at NM-AIST was published published on the NM-AIST website.
For the student exchange a student from TU Dresden is at Nelson Mandela African Institution of Science and Technology. Her task is to assist with the development of learning and teaching material for the smart grid courses. The focus lays on practical lab task in data security and data protection in Smart Grid systems. This includes task such as, using the NM-AIST lab equipment to simulating different attack scenarios e.g. network traffic sniffing, man-in-the-middle attacks and power load analyses for user behaviour tracking.
A master’s student from Nelson Mandela African Institution of Science and Technology is visiting Stellenbosch University as one of the exchange students. She said: “what I am doing here in Stellenbosch is attending some courses on smart grid technology and renewable energy technology which are very essential for my research.”
Further, she is working on a smart grid based research on demand side management of electricity, whereby the demand management will be done on the forecasted data using different methods like time of use tariffs, direct load control, load shifting and energy efficiency. Currently, she is working on short term prediction of electricity demand.