Below we list a number of
suggested projects for DAT300. Of course we would be open to hear your
suggestions too -- but the projects must relate to Energy and ICT. The suggestions
below are just a seed idea. It is then up to you to work out the details and
the work plan with the project demonstration at the end of the course.
Explores how processing paradigms such as data
streaming can be leveraged in this context.
What kind of data gathering networks in Smart Grids
are possible with today's technology, such as embedded platforms, wireless communications and open-source hardware? How can consumers get and process data from their own smart meters in
order to change their behavior?
How can data from the smart
grid best be visualized and what patterns can be determined? Use tools such as Spotfire, Gephi, and your programming skills.
What security features do smart meters have? Hhow are they different from classical computer systems?
What are some interesting attacks that smart meters should be protected
against?
What are the main features of smart meters? Implementation in Python (or other prototyping language) of a small
smart meter model. OPTIONAL (use a Arduino or a
RaspberryPi to collect data from a small electronic
circuit simulating a consumer, similar to http://www.openhomeautomation.net/power-monitoring-arduino-ina219/ ).
For most applications, such
as energy consumption measurements in the smart grid, it is important to
monitor the network connectivity in real time. The monitor results can be used
as an indication of the health of the sensors and the network, e.g. whether
they behave in the expected way. Visualizing the network dynamics and
highlighting irregularities help the network administrator to quickly locate
and pinpoint problems and make the appropriate decisions. Tools as Gephi or JUNG can be useful.
How can a
de facto IDS such as snort be adapted for protocols found in the smart
grid. This project is about understanding snort rules and how these can be
adapted to DLMS/COSEM or MBUS traffic.
Many government agencies
and other organizations provide open datasets. How can such datasets be used to
extend our understanding of enery consumption, or
other patterns in the smart grid datasets? One source for open data is found at
the hack for Sweden site.
What services would be
useful for consumers and companies to have in the smart grid? Are the data
available sufficient to create such services? See for example the following service from E.On to save energy.