Hackathon

Prizes

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  You and your team will receive a 500 € Amazon gift card (total mount for the team)

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  The first option is simpler, which means that we’ll expect a much-elaborated solution. The second option is more challenging and carries higher added value, which means that even experiments and not perfect solutions could be considered.

  The two options for the challenge:

  1. Starting from timeseries data made available from projects or other sources related to buildings (energy-IEQ), make (a software pipeline for) open datasets available
  2. Make a software pipeline that can generate a dataset (starting from Google Maps / Street View etc... and other datasets) in which buildings in a district/city are automatically described and classified by number of windows, presence of balconies, height, type etc...

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  You and your team will receive a 500 € Amazon Gift Card (total amount for the team)

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  Your project has  to allow to automatically detect and outline all objects that respect some given parameters (e.g. colour, shape) within an image, it then has return the list of objects detected inside the image as a list of JSON objects, as well as display the detected outline on top of the input image.

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  You and your team will receive a 500 € monni card (total amount for the team) and each team member gets a KONVERTO drinking bottle.

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  Optimal EV Charging Planner
  
  I know that in 30 minutes I’ll be in BZ. I want to charge my car at the nearest available compatible charging station to my destination. I have an appointment at the physio therapist that takes one hour. When I come back to my car, I want it to be fully charged. This means the app should not only send me to the nearest charger to my destination but should also take into consideration the charging speed (kW) of the charger and my current battery level. If my appointment takes 3h, a slower charger may be enough and more cost efficient. If the appointment takes only 15 minutes, a faster charger would be required to fully charge the car. This obviously depends on the cars battery level when I arrive at the charger. So, this should be considered. Additionally, I’d like to have the possibility to specify the percentage my car should have when I come back. If my appointment takes 15 minutes, but I’d just like to charge from 60% to 70%, a slower charger could be sufficient and cost less per kWh.
  
  App:
  Input:

  • App where you input your destination and how long you plan to stay in that area.
  • Additionally, I’d like to have the possibility to optionally enter the battery level that my car should have when I come back from my appointment.

  Output:

  • The charger nearest to my destination available with the right charging speed to charge my car in the time I’m at my appointment.

  The data required should be available on Open Data Hub.

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  Your team will receive a 500 € Amazon card and will be selected to collaborate with WaveLAB.

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  The proposal must demonstrate an innovative use of GenAI that adds significant value to WaveLAB stations, improving user interaction and satisfaction. 
  It should be technically feasible and integrable with the existing infrastracture.