We're 5 first year Computer Science students from Stockholm who love drinking coffee, knocking out code, and hanging out with fun people. This is our first hackathon and we've had a great time.
We chose to work on Intelligent Buildings, specifically on the GE Money Energy track. We set out to develop a solution which could help companies and private users minimise both their electricity bills and their environmental impact. Our application is split up into 3 distinct parts:
1: Data aggregation
Our algorithms scrape data from the web to gather information about a users previous electricity usage, as well as the prices for electricity for the coming days.
The data is then summarised in a heat map of electricity consumption represented in a ring on Komdo.tech.
Algorithms perform an analysis on the aggregated data in order to produce smart notifications about when the user is effectively consuming electricity. The smart notifications display how to change electricity usage to minimise both spending as well as carbon footprint. These notifications also display the amount of money saved if the changes where to be implemented. Users receive text messages about how they should change their energy consumption to save money and minimise their carbon footprint.
We developed a platform which can deliver data to be represented and analysed in very different contexts. The algorithms can be modified to produce insights about electricity usage, and the aggregated data can be represented in varying forms (we chose a circle, because circles are round, like our dear planet). We believe our platform holds the potential to be used for future projects to help users better understand their electricity usage, and change it to better fit their ambitions.
The future for Komdo
We are very proud of our achievements and what we managed to build here at Junction. Our ambition is to continue working on Komdo by developing a mobile application and a customised environment tailored to each users needs. We will also continue to develop our smart algorithms to provide more versatile suggestions which span over a wide spectrum of future predictive analysis. A key future usage example would be delivering our smart suggestions about energy consumption changes to automated electricity devices such as Google NEST for example.