My thesis, Using Mobile Phones for Sustainability in Domestic Environments, was a cooperation between the The Media Computing Group and Prof. Anind Dey. In my work I was interested how GPS-enabled thermostats could help us reduce energy consumption in domestic environments.
To achieve this goal I used a location predictive algorithm, which predicted when the user comes homes. Based on the predicted return time the temperature in the apartment is regulated. I also tried to reduce standby power consumption by deactivating devices which draw power unnecessary while the user is not at home.
I developed a prototype system with off-the-shelf home automation hardware, which allowed the user to control temperature and standby devices. The system was developed on a Google Android G1 smart phone and allowed the user remote control over the system.
My thesis was supervised by Prof. Anind Dey and Max Möllers.
- MobileSafer, a system that explores the possibility to influence a user's long-term behavior towards power consumption
- with data gathered through the MobileSafer project I also want to realize the idea of using a machine learning technique to accurately calculate power consumption of heating and cooling systems in an apartment
- Machine-Learning in relation to HCI and Sustainability
- Activity Prediction
- Mobile Technologies