Abstract:
Buildings contribute significantly to overall energy consumption across the world. Studies suggest that providing occupants with an energy breakdown: per-appliance
energy consumption, can help them save up to 15% energy. However, there are currently no practical solutions to provide an energy breakdown. There are three core problems impeding the practicality of energy breakdown: 1) comparability - it is virtually impossible to compare two energy breakdown techniques, 2) actionability - current research focuses mostly on giving an energy breakdown, without considering insights that can help users save energy, and 3) scalability - current research requires hardware in each home, and thus can not be scaled across all homes. In this thesis, we address these three core problems towards making energy breakdown more practical. First, we present open source tools and data sets that make it easier to compare energy breakdown methods. Second, we present techniques that create actionable energy saving insights from appliance energy traces. The generated insights such as modifying thermostat temperature setpoint can save up to 10% energy. Third, we propose new methods that can provide an energy breakdown, without installing any sensor in the home. Our methods are not only more scalable, they are also up to 37% more accurate compared to the state-of-the-art energy breakdown techniques. To summarise, our thesis attempts to make energy breakdown more practical, by making it comparable, actionable, and scalable.
