Researcher(s)
- Kayla Roth, Computer Science, University of Delaware
- Fatimah Mohammad A Alhassan, Computer Science, University of Delaware
Faculty Mentor(s)
- Matthew Mauriello, Computer Science, University of Delaware
Abstract
People consume energy in their homes for many different reasons, such as staying cool on a hot day, keeping food fresh, and being productive. Unfortunately, energy consumption can be expensive, both for someone’s bank account and for the environment. The aim of the Electric-Vis project is to provide feedback on home energy efficiency and lifestyles. To achieve this goal, we focus on incorporating more data about user behaviors such as sleep, location, and movement patterns, into the data modeling process. Still in the early stages, data from a residential home is currently collected using commercial APIs (Application Programming Interfaces) to access data from a FitBit activity tracker and an Emporia home energy monitor. This data has been integrated into a web-based interface, but only for step count and energy usage. The goal for this summer was to further integrate the FitBit API such that it would collect sleep and location data. The collection of these data points is necessary to establish a more thorough understanding of how a person’s energy consumption may change due to common behaviors, and the final aim of this work is to visualize this data once it has been successfully stored in our back-end database.