A collaborative framework for annotating energy datasets

Targeting human activities responsible for the energy consumption instead of focusing solely on single appliance feedback for achieving energy efficiency in residential homes would link human behaviors to the resulting energy consumption. To this end, learning when appliances are in an active or idle state and the related user activity is crucial. Until smart appliances become widespread and can communicate their internal state, identifying when the residents interact with the appliances has to be determined from the available information that can be recorded from these devices. Developing and validating learning models require ground truth in the form of annotations to indicate when an appliance is active or idle. Launching data collection campaigns to incorporate these missing ground truth data involves careful planning before the roll-out of the experiment. Prohibitive costs for the hardware and time investment to monitor the deployed equipment are necessary for quality data. As such, publicly released datasets containing appliance-level data offer a basis for most researchers. This paper addresses these challenges by providing a collaborative web-based framework to retrofit labeling on existing datasets. The platform is publicly available, applies the wisdom of the crowd in the realm of energy research and leverages gamification techniques to encourage users' active contribution. The access to the platform and furthermore to the expert manually labeled dataset intends to enable future research and foster more collaboration in this area.