Non-intrusive load identification for smart outlets

An increasing interest in energy-efficiency combined with the decreasing cost of embedded networked sensors is lowering the cost of outlet-level metering. If these trends continue, new buildings in the near future will be able to install \u27smart\u27 outlets, which monitor and transmit an outlets power usage in real time, for nearly the same cost as conventional outlets. One problem with the pervasive deployment of smart outlets is that users must currently identify the specific device plugged into each meter, and then manually update the outlets meta-data in software whenever a new device is plugged into the outlet. Correct meta-data is important in both interpreting historical outlet energy data and using the data for building management. To address this problem, we propose Non-Intrusive Load Identification (NILI), which automatically identifies the device attached to a smart outlet without any human intervention. In particular, in our approach to NILI, we identify an intuitive and simple-to-compute set of features from time-series energy data and then employ well-known classifiers. Our results achieve accuracy of over 90% across 15 device types on outlet-level energy traces collected from multiple real homes

Exploring Micro-Incentive Strategies for Participant Compensation in High-Burden Studies

Micro-incentives represent a new but little-studied trend in participant compensation for user studies. In this paper, we use a combination of statistical analysis and models from labor economics to evaluate three canonical micro-payment schemes in the context of high-burden user studies, where participants wear sensors for extended durations. We look at how these strategies affect compliance, data quality, and retention, and show that when used carefully, micro-payments can be highly beneficial. We find that data quality is different across the micro-incentive schemes we experimented with, and therefore the incentive strategy should be chosen with care. We think that adaptive micro-payment based incentives can be used to successfully incentivize future studies at much lower cost to the study designer, while ensuring high compliance, good data quality, and lower retention issues. Author Keywords micro-payments, micro-incentives, user study