SummaryNonintrusive load monitoring (NILM) is a technique for deducing the power consumption and operational schedule of individual loads in a building from measurements of the overall voltage and current feeding it, using information and communication technologies. In this article, we review the potential of this technology to enhance residential electricity audits. First, we review the currently commercially available whole-house and plug-level technology for residential electricity monitoring in the context of supporting audits. We then contrast this with NILM and show the advantages and disadvantages of the approach by discussing results from a prototype system installed in an apartment unit. Recommendations for improving the technology to allow detailed, continuous appliance-level auditing of residential buildings are provided, along with ideas for possible future work in the field.

We present Sensor Andrew, a multidisciplinary campus-wide scalable sensor network that is designed to host a wide range of sensor, actuator and low-power applications. The goals of Sensor Andrew are to support ubiquitous large-scale monitoring, operation and control of infrastructure in a way that is extensible, easy to use, and secure while maintaining privacy. Target applications currently being developed as part of Sensor Andrew include builing emergency, first-responder support, quality of life for the disabled, monitoring and optimization of water distribution systems, building power monitoring and control, social networking, and biometric sensors for campus security. Sensing devices that are used range from cameras and batteryoperated sensor nodes to energy-monitoring devices wired into building power supplies. Some of these sensing devices may also be mobile and require hand-off between different networked regions. Supporting multiple applications and heterogeneous devices requires a standardized communication medium capable of scaling to tens of thousands of sources. In this technical report, we present the architecture underlying Sensor Andrew for managing sensor data collection as well as server-side application interactions. Sensors and actuators are modeled as event nodes in a push-based publish-subscribe architecture. A data handler provides registration, discovery and data logging facilities for each device. The major elements of this architecture have been deployed in five buildings at Carnegie Mellon University, and are comprised of over 1000 sensing points reporting data from multiple communication interfaces. Finally, we describe two different case study applications currently using the infrastructure that benefit from shared information. Design choices, limitations and enhancements across various layers and protocols are also discussed.

Individual appliances' electricity consumption is automatically disaggregated from a single custom metering system on the main feed to an occupied residential building. A data acquisition system samples voltage and current at 100 kHz, then calculates real and reactive power, harmonics, and other features at 20Hz. A probabilistic eventdetector using the generalized likelihood ratio (GLR) matches human-labeled events to the time-series of features. Machine-learning classification was most successful with the 1-nearest-neighbor algorithm, correctly identifying 90% of the laboratorygenerated training events and 79% of validation examples. The challenge of obtaining adequate training data for the real-world home leads to the development of the Wire Spy, a wirelessly-networked event detector with an inductive sensor which clamps to the cable of an appliance.

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