We introduce the Plug-Level Appliance Identification Dataset (PLAID), a public and crowd-sourced dataset for load identification research consisting of short voltage and current measurements (in the order of a few seconds) for different residential appliances. The goal of PLAID is to provide a public library for high-resolution appliance measurements that can be integrated into existing or novel appliance identification algorithms. PLAID currently contains measurements for more than 200 different appliance instances, representing 11 appliance classes, and totaling more than a thousand records. In this demo we summarize the existing dataset, demonstrate how new records can be added to the library using a web interface and, finally, walk through a live example of how the library can be integrated into an existing non-intrusive load monitoring (NILM) algorithm framework.

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This paper presents the Plug-Load Appliance Identification Dataset (PLAID), a labelled dataset containing records of the electrical voltage and current of domestic electrical appliances obtained at a high sampling frequency (30 kHz). The dataset contains 1876 records of individually-metered appliances from 17 different appliance types (e.g., refrigerators, microwave ovens, etc.) comprising 330 different makes and models, and collected at 65 different locations in Pittsburgh, Pennsylvania (USA). Additionally, PLAID contains 1314 records of the combined operation of 13 of these appliance types (i.e., measurements obtained when multiple appliances were active simultaneously). Identifying electrical appliances based on electrical measurements is of importance in demand-side management applications for the electrical power grid including automated load control, load scheduling and nonintrusive load monitoring. This paper provides a systematic description of the measurement setup and dataset so that it can be used to develop and benchmark new methods in these and other applications, and so that extensions to it can be developed and incorporated in a consistent manner. 110 130 max 2 0 RMSThe main contributions of this paper are that it:The complete PLAID dataset and all mentioned scripts are available in 5 . In the same repository, code written to capture the data can be found. The files are two scripts, namely 'collecting_data.vi' (written with LabVIEW) and 'collecting_data.m' (written in MATLAB).

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Non-Intrusive Load Monitoring (NILM) is a method of extracting appliance-level power consumption information from aggregate circuit-level data with the goal of giving users feedback regarding their energy consumption so they can take control of their consumption habits. In this paper, we present a novel algorithm for classification of on and off states of appliances. We compare the performance of our algorithm in on state detection with a pervious paper that evaluated the same dataset and show that it performs up to 13% better. We also present the results of a case study where we collected data for different modes of a cooktop, microwave and dishwasher and used our algorithms to perform power estimation. The error on ten different setups in the test bed ranges from 1% to 32%. We discuss our results and lay out ideas for future work.

Non-Intrusive Load Monitoring (NILM), the set of techniques used for disaggregating total electricity consumption in a building into its constituent electrical loads, has recently received renewed interest in the research community, partly due to the roll-out of smart metering technology worldwide. Event-based NILM approaches (i.e., those that are based on first segmenting the power time-series and associating each segment with the operation of electrical appliances) are a commonly implemented solution but are prone to the propagation of errors through the data processing pipeline. Thus, during energy estimation (the final step in the process), many corrections need to be made to account for errors incurred during segmentation, feature extraction and classification (the other steps typically present in event-based approaches). A robust framework for energy estimation should use the labels from classification to (1) model the different state transitions that can occur in an appliance; (2) account for any misclassifications by correcting event labels that violate the extracted model; and (3) accurately estimate the energy consumed by that appliance over a period of time. In this paper, we address the second problem by proposing an error-correcting algorithm which looks at sequences generated by Finite State Machines (FSMs) and corrects for errors in the sequence; errors are defined as state transitions that violate the said FSM. We evaluate our framework on simulated data and find that it improves energy estimation errors. We further test it on data from 43 appliances collected from 19 houses and find that the framework significantly improves errors in energy estimates when compared to the case with no correction in 19 appliances, leaves 17 appliances unchanged, and has a slightly negative impact on 6 appliances.

Understanding where electricity is being used in buildings is an important tool for Cyber-Physical Systems (CPS) used in building energy conservation and efficiency. Current approaches for appliance-level energy metering typically require the installation of plug-through power meters, which is often difficult and costly for devices with inaccessible wires or outlets, or appliances that draw large amounts of current. In this paper, we present an energy measurement system that estimates the energy consumption of individual appliances using a wireless sensor network consisting of contactless electromagnetic field (EMF) sensors deployed near each appliance, and a whole-house power meter. We present the design of a battery-operated EMF sensor, which can detect appliance state transitions within close proximity based on magnetic and electric field fluctuations. Each detector wirelessly transmits state change events to a circuit-panel energy meter, in a time-synchronized fashion, so that the overall power measurements can be used to estimate appliance-level energy usage. Our EMF sensors are able to detect significant power state changes from a few inches away, thus making it possible to externally monitor in-wall wiring to devices. We experimentally evaluate our proposed EMF sensor, three-phase power meter and communication protocol in a residential building collecting data for over a week. The system is able to detect appliance state transitions with an accuracy of 95.8% and estimate the overall energy with an accuracy of 98.1%.