Nonintrusive appliance load monitoring (NIALM) allows disaggregation of total electricity consumption into particular appliances in domestic or industrial environments. NIALM systems operation is based on processing of electrical signals acquired at one point of a monitored area. The main objective of this paper was to present the state-of-the-art in NIALM technologies for the smart home. This paper focuses on sensors and measurement methods. Different intelligent algorithms for processing signals have been presented. Identification accuracy for an actual set of appliances has been compared. This article depicts the architecture of a unique NIALM laboratory, presented in detail. Results of developed NIALM methods exploiting different measurement data are discussed and compared to known methods. New directions of NIALM research are proposed.
The paper presents a novel method for non-intrusive appliances identification. It can be used for energy load disaggregation in a smart grid. The approach identifies changes in the state of the particular appliance by measuring and processing the common supply current signal. Analysis of the instantaneous changes in the aggregated current on the output of the analyzed circuit in the power network is exploited here. The signal is processed using the time alignment of the current and voltage signals samples represented in the array form. The scheme includes filtering, event detection and identification, which is performed by comparing parameters of the detected event against previously determined signatures of monitored appliances. The analysis is performed in the time domain; therefore (unlike other existing methods), the information contained in the original signal is not lost. The approach was tested in the laboratory designed specifically for this purpose. All tests have been conducted with up to 12 appliances operating at the same time in the single power supply circuit. The measurement setup was developed and used to record appliances’ switching on/off events. During tests, 2300 events for devices were recorded. Collected data were processed to identify particular devices with the accuracy of 98.8% and macro-averaged F-score measure of 0.9874. High identification accuracy was achieved despite the high number of devices operating in the background.
The paper presents the novel HF-GEN method for determining the characteristics of Electrical Appliance (EA) operating in the end-user environment. The method includes a measurement system that uses a pulse signal generator to improve the quality of EA identification. Its structure and the principles of operation are presented. A method for determining the characteristics of the current signals’ transients using the cross-correlation is described. Its result is the appliance signature with a set of features characterizing its state of operation. The quality of the obtained signature is evaluated in the standard classification task with the aim of identifying the particular appliance’s state based on the analysis of features by three independent algorithms. Experimental results for 15 EAs categories show the usefulness of the proposed approach.
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