Energy disaggregation is to discover the energy consumption of individual appliances from their aggregated energy values. To solve the problem, most existing approaches rely on either appliances' signatures or their state transition patterns, both hard to obtain in practice. Aiming at developing a simple, universal model that works without depending on sophisticated machine learning techniques or auxiliary equipments, we make use of easily accessible knowledge of appliances and the sparsity of the switching events to design a Sparse Switching Event Recovering (SSER) method. By minimizing the total variation (TV) of the (sparse) event matrix, SSER can effectively recover the individual energy consumption values from the aggregated ones. To speed up the process, a Parallel Local Optimization Algorithm (PLOA) is proposed to solve the problem in active epochs of appliance activities in parallel. Using real-world trace data, we compare the performance of our method with that of the state-of-the-art solutions, including Least Square Estimation (LSE) and iterative Hidden Markov Model (HMM). The results show that our approach has an overall higher detection accuracy and a smaller overhead.
@cite_18: This paper presents a new nonintrusive appliance load monitoring technique based on integer programming. Nonintrusive appliance load monitoring is the problem of identifying the operating conditions of electric appliances in a house by observing only the overall load current and voltage. Since the overall load current is expressed as a superposition of the currents of the operating appliances, the monitoring problem can be formulated as an integer quadratic programming problem by expressing the operating conditions as integer variables. This problem is solvable with a sufficiently small computational burden thanks to the recent development of commercial software. The proposed method does not require relearning even when a new appliance is installed in the house. Furthermore, the proposed formulation is applicable to cases in which some appliance has multiple modes, and cases in which some appliances of the same type are operating simultaneously. The usefulness of the proposed technique is verified by experimental results. © 2010 Wiley Periodicals, Inc. Electr Eng Jpn, 174(2): 18–25, 2011; Published online in Wiley Online Library (wileyonlinelibrary.com). DOI 10.1002 eej.21040
@cite_14: Abstract Increased interest in energy scorekeeping, load forecasting and improved control of electricity-consuming equipment has focused attention on the instrumentation required to obtain the desired data. Work performed by the authors and other researchers has shown that individual loads can be detected and separated from rapid sampling of power at a single point serving a number of pieces of equipment, for example the electrical service entrance for an entire house or all of the central space-conditioning equipment in a commercial building. This technique has worked well in tests in houses but faces more difficult challenges in commercial buildings. We present our results for this centralized or non-intrusive load monitoring technique, applied to the space-conditioning equipment in an office laboratory building in which equipment start-up and shut-down was centrally observed and analyzed on the basis of changes in steady-state power. We further describe our enhanced technique for distinguishing loads by matching start-up transients to known patterns, and present laboratory tests of fully automated detection hardware and software.
@cite_22: This paper describes a transient event classification scheme, system identification techniques, and implementation for use in nonintrusive load monitoring. Together, these techniques form a system that can determine the operating schedule and find parameters of physical models of loads that are connected to an AC or DC power distribution system. The monitoring system requires only off-the-shelf hardware and recognizes individual transients by disaggregating the signal from a minimal number of sensors that are installed at a central location in the distribution system. Implementation details and field tests for AC and DC systems are presented.
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@cite_3: The data collected by smart meters contain a lot of useful information. One potential use of the data is to track the energy consumptions and operating statuses of major home appliances. The results will enable homeowners to make sound decisions on how to save energy and how to participate in demand response programs. This paper presents a new method to breakdown the total power demand measured by a smart meter to those used by individual appliances. A unique feature of the proposed method is that it utilizes diverse signatures associated with the entire operating window of an appliance for identification. As a result, appliances with complicated middle process can be tracked. A novel appliance registration device and scheme is also proposed to automate the creation of appliance signature database and to eliminate the need of massive training before identification. The software and system have been developed and deployed to real houses in order to verify the proposed method.
@cite_23: This paper describes the theoretical foundation and prototype implementation of a power system transient event detector for use in a nonintrusive load monitor (NILM). The NILM determines the operating schedule of the major electrical loads in a building from measurements made at the electric utility service entry. The transient event detector extends the applicability of the NILM to challenging commercial and industrial sites. A spectral preprocessor for use in the transient event detector is introduced first. Then, the transient event detection algorithm is developed. The performance of the algorithm is illustrated with results from a prototype event detector. >
@cite_15: Nonintrusive load monitoring (NILM) can determine operating schedule of electrical loads in a target system from measurements made at a centralized location, such as the electric utility service entry. NILM is an ideal platform for extracting useful information about any system that uses electromechanical devices. It has a low installation cost and high reliability because it uses a bare minimum of sensors. It is possible to use modem state and parameter estimation algorithms to verify remotely the "health" of electromechanical loads by using NILM to analyze measured waveforms associated with the operation of individual loads. NILM can also monitor the operation of the electrical distribution system itself, identifying situations where two or more otherwise healthy loads interfere with each other's operation through voltage waveform distortion or power quality problems. Strategies for nonintrusive monitoring have developed over the last 20 years. Advances in computing technology make a new wealth of computational tools useful in practical, field-based NILM systems. This article reviews techniques for high-performance nonintrusive load and diagnostic monitoring and illustrates key points with results from field tests.
@cite_12: A load signature is an electrical expression that a load device or appliance distinctly possesses. Load signatures can be applied to produce many useful services and products, such as, determining the energy usage of individual appliances, monitoring the health of critical equipment, monitoring power quality, and developing facility management tools. Load signatures of typical yet extensive loads are needed to be collected before applying them to different services and products. As there are an enormous number of electrical appliances, it is beneficial to classify the appliances for building a well-organized load signature database. The objective of this study is to develop an effective method to classify the loads. A 2-dimensional form of load signatures, voltage-current (V-l) trajectory, is suggested for characterizing the typical household appliances.Hierarchical clustering method was employed to classify the appliances and construct the taxonomy of the appliances. The taxonomy based on V-l trajectory was compared to the taxonomies based on traditional power metrics and eigenvectors in the previous studies. It was found that the groups of appliances in the taxonomy based on V-I trajectory were well-separated and had engineering meanings .
Most approaches are based on appliances' signatures, , specific features such as the real reactive power, current, and voltage of running appliances @cite_8 . These methods need the support of high sampling rate and build either steady or transient signal features of appliances with labeled training datasets. The signal features are treated as the appliances' signatures @cite_14 @cite_15 , based on which event detection schemes are developed to detect appliances' on off as well as different running states. The detected events are ascribed to certain appliances' activities via classification @cite_3 @cite_18 @cite_12 . In addition to time-domain signal features, spectral analysis has also been adopted to search for appliances' signatures in the frequency domain @cite_23 @cite_22 @cite_21 .