A graph-based signal processing approach for low-rate energy disaggregation

Abstract

This version is available at https://strathprints.strath.ac.uk/51401/ Strathprints is designed to allow users to access the research output of the University of Strathclyde. Unless otherwise explicitly stated on the manuscript, Copyright © and Moral Rights for the papers on this site are retained by the individual authors and/or other copyright owners. Please check the manuscript for details of any other licences that may have been applied. You may not engage in further distribution of the material for any profitmaking activities or any commercial gain. You may freely distribute both the url (https://strathprints.strath.ac.uk/) and the content of this paper for research or private study, educational, or not-for-profit purposes without prior permission or charge.Any correspondence concerning this service should be sent to the Strathprints administrator: strathprints@strath.ac.ukThe Strathprints institutional repository (https://strathprints.strath.ac.uk) is a digital archive of University of Strathclyde research outputs. It has been developed to disseminate open access research outputs, expose data about those outputs, and enable the management and persistent access to Strathclyde's intellectual output.A graph-based signal processing approach for low-rate energy disaggregation Abstract-Graph-based signal processing (GSP) is an emerging field that is based on representing a dataset using a discrete signal indexed by a graph. Inspired by the recent success of GSP in image processing and signal filtering, in this paper, we demonstrate how GSP can be applied to non-intrusive appliance load monitoring (NALM) due to smoothness of appliance load signatures. NALM refers to disaggregating total energy consumption in the house down to individual appliances used. At low sampling rates, in the order of minutes, NALM is a difficult problem, due to significant random noise, unknown base load, many household appliances that have similar power signatures, and the fact that most domestic appliances (for example, microwave, toaster), have usual operation of just over a minute. In this paper, we proposed a different NALM approach to more traditional approaches, by representing the dataset of active power signatures using a graph signal. We develop a regularization on graph approach where by maximizing smoothness of the underlying graph signal, we are able to perform disaggregation. Simulation results using publicly available REDD dataset demonstrate potential of the GSP for energy disaggregation and competitive performance with respect to more complex Hidden Markov Model-based approaches.

Blind non-intrusive appliance load monitoring using graph-based signal processing

“…, a < b, and similarly for matrix L. Note that (3) is the same as in [20], [23], except that we replaced a vector of known samples used for training in supervised classification approaches with a randomly picked sample (s (1)). Since D is a diagonal matrix, L is also diagonally symmetric.…”

“…is often used in the literature [20], [21], [23], where dist(x, y) can be, for example, Euclidian distance between x and y. The graphs and signals on graphs defined above can be conveniently used to represent very different data structures, such as time series, images, sensors, tracked objects, social networks, hyperlinked documents etc.…”

“…., is constant, we simplify the notation as ∆P (i) = ∆P (t i ). To avoid detecting standby settings, the initial event threshold is set to T 0 = 10W, thus, all ∆P (i) ∈ (−∞, −T 0 ) ∪ (T 0 , ∞) will be regarded as candidates for event occurrences [23].…”

“…GSP offers an alternative to conventional signal processing and data mining approaches by embedding the structure of signals onto a graph, leading to a powerful scalable and flexible approach suitable to many data mining and signal processing problems, ranging from image denoising and data compression, to classification, biomedical, and environmental data processing (see [18]- [22] and references therein). We note that recently, the latter two authors of this paper propose a GSP-based NALM approach in [23]. However, the approach of [23] is supervised and employs GSP only for data classification based on [20].…”

“…We note that recently, the latter two authors of this paper propose a GSP-based NALM approach in [23]. However, the approach of [23] is supervised and employs GSP only for data classification based on [20]. The proposed algorithm operates purely on collected aggregate active power data, and uses GSP three times, first to find an optimal event threshold, then to perform clustering, and finally for feature matching.…”