An intelligent nonintrusive load monitoring scheme based on 2D phase encoding of power signals

Abstract

Nonintrusive load monitoring (NILM) is the de facto technique for extracting device-level power consumption fingerprints at (almost) no cost from only aggregated mains readings. Specifically, there is no need to install an individual meter for each appliance. However, a robust NILM system should incorporate a precise appliance identification module that can effectively discriminate between various devices. In this context, this paper proposes a powerful method to extract accurate power fingerprints for electrical appliance identification. Rather than relying solely on time-domain (TD) analysis, this framework abstracts the phase encoding of the TD description of power signals using a two-dimensional (2D) representation. This allows mapping power trajectories to a novel 2D binary representation space, and then performing a histogramming process after converting binary codes to new decimal representations. This yields the final histogram of 2D phase encoding of power signals, namely, 2D-PEP. An empirical performance evaluation conducted with three realistic power consumption databases collected at distinct resolutions indicates that the proposed 2D-PEP descriptor achieves

Scattering Transform for Classification in Non-Intrusive Load Monitoring

“…Accuracy results above 98% were obtained in [14], which presented a technique called 2D phase encoding (2DPEP) to generate a 2D image from the NILM signal. However, the proposed approach used other classification methods based on classical machine learning, increasing the complexity.…”

“…The main reasons for that, both in terms of classification and disaggregation, can be summarized as follows: (i) the feature extraction is done automatically through the training process in an end-to-end architecture, without requiring hand-crafted and specifically designed features [12]; (ii) well-known techniques for image processing and CNNs for image classification can be adapted for NILM signals [13]. However, the need for a significant amount of training data and the high computational cost spent on training convolution filters are challenges yet to be overcome [14].…”

Noninvasive Detection of Appliance Utilization Patterns in Residential Electricity Demand

“…The REDD dataset contains measurements from six different households in the United States with a 3-s granularity, with monitoring periods from 2.7 to 25 days. The monitoring periods are not the best for detecting utilization patterns (unfortunately, 2.7 days can include some holidays, for example, and even 25 days do not cover different seasons of the year), but on the other hand this dataset records a good quantity of individual channels (18,9,20,18,24,15), each of them representing one individual appliance. The information in Table 1 leads to a system state domain of high dimension: R 52 , R 18 , R 4 , R 5 , and R 24 and R 18 , R 9 , R 20 , R 24 , and R 15 , respectively, for the UK-DALE and the REDD datasets.…”

“…For the REDD dataset, with one sample every 3 s, the set of system statuses has 28,800 measurements for each monitored day. In addition, if each system status is represented by a binary vector that indicates the status of each appliance (i.e., on or off, or 1 or 0), the number of possible statuses for each house is 2 52 , 2 18 , 2 4 , 2 5 , and 2 24 for the UK-DALE and 2 18 , 2 9 , 2 20 , 2 24 , and 2 15 for the REDD dataset. These numbers render traditional statistical analysis limited.…”

“…The present work follows these lines and focuses on the detection of the utilization patterns of different appliances by using two strategies in steps: first, by using only linear and nonparametric algorithms (PCA, k-means clustering, and Minimum Spanning Tree methods) [17,18], the existence of appliance clusters is investigated, and a methodology for defining the groups of appliances inside each group is established. Second, by using nonlinear methods, specifically Self Organizing Maps (SOM), the possibility of using a trained map as a status classifier is explored.…”