196 research outputs found
Multi-timescale Event Detection in Nonintrusive Load Monitoring based on MDL Principle
Load event detection is the fundamental step for the event-based
non-intrusive load monitoring (NILM). However, existing event detection methods
with fixed parameters may fail in coping with the inherent multi-timescale
characteristics of events and their event detection accuracy is easily affected
by the load fluctuation. In this regard, this paper extends our previously
designed two-stage event detection framework, and proposes a novel
multi-timescale event detection method based on the principle of minimum
description length (MDL). Following the completion of step-like event detection
in the first stage, a long-transient event detection scheme with
variable-length sliding window is designed for the second stage, which is
intended to provide the observation and characterization of the same event at
different time scales. In that, the context information in the aggregated load
data is mined by motif discovery, and then based on the MDL principle, the
proper observation scales are selected for different events and the
corresponding detection results are determined. In the post-processing step, a
load fluctuation location method based on voice activity detection (VAD) is
proposed to identify and remove the unreasonable events caused by fluctuations.
Based on newly proposed evaluation metrics, the comparison tests on public and
private datasets demonstrate that our method achieves higher detection accuracy
and integrity for events of various appliances across different scenarios.Comment: 11 pages,16 figure
Recommended from our members
Advances in Multiscale Methods with Applications in Optimization, Uncertainty Quantification and Biomechanics
Advances in multiscale methods are presented from two perspectives which address the issue of computational complexity of optimizing and inverse analyzing nonlinear composite materials and structures at multiple scales. The optimization algorithm provides several solutions to meet the enormous computational challenge of optimizing nonlinear structures at multiple scales including: (i) enhanced sampling procedure that provides superior performance of the well-known ant colony optimization algorithm, (ii) a mapping-based meshing of a representative volume element that unlike unstructured meshing permits sensitivity analysis on coarse meshes, and (iii) a multilevel optimization procedure that takes advantage of possible weak coupling of certain scales. We demonstrate the proposed optimization procedure on elastic and inelastic laminated plates involving three scales. We also present an adaptive variant of the measure-theoretic approach (MTA) for stochastic characterization of micromechanical properties based on the observations of quantities of interest at the coarse (macro) scale. The salient features of the proposed nonintrusive stochastic inverse solver are: identification of a nearly optimal sampling domain using enhanced ant colony optimization algorithm for multiscale problems, incremental Latin-hypercube sampling method, adaptive discretization of the parameter and observation spaces, and adaptive selection of number of samples. A complete test data of the TORAY T700GC-12K-31E and epoxy #2510 material system from the NIAR report is employed to characterize and validate the proposed adaptive nonintrusive stochastic inverse algorithm for various unnotched and open-hole laminates. Advances in Multiscale methods also provides us a unique tool to study and analyze human bones, which can be seen as a composite material, too. We used two multiscale approaches for fracture analysis of full scale femur. The two approaches are the reduced order homogenization (ROH) and the novel accelerated reduced order homogenization (AROH). The AROH is based on utilizing ROH calibrated to limited data as a training tool to calibrate a simpler, single-scale anisotropic damage model. For bone tissue orientation, we take advantage of so-called Wolff’s law. The meso-phase properties are identified from the least square minimization of error between the overall cortical and trabecular bone properties and those predicted from the homogenization. The overall elastic and inelastic properties of the cortical and trabecular bone microstructure are derived from bone density that can be estimated from the Hounsfield units (HU). For model validation, we conduct ROH and AROH simulations of full scale finite element model of femur created from the QCT and compare the simulation results with available experimental data
Analysis of trends in seasonal electrical energy consumption via non-negative tensor factorization
This paper looks at the extraction of trends of household electrical seasonal consumption via load disaggregation. With the proviso that data for several home devices can be embedded in a tensor, non-negative multi-way array factorization is performed in order to extract the most relevant components. In the initial decomposition step the decomposed signals are incorporated in the test signal consisting of the whole-home measured consumption. After this the disaggregated data corresponding to each electrical device is obtained by factorizing the associated matrix through the learned model. Finally, we evaluate the performance of load disaggregation by the supervised method, and study the trends along several years and across seasons. Towards this end, computational experiments were yielded using real-world data from household electrical consumption measurements along several years. While breaking down the whole house energy consumption into appliance level gives less accurate estimates in the late years, we empirically show the adequacy of this method for handling the earlier years and the estimates of the underlying seasonal trend-cycle.info:eu-repo/semantics/acceptedVersio
Double Fourier Integral Analysis based Convolutional Neural Network Regression for High-Frequency Energy Disaggregation
© 2021 IEEE. This is the accepted manuscript version of an article which has been published in final form at https://doi.org/ 10.1109/TETCI.2021.3086226Non-Intrusive Load Monitoring aims to extract the energy consumption of individual electrical appliances through disaggregation of the total power load measured by a single smart-meter. In this article we introduce Double Fourier Integral Analysis in the Non-Intrusive Load Monitoring task in order to provide more distinct feature descriptions compared to current or voltage spectrograms. Specifically, the high-frequency aggregated current and voltage signals are transformed into two-dimensional unit cells as calculated by Double Fourier Integral Analysis and used as input to a Convolutional Neural Network for regression. The performance of the proposed methodology was evaluated in the publicly available U.K.-DALE dataset. The proposed approach improves the estimation accuracy by 7.2% when compared to the baseline energy disaggregation setup using current and voltage spectrograms.Peer reviewe
Non-intrusive Load Monitoring based on Self-supervised Learning
Deep learning models for non-intrusive load monitoring (NILM) tend to require
a large amount of labeled data for training. However, it is difficult to
generalize the trained models to unseen sites due to different load
characteristics and operating patterns of appliances between data sets. For
addressing such problems, self-supervised learning (SSL) is proposed in this
paper, where labeled appliance-level data from the target data set or house is
not required. Initially, only the aggregate power readings from target data set
are required to pre-train a general network via a self-supervised pretext task
to map aggregate power sequences to derived representatives. Then, supervised
downstream tasks are carried out for each appliance category to fine-tune the
pre-trained network, where the features learned in the pretext task are
transferred. Utilizing labeled source data sets enables the downstream tasks to
learn how each load is disaggregated, by mapping the aggregate to labels.
Finally, the fine-tuned network is applied to load disaggregation for the
target sites. For validation, multiple experimental cases are designed based on
three publicly accessible REDD, UK-DALE, and REFIT data sets. Besides,
state-of-the-art neural networks are employed to perform NILM task in the
experiments. Based on the NILM results in various cases, SSL generally
outperforms zero-shot learning in improving load disaggregation performance
without any sub-metering data from the target data sets.Comment: 12 pages,10 figure
An intelligent nonintrusive load monitoring scheme based on 2D phase encoding of power signals
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 outperformance for appliance identification in comparison with other recent techniques. Accordingly, high identification accuracies are attained on the GREEND, UK-DALE, and WHITED data sets, where 99.54%, 98.78%, and 100% rates have been achieved, respectively, using the proposed 2D-PEP descriptor. 2020 The Authors. International Journal of Intelligent Systems published by Wiley Periodicals LLCThis paper was made possible by National Priorities Research Program (NPRP) Grant No. 10-0130-170288 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors. Open Access funding provided by the Qatar National Library.Scopu
Robust event-based non-intrusive appliance recognition using multi-scale wavelet packet tree and ensemble bagging tree
open access articleProviding the user with appliance-level consumption data is the core of each energy efficiency system. To that
end, non-intrusive load monitoring is employed for extracting appliance specific consumption data at a low cost
without the need of installing separate submeters for each electrical device. In this context, we propose in this
paper a novel non-intrusive appliance recognition system based on (i) detecting events in the aggregated power
signal using a novel and powerful scheme, (ii) applying multiscale wavelet packet tree to collect comprehensive
energy consumption features, and (iii) adopting an ensemble bagging tree classifier along with comparing its
performance with various machine learning schemes. Moreover, to validate the proposed model, an empirical
investigation is conducted on two real and public energy consumption datasets, namely, the GREEND and REDD,
in which consumption readings are collected at low-frequencies. In addition, a comprehensive review of recent
non-intrusive load monitoring approaches has been conducted and presented, in which their characteristics,
performances and limitations are described. The proposed non-intrusive load monitoring system shows a high
appliance recognition performance in terms of the accuracy, F1 score and low time complexity when it has been
applied to different households from the GREEND and REDD repositories, in which every house includes various
domestic appliances. Obtained results have described, e.g., that average accuracies of 97.01% and 96.36% have
been reached on the GREEND and REDD datasets, respectively, which outperformed almost existing solutions
considered in this framework
A Practical Load Disaggregation Approach for Monitoring Industrial Users Demand with Limited Data Availability
The emergence of smart sensors has had a significant impact on the utility industry. In particular, it has made the planning and implementation of demand-side management (DSM) programmes easier. Nevertheless, for various reasons, some users may not implement smart meters for load monitoring. This paper addresses such cases, particularly large-scale industrial users, which, despite heavy electrical loads coming from many different processes, implement only simple energy measuring equipment for billing purposes. This necessitates the utilisation of novel methodologies for load disaggregation, often referred to as nonintrusive load monitoring (NILM). The availability of such tools can create multifold benefits for industrial park management, utility service providers, regulators, and policymakers. Here, we introduce an optimisation algorithm for nonintrusive load disaggregation that is low-cost, speedy, and acceptably accurate. As a case study, we used real network data of three industrial sectors: food processing, stonecutting, and glassmaking. For all cases, the optimisation framework developed a desegregated profile and estimated the load with an error of less than 5%. For non-workdays, given the higher uncertainty for the continuity of different processes, the estimation error was higher but still in an acceptable range of around 3.63–15.09% with an average of 8.10%.</jats:p
- …