7 research outputs found
Cross-correlation based classification of electrical appliances for non-intrusive load monitoring
This is the author accepted manuscript. The final version is available from the Institute of Electrical and Electronics Engineers via the DOI in this recordOver the last few decades, residential electrical load classification and identification have been one of the most challenging research in the area of non-intrusive load monitoring (NILM) for home energy management system. The application of NILM technique in the smart grid has gained enormous attention in recent years. Several methods, including information from the given domains into NILM, have been proposed. Recently, among these methods, machine learning techniques are shown to be significantly better based on large-scale data for load monitoring. In this paper, machine learning techniques are utilized for residential load classification on novel cross-correlation based features, which are extracted from the synthetic time series data. We also present a t-distributed stochastic neighbour embedding (t SNE) based dimensionality reduction from the high dimensional feature set so that the classification can be implemented on a general-purpose microcontroller for near real-time monitoring. Our experimental results show that the extracted features are suitable for reliable identification and classification of different and the combination of residential loads.Visvesvaraya PhD scheme, Government of Indi
New Appliance Detection for Nonintrusive Load Monitoring
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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
Data fusion strategies for energy efficiency in buildings: Overview, challenges and novel orientations
Recently, tremendous interest has been devoted to develop data fusion
strategies for energy efficiency in buildings, where various kinds of
information can be processed. However, applying the appropriate data fusion
strategy to design an efficient energy efficiency system is not
straightforward; it requires a priori knowledge of existing fusion strategies,
their applications and their properties. To this regard, seeking to provide the
energy research community with a better understanding of data fusion strategies
in building energy saving systems, their principles, advantages, and potential
applications, this paper proposes an extensive survey of existing data fusion
mechanisms deployed to reduce excessive consumption and promote sustainability.
We investigate their conceptualizations, advantages, challenges and drawbacks,
as well as performing a taxonomy of existing data fusion strategies and other
contributing factors. Following, a comprehensive comparison of the
state-of-the-art data fusion based energy efficiency frameworks is conducted
using various parameters, including data fusion level, data fusion techniques,
behavioral change influencer, behavioral change incentive, recorded data,
platform architecture, IoT technology and application scenario. Moreover, a
novel method for electrical appliance identification is proposed based on the
fusion of 2D local texture descriptors, where 1D power signals are transformed
into 2D space and treated as images. The empirical evaluation, conducted on
three real datasets, shows promising performance, in which up to 99.68%
accuracy and 99.52% F1 score have been attained. In addition, various open
research challenges and future orientations to improve data fusion based energy
efficiency ecosystems are explored