Non-intrusive appliance load monitoring with bagging classifiers

Krämer, Oliver; Klingenberg, Thole; Sonnenschein, Michael; Wilken, Olaf

doi:10.1093/jigpal/jzv016

Cited by 8 publications

(4 citation statements)

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“…where a 1 is a constant, usually 2, to regulate the global search capability, and the larger a 1 is, the stronger the global search capability; and sign(r − 0.5) regulates the search direction, where r is a random number between 0 and 1. Substituting Formulas (10) and (11) into Formula ( 9), the F expression can be redefined as:…”

Section: Concentration Updatementioning

confidence: 99%

“…(10) CNN: the novel structural convolutional neural network method used in reference [23]. (11) CNN-LSTM: the method based on the CNN-LSTM deep learning model used in reference [27].…”

Section: Experimental Design and Evaluation Metricsmentioning

confidence: 99%

“…Non-intrusive load monitoring is divided into four steps: data acquisition, event monitoring, feature extraction and load identification, among which load identification is the most important [5]. In recent years, there has been significant research conducted by numerous scholars in the field of load identification, and the common recognition algorithms include k-nearest neighbor (k-NN) [6], artificial neural network (ANN) [7,8], support vector machines (SVM) [9], logistic regression (LR) [10], decision tree (DT) [11], etc. Ref.…”

Section: Introductionmentioning

confidence: 99%

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Research on Non-Intrusive Load Recognition Method Based on Improved Equilibrium Optimizer and SVM Model

2023

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Non-intrusive load monitoring is the main trend of green energy-saving electricity consumption at present, and load identification is a core part of non-invasive load monitoring. A support vector machine (SVM) is commonly used in load recognition, but there are still some problems in the parameter selection, resulting in a low recognition accuracy. Therefore, an improved equilibrium optimizer (IEO) is proposed to optimize the parameters of the SVM. Firstly, household appliance data are collected, and load features are extracted to build a self-test dataset; and secondly, Bernoulli chaotic mapping, adaptive factors and the Levy flight were introduced to improve the traditional equilibrium optimizer algorithm. The performance of the IEO algorithm is validated on test functions, and the SVM is optimized using the IEO algorithm to establish the IEO-SVM load identification model. Finally, the recognition effect of the IEO-SVM model is verified based on the self-test dataset and the public dataset. The results show that the IEO algorithm has good optimization accuracy and convergence speed on the test function. The IEO-SVM load recognition model achieves an accuracy of 99.428% on the self-test dataset and 100% accuracy on the public dataset, and the classification performance is significantly better than other classification algorithms, which can complete the load recognition task well.

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Section: Concentration Updatementioning

confidence: 99%

“…(10) CNN: the novel structural convolutional neural network method used in reference [23]. (11) CNN-LSTM: the method based on the CNN-LSTM deep learning model used in reference [27].…”

Section: Experimental Design and Evaluation Metricsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Research on Non-Intrusive Load Recognition Method Based on Improved Equilibrium Optimizer and SVM Model

2023

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“…Kramer et al [31] have recently performed an analysis for comparing disaggregation accuracy results achieved by different classifiers such as SVM, NN and Random Forests. As it has been shown that the temporal transitions information could improve the disaggregation [12], few algorithms that could manage this combination have been investigated.…”

Section: Disaggregation Approachesmentioning

confidence: 99%

Context-Based Energy Disaggregation in Smart Homes

et al. 2016

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Abstract:In this paper, we address the problem of energy conservation and optimization in residential environments by providing users with useful information to solicit a change in consumption behavior. Taking care to highly limit the costs of installation and management, our work proposes a Non-Intrusive Load Monitoring (NILM) approach, which consists of disaggregating the whole-house power consumption into the individual portions associated to each device. State of the art NILM algorithms need monitoring data sampled at high frequency, thus requiring high costs for data collection and management. In this paper, we propose an NILM approach that relaxes the requirements on monitoring data since it uses total active power measurements gathered at low frequency (about 1 Hz). The proposed approach is based on the use of Factorial Hidden Markov Models (FHMM) in conjunction with context information related to the user presence in the house and the hourly utilization of appliances. Through a set of tests, we investigated how the use of these additional context-awareness features could improve disaggregation results with respect to the basic FHMM algorithm. The tests have been performed by using Tracebase, an open dataset made of data gathered from real home environments.

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