Residential Load Disaggregation Considering State Transitions

Zeinal-Kheiri, Sevda; Shotorbani, Amin Mohammadpour; Mohammadi‐Ivatloo, Behnam

doi:10.1109/tii.2019.2925323

Cited by 31 publications

(25 citation statements)

References 27 publications

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“…], with t 0 being the first sample of i agg and v agg respectively. In DFIA an output function f (•) is defined [24] by the cyclically varying signals i τ agg and v τ agg , i.e. f (v τ agg , i τ agg ), which in our case is the instantaneous power p τ…”

Section: B Double Fourier Integral Analysismentioning

confidence: 99%

“…Combinations of machine learning algorithms for fusion of information [19] and modelling of temporal dynamics [20] have also been proposed. As regards methods using source separation algorithms they are based on non-negative matrix/tensor factorization [21]- [23], integer non-linear programming [24] and sparse-coding [25], [26] using additional constraints (e.g. sum-to-one or cross-entropy) on the optimization problem [27], [28].…”

Section: Introductionmentioning

confidence: 99%

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Double Fourier Integral Analysis Based Convolutional Neural Network Regression for High-Frequency Energy Disaggregation

Schirmer

Mporas

2022

IEEE Trans. Emerg. Top. Comput. Intell.

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Non-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 UK-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.

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Section: B Double Fourier Integral Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Double Fourier Integral Analysis Based Convolutional Neural Network Regression for High-Frequency Energy Disaggregation

Schirmer

Mporas

2022

IEEE Trans. Emerg. Top. Comput. Intell.

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“…State-optimization-based approaches have been proposed to estimate optimal state transitions [9,10]. In [9], it was shown that the computation complexity can be reduced by limiting the possible appliance transition modes.…”

Section: Introductionmentioning

confidence: 99%

Time-Lapse Image Method for Classifying Appliances in Nonintrusive Load Monitoring

et al. 2021

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In this paper, a time-lapse image method is proposed to improve the classification accuracy for multistate appliances with complex patterns based on nonintrusive load monitoring (NILM). A log-likelihood ratio detector with a maxima algorithm was applied to construct a real-time event detection of home appliances. Moreover, a novel image-combining method was employed to extract information from the data based on the Gramian angular field (GAF) and recurrence plot (RP) transformations. From the simulation results, it was confirmed that the classification accuracy can be increased by up to 30% with the proposed method compared with the conventional approaches in classifying multistate appliances.

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“…This method is expensive and inconvenient, because an ILM-based power system with several appliances to read and record data needs a magnitude of sensors, which incur a prohibitive extension cost, and it does not respect consumer privacy. In contrast, NILM, or load disaggregation, can analyze the aggregated power consumption data of the appliances' exclusive power consumption via the appliance power consumption patterns, with no need to have data recorder sensors ( Figure 1) [8,9]. Nowadays, worldwide smart electricity meters are widely installed and used in homes and other places.…”

Section: Introductionmentioning

confidence: 99%

“…Recognition of the simultaneous on and off state of multiple devices is dealt with a Cepstrum smoothing-based load disaggregation in [18]. Optimization-based methods to solve the NILM problem are proposed in [9,[19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Improving Residential Load Disaggregation for Sustainable Development of Energy via Principal Component Analysis

et al. 2020

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The useful planning and operation of the energy system requires a sustainability assessment of the system, in which the load model adopted is the most important factor in sustainability assessment. Having information about energy consumption patterns of the appliances allows consumers to manage their energy consumption efficiently. Non-intrusive load monitoring (NILM) is an effective tool to recognize power consumption patterns from the measured data in meters. In this paper, an unsupervised approach based on dimensionality reduction is applied to identify power consumption patterns of home electrical appliances. This approach can be utilized to classify household activities of daily life using data measured from home electrical smart meters. In the proposed method, the power consumption curves of the electrical appliances, as high-dimensional data, are mapped to a low-dimensional space by preserving the highest data variance via principal component analysis (PCA). In this paper, the reference energy disaggregation dataset (REDD) has been used to verify the proposed method. REDD is related to real-world measurements recorded at low-frequency. The presented results reveal the accuracy and efficiency of the proposed method in comparison to conventional procedures of NILM.

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Residential Load Disaggregation Considering State Transitions

Cited by 31 publications

References 27 publications

Double Fourier Integral Analysis Based Convolutional Neural Network Regression for High-Frequency Energy Disaggregation

Double Fourier Integral Analysis Based Convolutional Neural Network Regression for High-Frequency Energy Disaggregation

Time-Lapse Image Method for Classifying Appliances in Nonintrusive Load Monitoring

Improving Residential Load Disaggregation for Sustainable Development of Energy via Principal Component Analysis

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