Non-Intrusive Load Monitoring Using a CNN-LSTM-RF Model Considering Label Correlation and Class-Imbalance

Zhang, Xiao; Li, Shujian; Liu, Chengxi; Zhu, Hongbo; Dong, Nan; Xiao, Tianying

doi:10.1109/access.2021.3087696

Cited by 28 publications

(18 citation statements)

References 33 publications

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“…In addressing imbalances, Lin et al [ 35 ] proposed a new loss function Focal Loss and applied it to dense object detection. In the field of NILM, Zhou et al [ 36 ] proposed a method that considers both label relevance and class imbalance. The method is implemented by a CNN-LSTM-RF model, where the class imbalance problem is mainly solved by a compound reweighting approach, while the label relevance problem is solved by the method proposed by [ 33 ].…”

Section: Related Workmentioning

confidence: 99%

Deep Learning-Based Non-Intrusive Commercial Load Monitoring

Zhou

Shao

Wang

et al. 2022

Sensors

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Commercial load is an essential demand-side resource. Monitoring commercial loads helps not only commercial customers understand their energy usage to improve energy efficiency but also helps electric utilities develop demand-side management strategies to ensure stable operation of the power system. However, existing non-intrusive methods cannot monitor multiple commercial loads simultaneously and do not consider the high correlation and severe imbalance among commercial loads. Therefore, this paper proposes a deep learning-based non-intrusive commercial load monitoring method to solve these problems. The method takes the total power signal of the commercial building as input and directly determines the state and power consumption of several specific appliances. The key elements of the method are a new neural network structure called TTRNet and a new loss function called MLFL. TTRNet is a multi-label classification model that can autonomously learn correlation information through its unique network structure. MLFL is a loss function specifically designed for multi-label classification tasks, which solves the imbalance problem and improves the monitoring accuracy for challenging loads. To validate the proposed method, experiments are performed separately in seen and unseen scenarios using a public dataset. In the seen scenario, the method achieves an average F1 score of 0.957, which is 7.77% better than existing multi-label classification methods. In the unseen scenario, the average F1 score is 0.904, which is 1.92% better than existing methods. The experimental results show that the method proposed in this paper is both effective and practical.

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Section: Related Workmentioning

confidence: 99%

Deep Learning-Based Non-Intrusive Commercial Load Monitoring

Zhou

Shao

Wang

et al. 2022

Sensors

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“…They showed using the Reference Energy Disaggregation Data Set (REDD) [53] and UK Domestic Appliance-Level Electricity (DALE) [54] datasets that the sequence-topoint technique outperforms the sequence-to-sequence state of the art approaches. In [55], a deep learning based on the convolutional neural network, long-term short-term memory network, and random forest (RF) algorithm was presented. They investigated the concept of label correlations and tested their model on the Pecan Street [56] and REDD [49] datasets.…”

Section: Related Workmentioning

confidence: 99%

Non-Intrusive Load Monitoring of Household Devices Using a Hybrid Deep Learning Model through Convex Hull-Based Data Selection

Laouali

Ruano

et al. 2022

Energies

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The availability of smart meters and IoT technology has opened new opportunities, ranging from monitoring electrical energy to extracting various types of information related to household occupancy, and with the frequency of usage of different appliances. Non-intrusive load monitoring (NILM) allows users to disaggregate the usage of each device in the house using the total aggregated power signals collected from a smart meter that is typically installed in the household. It enables the monitoring of domestic appliance use without the need to install individual sensors for each device, thus minimizing electrical system complexities and associated costs. This paper proposes an NILM framework based on low frequency power data using a convex hull data selection approach and hybrid deep learning architecture. It employs a sliding window of aggregated active and reactive powers sampled at 1 Hz. A randomized approximation convex hull data selection approach performs the selection of the most informative vertices of the real convex hull. The hybrid deep learning architecture is composed of two models: a classification model based on a convolutional neural network trained with a regression model based on a bidirectional long-term memory neural network. The results obtained on the test dataset demonstrate the effectiveness of the proposed approach, achieving F1 values ranging from 0.95 to 0.99 for the four devices considered and estimation accuracy values between 0.88 and 0.98. These results compare favorably with the performance of existing approaches.

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“…In [27], the authors applied a spatial clustering using density-based for applications with noise to classify different load curves. The idea of combining expert knowledge and deep learning models led up to 95% accuracy, overcoming other state-of-the-art deep learning methods.…”

Section: Cnn For Nilm Using Low-frequency Datamentioning

confidence: 99%

“…The idea of combining expert knowledge and deep learning models led up to 95% accuracy, overcoming other state-of-the-art deep learning methods. However, the method in [27] needed a multifeature method to transform the 1D load data into 2D matrix data. Authors in [28] proposed a 2D CNN structure that recognizes the load status.…”

Section: Cnn For Nilm Using Low-frequency Datamentioning

confidence: 99%

Scattering Transform for Classification in Non-Intrusive Load Monitoring

Aguiar

Lazzaretti

Mulinari³

et al. 2021

Energies

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Nonintrusive Load Monitoring (NILM) uses computational methods to disaggregate and classify electrical appliances signals. The classification is usually based on the power signatures of the appliances obtained by a feature extractor. State-of-the-art results were obtained extracting NILM features with convolutional neural networks (CNN). However, it depends on the training process with large datasets or data augmentation strategies. In this paper, we propose a feature extraction strategy for NILM using the Scattering Transform (ST). The ST is a convolutional network analogous to CNN. Nevertheless, it does not need a training process in the feature extraction stage, and the filter coefficients are analytically determined (not empirically, like CNN). We perform tests with the proposed method on different publicly available datasets and compare the results with state-of-the-art deep learning-based and traditional approaches (including wavelet transform and V-I representations). The results show that ST classification accuracy is more robust in terms of waveform parameters, such as signal length, sampling frequency, and event location. Besides, ST overcame the state-of-the-art techniques for single and aggregated loads (accuracies above 99% for all evaluated datasets), in different training scenarios with single and aggregated loads, indicating its feasibility in practical NILM scenarios.

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Non-Intrusive Load Monitoring Using a CNN-LSTM-RF Model Considering Label Correlation and Class-Imbalance

Cited by 28 publications

References 33 publications

Deep Learning-Based Non-Intrusive Commercial Load Monitoring

Deep Learning-Based Non-Intrusive Commercial Load Monitoring

Non-Intrusive Load Monitoring of Household Devices Using a Hybrid Deep Learning Model through Convex Hull-Based Data Selection

Scattering Transform for Classification in Non-Intrusive Load Monitoring

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