A Dataset for Non-Intrusive Load Monitoring: Design and Implementation

Renaux, Douglas P. B.; Pöttker, Fabiana; Ancelmo, Hellen Cristina; Lazzaretti, André E.; Lima, Carlos R. Erig; Linhares, Robson R.; Oroski, Elder; Nolasco, Lucas da Silva; Lima, Lucas Tokarski; Mulinari, Bruna Machado; Silva, José Reinaldo Lopes da; Omori, Júlio Shigeaki; Santos, Rodrigo Braun dos

doi:10.3390/en13205371

Cited by 28 publications

(18 citation statements)

References 35 publications

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“…Both datasets contain individual HEAs measurements which are convenient for extracting features, training models, conducting performance evaluation and performing benchmarking on a common basis. Indeed, some existing datasets include scenarios of multiple simultaneous loads [27]. However, before conducting disaggregation (i.e., decomposing the whole energy consumption of a dwelling into the energy usage of individual HEAs), it is important to build an initial signature database that is key to many NILM techniques.…”

Section: Heas Datasetsmentioning

confidence: 99%

Comparative Evaluation of Non-Intrusive Load Monitoring Methods Using Relevant Features and Transfer Learning

et al. 2021

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Non-Intrusive Load Monitoring (NILM) refers to the analysis of the aggregated current and voltage measurements of Home Electrical Appliances (HEAs) recorded by the house electrical panel. Such methods aim to identify each HEA for a better control of the energy consumption and for future smart grid applications. Here, we are interested in an event-based NILM pipeline, and particularly in the HEAs’ recognition step. This paper focuses on the selection of relevant and understandable features for efficiently discriminating distinct HEAs. Our contributions are manifold. First, we introduce a new publicly available annotated dataset of individual HEAs described by a large set of electrical features computed from current and voltage measurements in steady-state conditions. Second, we investigate through a comparative evaluation a large number of new methods resulting from the combination of different feature selection techniques with several classification algorithms. To this end, we also investigate an original feature selection method based on a deep neural network architecture. Then, through a machine learning framework, we study the benefits of these methods for improving Home Electrical Appliance (HEA) identification in a supervised classification scenario. Finally, we introduce new transfer learning results, which confirm the relevance and the robustness of the selected features learned from our proposed dataset when they are transferred to a larger dataset. As a result, the best investigated methods outperform the previous state-of-the-art results and reach a maximum recognition accuracy above 99% on the PLAID evaluation dataset.

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Section: Heas Datasetsmentioning

confidence: 99%

Comparative Evaluation of Non-Intrusive Load Monitoring Methods Using Relevant Features and Transfer Learning

et al. 2021

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“…The first dataset used in this work is the LIT Synthetic (LIT-SYN) [20]. This is a subset of the full LIT-Dataset and it refers to acquisitions collected from a bench, in which real loads are connected, but the network loads' switching instant is controlled.…”

Section: Lit Syntetic Datasetmentioning

confidence: 99%

“…We compare our results with state-of-the-art methods, considering two publicly available high-frequency datasets: LIT-Synthetic [20] and PLAID [21]. Among the main advantages of the proposed method, especially when compared to deep learning techniques, we can highlight:…”

Section: Introductionmentioning

confidence: 99%

Scattering Transform for Classification in Non-Intrusive Load Monitoring

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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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“…The accuracy of at least 95% was obtained for nine appliances: 1 (vacuum cleaner), 2 (slow juicer), 7 (laptop), (iron), 11 (kettle), 12 (jigsaw), 13 (coffee machine), 14 (air conditioner), and 15 (planer). Signatures for EAs 11-15 significantly differ from all others, which makes them easily identifiable (see columns [11][12][13][14][15]. The lowest classification accuracy (24%) was achieved for category 9 (sharpener) and for category 3 (lamp with the "Osram" bulb) at the level of 28%.…”

Section: Neural Networkmentioning

confidence: 99%

“…Over the past 20 years, the topic was widely explored [7][8][9][10][11]. Public databases were prepared to allow for the verification of new approaches [12][13][14]. The main achievements are summarized, for instance, in [15].…”

Section: Introductionmentioning

confidence: 99%

Identification of the State of Electrical Appliances with the Use of a Pulse Signal Generator

et al. 2021

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The paper presents the novel HF-GEN method for determining the characteristics of Electrical Appliance (EA) operating in the end-user environment. The method includes a measurement system that uses a pulse signal generator to improve the quality of EA identification. Its structure and the principles of operation are presented. A method for determining the characteristics of the current signals’ transients using the cross-correlation is described. Its result is the appliance signature with a set of features characterizing its state of operation. The quality of the obtained signature is evaluated in the standard classification task with the aim of identifying the particular appliance’s state based on the analysis of features by three independent algorithms. Experimental results for 15 EAs categories show the usefulness of the proposed approach.

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A Dataset for Non-Intrusive Load Monitoring: Design and Implementation

Cited by 28 publications

References 35 publications

Comparative Evaluation of Non-Intrusive Load Monitoring Methods Using Relevant Features and Transfer Learning

Comparative Evaluation of Non-Intrusive Load Monitoring Methods Using Relevant Features and Transfer Learning

Scattering Transform for Classification in Non-Intrusive Load Monitoring

Identification of the State of Electrical Appliances with the Use of a Pulse Signal Generator

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