On time series representations for multi-label NILM

Nalmpantis, Christoforos; Vrakas, Dimitris

doi:10.1007/s00521-020-04916-5

Cited by 32 publications

(19 citation statements)

References 49 publications

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“…The F1 score is used as a general evaluation indicator for evaluating multi-classification problems, which is selected for evaluation of models in most essays. Except that [26] and [27] use the low-frequency dataset of UK-DALE and REDD, all the others use highfrequency datasets. As seen from TABLE II, on the PLAID dataset, compared with the ordinary CNN, the algorithm proposed in this paper has increased by 3.88%.…”

Section: E Experiments Resultsmentioning

confidence: 99%

Non-Intrusive Adaptive Load Identification Based on Siamese Network

Wang

et al. 2022

IEEE Access

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The traditional non-intrusive load monitoring (NILM) algorithms are mostly based on classification models, which have several deficiencies. Firstly, a large amount of labeled data is required to train the classification model. Secondly, these algorithms cannot identify unknown devices that frequently encountered in practical application. Finally, these models have poor performance in versatility which means they only adapt to the trained data. These shortcomings greatly influence the practicality of these NILM algorithms. To tackle these problems, this paper has proposed a non-intrusive adaptive load identification model based on the Siamese network, which uses both the V-I trajectory and active power as the load signatures. The Siamese network is utilized to calculate the similarity of the V-I trajectory, and the load identification is realized by matching the signature with the feature library. Through adding new features to the feature library dynamically, the identification of unknown load can be realized. In addition, the Siamese network is a typical network for few-shot learning, thus the proposed model can be trained with a small number of samples to achieve ideal recognition effect. At last, the validity and versatility of the model are verified in PLAID dataset and COOLL dataset.

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Section: E Experiments Resultsmentioning

confidence: 99%

Non-Intrusive Adaptive Load Identification Based on Siamese Network

Wang

et al. 2022

IEEE Access

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“…Furthermore, the authors compare their algorithm to other cutting edge multi-label NILM approaches such as classification based on extreme learning machines (ELM) [34], graph-based semi-supervised learning [35], and an approach based on deep dictionary learning and deep transform learning [36]. Nalmpantis and Vrakas [37] present a multi-label NILM based on the Signal2Vec algorithm that maps any time series into a vector space. A deep neural network (DNN) based multi-label NILM applying active power features at low-sampling frequency is proposed in [23,24].…”

Section: Related Workmentioning

confidence: 99%

“…Moreover, our results cannot be directly compared with the ones presented in [49], as these were obtained from a private dataset, besides the very different experimental settings including a different performance metric. In [23,37], the F 1 macro score for TCNN and FCNN DNN based multi-label classifiers are given; however, they use UK-DALE dataset making the comparison irrelevant.…”

Section: Complexity Analysismentioning

confidence: 99%

Multi-Label Learning for Appliance Recognition in NILM Using Fryze-Current Decomposition and Convolutional Neural Network

Faustine

Pereira²

2020

Energies

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The advance in energy-sensing and smart-meter technologies have motivated the use of a Non-Intrusive Load Monitoring (NILM), a data-driven technique that recognizes active end-use appliances by analyzing the data streams coming from these devices. NILM offers an electricity consumption pattern of individual loads at consumer premises, which is crucial in the design of energy efficiency and energy demand management strategies in buildings. Appliance classification, also known as load identification is an essential sub-task for identifying the type and status of an unknown load from appliance features extracted from the aggregate power signal. Most of the existing work for appliance recognition in NILM uses a single-label learning strategy which, assumes only one appliance is active at a time. This assumption ignores the fact that multiple devices can be active simultaneously and requires a perfect event detector to recognize the appliance. In this paper proposes the Convolutional Neural Network (CNN)-based multi-label learning approach, which links multiple loads to an observed aggregate current signal. Our approach applies the Fryze power theory to decompose the current features into active and non-active components and use the Euclidean distance similarity function to transform the decomposed current into an image-like representation which, is used as input to the CNN. Experimental results suggest that the proposed approach is sufficient for recognizing multiple appliances from aggregated measurements.

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“…Recognizing many appliances with one model has attracted the interest of many researchers as well. Multi-label approaches usually identify on-off states of a predefined number of appliances [3,4]. This research focuses on the single regression approach, aiming to develop a computationally efficient energy disaggregator.…”

Section: Introductionmentioning

confidence: 99%

Neural Fourier Energy Disaggregation

Nalmpantis

Gkalinikis

Vrakas

2022

Sensors

Self Cite

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Deploying energy disaggregation models in the real-world is a challenging task. These models are usually deep neural networks and can be costly when running on a server or prohibitive when the target device has limited resources. Deep learning models are usually computationally expensive and they have large storage requirements. Reducing the computational cost and the size of a neural network, without trading off any performance is not a trivial task. This paper suggests a novel neural architecture that has less learning parameters, smaller size and fast inference time without trading off performance. The proposed architecture performs on par with two popular strong baseline models. The key characteristic is the Fourier transformation which has no learning parameters and it can be computed efficiently.

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On time series representations for multi-label NILM

Cited by 32 publications

References 49 publications

Non-Intrusive Adaptive Load Identification Based on Siamese Network

Non-Intrusive Adaptive Load Identification Based on Siamese Network

Multi-Label Learning for Appliance Recognition in NILM Using Fryze-Current Decomposition and Convolutional Neural Network

Neural Fourier Energy Disaggregation

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