Fully-Convolutional Denoising Auto-Encoders for NILM in Large Non-Residential Buildings

García-Pérez, Diego; Pérez, Daniel; Díaz-Blanco, Ignacio; González-Muñiz, Ana; Domínguez-González, Manuel; Cuadrado, Abel A.

doi:10.1109/tsg.2020.3047712

Cited by 42 publications

(20 citation statements)

References 30 publications

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“…There are five main preliminary algorithms that mostly operate on the energetic load profile, which can be categorized according to AI architecture: (i) Denoising autoencoders: a survey and comparative study on "denoising auto encoders (DAE) was performed by Piazza et al [35]. A convolutional deep learning DAE model was developed by Dominguez-Gonzalez et al [36]. A denoising autoencoder prevents the autoencoder from performing identity operation by intentionally injecting noise into it.…”

Section: A Survey Of Algorithms In Order To Comprehend How To Approach the Problemmentioning

confidence: 99%

A Highly Accurate NILM: With an Electro-Spectral Space That Best Fits Algorithm’s National Deployment Requirements

Calamaro¹,

Donko²,

Shmilovitz³

2021

Energies

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The central problems of some of the existing Non-Intrusive Load Monitoring (NILM) algorithms are indicated as: (1) higher required electrical device identification accuracy; (2) the fact that they enable training over a larger device count; and (3) their ability to be trained faster, limiting them from usage in industrial premises and external grids due to their sensitivity to various device types found in residential premises. The algorithm accuracy is higher compared to previous work and is capable of training over at least thirteen electrical devices collaboratively, a number that could be much higher if such a dataset is generated. The algorithm trains the data around 1.8×108 faster due to a higher sampling rate. These improvements potentially enable the algorithm to be suitable for future “grids and industrial premises load identification” systems. The algorithm builds on new principles: an electro-spectral features preprocessor, a faster waveform sampling sensor, a shorter required duration for the recorded data set, and the use of current waveforms vs. energy load profile, as was the case in previous NILM algorithms. Since the algorithm is intended for operation in any industrial premises or grid location, fast training is required. Known classification algorithms are comparatively trained using the proposed preprocessor over residential datasets, and in addition, the algorithm is compared to five known low-sampling NILM rate algorithms. The proposed spectral algorithm achieved 98% accuracy in terms of device identification over two international datasets, which is higher than the usual success of NILM algorithms.

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Section: A Survey Of Algorithms In Order To Comprehend How To Approach the Problemmentioning

confidence: 99%

A Highly Accurate NILM: With an Electro-Spectral Space That Best Fits Algorithm’s National Deployment Requirements

Calamaro¹,

Donko²,

Shmilovitz³

2021

Energies

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“…These include heuristic search algorithms that create rules for each appliance [3], decision trees [4], and long short-term memory for event detection [5]. Given that the worldwide deployment of AMIs results in abundant load data, research on deep-learning-based methods to accomplish NILM has become a hot topic in recent years [6][7][8][9][10][11][12][13][14][15]. In recent studies, distinct structures of deep neural networks (DNNs) have been established to represent mapping relationships.…”

Section: Introductionmentioning

confidence: 99%

Fed‐NILM: A federated learning‐based non‐intrusive load monitoring method for privacy‐protection

Wang

Liu

et al. 2022

Energy Conversion and Econom

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Non-intrusive load monitoring (NILM) is essential for understanding consumer power consumption patterns and may have wide applications such as in carbon emission reduction and energy conservation. Determining NILM models requires massive load data containing different types of appliances. However, inadequate load data and the risk of power consumer privacy breaches may be encountered by local data owners when determining the NILM model. To address these problems, a novel NILM method based on federated learning (FL) called Fed-NILM is proposed. In Fed-NILM, instead of local load data, local model parameters are shared among multiple data owners. The global NILM model is obtained by averaging the parameters with the appropriate weights. Experiments based on two measured load datasets are performed to explore the generalization capability of Fed-NILM. In addition, a comparison of Fed-NILM with locally trained NILM models and the centrally trained NILM model is conducted. Experimental results show that the Fed-NILM exhibits superior performance in terms of scalability and convergence. Fed-NILM out performs locally trained NILM models operated by local data owners and approaches the centrally trained NILM model, which is trained on the entire load dataset without privacy protection. The proposed Fed-NILM significantly improves the co-modelling capabilities of local data owners while protecting the privacy of power consumers.

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“…In detail, within the last few decades, NILM has been employed in many utility and non-utility applications. As regards the utility applications, energy-consumption reduction for residential [ 13 , 14 ] and industrial [ 15 ] areas is the most common application. Furthermore, NILM has been used in energy management of smart-grids to optimize load schedules as well as to increase customers’ satisfaction [ 16 , 17 ].…”

Section: Introductionmentioning

confidence: 99%

2D Transformations of Energy Signals for Energy Disaggregation

Schirmer

Mporas

2022

Sensors

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The aim of Non-Intrusive Load Monitoring is to estimate the energy consumption of individual electrical appliances by disaggregating the overall power consumption that has been sampled from a smart meter at a house or commercial/industrial building. Last decade’s developments in deep learning and the utilization of Convolutional Neural Networks have improved disaggregation accuracy significantly, especially when utilizing two-dimensional signal representations. However, converting time series’ to two-dimensional representations is still an open challenge, and it is not clear how it influences the performance of the energy disaggregation. Therefore, in this article, six different two-dimensional representation techniques are compared in terms of performance, runtime, influence on sampling frequency, and robustness towards Gaussian white noise. The evaluation results show an advantage of two-dimensional imaging techniques over univariate and multivariate features. In detail, the evaluation results show that: first, the active and reactive power-based signatures double Fourier based signatures, as well as outperforming most of the other approaches for low levels of noise. Second, while current and voltage signatures are outperformed at low levels of noise, they perform best under high noise conditions and show the smallest decrease in performance with increasing noise levels. Third, the effect of the sampling frequency on the energy disaggregation performance for time series imaging is most prominent up to 1.2 kHz, while, above 1.2 kHz, no significant improvements in terms of performance could be observed.

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Fully-Convolutional Denoising Auto-Encoders for NILM in Large Non-Residential Buildings

Cited by 42 publications

References 30 publications

A Highly Accurate NILM: With an Electro-Spectral Space That Best Fits Algorithm’s National Deployment Requirements

A Highly Accurate NILM: With an Electro-Spectral Space That Best Fits Algorithm’s National Deployment Requirements

Fed‐NILM: A federated learning‐based non‐intrusive load monitoring method for privacy‐protection

2D Transformations of Energy Signals for Energy Disaggregation

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