A Practical Solution for Non-Intrusive Type II Load Monitoring Based on Deep Learning and Post-Processing

Kong, Weicong; Zhang, Dong; Bo, Wang; Zhao, Junhua; Huang, Jie

doi:10.1109/tsg.2019.2918330

Cited by 128 publications

(98 citation statements)

References 32 publications

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“…In recent years, the application of deep learning has been significantly considered and used in various scientific and industrial fields. As such, deep learning techniques are used today in various applications in power and energy systems, such as fault detection [25,26], cyberattack detection [27], renewable power plant potential measurement [28], non-intrusive load monitoring [29,30], and load forecasting [14,31]. Deep learning has different techniques, each of which is skilled in specific applications due to its unique structure.…”

Section: Bidirectional Long Short-term Memory (Bi-lstm)mentioning

confidence: 99%

Deep Learning-Assisted Short-Term Load Forecasting for Sustainable Management of Energy in Microgrid

et al. 2021

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Nowadays, supplying demand load and maintaining sustainable energy are important issues that have created many challenges in power systems. In these types of problems, short-term load forecasting has been proposed as one of the management and energy supply modes in power systems. In this paper, after reviewing various load forecasting techniques, a deep learning method called bidirectional long short-term memory (Bi-LSTM) is presented for short-term load forecasting in a microgrid. By collecting relevant features available in the input data at the training stage, it is shown that the proposed procedure enjoys important properties, such as its great ability to process time series data. A microgrid in rural Sub-Saharan Africa, including household and commercial loads, was selected as the case study. The parameters affecting the formation of household and commercial load profiles are considered as input variables, and the total household and commercial load profiles of the microgrid are considered as the target. The Bi-LSTM network is trained by input variables to forecast the microgrid load on an hourly basis by recognizing the consumption pattern. Various performance evaluation indicators such as the correlation coefficient (R), mean squared error (MSE), and root mean squared error (RMSE) are utilized to analyze the forecast results. In addition, in a comparative approach, the performance of the proposed method is compared and evaluated with other methods used in similar studies. The results presented for the training phase show an accuracy of R = 99.81% for the Bi-LSTM network. The test and load forecasting stage are performed by the Bi-STLM network, with an accuracy of R = 99.34% and forecasting errors of MSE = 0.1042 and RMSE = 0.3243. The results confirm the high performance of the proposed Bi-LSTM technique, with a high correlation coefficient when compared to other methods used for short-term load forecasting.

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Section: Bidirectional Long Short-term Memory (Bi-lstm)mentioning

confidence: 99%

Deep Learning-Assisted Short-Term Load Forecasting for Sustainable Management of Energy in Microgrid

et al. 2021

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“…It is noteworthy that in two cases data were upsampled to have a higher frequency than the original dataset [63,64]. Results on the influence of the sampling frequency on disaggregation results are presented in different studies [62,[65][66][67]. Most of these studies find a marked dependence on the device [62,65,66].…”

Section: Preprocessingmentioning

confidence: 99%

Review on Deep Neural Networks Applied to Low-Frequency NILM

Huber¹,

Calatroni²,

Rumsch³

et al. 2021

Preprint

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This paper reviews non-intrusive load monitoring (NILM) approaches that employ deep neural networks to disaggregate appliances from low frequency data, i.e. data with sampling rates lower than the AC base frequency. We first review the many degrees of freedom of these approaches, what has already been done in literature, and compile the main characteristics of the reviewed publications in an extensive overview table. The second part of the paper discusses selected aspects of the literature and corresponding research gaps. In particular, we do a performance comparison with respect to reported MAE and F$_1$-scores and observe different recurring elements in the best performing approaches, namely data sampling intervals below 10\,s, a large field of view, the usage of GAN losses, multi-task learning, and post-processing. Subsequently, multiple input features, multi-task learning and related research gaps are discussed, the need for comparative studies is highlighted, and finally, missing elements for a successful deployment of NILM approaches based on deep neural networks are pointed out. We conclude the review with an outlook on possible future scenarios.

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“…In fact, in the NILM literature, CNNs have always shown better performance than RNNs, even though RNNs are still widely employed for sequence modeling tasks. In [19], a CNN-based DNN has been combined with data augmentation and an effective postprocessing phase, improving its ability to correctly detect the activation of each appliance with a small amount of data available. The attention mechanism applied to NILM is a relatively new idea [20].…”

Section: Introductionmentioning

confidence: 99%

Improving Non-Intrusive Load Disaggregation through an Attention-Based Deep Neural Network

Piccialli

Sudoso

2021

Energies

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Energy disaggregation, known in the literature as Non-Intrusive Load Monitoring (NILM), is the task of inferring the power demand of the individual appliances given the aggregate power demand recorded by a single smart meter which monitors multiple appliances. In this paper, we propose a deep neural network that combines a regression subnetwork with a classification subnetwork for solving the NILM problem. Specifically, we improve the generalization capability of the overall architecture by including an encoder–decoder with a tailored attention mechanism in the regression subnetwork. The attention mechanism is inspired by the temporal attention that has been successfully applied in neural machine translation, text summarization, and speech recognition. The experiments conducted on two publicly available datasets—REDD and UK-DALE—show that our proposed deep neural network outperforms the state-of-the-art in all the considered experimental conditions. We also show that modeling attention translates into the network’s ability to correctly detect the turning on or off an appliance and to locate signal sections with high power consumption, which are of extreme interest in the field of energy disaggregation.

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A Practical Solution for Non-Intrusive Type II Load Monitoring Based on Deep Learning and Post-Processing

Cited by 128 publications

References 32 publications

Deep Learning-Assisted Short-Term Load Forecasting for Sustainable Management of Energy in Microgrid

Deep Learning-Assisted Short-Term Load Forecasting for Sustainable Management of Energy in Microgrid

Review on Deep Neural Networks Applied to Low-Frequency NILM

Improving Non-Intrusive Load Disaggregation through an Attention-Based Deep Neural Network

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