A practical solution based on convolutional neural network for non-intrusive load monitoring

Moradzadeh, Arash; Mohammadi‐Ivatloo, Behnam; Abapour, Mehdi; Anvari‐Moghaddam, Amjad; Farkoush, Saeid Gholami; Rhee, Sang-Bong

doi:10.1007/s12652-020-02720-6

Cited by 50 publications

(32 citation statements)

References 47 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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“…Several works use low-frequency datasets to train CNN architectures for disaggregation or NILM classification [1,[22][23][24][25][26]. In [22], for instance, the authors proposed a sequence-to-sequence 1D-CNN architecture for NILM with superior disaggregation results for high-power loads, but with an inability to disaggregate low power loads.…”

Section: Cnn For Nilm Using Low-frequency Datamentioning

confidence: 99%

“…Chen et al [23] explain in the conclusions section the intention to use data augmentation to improve the results with more aggregated loads. In [1], the authors proposed a deep CNN architecture for NILM classification, with accuracy results greater than 96% for the REDD dataset. Moradzadeh et al [1] proposed to classify appliances of households not included in the training stage, but the disaggregated load curves were not available, and the results are limited to only three selected loads.…”

Section: Cnn For Nilm Using Low-frequency Datamentioning

confidence: 99%

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Scattering Transform for Classification in Non-Intrusive Load Monitoring

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Lazzaretti

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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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“…International Energy Agency, the Worldwide electricity demand is expected to increase by more than 65% by the year 2035 [3]. Recent studies have shown that about 60% of the world's energy is consumed in residential and commercial buildings [4]. Accordingly, energy management in buildings can be one of the most important tools in saving energy and reducing CO2 in the environment.…”

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A Secure Federated Deep Learning-Based Approach for Heating Load Demand Forecasting in Building Environment

et al. 2022

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A practical solution based on convolutional neural network for non-intrusive load monitoring

Cited by 50 publications

References 47 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

Scattering Transform for Classification in Non-Intrusive Load Monitoring

A Secure Federated Deep Learning-Based Approach for Heating Load Demand Forecasting in Building Environment

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