Event Classification in Non-Intrusive Load Monitoring Using Convolutional Neural Network

Medeiros, A.; Canha, Luciane Neves; Bertineti, Daniel Pegoraro; Azevedo, R. M. de

doi:10.1109/isgt-la.2019.8895291

Cited by 14 publications

(12 citation statements)

References 10 publications

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“…Commercial Energy management systems and off the shelf solutions are commonly used, including PowerScout 24 [50], eGauge energy monitoring systems [51], EnviR energy aggregator [52], and oscilloscopes [53]. Alternatively, some researchers have developed their own experimental data acquisition systems using devices such as a Raspberry Pi 3 and a Arduino Mega 2560 [54] or the modular open-source phasor measurement unit called OpenPMU [55].…”

Section: Toolsmentioning

confidence: 99%

“…Representing the operation of the human brain by defining evaluating nodes as neurons and weighted connections as axons, neural networks have been implemented in several areas of investigation [59]. Convolutional neural networks [53] and feedforward neural networks [91] represent the most common deep learning techniques used for NILM. Using these types of methods, considerable high performance is commonly achieved.…”

Section: Load Classificationmentioning

confidence: 99%

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Load modelling and non-intrusive load monitoring to integrate distributed energy resources in low and medium voltage networks

et al. 2021

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Section: Toolsmentioning

confidence: 99%

Section: Load Classificationmentioning

confidence: 99%

Load modelling and non-intrusive load monitoring to integrate distributed energy resources in low and medium voltage networks

et al. 2021

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“…Yang proposes an imaging rule to convert current waveforms to greyscale image and constructs a CNN architecture premised upon VGG-16 to tackle the issue of NILM [25]. Medeiros transforms the values of active and reactive power into a matrix form for CNN training, and reaches a high accuracy in NILM [26]. Fabrizio proposes a new CNN-based system for NILM applications that achieves encouraging results on the public-use dataset BLUED [27].…”

Section: Related Workmentioning

confidence: 99%

Non-Intrusive Load Monitoring Using a CNN-LSTM-RF Model Considering Label Correlation and Class-Imbalance

Zhang

Liu

et al. 2021

IEEE Access

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Non-Intrusive Load Monitoring (NILM) is particularly important for demand response. This paper proposes an innovative method based on a deep learning model to recognize the working state of electrical appliances using low frequency load data. The approach includes a data processing step, a deep learning model and a new accuracy calculation method. The data processing step consists of a multi-feature and high-dimensional method (MFHDM) and a pre-training process. The deep learning model consists of a convolutional neural network (CNN), a long-term short-term memory network (LSTM) and a randomforest (RF) algorithm. The proposed method addresses the label correlation problem and the classimbalance problem. To test the proposed method, the Reference Energy Disaggregation Dataset (REDD) and the Pecan Street dataset (PSD) are used. A comparative analysis with several models shows that the proposed method can effectively improve electrical appliance recognition accuracy and realize NILM.

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“…In particular, r is the sum of the actual power consumption, andr represents the sum of the predicted power consumption. The specific computation method is shown in Formulas ( 5) and (6).…”

Section: Model Trainingmentioning

confidence: 99%

“…This periodic trend constitutes the fundamental principle of load identification [5]. Traditional NILM methods build equipment feature databases by manually extracting features, while deep neural networks can realize automatic learning from data, thereby avoiding the step of manually extracting features [6,7]. As deep learning has been effectively applied in image and voice identification, some people have started to explore its application in NILM.…”

Section: Introductionmentioning

confidence: 99%

Nonintrusive Residential Electricity Load Decomposition Based on Transfer Learning

Yang

Liu

2021

Sustainability

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Monitoring electricity consumption in residential buildings is an important way to help reduce energy usage. Nonintrusive load monitoring is a technique to separate the total electrical load of a single household into specific appliance loads. This problem is difficult because we aim to extract the energy consumption of each appliance by only using the total electrical load. Deep transfer learning is expected to solve this problem. This paper proposes a deep neural network model based on an attention mechanism. This model improves the traditional sequence-to-sequence model with a time-embedding layer and an attention layer so that it can be better applied in nonintrusive load monitoring. In particular, the improved model abandons the recurrent neural network structure and shortens the training time, which means it is more appropriate for use in model pretraining with large datasets. To verify the validity of the model, we selected three open datasets and compared them with the current leading model. The results show that transfer learning can effectively improve the prediction ability of the model, and the model proposed in this study has a better performance than the most advanced available model.

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Event Classification in Non-Intrusive Load Monitoring Using Convolutional Neural Network

Cited by 14 publications

References 10 publications

Load modelling and non-intrusive load monitoring to integrate distributed energy resources in low and medium voltage networks

Load modelling and non-intrusive load monitoring to integrate distributed energy resources in low and medium voltage networks

Non-Intrusive Load Monitoring Using a CNN-LSTM-RF Model Considering Label Correlation and Class-Imbalance

Nonintrusive Residential Electricity Load Decomposition Based on Transfer Learning

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