A demonstration of reproducible state-of-the-art energy disaggregation using NILMTK

Batra, Nipun; Kukunuri, Rithwik; Pandey, Ayush; Malakar, Raktim; Kumar, Rajat; Krystalakos, Odysseas; Zhong, Mingjun; Meira, Paulo C. M.; Parson, Oliver

doi:10.1145/3360322.3360999

Cited by 7 publications

(5 citation statements)

References 8 publications

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“…Therefore, this study likewise used these two kinds of performance metrics to analyze the load decomposition accuracy. The calculation methods are shown in Equations ( 9) and (10), respectively.…”

Section: Performance Metricsmentioning

confidence: 99%

“…Then, they started investigating the application of deep learning algorithms in this field and used long shortterm memory (LSTM) networks, denoising autoencoders (DAE), and convolutional neural networks (CNN) to solve non-intrusive load decomposition problems in 2015 [9]. In 2019, Batra et al updated the toolkit by including the latest algorithms capable of performing load decomposition tasks at that time [10].…”

Section: Introductionmentioning

confidence: 99%

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Non-Intrusive Load Decomposition Based on Instance-Batch Normalization Networks

Wang

Liu

2023

Energies

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At present, the non-intrusive load decomposition method for low-frequency sampling data is as yet insufficient within the context of generalization performance, failing to meet the decomposition accuracy requirements when applied to novel scenarios. To address this issue, a non-intrusive load decomposition method based on instance-batch normalization network is proposed. This method uses an encoder-decoder structure with attention mechanism, in which skip connections are introduced at the corresponding layers of the encoder and decoder. In this way, the decoder can reconstruct a more accurate power sequence of the target. The proposed model was tested on two public datasets, REDD and UKDALE, and the performance was compared with mainstream algorithms. The results show that the F1 score was higher by an average of 18.4 when compared with mainstream algorithms. Additionally, the mean absolute error reduced by an average of 25%, and the root mean square error was reduced by an average of 22%.

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Section: Performance Metricsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Non-Intrusive Load Decomposition Based on Instance-Batch Normalization Networks

Wang

Liu

2023

Energies

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“…In 2014, Kelly [31] developed an opensource toolkit specifically for NILM, which provides a processing interface of mainstream open datasets to simplify NILM data processing tasks. Additionally, the toolkit provides a baseline model and performance metrics, which facilitates the development of state-ofart models [31,32]. The ElecMeter object is the core of the NILM toolkit (NILMTK), encapsulating the method for obtaining power data from the circuit.…”

Section: Dataset and Nilmtk Toolkitmentioning

confidence: 99%

Sequence to Point Learning Based on an Attention Neural Network for Nonintrusive Load Decomposition

Yang

Liu

2021

Electronics

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Nonintrusive load monitoring (NILM) analyzes only the main circuit load information with an algorithm to decompose the load, which is an important way to help reduce energy usage. Recent research shows that deep learning has become popular for this problem. However, the ability of a neural network to extract load features depends on its structure. Therefore, more research is required to determine the best network architecture. This study proposed two deep neural networks based on the attention mechanism to improve the current sequence to point (s2p) learning model. The first model employs Bahdanau style attention and RNN layers, and the second model replaces the RNN layer with a self-attention layer. The two models are both based on a time embedding layer. Therefore, they can be better applied in NILM. To verify the effectiveness of the algorithms, we selected two open datasets and compared them with the original s2p model. The results show that attention mechanisms can effectively improve the model’s performance.

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“…Since we focus mainly in this framework on proposing a novel feature descriptor, the event detection task has been conducted using the edge detector described in the NILM toolkit. 41…”

Section: Event Detectionmentioning

confidence: 99%

“…An event detection scheme is applied to the aggregated power signal

s_{A}

collected from the main supply to capture an individual event vector

e_{i} = [e (1), e (2), …, e (L)]

for each appliance, with

M

is the length of the event vector. Since we focus mainly in this framework on proposing a novel feature descriptor, the event detection task has been conducted using the edge detector described in the NILM toolkit 41 …”

Section: Proposed Systemmentioning

confidence: 99%

An intelligent nonintrusive load monitoring scheme based on 2D phase encoding of power signals

et al. 2020

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Nonintrusive load monitoring (NILM) is the de facto technique for extracting device-level power consumption fingerprints at (almost) no cost from only aggregated mains readings. Specifically, there is no need to install an individual meter for each appliance. However, a robust NILM system should incorporate a precise appliance identification module that can effectively discriminate between various devices. In this context, this paper proposes a powerful method to extract accurate power fingerprints for electrical appliance identification. Rather than relying solely on time-domain (TD) analysis, this framework abstracts the phase encoding of the TD description of power signals using a two-dimensional (2D) representation. This allows mapping power trajectories to a novel 2D binary representation space, and then performing a histogramming process after converting binary codes to new decimal representations. This yields the final histogram of 2D phase encoding of power signals, namely, 2D-PEP. An empirical performance evaluation conducted with three realistic power consumption databases collected at distinct resolutions indicates that the proposed 2D-PEP descriptor achieves

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A demonstration of reproducible state-of-the-art energy disaggregation using NILMTK

Cited by 7 publications

References 8 publications

Non-Intrusive Load Decomposition Based on Instance-Batch Normalization Networks

Non-Intrusive Load Decomposition Based on Instance-Batch Normalization Networks

Sequence to Point Learning Based on an Attention Neural Network for Nonintrusive Load Decomposition

An intelligent nonintrusive load monitoring scheme based on 2D phase encoding of power signals

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