Non-intrusive load monitoring system for similar loads identification using feature mapping and deep learning techniques

Kumar, Mukesh; Gopinath, R.; Harikrishna, P; Srinivas, K.

doi:10.1088/1361-6501/ac271f

Cited by 6 publications

(4 citation statements)

References 36 publications

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“…Introducing activereactive power pairs into the Additive Factorial Hidden Markov Models framework, [141] demonstrates that the algorithm can output disaggregated profiles in both active and reactive power components with a significant improvement. More steady-state features, such as apparent power, current, voltage, power factor, and harmonics, are also considered characteristic loads for Transient-State Features (≥ kHz) Non-Traditional Features • Active Power [1], [109]- [113], [116], [117], [124], [139]- [141], [144]- [158] • Transient V-I Trajectories [137], [151], [159]- [169] • Weather [145], [154], [158] • Reactive Power [139]- [141], [144], [146], [148], [151]- [154] • Transient Power [1], [137], [162], [163], [168], [170], [171] • Temperature [150], [157] • Apparent Power [142], [151], [172] • Transient Harmonics [137], [163], [166], [167], [170], [173], [174] • Oc...…”

Section: … …mentioning

confidence: 99%

Non-Intrusive Load Monitoring by Voltage–Current Trajectory Enabled Transfer Learning

Liu

Wang

You

2019

IEEE Trans. Smart Grid

163

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Non-Intrusive Load Monitoring (NILM) is pivotal in today's energy landscape, offering vital solutions for energy conservation and efficient management. Its growing importance in enhancing energy savings and understanding consumer behavior makes it a pivotal technology for addressing global energy challenges. This paper delivers an in-depth review of NILM, highlighting its critical role in smart homes and smart grids. The significant contributions of this study are threefold: Firstly, it compiles a comprehensive global dataset table, providing a valuable tool for researchers and engineers to select appropriate datasets for their NILM studies. Secondly, it categorizes NILM approaches, simplifying the understanding of various algorithms by focusing on technologies, label data requirements, feature usage, and monitoring states. Lastly, by identifying gaps in current NILM research, this work sets a clear direction for future studies, discussing potential areas of innovation.

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Section: … …mentioning

confidence: 99%

Non-Intrusive Load Monitoring by Voltage–Current Trajectory Enabled Transfer Learning

Liu

Wang

You

2019

IEEE Trans. Smart Grid

163

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“…First, the accuracy of disaggregation for similar simultaneously switched devices can be further improved.Secondly, industrial equipment consumes more electricity and encompasses a wider variety of device types except for NILM for household devices; implementing NILM for industrial equipment effectively have a significant impact on energy conservation. Finally, developing unsupervised or semi supervised methods is crucial for practical applications for NILM tasks [49]; addressing at achieving low classification error in situations with limited or no labeled device data remains an important research topic [50].…”

Section: Discussionmentioning

confidence: 99%

An intelligent non-intrusive load monitoring model based on power encoding and convolutional state modules

Xu,

Jiang,

Zhang

et al. 2024

Meas. Sci. Technol.

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Non-Intrusive Load Monitoring (NILM) identifies device power consumption or on/off states solely based on total power data, which is highly valuable for consumers to understand their appliance usage behavior and take necessary measures to reduce energy consumption, especially for the benefit of energy consumers' living production. However, a challenge faced by NILM is the tendency to focus excessively on power disaggregation while neglecting the disaggregation of on/off states, leading to lower classification accuracy, particularly owning to imbalanced states. This study proposes a model that integrates the power and on/off states to simultaneously disaggregate the power and device on/off states. The model comprises two main modules: a power encoding module for power disaggregation, and a convolutional state module for on/off state disaggregation. The power encoding module utilizes BERT-LSTM and Long Short-Term Memory (LSTM) networks for initial energy disaggregation. In contrast, the convolutional state module employs Convolutional Neural Networks (CNNs) for device state disaggregation. The output of the power-encoding module is multiplied by the probability of on/off states to obtain the final power.The proposed model is evaluated using the REDD and UK-DALE datasets. Compared to the baseline models, the results show an improvement in the device state classification average accuracy from 0.948 to 0.957, and a decrease in the average error between the real power and disaggregated power from 26.356W to 25.108W. Additionally, real-world experiments conducted using the designed platform for collecting and disaggregating power data achieve an average accuracy of 0.997. The proposed model demonstrates competitiveness in the NILM field and underscores its significance in aiding energy-consumption reduction efforts.

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“…Based on the residual connection, this section proposes a multiscale fusion residual module for the network model to extract the base load features [28], using the features of different resolutions to improve the multiscale capability of the load and allow the network to have more perceptual fields, with the structure shown in Figure 4.…”

Section: Proposed Methodsmentioning

confidence: 99%

Non-Intrusive Load Disaggregation Based on a Feature Reused Long Short-Term Memory Multiple Output Network

et al. 2022

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Load decomposition technology is an important aspect of power intelligence. At present, there are mainly machine learning methods based on artificial features and deep learning methods for load decomposition. The method based on artificial features has a difficult time obtaining effective load features, leading to low accuracy. The method based on deep learning can automatically extract load characteristics, which improves the accuracy of load decomposition. However, with the deepening of the model structure, the number of parameters becomes too large, the training speed is slow, and the computing cost is high, which leads to the reduction of redundant features and the learning ability in some shallow networks, and the traditional deep learning model has a difficult time obtaining effective features on the time scale. To address these problems, a feature reused long short-term memory multiple output network (M-LSTM) is proposed and used for non-invasive load decomposition tasks. The network proposes an improved multiscale fusion residual module to extract basic load features and proposes the use of LSTM cyclic units to extract time series information. Feature reuse is achieved by combining it with the reorganization of the input data into multiple branches. The proposed structure reduces the difficulty of network optimization, and multi-scale fusion can obtain features on multiple time scales, which improves the ability of model feature extraction. Compared with common network models that tend to train network models for a single target load, the structure can simultaneously decompose the target load power while ensuring the accuracy of load decomposition, thus reducing computational costs, avoiding repetitive model training, and improving training efficiency.

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Non-intrusive load monitoring system for similar loads identification using feature mapping and deep learning techniques

Cited by 6 publications

References 36 publications

Non-Intrusive Load Monitoring by Voltage–Current Trajectory Enabled Transfer Learning

Non-Intrusive Load Monitoring by Voltage–Current Trajectory Enabled Transfer Learning

An intelligent non-intrusive load monitoring model based on power encoding and convolutional state modules

Non-Intrusive Load Disaggregation Based on a Feature Reused Long Short-Term Memory Multiple Output Network

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