Adaptive modeling for Non-Intrusive Load Monitoring

Wang, Chao; Wu, Zhao; Peng, Wenxiong; Liu, Weihua; Xiong, Liansong; Wu, Tao; Yu, Lili; Zhang, Huaiqing

doi:10.1016/j.ijepes.2022.107981

Cited by 13 publications

(9 citation statements)

References 11 publications

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“…The non-invasive power load monitoring method obtains the operation status of a single electric equipment by monitoring and analyzing the total load power consumption. The whole implementation process is simpler and more convenient, and has wide application value for enterprise users or individual users [4,5].…”

Section: Introductionmentioning

confidence: 99%

Non Intrusive Power Load Monitoring Method Based on Machine Learning

Guan

Song

Huangfu

2023

J. Phys.: Conf. Ser.

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In order to improve the accuracy of non-invasive power load monitoring and shorten the monitoring time, a new non-invasive power load monitoring method based on machine learning is proposed. Firstly, the power load data is collected by sensors and normalized. Secondly, based on the normalized results of power load data, the RBF neural network model in machine learning is used for iterative training to extract power load characteristics. Finally, according to the characteristics of power load, the hidden Markov model is used to transform the non-invasive power load monitoring problem into a decoding problem, and the improved Viterbi algorithm is used to solve the hidden Markov model, and the non-invasive power load monitoring is completed according to the obtained state sequence.

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Section: Introductionmentioning

confidence: 99%

Non Intrusive Power Load Monitoring Method Based on Machine Learning

Guan

Song

Huangfu

2023

J. Phys.: Conf. Ser.

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“…In the second stage, the power consumption of each appliance was estimated based on the identification result, and a simple integer linear programmer and the density‐based spatial clustering of applications with noise algorithms were used to determine which ON‐state is a multistate appliance. Furthermore, an adaptive FHMM was proposed before 21 to identify the transition between the working states of each appliance, where an adaptive clustering process was presented in the proposed model. The adaptive clustering process can automatically identify the hidden states' numbers based on the power variation at various working stages.…”

Section: Introductionmentioning

confidence: 99%

Towards energy‐efficient smart homes via precise nonintrusive load disaggregation based on hybrid ANN–PSO

Ramadan

Huang

Bamisile

et al. 2023

Energy Science & Engineering

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Nowadays, the load monitoring system is an important element in smart buildings to reduce energy consumption. Nonintrusive load monitoring (NILM) is utilized to determine the power consumption of each appliance in smart homes. The main problem of NILM is how to separate each appliance's power from the signal of aggregated consumption. In this regard, this paper presents a combination between particle swarm optimization (PSO) and artificial neural networks (ANNs) to identify electrical appliances for demand‐side management. ANN is applied in NILM as a load identification task, and PSO is used to train the ANN algorithm. This combination enhances the NILM technique's accuracy, which is further verified by experiments on different datasets like Reference Energy Disaggregation Dataset, UK Domestic Appliance‐Level ElectricityUK‐DALE, and Indian data for Ambient Water and electricity Sensing. The high accuracy of the proposed algorithm is verified by comparisons with state of the art methods. Compared with other approaches, the total mean absolute error has decreased from 39.3566 to 18.607. Also, the normalized root mean square error (NRMSE) method has been used to compare the measured and predicted results. The NRMSE is in the range of 1.719%–16.514%, which means perfect consistency. This demonstrates the effectiveness of the proposed approach for home energy management. Furthermore, customer behavior has been studied, considering energy costs during day hours.

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“…In NILD, the power fingerprint process often contains two key aspects, feature extraction and load identification. Earlier, the steady state characteristics of active power P and reactive power Q were often used for identification [ 6 ]. References [ 7 , 8 ] added features such as current waveform, harmonics, transient power waveform, and switching transient waveform for load identification based on the use of active and reactive power.…”

Section: Introductionmentioning

confidence: 99%

Optimized LightGBM Power Fingerprint Identification Based on Entropy Features

Lin

Zhang

Zhang³

et al. 2022

Entropy

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The huge amount of power fingerprint data often has the problem of unbalanced categories and is difficult to upload by the limited data transmission rate for IoT communications. An optimized LightGBM power fingerprint extraction and identification method based on entropy features is proposed. First, the voltage and current signals were extracted on the basis of the time-domain features and V-I trajectory features, and a 56-dimensional original feature set containing six entropy features was constructed. Then, the Boruta algorithm with a light gradient boosting machine (LightGBM) as the base learner was used for feature selection of the original feature set, and a 23-dimensional optimal feature subset containing five entropy features was determined. Finally, the Optuna algorithm was used to optimize the hyperparameters of the LightGBM classifier. The classification performance of the power fingerprint identification model on imbalanced datasets was further improved by improving the loss function of the LightGBM model. The experimental results prove that the method can effectively reduce the computational complexity of feature extraction and reduce the amount of power fingerprint data transmission. It meets the recognition accuracy and efficiency requirements of a massive power fingerprint identification system.

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Adaptive modeling for Non-Intrusive Load Monitoring

Cited by 13 publications

References 11 publications

Non Intrusive Power Load Monitoring Method Based on Machine Learning

Non Intrusive Power Load Monitoring Method Based on Machine Learning

Towards energy‐efficient smart homes via precise nonintrusive load disaggregation based on hybrid ANN–PSO

Optimized LightGBM Power Fingerprint Identification Based on Entropy Features

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