Pre-trained non-intrusive load monitoring model for recognizing activity of daily living

Kim, Gyubaek; Park, Sang‐Hyun

doi:10.1007/s10489-022-04053-7

Cited by 4 publications

(1 citation statement)

References 45 publications

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“…Health aspects may also be studied from electrical consumption data, such as possible inactivity, sleep disorders, or low-activity routines [ 21 ]. These systems have been proven a suitable tool for the detection of unusual activity patterns, as well as for launching alerts to caregivers or relatives [ 22 ].…”

Section: Introductionmentioning

confidence: 99%

Detection of Anomalies in Daily Activities Using Data from Smart Meters

Hernández,

Nieto,

de Diego-Otón

et al. 2024

Sensors

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The massive deployment of smart meters in most Western countries in recent decades has allowed the creation and development of a significant variety of applications, mainly related to efficient energy management. The information provided about energy consumption has also been dedicated to the areas of social work and health. In this context, smart meters are considered single-point non-intrusive sensors that might be used to monitor the behaviour and activity patterns of people living in a household. This work describes the design of a short-term behavioural alarm generator based on the processing of energy consumption data coming from a commercial smart meter. The device captured data from a household for a period of six months, thus providing the consumption disaggregated per appliance at an interval of one hour. These data were used to train different intelligent systems, capable of estimating the predicted consumption for the next one-hour interval. Four different approaches have been considered and compared when designing the prediction system: a recurrent neural network, a convolutional neural network, a random forest, and a decision tree. By statistically analysing these predictions and the actual final energy consumption measurements, anomalies can be detected in the undertaking of three different daily activities: sleeping, breakfast, and lunch. The recurrent neural network achieves an F1-score of 0.8 in the detection of these anomalies for the household under analysis, outperforming other approaches. The proposal might be applied to the generation of a short-term alarm, which can be involved in future deployments and developments in the field of ambient assisted living.

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Section: Introductionmentioning

confidence: 99%

Detection of Anomalies in Daily Activities Using Data from Smart Meters

Hernández,

Nieto,

de Diego-Otón

et al. 2024

Sensors

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A mother-son model for multi-objective non-invasive load monitoring

Mei,

Liu,

Cao

et al. 2023

Energy and Buildings

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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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Pre-trained non-intrusive load monitoring model for recognizing activity of daily living

Cited by 4 publications

References 45 publications

Detection of Anomalies in Daily Activities Using Data from Smart Meters

Detection of Anomalies in Daily Activities Using Data from Smart Meters

A mother-son model for multi-objective non-invasive load monitoring

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

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