Anomaly Detection for an Elderly Person Watching System using Multiple Power Consumption Models

Hori, Maiya; Harada, Togoro; Taniguchi, Rin Ichiro

doi:10.5220/0006247006690675

Cited by 4 publications

(5 citation statements)

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“…To demonstrate how SEPAD achieves a high degree of anomaly detection in monitoring the power consumption data and detects anomalies according to the electricity usage habits of residents, we compared SEPAD to three other detectors, based on two parameters: accuracy and training cost and speed. We compared SEPAD against the results of Nagi et al [12], Chou and Telaga [22], and Hori et al [27] 2.…”

Section: Comparison Results and Evaluationmentioning

confidence: 99%

“…However, the detecting speed and training cost are not discussed in this work. Hori et al [27] introduced a power consumption-based home health monitoring system in which a kNN model was used to monitor anomalies in real time. By dividing time into different time zones, a larger anomaly score in a specific time zone may indicate the occurrence of abnormal events.…”

Section: Related Workmentioning

confidence: 99%

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Sample Efficient Home Power Anomaly Detection in Real Time Using Semi-Supervised Learning

Wang

Yang

2019

IEEE Access

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Anomaly detection in home power monitoring can be categorized into two main types: detection of electrical theft, leakage, or nontechnical loss and monitoring anomalies in the daily activities of residents. Focusing on the application and practicality of anomaly detection, we propose sample efficient home power anomaly detection (SEPAD) with improved monitoring performance in terms of electricity usage as well as changes in the daily living activities of residents via provision of detailed feedback. SEPAD consists of two classifiers: an appliance pattern matching classifier (APMC) and an energy consumption habit classifier (ECHC). The APMC uses a single-source separation framework based on a semi-supervised support vector machine (semi-SVM) model. This semi-supervised learning method requires only a small amount of labeled data to achieve high accuracy in near real time and is a sample efficient detection method. The hidden Markov model (HMM)-based ECHC improves the rationality of SEPAD by providing anomaly detection functionality with respect to the daily activities of householders, especially the elderly and residents in developing areas. When SEPAD detects the appearance of an unknown pattern or known patterns contrary to the household's electricity usage habits, it triggers an alarm. SEPAD was applied to monitor power consumption data from Mkalama, a rural area in Tanzania with 52 households containing nearly 150 occupants connected to a solar powered off-grid network. The results of the practical test demonstrate the high accuracy and practicality of the proposed method.

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Section: Comparison Results and Evaluationmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Sample Efficient Home Power Anomaly Detection in Real Time Using Semi-Supervised Learning

Wang

Yang

2019

IEEE Access

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“…IoT Device Utility or Communication Data Activity detection [1,2,4,[11][12][13][14], [6][7][8][9] * [16][17][18][19][20][21] Anomaly detection [3,5,10], [6][7][8][9] * [22][23][24][25][26][27][28][29] * Studies that used both analysis methods.…”

Section: Methodsmentioning

confidence: 99%

“…Communication usage data processed from call detail records (CDRs) are used to find call patterns and location movements [ 20 , 21 ]; Anomaly detection-based: It checks whether an abnormality exceeding a specific threshold has occurred. Various techniques such as Bayesian networks, support vector machines, nearest neighbors, clustering, hidden Markov models (HMMs), and neural networks have been applied to detect such anomalies [ 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 ]. …”

Section: Related Workmentioning

confidence: 99%

“…For this reason, the use of utility data, such as electricity, water, and gas, has been gaining significant attention [ 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 ]. This method provides an advantage in that the required data can be easily collected without causing discomfort to single-person households.…”

Section: Introductionmentioning

confidence: 99%

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Activity Detection from Electricity Consumption and Communication Usage Data for Monitoring Lonely Deaths

Kim

Park

2021

Sensors

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As the number of single-person households grows worldwide, the need to monitor their safety is gradually increasing. Among several approaches developed previously, analyzing the daily lifelog data generated unwittingly, such as electricity consumption or communication usage, has been discussed. However, data analysis methods in the domain are currently based on anomaly detection. This presents accuracy issues and the challenge of securing service reliability. We propose a new analysis method that finds activities such as operation or movement from electricity consumption and communication usage data. This is evidence of safety. As a result, we demonstrate better performance through comparative verification. Ultimately, this study aims to contribute to a more reliable implementation of a service that enables monitoring of lonely deaths.

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Analysis of household power consumption data for social safety net services

Kim

Park

2023

Energy Reports

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Anomaly Detection for an Elderly Person Watching System using Multiple Power Consumption Models

Cited by 4 publications

References 0 publications

Sample Efficient Home Power Anomaly Detection in Real Time Using Semi-Supervised Learning

Sample Efficient Home Power Anomaly Detection in Real Time Using Semi-Supervised Learning

Activity Detection from Electricity Consumption and Communication Usage Data for Monitoring Lonely Deaths

Analysis of household power consumption data for social safety net services

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