Energy disaggregation for identifying anomalous appliance

Rashid, Haroon; Singh, Pushpendra

doi:10.1145/3137133.3141438

Cited by 9 publications

(33 citation statements)

References 4 publications

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“…Daily Aggregation: To aggregate data daily, all the consumption readings are collected to produce statistical metrics, that us, mean, variance, minimum, maximum, or sum of electricity consumption during a day for a single consumer. These parameters reveal daily average, spikes of low and high usage [26–30]. Figures 6 and 7 depict the trend of a consumer over longer and shorter periods, respectively.…”

Section: Methodology Proposalsmentioning

confidence: 99%

Using machine learning ensemble method for detection of energy theft in smart meters

Kawoosa,

Prashar,

Faheem

et al. 2023

IET Generation Trans & Dist

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Electricity theft is a primary concern for utility providers, as it leads to substantial financial losses. To address the issue, a novel extreme gradient boosting (XGBoost)‐based model utilizing the consumers’ electricity consumption patterns for analysis is proposed for electricity theft detection (ETD). To remove the imbalance in the real‐world electricity consumption dataset and ensure an even distribution of theft and non‐theft data instances, six different artificially created theft attacks were used. Moreover, the utilization of the XGBoost algorithm for classification, especially to identify malicious instances of electricity theft, yielded commendable accuracy rates and a minimal occurrence of false positives. The proposed model identifies electricity theft specific to the regions, utilizing electricity consumption parameters, and other variables as input features. The authors’ model outperformed existing benchmarks like k‐neural networks, light gradient boost, random forest, support vector machine, decision tree, and AdaBoost. The simulation results using the false attacks for balancing the dataset have improved the proposed model's performance, achieving a precision, recall, and F1‐score of 96%, 95%, and 95%, respectively. The results of the detection rate and the false positive rate (FPR) of the proposed XGBoost‐based detection model have achieved 96% and 3%, respectively.

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Section: Methodology Proposalsmentioning

confidence: 99%

Using machine learning ensemble method for detection of energy theft in smart meters

Kawoosa,

Prashar,

Faheem

et al. 2023

IET Generation Trans & Dist

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“…For instance, in Figure 1 anomaly detection algorithm, UNUM, on both AC and refrigerator disaggregated power consumption traces to identify anomalies. We build UNUM upon our preliminary work presented in a poster paper [52] by including more detailed rules. This has resulted in improvement in accuracy and the scope; the algorithm has also been evaluated thoroughly since its inception.…”

Section: Methodsmentioning

confidence: 99%

Can non-intrusive load monitoring be used for identifying an appliance’s anomalous behaviour?

2019

Self Cite

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Identification of faulty appliance behaviour in real time can signal energy wastage and the need for appliance servicing or replacement leading to energy savings.The problem of appliance fault or anomaly detection has been tackled vastly in relation to submetering, which is not scalable since it requires separate meters for each appliance. At the same time, for applications such as energy feedback, Non-intrusive load monitoring (NILM) has been recognised as a scalable and practical alternative to submetering. However, the usability of NILM for anomaly detection has not yet been investigated. Since the goal of NILM is to provide energy consumption estimate, it is unclear if the signal fidelity of appliance signatures generated by state-of-the-art NILM is sufficient to enable accurate appliance fault detection. In this paper, we attempt to determine whether appliance signatures detected by NILM can be used directly for anomaly detection. This is carried out by proposing an anomaly detection algorithm which performs well for submetering data and evaluate its ability to identify the same faulty behaviour of appliances but with NILM-generated appliance power traces. Our results on a dataset of six residential homes using four state-of-the-art NILM algorithms show that, on average, NILM traces are not as robust to identification of faulty behaviour as compared to using submetered data. We discuss in detail observations pertaining to the reconstructed appliance signatures following NILM and their fidelity with respect to noise-free

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“…Following, a statistical rule‐based method is deployed to detect outliers. In References [22,23] an NILM method is proposed to extract appliance‐level data and then to detect appliance abnormalities using a rule‐based algorithm. Furthermore, this helps in investigating the impact of NILM on abnormality detection performance.…”

Section: Related Workmentioning

confidence: 99%

Smart power consumption abnormality detection in buildings using micromoments and improved K‐nearest neighbors

et al. 2021

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Anomaly detection in energy consumption is a crucial step towards developing efficient energy saving systems, diminishing overall energy expenditure and reducing carbon emissions. Therefore, implementing powerful techniques to identify anomalous consumption in buildings and providing this information to end‐users and managers is of significant importance. Accordingly, two novel schemes are proposed in this paper; the first one is an unsupervised abnormality detection based on one‐class support vector machine, namely UAD‐OCSVM, in which abnormalities are extracted without the need of annotated data; the second is a supervised abnormality detection based on micromoments (SAD‐M2), which is implemented in the following steps: (i) normal and abnormal power consumptions are defined and assigned; (ii) a rule‐based algorithm is introduced to extract the micromoments representing the intent‐rich moments, in which the end‐users make decisions to consume energy; and (iii) an improved K‐nearest neighbors model is introduced to automatically classify consumption footprints as normal or abnormal. Empirical evaluation conducted in this framework under three different data sets demonstrates that SAD‐M2 achieves both a highest abnormality detection performance and real‐time processing capability with considerably lower computational cost in comparison with other machine learning methods. For instance, up to 99.71% accuracy and 99.77% F1 score have been achieved using a real‐world data set collected at the Qatar University energy lab.

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Energy disaggregation for identifying anomalous appliance

Cited by 9 publications

References 4 publications

Using machine learning ensemble method for detection of energy theft in smart meters

Using machine learning ensemble method for detection of energy theft in smart meters

Can non-intrusive load monitoring be used for identifying an appliance’s anomalous behaviour?

Smart power consumption abnormality detection in buildings using micromoments and improved K‐nearest neighbors

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