Deep Learning Detection of Inaccurate Smart Electricity Meters: A Case Study

Liu, Ming; Liu, Dongpeng; Sun, Guangyu; Zhao, Yi; Wang, Duolin; Liu, Fangxing; Fang, Xiang; He, Qing; Xu, Dong

doi:10.1109/mie.2020.3026197

Cited by 17 publications

(10 citation statements)

References 24 publications

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“…Further use cases based on the application of machine learning for anomaly detection in smart meter data have emerged and manifested themselves in areas such as energy theft detection [103,104], detecting inaccurate smart meters [105], and detecting abnormal consumption behavior in general [106]. In [104], two anomaly detection schemes for detecting energy theft attacks and locating metering defects in smart meter data are presented.…”

Section: Recognizing Patterns and Anomaliesmentioning

confidence: 99%

“…The work by Sial et al [106] investigates heuristic approaches for identifying abnormal energy consumption from smart meter data, based on a combination of four distinct power-, energy-, and time-related features used in conjunction to detect anomalies. An even more sophisticated approach was presented by Liu et al [105], who applied a deep neural network in detecting inaccurate meters to prevent the unnecessary replacement of smart meters, thus increasing their service life span. Lastly, the detection and quantification of anomalies in smart meter energy data play a crucial role in assessing the energy quality, which is essential for detecting faulty appliances, malfunctioning appliances, and non-technical losses [107][108][109][110].…”

Section: Recognizing Patterns and Anomaliesmentioning

confidence: 99%

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Watt’s up at Home? Smart Meter Data Analytics from a Consumer-Centric Perspective

et al. 2021

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The key advantage of smart meters over traditional metering devices is their ability to transfer consumption information to remote data processing systems. Besides enabling the automated collection of a customer’s electricity consumption for billing purposes, the data collected by these devices makes the realization of many novel use cases possible. However, the large majority of such services are tailored to improve the power grid’s operation as a whole. For example, forecasts of household energy consumption or photovoltaic production allow for improved power plant generation scheduling. Similarly, the detection of anomalous consumption patterns can indicate electricity theft and serve as a trigger for corresponding investigations. Even though customers can directly influence their electrical energy consumption, the range of use cases to the users’ benefit remains much smaller than those that benefit the grid in general. In this work, we thus review the range of services tailored to the needs of end-customers. By briefly discussing their technological foundations and their potential impact on future developments, we highlight the great potentials of utilizing smart meter data from a user-centric perspective. Several open research challenges in this domain, arising from the shortcomings of state-of-the-art data communication and processing methods, are furthermore given. We expect their investigation to lead to significant advancements in data processing services and ultimately raise the customer experience of operating smart meters.

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Section: Recognizing Patterns and Anomaliesmentioning

confidence: 99%

Section: Recognizing Patterns and Anomaliesmentioning

confidence: 99%

Watt’s up at Home? Smart Meter Data Analytics from a Consumer-Centric Perspective

et al. 2021

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“…Abnormal judgment is a necessary part of anomaly detection. In [16], the submeters are defined as inaccurate if the predicted residual errors exceed threshold. In [17], it adopts three-sigma rule as the rule of abnormal judgment.…”

Section: Abnormal Judgmentmentioning

confidence: 99%

“…However, the abnormal dataset needs to be adjusted manually before detecting anomalies. In [16], it proposed a novel deep learning method based on long short-term memory (LSTM) and a modified convolutional neural network (CNN), which aims to extract spatial and temporal characteristics of the power consumption; it detects abnormal stations based on a prediction residual. Z. Zheng [17] introduced a novel electricity theft detection method based on…”

Section: Introductionmentioning

confidence: 99%

“…The prediction accuracy of deep learning models strongly depends on the hyperparameter optimization (HPO) process. In [16][17][18][19][20][21], the authors ignored the problem of hyperparameter optimization, so the accuracy of the model cannot be improved. In [22], the authors proposed a novel optimization method that combines generalized normal distribution optimization, Nelder-Mead (NM) simplex direct search method and differential mutation operator to address optimization problems.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation Model of Operation State Based on Deep Learning for Smart Meter

Zhao

Han

et al. 2021

Energies

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The operation state detection of numerous smart meters is a significant problem caused by manual on-site testing. This paper addresses the problem of improving the malfunction detection efficiency of smart meters using deep learning and proposes a novel evaluation model of operation state for smart meter. This evaluation model adopts recurrent neural networks (RNN) to predict power consumption. According to the prediction residual between predicted power consumption and the observed power consumption, the malfunctioning smart meter is detected. The training efficiency for the prediction model is improved by using transfer learning (TL). This evaluation uses an accumulator algorithm and threshold setting with flexibility for abnormal detection. In the simulation experiment, the detection principle is demonstrated to improve efficient replacement and extend the average using time of smart meters. The effectiveness of the evaluation model was verified on the actual station dataset. It has accurately detected the operation state of smart meters.

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Regularized DNN Based Adaptive Compensation Algorithm for Gateway Power Meter in Ultra-High Voltage Substations

Wang,

Feng,

et al. 2024

Communications in Computer and Information Science

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Deep Learning Detection of Inaccurate Smart Electricity Meters: A Case Study

Cited by 17 publications

References 24 publications

Watt’s up at Home? Smart Meter Data Analytics from a Consumer-Centric Perspective

Watt’s up at Home? Smart Meter Data Analytics from a Consumer-Centric Perspective

Evaluation Model of Operation State Based on Deep Learning for Smart Meter

Regularized DNN Based Adaptive Compensation Algorithm for Gateway Power Meter in Ultra-High Voltage Substations

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