An Analysis of Customer Power Profile Events for Non-invasive Energy Meter Error Monitoring

Nakutis, Žilvinas; Rinaldi, Stefano; Kuzas, Pranas; Lukočius, Robertas

doi:10.1109/amps.2019.8897778

Cited by 4 publications

(5 citation statements)

References 13 publications

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“…In [7] 1s load power profiles for residential consumers are analysed with the goal of detecting power steps in a sampled load power profile. A noninvasive error monitoring technique is devised through comparison of the tested and reference meters and synchronized statistical processing between the two.…”

Section: Related Workmentioning

confidence: 99%

Reporting Interval Impact on Deep Residential Energy Measurement Prediction

Stâmâtescu

Ciornei

Plamanescu

et al. 2021

2021 IEEE 11th International Workshop on Applied Measurements for Power Systems (AMPS)

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Forecasting and anomaly detection for energy time series is emerging as an important application area for computational intelligence and learning algorithms. The training of robust data-driven models relies on large measurement datasets sampled at ever increasing rates and demand large computational and storage resources for off-line power quality analysis and on-line control in energy management schemes. We analyze the impact of the reporting rate of energy measurements on deep learning based forecasting models in a residential scenario. The work is also motivated by the development of embedded energy gateways for online inference and anomaly detection that avoids the dependence on costly, high-latency, cloud systems for data storage and algorithm evaluation. This in turn requires increased local computation and memory requirements to generate predictions within the control sampling period. We report quantitative forecasting metrics (MSE, MAE, MAPE) to establish an empirical trade-off between reporting rate and model accuracy. Additional results consider the rate-variable feature extraction using a time series data mining algorithm for multi-scale analytics.

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Section: Related Workmentioning

confidence: 99%

Reporting Interval Impact on Deep Residential Energy Measurement Prediction

Stâmâtescu

Ciornei

Plamanescu

et al. 2021

2021 IEEE 11th International Workshop on Applied Measurements for Power Systems (AMPS)

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“…An adjustment (multiplicative) coefficient (meter constant) can be then determined for every meter minimizing the difference between the sum energy of all observed meters and energy measured by sum (reference) meter. Other methods [3][4][5] also rely on a reference meter (installed in the sum meter of the subgrid location), but individually selected meter's readings are synchronously compared with energy consumption at the sum meter. In [3], an injected 10 W load is suggested while the method described in [4,5] utilize natural power usage changes due to consumer activities.…”

Section: Remote Error Monitoring Techniquesmentioning

confidence: 99%

“…Other methods [3][4][5] also rely on a reference meter (installed in the sum meter of the subgrid location), but individually selected meter's readings are synchronously compared with energy consumption at the sum meter. In [3], an injected 10 W load is suggested while the method described in [4,5] utilize natural power usage changes due to consumer activities. The common feature of these already introduced techniques is that meter error estimation uncertainty is significant.…”

Section: Remote Error Monitoring Techniquesmentioning

confidence: 99%

“…During exploitation (in-service) the meter can be inspected in a certified lab or on-site only in response to consumer complaints or suspicion by the energy provider. Recently, various remote [3][4][5], built-in [6], based on automatic meter reading data [7] meter error monitoring techniques have emerged and are gaining more interest for implementation in smart energy meters. These new technologies introduce an opportunity to monitor meter error nearly constantly.…”

Section: Introductionmentioning

confidence: 99%

“…However, remote error monitoring systems are not yet traceable to any power or energy units. For example, the approaches introduced in [3][4][5] include a traceable reference meter, but it also includes fluctuating media (distribution grid with all consumers' loads), whose unpredictable and random nature prevents achievement of error estimation (error measurement) traceability. Moreover, the uncertainty of remote monitoring is way larger than achievable in certified laboratories.…”

Section: Introductionmentioning

confidence: 99%

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A Contemplation on Electricity Meters In-Service Surveillance Assisted by Remote Error Monitoring

Nakutis

Kaškonas

2020

Energies

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In this paper, remote error monitoring techniques for electricity meters are overviewed suggesting their utilization for in-service surveillance assistance. It is discussed that in-service error observation could provide valuable input, contributing to the timely detection of batches of meters reaching nonconformance status. The payback period analysis of the deployment of a remote error monitoring solution is considered. However, it is pointed out that such an analysis lacks input information describing the relationship between the remote monitoring system’s performance and its ability to detect nonconformance of the batch. It is also noticed that there is no published methodology for grading the status of an entire batch of meters referring to error estimates of a subset of the meters, when the uncertainty of estimation is rather high.

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