Data Analytics for Profiling Low-Voltage Customers with Smart Meter Readings

Pilo, Fabrizio; Pisano, Giuditta; Ruggeri, Simona; Troncia, Matteo

doi:10.3390/app11020500

Cited by 8 publications

(5 citation statements)

References 34 publications

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“…assumed that the measurement period of the total meter is a, and c a is the power consumption of the period a. The number of users in this area is n, the actual power consumption of a user is m, the actual measured power of the smart meter is a x,y , and the actual power consumption during the power consumption process is z [23,24]. Then, in the case that there is no error in the measured value of the total meter, the relationship between the actual power consumption of the user during a certain period of time and the actual power loss during the power process is shown as follows:…”

Section: Related Theories and Methods For Thementioning

confidence: 99%

Reliability Analysis of Intelligent Electric Energy Meter under Fusion Model Illness Analysis Algorithm

Xie

Zhang

et al. 2021

Journal of Sensors

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This work is aimed at solving the morbidity problem of the smart meter fusion model and improve the measurement accuracy and reliability of the smart meter. Starting with the topology of the smart meter, the reason for the serious morbidity of the smart meter model is discussed. First, the basic process of power system state estimation of smart meters is introduced, and the concept of error analysis of smart meters is clarified. Then, the causes and mechanisms of the ill-conditioned problems of the smart meter model are analyzed, and methods to reduce the morbidity of the smart meter calculation model are analyzed. Finally, a data optimization algorithm based on a greedy strategy and an improved Tikhonov regularization method is proposed. The model data is processed and optimized to reduce the morbidity of the smart meter measurement model. The results show that the analysis algorithm for reducing the morbidity error of the smart meter proposed in this study can effectively interfere with the morbidity of the smart meter calculation model. The processing effect shows that it can reduce the measurement error of the smart meter to about 5%, which is an order of magnitude lower than the error before processing, and the processing effect of the least square method is improved by more than 70%. From the perspective of processing speed, when the user number is between 50 and 100, the running time of the algorithm ranges between 1.5 and 3.5 s, which can be fully adapted to the actual situation and has strong practicability. In short, this study is helpful in improving the accuracy and reliability of smart meter calculations and provides a certain reference for related research.

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Section: Related Theories and Methods For Thementioning

confidence: 99%

Reliability Analysis of Intelligent Electric Energy Meter under Fusion Model Illness Analysis Algorithm

Xie

Zhang

et al. 2021

Journal of Sensors

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“…Four feeders deliver energy to different kinds of customers of the network, residential and non-residential, with both three-phases and single-phase connections. Real daily profiles of consumption derived from a measurement campaign are used for simulating the end users' behavior for one month [33]. The length of the feeders varies from a few meters (feeder F_3) to approximately 1 km (feeder F_2).…”

Section: B Distribution Network Modelmentioning

confidence: 99%

Impact of Electrical Vehicle Private Charging Stations on the Quality of the Low Voltage Network Supply

Pisano

Ruggeri

Soma

et al. 2023

IEEE Open J. Power Energy

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The appearance of new single-phase intensive and often coincident loads, such as the electric vehicle private charging stations (i.e., residential or private parking lots of shopping malls or workplaces), can affect the operation of low voltage (LV) distribution networks. Unbalances among the three-phase loading are extremely common, even if the end-users of a given LV network have similar behaviors. Due to the coincidence of such loads, excessive voltage drops may often appear, altering the quality of supply voltage provided to the LV end-users. This work evaluates the effects of residential and other charging stations located in parking lots of shopping malls or workplaces on the quality of supply voltage of LV grids. Then, a decentralized approach is proposed, based on the multi-agent system, which allows the different flexibility resources available on the grid to act together to improve the network quality. The agents cooperate to regulate the network nodal voltage, taking into account the characteristics of the resources they are controlling and the needs of their owners. A case study derived from a real portion of the Italian distribution system demonstrates the validity of the approach in solving the network operation criticalities.

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“…The advent of an SG implies more comprehensive load modeling that considers individual appliance behavior [ 14 ]. A study of household power usage in the United States discovered that 66% of the energy (out of the total) is consumed by space heating, water and air conditioning [ 15 ].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Feature engineering is the process of selecting and extracting features. The RF and XGBoost machine learning approaches are utilized to choose relevant characteristics [ 14 ]. The influence of attributes on the target is computed using these methods.…”

Section: Proposed System Modelmentioning

confidence: 99%

Ensemble learning approach for advanced metering infrastructure in future smart grids

Irfan,

Ayub,

Althobiani

et al. 2023

PLoS ONE

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Typically, load forecasting models are trained in an offline setting and then used to generate predictions in an online setting. However, this approach, known as batch learning, is limited in its ability to integrate new load information that becomes available in real-time. On the other hand, online learning methods enable load forecasting models to adapt efficiently to new incoming data. Electricity Load and Price Forecasting (ELPF) is critical to maintaining energy grid stability in smart grids. Existing forecasting methods cannot handle the available large amount of data, which are limited by different issues like non-linearity, un-adjusted high variance and high dimensions. A compact and improved algorithm is needed to synchronize with the diverse procedure in ELPF. Our model ELPF framework comprises high/low consumer data separation, handling missing and unstandardized data and preprocessing method, which includes selecting relevant features and removing redundant features. Finally, it implements the ELPF using an improved method Residual Network (ResNet-152) and the machine-improved Support Vector Machine (SVM) based forecasting engine to forecast the ELP accurately. We proposed two main distinct mechanisms, regularization, base learner selection and hyperparameter tuning, to improve the performance of the existing version of ResNet-152 and SVM. Furthermore, it reduces the time complexity and the overfitting model issue to handle more complex consumer data. Furthermore, numerous structures of ResNet-152 and SVM are also explored to improve the regularization function, base learners and compatible selection of the parameter values with respect to fitting capabilities for the final forecasting. Simulated results from the real-world load and price data confirm that the proposed method outperforms 8% of the existing schemes in performance measures and can also be used in industry-based applications.

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Data Analytics for Profiling Low-Voltage Customers with Smart Meter Readings

Cited by 8 publications

References 34 publications

Reliability Analysis of Intelligent Electric Energy Meter under Fusion Model Illness Analysis Algorithm

Reliability Analysis of Intelligent Electric Energy Meter under Fusion Model Illness Analysis Algorithm

Impact of Electrical Vehicle Private Charging Stations on the Quality of the Low Voltage Network Supply

Ensemble learning approach for advanced metering infrastructure in future smart grids

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