On the non-intrusive extraction of residents’ privacy- and security-sensitive information from energy smart meters

Schirmer, Pascal A.; Mporas, Iosif

doi:10.1007/s00521-020-05608-w

Cited by 7 publications

(3 citation statements)

References 42 publications

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“…In the process of energy management, data acquisition is an important link (Mahapatra and Nayyar, 2022;Schirmer and Mporas, 2023). Data collection mainly collects data related to energy use and consumption through various sensors, equipment and systems.…”

Section: Data Collectionmentioning

confidence: 99%

Path analysis of energy economic management standardization in the context of carbon neutralization and carbon peak

Liu

2023

Front. Ecol. Evol.

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Carbon neutrality and carbon peak are two important measures to control climate change. They have a huge impact on many companies in the fields of energy, industry, construction, transportation, etc. They can change the development pattern of related industries and increase many new investment and development opportunities. This paper proposes a path analysis of the standardization of energy economic management under the background of carbon neutrality and carbon peak, aiming to study the development forecast of energy economic management under low-carbon conditions. The algorithm proposed in this paper is an energy consumption analysis algorithm based on the IPAT model, which can be combined with the IPAT model to analyze and process energy consumption data. In addition, by analyzing and evaluating the contribution of various factors, people can better understand the impact of energy consumption on the environment and formulate corresponding solutions. The experimental analysis results of energy economic management based on carbon neutrality and carbon peak show that, from 2013 to 2017, under the baseline scenario, carbon emissions increased year by year, from 9.25 billion tons to 10.48 billion tons. Under the carbon neutral scenario, its carbon emissions are 9.22 billion tons, 9.24 billion tons, 9.22 billion tons, 9.19 billion tons and 9.21 billion tons respectively. Obviously, under the carbon neutral scenario, carbon emissions can be better controlled through carbon neutral and carbon peak strategies. Through these experimental prediction results, it can be proved that carbon neutrality and carbon peaking have excellent effects on promoting energy economic management. At the same time, this study also provides valuable reference information for further research on carbon neutrality and carbon peaks.

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Section: Data Collectionmentioning

confidence: 99%

Path analysis of energy economic management standardization in the context of carbon neutralization and carbon peak

Liu

2023

Front. Ecol. Evol.

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“…Moreover, the privacy-persevering nature of NILM has been used for human behavior monitoring [ 20 ]. Moreover, NILM was evaluated in terms of its ability to extract socio-economic information and consumer behavior [ 21 ].…”

Section: Introductionmentioning

confidence: 99%

2D Transformations of Energy Signals for Energy Disaggregation

Schirmer

Mporas

2022

Sensors

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The aim of Non-Intrusive Load Monitoring is to estimate the energy consumption of individual electrical appliances by disaggregating the overall power consumption that has been sampled from a smart meter at a house or commercial/industrial building. Last decade’s developments in deep learning and the utilization of Convolutional Neural Networks have improved disaggregation accuracy significantly, especially when utilizing two-dimensional signal representations. However, converting time series’ to two-dimensional representations is still an open challenge, and it is not clear how it influences the performance of the energy disaggregation. Therefore, in this article, six different two-dimensional representation techniques are compared in terms of performance, runtime, influence on sampling frequency, and robustness towards Gaussian white noise. The evaluation results show an advantage of two-dimensional imaging techniques over univariate and multivariate features. In detail, the evaluation results show that: first, the active and reactive power-based signatures double Fourier based signatures, as well as outperforming most of the other approaches for low levels of noise. Second, while current and voltage signatures are outperformed at low levels of noise, they perform best under high noise conditions and show the smallest decrease in performance with increasing noise levels. Third, the effect of the sampling frequency on the energy disaggregation performance for time series imaging is most prominent up to 1.2 kHz, while, above 1.2 kHz, no significant improvements in terms of performance could be observed.

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“…Also, the NILM's privacy-preserving characteristics have been used for human monitoring and assisted living [79]. The potential of NILM to extract socioeconomic data and customer behavior was examined as well [80]. In the past decade, a number of NILM surveys have been produced.…”

Section: Non-intrusive Loading Monitoringmentioning

confidence: 99%

Advanced data-analytics for power system security enhancement

Chen

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Security in power systems is of utmost importance to ensure the reliable and safe operation of electricity infrastructure. With the increasing integration of digital technologies and the advent of the smart grid, modern power systems infrastructure have become more interconnected and reliant on data communication networks. Nevertheless, they are also exposed to various cybersecurity threats and vulnerabilities at the same time.To solve the security problems mentioned above, this Ph.D. thesis aims to develop data-analytics methods for security-relevant problems in power systems. This thesis includes 2 aspects, namely dynamic security assessment and nonintrusive loading monitoring, where the common thread is to identify the abnormal situations and raise potential solutions. With a vast amount of real-time data collected and utilized in modern power systems, data-driven methods can be a powerful tool to deal with these problems for their computational efficiency. The techniques employed in this thesis involve anomaly detection (to detect cyberattacks, e.g., Long Short-Term Memory, Local Outlier Factor, etc.), interpretation analysis (to quantify the contributions of input features against the machine learning models, e.g., SHapley Additive exPlanations and Shapley Additive Global importancE), adversarial attacks and mitigation (to measure the model vulnerability), and ensemble learning (to enhance the reliability of the predictions).All the proposed methods have been well verified on simulations over the corresponding data sets recorded from power system operations. Throughout the data experiments, it is shown that proper employment of ML algorithms could efficiently solve the security-relevant problems in power systems, and interpretation analysis will help to better understand the internal operation modes of the trained ML models as well as identify these crucial data features that dominate the com-

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On the non-intrusive extraction of residents’ privacy- and security-sensitive information from energy smart meters

Cited by 7 publications

References 42 publications

Path analysis of energy economic management standardization in the context of carbon neutralization and carbon peak

Path analysis of energy economic management standardization in the context of carbon neutralization and carbon peak

2D Transformations of Energy Signals for Energy Disaggregation

Advanced data-analytics for power system security enhancement

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