Comparative Analysis of Load-Shaping-Based Privacy Preservation Strategies in a Smart Grid

Kement, Cihan Emre; Gültekin, Hakan; Tavlı, Bülent; Girici, Tolga; Uludag, Suleyman

doi:10.1109/tii.2017.2718666

Cited by 24 publications

(5 citation statements)

References 34 publications

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“…The privacy was evaluated by the F-score of NILM. The privacy preservation problem was formulated as an optimization problem in [185], where the objective was to minimize the sum of the expected cost, disutility of consumers caused by the late use of appliances, and information leakage. Eight privacyenhanced scheduling strategies considering on-site battery, renewable energy resources, and appliance load moderation were comprehensively compared.…”

Section: Data Privacymentioning

confidence: 99%

Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges

Wang

Chen

Hong

et al. 2019

IEEE Trans. Smart Grid

923

370

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The widespread popularity of smart meters enables an immense amount of fine-grained electricity consumption data to be collected. Meanwhile, the deregulation of the power industry, particularly on the delivery side, has continuously been moving forward worldwide. How to employ massive smart meter data to promote and enhance the efficiency and sustainability of the power grid is a pressing issue. To date, substantial works have been conducted on smart meter data analytics. To provide a comprehensive overview of the current research and to identify challenges for future research, this paper conducts an application-oriented review of smart meter data analytics. Following the three stages of analytics, namely, descriptive, predictive and prescriptive analytics, we identify the key application areas as load analysis, load forecasting, and load management. We also review the techniques and methodologies adopted or developed to address each application. In addition, we also discuss some research trends, such as big data issues, novel machine learning technologies, new business models, the transition of energy systems, and data privacy and security.

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Section: Data Privacymentioning

confidence: 99%

Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges

Wang

Chen

Hong

et al. 2019

IEEE Trans. Smart Grid

923

370

View full text Add to dashboard Cite

show abstract

“…However, in 2012, the European Commission recommended keeping a frequency under 15 min to "allow the information to be used to achieve energy savings" [12]. Several works explore the trade-offs between privacy and the operational needs of Smart Grid data mainly by investigating different data resolution schemes and load shaping [2,8,26,42,43], but this research field is still considered to have many open challenges. In fact, the Smart Grid data minimisation is a well-studied case study for the more general problem of time series compression [9].…”

Section: Data Minimisation and Purpose Limitationmentioning

confidence: 99%

“…In addition, it could have the consequence that users start adopting techniques to preserve their privacy. Known techniques are charging and discharging batteries [41] or the use of load shaping with storage and distributed renewable energy sources [26].…”

Section: Data Minimisation and Purpose Limitationmentioning

confidence: 99%

Smart Grid Challenges Through the Lens of the European General Data Protection Regulation

Martínez

Ruíz

Puelles

et al. 2020

Lecture Notes in Information Systems and Organisation

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The General Data Protection Regulation (GDPR) was conceived to remove the obstacles to the free movement of personal data while ensuring the protection of natural persons with regard to the processing of such data. The Smart Grid has similar features as any privacy-critical system but, in comparison to the engineering of other architectures, has the peculiarity of being the source of energy consumption data. Electricity consumption constitutes an indirect means to infer personal information. This work looks at the Smart Grid from the perspective of the GDPR, which is especially relevant now given the current growth and diversification of the Smart Grid ecosystem. We provide a review of existing works highlighting the importance of energy consumption as valuable personal data as well as an analysis of the established Smart Grid Architecture Model and its main challenges from a legal viewpoint, in particular the challenge of sharing data with third parties.

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“…The fine-grained energy utilization information cannot only reveal one's presence in the house, but such data can also help to deduce the activities and habits of the consumer [67,68]. Lisovich et al [69] experimented to determine what kind of information can be extracted from the energy consumption data.…”

Section: Privacy and Data Confidentialitymentioning

confidence: 99%

Prospects of Appliance-Level Load Monitoring in Off-the-Shelf Energy Monitors: A Technical Review

Haq

Jacobsen

2018

Energies

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Abstract:The smart grid initiative has encouraged utility companies worldwide to roll-out new and smarter versions of energy meters. Before an extensive roll-out, which is both labor-intensive and incurs high capital costs, consumers need to be incentivised to reap the long-term benefits of such smart meters. Off-the-shelf energy monitors (e-monitors) can provide consumers with an insight into such potential benefits. As e-monitors are owned by the consumer, the consumer has greater control over the data, which significantly reduces the privacy and data confidentiality concerns. Because only limited online technical information is available about e-monitors, we evaluate several existing e-monitors using an online technical survey directly from the vendors. Besides automated e-monitoring, the use of different off-the-shelf e-monitors can also help to demonstrate state-of-the-art techniques such as non-intrusive load monitoring (NILM), data analytics, and the predictive maintenance of appliances. Our survey indicates a trend towards the incorporation of such state-of-the-art capabilities, particularly the appliance-level e-monitoring and load disaggregation. We have also discussed some essential requirements to implement load disaggregation in the next generation e-monitors. In future, these intelligent e-monitoring techniques will encourage effective consumer participation in the demand-side management (DSM) programs.

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Comparative Analysis of Load-Shaping-Based Privacy Preservation Strategies in a Smart Grid

Cited by 24 publications

References 34 publications

Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges

Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges

Smart Grid Challenges Through the Lens of the European General Data Protection Regulation

Prospects of Appliance-Level Load Monitoring in Off-the-Shelf Energy Monitors: A Technical Review

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