Artificial Intelligence Assisted Consumer Privacy and  Electrical Energy Management

Artificial Intelligence Assisted Consumer Privacy and Electrical Energy Management

Raziq Yaqub

Contact

Sadiq Ahmad

Contact

Alabama Agricultural and Mechanical University

Artificial Intelligence Assisted Consumer Privacy and Electrical Energy Management

Article Fingerprint

ReserarchID

P171H

Artificial Intelligence Assisted Consumer Privacy and Electrical Energy Management Banner

AI TAKEAWAY

Connecting with the Eternal Ground

Abstract

Smart metering infrastructure brings unique benefits for Utility Companies as well as consumers, however, massive consumer data collected and transmitted by the smart meters have raised consumers’ privacy concerns. This paper presents a novel solution that is based on Artificial Intelligence Agent that continuously computes the gap between “Average Daily Demand” and “Instantaneous Demand” of a consumer, and allows the Battery Banks to discharge just enough to fill the gaps and eliminate kinks in the energy usage graph to mask the energy usage. This novel approach offers several benefits, such as, it conceals the utility usage patterns and thus ensures privacy, eliminates excessive discharging and charging of batteries that lifts operational constraints of the batteries, employs scheduling that renders utility bill reduction as an add-on.

References

19 Cites in Article

Reference Format

M Asghar,D Miorandi (2013). A Holistic View of Security and Privacy Issues in Smart Grids.
Soren Finster,Ingmar Baumgart (2015). Privacy-Aware Smart Metering: A Survey.
C Gentile,D Griffith,M Souryal (2012). Wireless network deployment in the smart grid: Design and evaluation issues.
Murat Kuzlu,Manisa Pipattanasomporn,Saifur Rahman (2014). Communication network requirements for major smart grid applications in HAN, NAN and WAN.
E Ancillotti,R Bruno,M Conti (2013). Review: The role of communication systems in smart grids: Architectures, technical solutions and research challenges.
Y Xu,W Wang (2013). Wireless mesh network in smart grid: Modeling and analysis for time critical communications.
Ulrich Greveler,Benjamin Justus,Dennis Loehr (2012). Forensic content detection through power consumption.
H Su,M Qiu,H Wang (2012). Secure wireless communication system for smart grid with rechargeable electric vehicles.
Andrés Molina-Markham,Prashant Shenoy,Kevin Fu,Emmanuel Cecchet,David Irwin (2010). Private memoirs of a smart meter.
J Zhao,T Jung,Y Wang,X Li (2014). Achieving differential privacy of data disclosure in the smart grid.
Endong Liu,Peng Cheng (2017). Achieving Privacy Protection Using Distributed Load Scheduling: A Randomized Approach.
Junwei Han,Xuesong Gao,Kunhong Gu (2017). Lithium-ion batteries: Future market, challenges, and recycling.
Raziq Yaqub,Sadiq Ahmad,Ayaz Ahmad,Muhammad Amin (2015). Smart energy-consumption management system considering consumers' spending goals (SEMS-CCSG).
Joaquim Delgado,Ricardo Faria,Pedro Moura,Aníbal De Almeida (2018). Impacts of plug-in electric vehicles in the portuguese electrical grid.
J Lopes,F Soares,P Almeida (2011). Integration of electric vehicles in the electric power system.
Ramez Naam (2015). How Cheap Can Energy Storage Get? Pretty Darn Cheap.
M Black (2016). Fireball from Solar System’s edge isn’t what astronomers expected.
Simone Barcellona,Luigi Piegari,Vincenzo Musolino,Christophe Ballif (2018). Economic viability for residential battery storage systems in grid‐connected PV plants.
Y Kabuli (2016). A survey on smart metering and smart grid communication.

Download References

Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Raziq Yaqub. 2020. \u201cArtificial Intelligence Assisted Consumer Privacy and Electrical Energy Management\u201d. Global Journal of Computer Science and Technology - D: Neural & AI GJCST-D Volume 20 (GJCST Volume 20 Issue D1).

More Citation Formats

Select Citation Style:

Download Citation

GJCST Volume 20 Issue D1

Explore Journals Explore Volume Read This Issue

Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords

Not Found

Classification

GJCST-D Classification I.2.1

Submission ReceivedDecember 15, 2019
Peer Review Double Blind
Handling Editor
Accepted January 4, 2020
Published January 15, 2020

Version of record

v1.2

Issue date

June 8, 2020

Language

Experiance in AR

Explore published articles in an immersive Augmented Reality environment. Our platform converts research papers into interactive 3D books, allowing readers to view and interact with content using AR and VR compatible devices.

View in VR

Read in 3D

Your published article is automatically converted into a realistic 3D book. Flip through pages and read research papers in a more engaging and interactive format.

View in 3D

Article Matrices

Total Views: 4382

Total Downloads: 1154

2026 Trends

Our website is actively being updated, and changes may occur frequently. Please clear your browser cache if needed. For feedback or error reporting, please email [email protected]