research-article

Accountability challenges of AI in smart grid services

Authors:

Amit Dilip Patil,

Seyyed Ahmad Javadi,

Hermann de MeerAuthors Info & Claims

e-Energy '22: Proceedings of the Thirteenth ACM International Conference on Future Energy Systems

Pages 597 - 600

https://doi.org/10.1145/3538637.3539636

Published: 28 June 2022 Publication History

Abstract

The rising importance and efficiency of Artificial Intelligence (AI) has led to AI being substantially deployed in various domains and its usage growing rapidly. For the smart grid, adopting AI-based methods has transformed established power system functions into more advanced analogs. However, the unstable operation of AI-based services may misguide operators to make incorrect operational decisions leading to substantial consequences. Accordingly, the rapidly growing market of AI-based services calls for raising awareness of accountability of these services. Accountability in grid services is getting even more challenging with respect to the wide availability of the AI-based methods implemented as libraries and the emergence of AI as a service.

A key way forward is through smart grid operators monitoring their AI-based services to enable accountability. Accountable AI increases trustworthiness and accelerates acceptance of these services by society. Toward this, a conceptual representation of a monitoring regime for AI-based services is proposed and major challenges to realising it are identified. In addition, this work aims to draw more attention to AI-based services and the issues they raise, given AI increasing prominence and the general calls for the more responsible and accountable use of AI.

References

[1]

Mohamed Abdel-Nasser and Karar Mahmoud. 2019. Accurate photovoltaic power forecasting models using deep lstm-rnn. Neural Computing and Applications, 31, 7, 2727--2740.

Digital Library

[2]

Ashraf Abdul, Jo Vermeulen, Danding Wang, Brian Y. Lim, and Mohan Kankanhalli. 2018. Trends and trajectories for explainable, accountable and intelligible systems: an hci research agenda. In vol. 2018-April. Association for Computing Machinery, (Apr. 2018). isbn: 9781450356206.

Digital Library

[3]

Fiaz Ahmad, Muhammad Tariq, and Ajmal Farooq. 2019. A novel ann-based distribution network state estimator. International Journal of Electrical Power & Energy Systems, 107, 200--212.

[4]

Moin Ahmed, Yun Zheng, Anna Amine, Hamed Fathiannasab, and Zhongwei Chen. 2021. The role of artificial intelligence in the mass adoption of electric vehicles. Joule, 5, 9, 2296--2322.

[5]

Yves Bamberger et al. 2006. Vision and strategy for europe's electricity networks of the future: european technology platformsmartgrids. isbn: 92-79-01414-5.

[6]

Alejandro Barredo Arrieta et al. 2020. Explainable artificial intelligence (xai): concepts, taxonomies, opportunities and challenges toward responsible ai. Information Fusion, 58, (June 2020), 82--115.

Digital Library

[7]

Adrien Bécue, Isabel Praça, and João Gama. 2021. Artificial intelligence, cyberthreats and industry 4.0: challenges and opportunities. Artificial Intelligence Review, 54, 5, 3849--3886.

Digital Library

[8]

Xie Chuansheng, Dong Dapeng, Hua Shengping, Xu Xin, and Chen Yingjie. 2012. Safety evaluation of smart grid based on ahp-entropy method. Systems Engineering Procedia, 4, 203--209.

[9]

Yu-Wei Chung, Behnam Khaki, Tianyi Li, Chicheng Chu, and Rajit Gadh. 2019. Ensemble machine learning-based algorithm for electric vehicle user behavior prediction. Applied Energy, 254, 113732.

[10]

Mingjian Cui, Jianhui Wang, and Meng Yue. 2019. Machine learning-based anomaly detection for load forecasting under cyberattacks. IEEE Transactions on Smart Grid, 10, 5, 5724--5734.

[11]

Dominik Danner and Hermann de Meer. 2021. Quality of service and fairness for electric vehicle charging as a service. Energy Informatics, 4, 3, 1--20.

[12]

Dominik Danner, Jan Seidemann, Michael Lechl, and Hermann de Meer. 2021. Flexibility disaggregation under forecast conditions. In Proceedings of the Twelfth ACM International Conference on Future Energy Systems, 27--38.

Digital Library

[13]

Zhuofu Deng, Binbin Wang, Yanlu Xu, Tengteng Xu, Chenxu Liu, and Zhiliang Zhu. 2019. Multi-scale convolutional neural network with time-cognition for multi-step short-term load forecasting. IEEE Access, 7, 88058--88071.

[14]

G. Dileep. 2020. A survey on smart grid technologies and applications. Renewable energy, 146, 2589--2625.

[15]

Mohamed Amine Ferrag, Leandros A Maglaras, Helge Janicke, Jianmin Jiang, and Lei Shu. 2018. A systematic review of data protection and privacy preservation schemes for smart grid communications. Sustainable cities and society, 38, 806--835.

[16]

Ángel Manuel Guerrero-Higueras, Francisco J Rodríguez-Lera, Francisco Martín-Rico, Jesús Balsa-Comerón, and Vicente Matellán-Olivera. 2018. Accountability in mobile service robots. In Workshop of Physical Agents. Springer, 242--254.

[17]

Murtaza Hashmi. 2011. Survey of smart grids concepts worldwide. VTT Technical Research Centre of Finland. isbn: 978-951-38-7507-7.

[18]

Eklas Hossain, Imtiaj Khan, Fuad Un-Noor, Sarder Shazali Sikander, and Md Samiul Haque Sunny. 2019. Application of big data and machine learning in smart grid, and associated security concerns: a review. IEEE Access, 7, 13960--13988.

[19]

2009. Ieee application guide for ieee std 1547(tm), ieee standard for interconnecting distributed resources with electric power systems. IEEE Std 1547.2-2008, 1--217.

[20]

Seyyed Ahmad Javadi, Richard Cloete, Jennifer Cobbe, Michelle Seng Ah Lee, and Jatinder Singh. 2020. Monitoring misuse for accountable 'artificial intelligence as a service'. In Proceedings of the 20220 AAAI/ACM Conference on AI, Ethics, and Society, 300--306.

Digital Library

[21]

Seyyed Ahmad Javadi, Chris Norval, Richard Cloete, and Jatinder Singh. 2021. Monitoring ai services for misuse. In Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society, 597--607.

Digital Library

[22]

M Humayun Kabir, Khondokar Fida Hasan, Mohammad Kamrul Hasan, and Keyvan Ansari. 2022. Explainable artificial intelligence for smart city application: a secure and trusted platform. In Explainable Artificial Intelligence for Cyber Security. Springer, 241--263.

[23]

Igor Kotsiuba, Inna Skarga-Bandurova, Alkiviadis Giannakoulias, and Oksana Bulda. 2019. Basic forensic procedures for cyber crime investigation in smart grid networks. In 2019 IEEE International Conference on Big Data (Big Data). IEEE, 4255--4264.

[24]

Nallapaneni Manoj Kumar, Aneesh A Chand, Maria Malvoni, Kushal A Prasad, Kabir A Mamun, FR Islam, and Shauhrat S Chopra. 2020. Distributed energy resources and the application of ai, iot, and block chain in smart grids. Energies, 13, 21, 5739.

[25]

Hui Liu, Chao Chen, Xinwei Lv, Xing Wu, and Min Liu. 2019. Deterministic wind energy forecasting: a review of intelligent predictors and auxiliary methods. Energy Conversion and Management, 195, 328--345.

[26]

Jing Liu, Yang Xiao, and Jingcheng Gao. 2013. Achieving accountability in smart grid. IEEE Systems Journal, 8, 2, 493--508.

[27]

Efthymios Manitsas, Ravindra Singh, Bikash C Pal, and Goran Strbac. 2012. Distribution system state estimation using an artificial neural network approach for pseudo measurement modeling. IEEE Transactions on power systems, 27, 4, 1888--1896.

[28]

Daniel Omeiza, Helena Web, Marina Jirotka, and Lars Kunze. 2021. Towards accountability: providing intelligible explanations in autonomous driving. In 2021 IEEE Intelligent Vehicles Symposium (IV). IEEE, 231--237.

Digital Library

[29]

Olufemi A Omitaomu and Haoran Niu. 2021. Artificial intelligence techniques in smart grid: a survey. Smart Cities, 4, 2, 548--568.

[30]

Amit Dilip Patil, Jonas Haack, Martin Braun, and Hermann de Meer. 2020. Modeling interconnected ict and power systems for resilience analysis. Energy Informatics, 3, 1, 1--20.

[31]

Ashish Shrestha et al. 2019. Comparative study of different approaches for islanding detection of distributed generation systems. Applied System Innovation, 2, 3, 25.

[32]

Jatinder Singh, Christopher Millard, Chris Reed, Jennifer Cobbe, and Jon Crowcroft. 2018. Accountability in the iot: systems, law, and ways forward. Computer, 51, 7, 54--65.

[33]

Sanja Stark, Anna Volkova, Sebastian Lehnhoff, and Hermann de Meer. 2021. Why your power system restoration does not work and what the ict system can do about it. In Proceedings of the twelfth ACM international conference on future energy systems, 269--273.

Digital Library

[34]

United States. 2007. Energy independence and security act of 2007. US Government Printing Office.

[35]

VDE. 2013. The German Standardization Roadmap E-Energy/Smart Grid 2.0. VDE-Verlag.

[36]

Anna Volkova, Michael Niedermeier, Robert Basmadjian, and Hermann de Meer. 2018. Security challenges in control network protocols: a survey. IEEE Communications Surveys & Tutorials, 21, 1, 619--639.

[37]

Rebecca Williams et al. 2022. From transparency to accountability of intelligent systems: moving beyond aspirations. Data & Policy, 4.

[38]

Ahmed S Zamzam, Xiao Fu, and Nicholas D Sidiropoulos. 2019. Data-driven learning-based optimization for distribution system state estimation. IEEE Transactions on Power Systems, 34, 6, 4796--4805.

Cited By

Volkova AHatamian MAnapyanova Ade Meer H(2024)Being Accountable is Smart: Navigating the Technical and Regulatory Landscape of AI-based Services for Power GridProceedings of the 2024 International Conference on Information Technology for Social Good10.1145/3677525.3678651(118-126)Online publication date: 4-Sep-2024
https://dl.acm.org/doi/10.1145/3677525.3678651
Nelufule NMasango MSenamela PMawela HLatakgomo MMoloi P(2024)Digital Forensics: A Survey of Emerging Threats, Challenges, and Opportunities in Smart Grids2024 IEEE 12th International Conference on Smart Energy Grid Engineering (SEGE)10.1109/SEGE62220.2024.10739495(109-114)Online publication date: 18-Aug-2024
https://doi.org/10.1109/SEGE62220.2024.10739495
Tabassum ZSamantaray RMohammadi SFathima A(2023)Artificial Intelligence and Blockchain Technology for Secure Smart Grid and Power Distribution AutomationAI and Blockchain Applications in Industrial Robotics10.4018/979-8-3693-0659-8.ch009(226-252)Online publication date: 29-Dec-2023
https://doi.org/10.4018/979-8-3693-0659-8.ch009

Index Terms

Accountability challenges of AI in smart grid services
1. Applied computing
  1. Computer forensics
    1. Investigation techniques
2. Security and privacy
  1. Human and societal aspects of security and privacy
    1. Social aspects of security and privacy

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Published In

cover image ACM Conferences

e-Energy '22: Proceedings of the Thirteenth ACM International Conference on Future Energy Systems

June 2022

630 pages

ISBN:9781450393973

DOI:10.1145/3538637

General Chairs:
Sebastian Lehnhoff
University of Oldenburg, Germany
,
David Irwin
University of Massachusetts, Amherst
,
Dan Wang
The Hong Kong Polytechnic University, Hong Kong

Copyright © 2022 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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New York, NY, United States

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Published: 28 June 2022

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e-Energy '22: The Thirteenth ACM International Conference on Future Energy Systems

June 28 - July 1, 2022

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Overall Acceptance Rate 160 of 446 submissions, 36%

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Cited By

Volkova AHatamian MAnapyanova Ade Meer H(2024)Being Accountable is Smart: Navigating the Technical and Regulatory Landscape of AI-based Services for Power GridProceedings of the 2024 International Conference on Information Technology for Social Good10.1145/3677525.3678651(118-126)Online publication date: 4-Sep-2024
https://dl.acm.org/doi/10.1145/3677525.3678651
Nelufule NMasango MSenamela PMawela HLatakgomo MMoloi P(2024)Digital Forensics: A Survey of Emerging Threats, Challenges, and Opportunities in Smart Grids2024 IEEE 12th International Conference on Smart Energy Grid Engineering (SEGE)10.1109/SEGE62220.2024.10739495(109-114)Online publication date: 18-Aug-2024
https://doi.org/10.1109/SEGE62220.2024.10739495
Tabassum ZSamantaray RMohammadi SFathima A(2023)Artificial Intelligence and Blockchain Technology for Secure Smart Grid and Power Distribution AutomationAI and Blockchain Applications in Industrial Robotics10.4018/979-8-3693-0659-8.ch009(226-252)Online publication date: 29-Dec-2023
https://doi.org/10.4018/979-8-3693-0659-8.ch009

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