IDEAS home Printed from https://ideas.repec.org/p/hal/journl/hal-04167091.html
   My bibliography  Save this paper

Regulating Artificial Intelligence in the EU, United States and China - Implications for energy systems

Author

Listed:
  • Fabian Heymann

    (SFOE - Swiss Federal Office of Energy)

  • Konstantinos Parginos

    (PERSEE - Centre Procédés, Énergies Renouvelables, Systèmes Énergétiques - Mines Paris - PSL (École nationale supérieure des mines de Paris) - PSL - Université Paris Sciences et Lettres)

  • Ali Hariri

    (EPFL - Ecole Polytechnique Fédérale de Lausanne)

  • Gabriele Franco

    (PANETTA Law Firm)

Abstract

The growing prevalence and potential impact of artificial intelligence (AI) on society rises the need for regulation. In return, the shape of regulations will affect the application potential of AI across all economic sectors. This study compares the approaches to regulate AI in the European Union (EU), the United States (US) and China (CN). We then apply the findings of our comparative analysis on the energy sector, assessing the effects of each regulatory approach on the operation of a AI-based short-term electricity demand forecasting application. Our findings show that operationalizing AI applications will face very different challenges across geographies, with important implications for policy making and business development.

Suggested Citation

  • Fabian Heymann & Konstantinos Parginos & Ali Hariri & Gabriele Franco, 2023. "Regulating Artificial Intelligence in the EU, United States and China - Implications for energy systems," Post-Print hal-04167091, HAL.
    • Handle: RePEc:hal:journl:hal-04167091
    Note: View the original document on HAL open archive server: https://hal.science/hal-04167091
    as

    Download full text from publisher

    File URL: https://hal.science/hal-04167091/document
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Tan Yigitcanlar & Kevin C. Desouza & Luke Butler & Farnoosh Roozkhosh, 2020. "Contributions and Risks of Artificial Intelligence (AI) in Building Smarter Cities: Insights from a Systematic Review of the Literature," Energies, MDPI, vol. 13(6), pages 1-38, March.
    2. Heymann, Fabian & Milojevic, Tatjana & Covatariu, Andrei & Verma, Piyush, 2023. "Digitalization in decarbonizing electricity systems – Phenomena, regional aspects, stakeholders, use cases, challenges and policy options," Energy, Elsevier, vol. 262(PB).
    3. Hong, Tao & Fan, Shu, 2016. "Probabilistic electric load forecasting: A tutorial review," International Journal of Forecasting, Elsevier, vol. 32(3), pages 914-938.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Niematallah Elamin & Mototsugu Fukushige, 2016. "A Quantile Regression Model for Electricity Peak Demand Forecasting: An Approach to Avoiding Power Blackouts," Discussion Papers in Economics and Business 16-22, Osaka University, Graduate School of Economics.
    2. Rostami-Tabar, Bahman & Ali, Mohammad M. & Hong, Tao & Hyndman, Rob J. & Porter, Michael D. & Syntetos, Aris, 2022. "Forecasting for social good," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1245-1257.
    3. Yu Cao & Cong Xu & Syahrul Nizam Kamaruzzaman & Nur Mardhiyah Aziz, 2022. "A Systematic Review of Green Building Development in China: Advantages, Challenges and Future Directions," Sustainability, MDPI, vol. 14(19), pages 1-29, September.
    4. Ozdemir, Ali Can & Buluş, Kurtuluş & Zor, Kasım, 2022. "Medium- to long-term nickel price forecasting using LSTM and GRU networks," Resources Policy, Elsevier, vol. 78(C).
    5. Palmyra Repette & Jamile Sabatini-Marques & Tan Yigitcanlar & Denilson Sell & Eduardo Costa, 2021. "The Evolution of City-as-a-Platform: Smart Urban Development Governance with Collective Knowledge-Based Platform Urbanism," Land, MDPI, vol. 10(1), pages 1-25, January.
    6. Elisabeth A. Shrimpton & Dexter Hunt & Chris D.F. Rogers, 2021. "Justice in (English) Water Infrastructure: A Systematic Review," Sustainability, MDPI, vol. 13(6), pages 1-18, March.
    7. Tan Yigitcanlar & Rashid Mehmood & Juan M. Corchado, 2021. "Green Artificial Intelligence: Towards an Efficient, Sustainable and Equitable Technology for Smart Cities and Futures," Sustainability, MDPI, vol. 13(16), pages 1-14, August.
    8. Sanjeev Sharma & Renu Popli & Sajjan Singh & Gunjan Chhabra & Gurpreet Singh Saini & Maninder Singh & Archana Sandhu & Ashutosh Sharma & Rajeev Kumar, 2024. "The Role of 6G Technologies in Advancing Smart City Applications: Opportunities and Challenges," Sustainability, MDPI, vol. 16(16), pages 1-53, August.
    9. Alfredo Candela Esclapez & Miguel López García & Sergio Valero Verdú & Carolina Senabre Blanes, 2022. "Automatic Selection of Temperature Variables for Short-Term Load Forecasting," Sustainability, MDPI, vol. 14(20), pages 1-22, October.
    10. Asaad Mohammad & Ramon Zamora & Tek Tjing Lie, 2020. "Integration of Electric Vehicles in the Distribution Network: A Review of PV Based Electric Vehicle Modelling," Energies, MDPI, vol. 13(17), pages 1-20, September.
    11. Nowotarski, Jakub & Weron, Rafał, 2018. "Recent advances in electricity price forecasting: A review of probabilistic forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1548-1568.
    12. Wang, Jianzhou & Wang, Shuai & Zeng, Bo & Lu, Haiyan, 2022. "A novel ensemble probabilistic forecasting system for uncertainty in wind speed," Applied Energy, Elsevier, vol. 313(C).
    13. Jessica Walther & Matthias Weigold, 2021. "A Systematic Review on Predicting and Forecasting the Electrical Energy Consumption in the Manufacturing Industry," Energies, MDPI, vol. 14(4), pages 1-24, February.
    14. Ewa Chodakowska & Joanicjusz Nazarko & Łukasz Nazarko, 2021. "ARIMA Models in Electrical Load Forecasting and Their Robustness to Noise," Energies, MDPI, vol. 14(23), pages 1-22, November.
    15. Antonio De Nicola & Maria Luisa Villani, 2021. "Smart City Ontologies and Their Applications: A Systematic Literature Review," Sustainability, MDPI, vol. 13(10), pages 1-40, May.
    16. Luo, Jian & Hong, Tao & Gao, Zheming & Fang, Shu-Cherng, 2023. "A robust support vector regression model for electric load forecasting," International Journal of Forecasting, Elsevier, vol. 39(2), pages 1005-1020.
    17. Hanna Obracht-Prondzyńska & Ewa Duda & Helena Anacka & Jolanta Kowal, 2022. "Greencoin as an AI-Based Solution Shaping Climate Awareness," IJERPH, MDPI, vol. 19(18), pages 1-25, September.
    18. Buzna, Luboš & De Falco, Pasquale & Ferruzzi, Gabriella & Khormali, Shahab & Proto, Daniela & Refa, Nazir & Straka, Milan & van der Poel, Gijs, 2021. "An ensemble methodology for hierarchical probabilistic electric vehicle load forecasting at regular charging stations," Applied Energy, Elsevier, vol. 283(C).
    19. Leonard Burg & Gonca Gürses-Tran & Reinhard Madlener & Antonello Monti, 2021. "Comparative Analysis of Load Forecasting Models for Varying Time Horizons and Load Aggregation Levels," Energies, MDPI, vol. 14(21), pages 1-16, November.
    20. Chabouni, Naima & Belarbi, Yacine & Benhassine, Wassim, 2020. "Electricity load dynamics, temperature and seasonality Nexus in Algeria," Energy, Elsevier, vol. 200(C).

    More about this item

    Keywords

    Artificial Intelligence; energy policy; load fore- casting; regulation;
    All these keywords.

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hal:journl:hal-04167091. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: CCSD (email available below). General contact details of provider: https://hal.archives-ouvertes.fr/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.