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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
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    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)

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    More about this item

    Keywords

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

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