IDEAS home Printed from https://ideas.repec.org/a/eee/telpol/v44y2020i6s0308596120300690.html
   My bibliography  Save this article

Artificial Intelligence Applications in Telecommunications and other network industries

Author

Listed:
  • Balmer, Roberto E.
  • Levin, Stanford L.
  • Schmidt, Stephen

Abstract

Artificial intelligence applications in network industries have the potential to reduce network roll-out and operating costs, improve performance, enhance customer service, and support the development and introduction of new services. This paper identifies and analyzes the range of AI applications already in place and those expected to be in place in the near future. After analyzing the applications found in the literature, we have conducted interviews with senior executives responsible for AI in telecommunications, electricity, gas and water companies headquartered in North America and Europe, as well as with suppliers incorporating AI into their equipment. These interviews have provided information on which AI applications are actually being used in these network industries and which are expected to be implemented in the future. While some of these AI applications, like predictive maintenance, have already been implemented today by most operators, more complex applications, such as traffic management AIs for 5G, are being considered by only a few large operators. Finally, the paper explores the consequences of AI for regulation. At the least, regulators will need to understand AI in order to determine if AI applications raise regulatory concerns. At this point, however, as AI becomes increasingly integrated into network operations, regulatory issues are only beginning to emerge.

Suggested Citation

  • Balmer, Roberto E. & Levin, Stanford L. & Schmidt, Stephen, 2020. "Artificial Intelligence Applications in Telecommunications and other network industries," Telecommunications Policy, Elsevier, vol. 44(6).
    • Handle: RePEc:eee:telpol:v:44:y:2020:i:6:s0308596120300690
    DOI: 10.1016/j.telpol.2020.101977
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0308596120300690
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.telpol.2020.101977?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Roozemond, Danko A., 2001. "Using intelligent agents for pro-active, real-time urban intersection control," European Journal of Operational Research, Elsevier, vol. 131(2), pages 293-301, June.
    2. Ajay Agrawal & Joshua Gans & Avi Goldfarb, 2019. "Economic Policy for Artificial Intelligence," Innovation Policy and the Economy, University of Chicago Press, vol. 19(1), pages 139-159.
    3. Raza, Muhammad Qamar & Khosravi, Abbas, 2015. "A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 50(C), pages 1352-1372.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Lim, Chulmin & Rowsell, Joe & Kim, Seongcheol, 2023. "Exploring the killer domains to create new value: A Comparative case study of Canadian and Korean telcos," 32nd European Regional ITS Conference, Madrid 2023: Realising the digital decade in the European Union – Easier said than done? 277998, International Telecommunications Society (ITS).
    2. Chen, Yan & Zhang, Ruiqian & Lyu, Jiayi & Hou, Yuqi, 2024. "AI and Nuclear: A perfect intersection of danger and potential?," Energy Economics, Elsevier, vol. 133(C).
    3. Amit Kumar Kushwaha & Ruchika Pharswan & Prashant Kumar & Arpan Kumar Kar, 2023. "How Do Users Feel When They Use Artificial Intelligence for Decision Making? A Framework for Assessing Users’ Perception," Information Systems Frontiers, Springer, vol. 25(3), pages 1241-1260, June.
    4. Lim, Chulmin & Rowsell, Joe & Kim, Seongcheol, 2024. "Exploring killer domains to create new value: A comparative case study of Canadian and Korean telcos," Telecommunications Policy, Elsevier, vol. 48(4).

    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. Czarnitzki, Dirk & Fernández, Gastón P. & Rammer, Christian, 2023. "Artificial intelligence and firm-level productivity," Journal of Economic Behavior & Organization, Elsevier, vol. 211(C), pages 188-205.
    2. Ahmed, R. & Sreeram, V. & Mishra, Y. & Arif, M.D., 2020. "A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 124(C).
    3. Ehsan Valavi & Joel Hestness & Newsha Ardalani & Marco Iansiti, 2022. "Time and the Value of Data," Papers 2203.09118, arXiv.org.
    4. Namrye Son, 2021. "Comparison of the Deep Learning Performance for Short-Term Power Load Forecasting," Sustainability, MDPI, vol. 13(22), pages 1-25, November.
    5. 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.
    6. Ajay Agrawal & Joshua Gans & Avi Goldfarb, 2019. "Economic Policy for Artificial Intelligence," Innovation Policy and the Economy, University of Chicago Press, vol. 19(1), pages 139-159.
    7. Zhu, Jun & Zhang, Jingting & Feng, Yiqing, 2022. "Hard budget constraints and artificial intelligence technology," Technological Forecasting and Social Change, Elsevier, vol. 183(C).
    8. Chou, Jui-Sheng & Tran, Duc-Son, 2018. "Forecasting energy consumption time series using machine learning techniques based on usage patterns of residential householders," Energy, Elsevier, vol. 165(PB), pages 709-726.
    9. Gautham Krishnadas & Aristides Kiprakis, 2020. "A Machine Learning Pipeline for Demand Response Capacity Scheduling," Energies, MDPI, vol. 13(7), pages 1-25, April.
    10. Caleb Peppiatt, 2024. "The Future of Work: Inequality, Artificial Intelligence, and What Can Be Done About It. A Literature Review," Papers 2408.13300, arXiv.org.
    11. Ulaa AlHaddad & Abdullah Basuhail & Maher Khemakhem & Fathy Elbouraey Eassa & Kamal Jambi, 2023. "Towards Sustainable Energy Grids: A Machine Learning-Based Ensemble Methods Approach for Outages Estimation in Extreme Weather Events," Sustainability, MDPI, vol. 15(16), pages 1-19, August.
    12. Cebreros Alfonso & Heffner-Rodríguez Aldo & Livas René & Puggioni Daniela, 2020. "Automation Technologies and Employment at Risk: The Case of Mexico," Working Papers 2020-04, Banco de México.
    13. Yang, Zhongshan & Wang, Jian, 2018. "A hybrid forecasting approach applied in wind speed forecasting based on a data processing strategy and an optimized artificial intelligence algorithm," Energy, Elsevier, vol. 160(C), pages 87-100.
    14. Guillaume Guerard & Hugo Pousseur & Ihab Taleb, 2021. "Isolated Areas Consumption Short-Term Forecasting Method," Energies, MDPI, vol. 14(23), pages 1-23, November.
    15. Emilio Ghiani & Alessandro Serpi & Virginia Pilloni & Giuliana Sias & Marco Simone & Gianluca Marcialis & Giuliano Armano & Paolo Attilio Pegoraro, 2018. "A Multidisciplinary Approach for the Development of Smart Distribution Networks," Energies, MDPI, vol. 11(10), pages 1-29, September.
    16. Ma, Weiwu & Fang, Song & Liu, Gang & Zhou, Ruoyu, 2017. "Modeling of district load forecasting for distributed energy system," Applied Energy, Elsevier, vol. 204(C), pages 181-205.
    17. Zulfiqar, M. & Kamran, M. & Rasheed, M.B. & Alquthami, T. & Milyani, A.H., 2023. "A hybrid framework for short term load forecasting with a navel feature engineering and adaptive grasshopper optimization in smart grid," Applied Energy, Elsevier, vol. 338(C).
    18. Federico Divina & Aude Gilson & Francisco Goméz-Vela & Miguel García Torres & José F. Torres, 2018. "Stacking Ensemble Learning for Short-Term Electricity Consumption Forecasting," Energies, MDPI, vol. 11(4), pages 1-31, April.
    19. Oracio I. Barbosa-Ayala & Jhon A. Montañez-Barrera & Cesar E. Damian-Ascencio & Adriana Saldaña-Robles & J. Arturo Alfaro-Ayala & Jose Alfredo Padilla-Medina & Sergio Cano-Andrade, 2020. "Solution to the Economic Emission Dispatch Problem Using Numerical Polynomial Homotopy Continuation," Energies, MDPI, vol. 13(17), pages 1-15, August.
    20. Jimyung Kang & Soonwoo Lee, 2018. "Data-Driven Prediction of Load Curtailment in Incentive-Based Demand Response System," Energies, MDPI, vol. 11(11), pages 1-14, October.

    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:eee:telpol:v:44:y:2020:i:6:s0308596120300690. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/30471/description#description .

    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.