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AI and Data Democratisation for Intelligent Energy Management

Author

Listed:
  • Vangelis Marinakis

    (School of Electrical & Computer Engineering, National Technical University of Athens, 15780 Zografou (Athens), Greece)

  • Themistoklis Koutsellis

    (School of Electrical & Computer Engineering, National Technical University of Athens, 15780 Zografou (Athens), Greece)

  • Alexandros Nikas

    (School of Electrical & Computer Engineering, National Technical University of Athens, 15780 Zografou (Athens), Greece)

  • Haris Doukas

    (School of Electrical & Computer Engineering, National Technical University of Athens, 15780 Zografou (Athens), Greece)

Abstract

Despite the large number of technology-intensive organisations, their corporate know-how and underlying workforce skill are not mature enough for a successful rollout of Artificial Intelligence (AI) services in the near-term. However, things have started to change, owing to the increased adoption of data democratisation processes, and the capability offered by emerging technologies for data sharing while respecting privacy, protection, and security, as well as appropriate learning-based modelling capabilities for non-expert end-users. This is particularly evident in the energy sector. In this context, the aim of this paper is to analyse AI and data democratisation, in order to explore the strengths and challenges in terms of data access problems and data sharing, algorithmic bias, AI transparency, privacy and other regulatory constraints for AI-based decisions, as well as novel applications in different domains, giving particular emphasis on the energy sector. A data democratisation framework for intelligent energy management is presented. In doing so, it highlights the need for the democratisation of data and analytics in the energy sector, toward making data available for the right people at the right time, allowing them to make the right decisions, and eventually facilitating the adoption of decentralised, decarbonised, and democratised energy business models.

Suggested Citation

  • Vangelis Marinakis & Themistoklis Koutsellis & Alexandros Nikas & Haris Doukas, 2021. "AI and Data Democratisation for Intelligent Energy Management," Energies, MDPI, vol. 14(14), pages 1-14, July.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:14:p:4341-:d:596969
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    References listed on IDEAS

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    Cited by:

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    2. Adam Sulich & Letycja Sołoducho-Pelc, 2022. "Changes in Energy Sector Strategies: A Literature Review," Energies, MDPI, vol. 15(19), pages 1-26, September.
    3. Shin-Cheng Yeh & Ai-Wei Wu & Hui-Ching Yu & Homer C. Wu & Yi-Ping Kuo & Pei-Xuan Chen, 2021. "Public Perception of Artificial Intelligence and Its Connections to the Sustainable Development Goals," Sustainability, MDPI, vol. 13(16), pages 1-34, August.

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