AI and Data Democratisation for Intelligent Energy Management

Vangelis Marinakis, Themistoklis Koutsellis, Alexandros Nikas and Haris Doukas
Additional contact information
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

Energies, 2021, vol. 14, issue 14, 1-14

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.

Keywords: artificial intelligence; data democratisation; energy data spaces; interoperability; data sharing; energy management; decarbonisation; decision support (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
https://www.mdpi.com/1996-1073/14/14/4341/pdf (application/pdf)
https://www.mdpi.com/1996-1073/14/14/4341/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:14:y:2021:i:14:p:4341-:d:596969

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().