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Energy Data Spaces: Architectures, Concepts and Applications

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F5: Artificial Intelligence and Smart Energy".

Deadline for manuscript submissions: 10 October 2025 | Viewed by 1627

Special Issue Editor


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Guest Editor
School of Electrical and Computer Engineering, National Technical University of Athens, 157 73 Zografou, Greece
Interests: power system operations; renewable energy sources; artificial intelligence on power systems; smart grids and control software development

Special Issue Information

Dear Colleagues,

As the energy landscape evolves towards a more sustainable, decentralized model, the integration of renewable resources and distributed energy systems presents unprecedented challenges to traditional operational frameworks. This transition demands a re-evaluation of the existing physical models, which struggle to accurately monitor and plan within this dynamic environment. To address these complexities, the development of an integrated ecosystem of data value chains becomes essential. This ecosystem will empower stakeholders in the energy sector with data-driven insights for optimization and coordination.

Central to this endeavor are energy data spaces, as underscored by the “Digitalization of Energy Action Plan” and the European Data Act. These spaces provide a secure platform for the exchange of data across energy sectors, promoting interoperability, innovation, and privacy protection.

This Special Issue focusses on advancements in this field, with a spotlight on federated architecture. Specifically, we delve into topics such as reference architectures for energy data spaces, innovative business models, intelligent applications, and the emergence of data-driven digital twins. By exploring these themes, we aim to foster a deeper understanding of the transformative potential of energy data spaces and their role in shaping the future of the energy sector.

Dr. Aris Dimeas
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • energy data spaces
  • federated architectures
  • interoperability
  • data driven application
  • digital twin
  • innovative business models for data spaces

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Published Papers (1 paper)

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Research

23 pages, 7094 KiB  
Article
Using Time-Series Databases for Energy Data Infrastructures
by Christos Hadjichristofi, Spyridon Diochnos, Kyriakos Andresakis and Vassilios Vescoukis
Energies 2024, 17(21), 5478; https://doi.org/10.3390/en17215478 - 1 Nov 2024
Cited by 1 | Viewed by 1050
Abstract
The management of energy market data, such as load, production, forecasts, and prices, is critical for energy market participants, who develop in-house energy data infrastructure services to aggregate data from many sources to support their business operations. Energy data management frequently involves time [...] Read more.
The management of energy market data, such as load, production, forecasts, and prices, is critical for energy market participants, who develop in-house energy data infrastructure services to aggregate data from many sources to support their business operations. Energy data management frequently involves time sensitive operations, including rapid data ingestion, real-time querying, filling in gaps from missing or delayed data, and updating large volumes of timestamped and loosely structured data, all of which demand high processing power. Traditional relational database management systems (RDBMSs) often struggle with these operations, whereas time series databases (TSDBs) appear to be a more efficient solution, providing enhanced scalability, reliability, real-time data availability and superior performance. This paper examines the advantages of TSDBs over RDBMS for energy data management, demonstrating that TSDBs can either replace or complement RDBMSs. We present quantitative improvements in digestion, integration, architecture, and performance, demonstrating that operations such as importing and querying time-series energy data, along with the overall system’s efficiency, can be significantly improved, achieving up to 100 times faster operations compared to relational databases, all without requiring extensive modifications to the existing information system’s architecture. Full article
(This article belongs to the Special Issue Energy Data Spaces: Architectures, Concepts and Applications)
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