DBSEJul 5, 2021

Managing Knowledge in Energy Data Spaces

arXiv:2107.01965v110 citations
Originality Synthesis-oriented
AI Analysis

This work addresses data management issues for the energy sector, but it is incremental as it applies an existing architecture to a specific domain.

The paper tackles the challenge of fragmented energy data applications by proposing Energy Data Ecosystems (EDEs) based on the International Data Space (IDS) reference architecture to enable controlled data exchange and integration, and illustrates their applicability in real-world scenarios from the energy sector.

Data in the energy domain grows at unprecedented rates and is usually generated by heterogeneous energy systems. Despite the great potential that big data-driven technologies can bring to the energy sector, general adoption is still lagging. Several challenges related to controlled data exchange and data integration are still not wholly achieved. As a result, fragmented applications are developed against energy data silos, and data exchange is limited to few applications. In this paper, we analyze the challenges and requirements related to energy-related data applications. We also evaluate the use of Energy Data Ecosystems (EDEs) as data-driven infrastructures to overcome the current limitations of fragmented energy applications. EDEs are inspired by the International Data Space (IDS) initiative launched in Germany at the end of 2014 with an overall objective to take both the development and use of the IDS reference architecture model to a European/global level. The reference architecture model consists of four architectures related to business, security, data and service, and software aspects. This paper illustrates the applicability of EDEs and IDS reference architecture in real-world scenarios from the energy sector. The analyzed scenario is positioned in the context of the EU-funded H2020 project PLATOON.

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