Managing Knowledge in Energy Data Spaces

Janev, Valentina; Vidal, María-Esther; Endris, Kemele M.; Pujić, Dea

doi:10.1145/3442442.3453541

Cited by 10 publications

(6 citation statements)

References 12 publications

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“…The work presented in this paper is built on our previous work (conference paper) on knowledge-driven frameworks for managing energy data spaces [8] and in the knowledgedriven data ecosystems [12]. With the focus of achieving the targets envisioned in the latest EU energy strategy and the European Green Deal Action Plan [13], we present a knowledgedriven data ecosystem to encapsulate, communicate, and manage the distributed assets in the energy value chain.…”

Section: Overview Of Main Contributionsmentioning

confidence: 99%

“…If the datasets are already included in the materialized knowledge graph, then the federated query processing component can directly access them without performing data transformation at query time. However, if the data sources are stored in raw format, then the data transformation rules will be applied only for the part of the dataset required to answer the query; see also Janev et al (2021) [8].…”

Section: Unified Knowledge Graph Creation Processmentioning

confidence: 99%

“…Virtual integration approach can also be applied over heterogeneous data sources. In this case, the query processing engine not only queries each data source and merges results, but also should be able to transform raw data into the semantic models specified in the mappings during query time [8].…”

Section: Traversing the Knowledge Graphmentioning

confidence: 99%

“…ACM, New York, NY, USA, 11 pages. https://doi.org/10.1145/3442442.3453541, accessed on 23 May 2022), the authors assess the applicability of the technologies for managing knowledge [8] in the energy sector and consequently for specifying new business models.…”

Section: Introductionmentioning

confidence: 99%

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Responsible Knowledge Management in Energy Data Ecosystems

et al. 2022

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This paper analyzes the challenges and requirements of establishing energy data ecosystems (EDEs) as data-driven infrastructures that overcome the limitations of currently fragmented energy applications. It proposes a new data- and knowledge-driven approach for management and processing. This approach aims to extend the analytics services portfolio of various energy stakeholders and achieve two-way flows of electricity and information for optimized generation, distribution, and electricity consumption. The approach is based on semantic technologies to create knowledge-based systems that will aid machines in integrating and processing resources contextually and intelligently. Thus, a paradigm shift in the energy data value chain is proposed towards transparency and the responsible management of data and knowledge exchanged by the various stakeholders of an energy data space. The approach can contribute to innovative energy management and the adoption of new business models in future energy data spaces.

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Section: Overview Of Main Contributionsmentioning

confidence: 99%

Section: Unified Knowledge Graph Creation Processmentioning

confidence: 99%

Section: Traversing the Knowledge Graphmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Responsible Knowledge Management in Energy Data Ecosystems

et al. 2022

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“…Power network operators started to consolidate redispatch measures via central platforms 1 but these are still in their infancies. In research, related work on anticipating energy-driven disruptions in process industry focuses on linear programming models for estimation of energy disruptions from earth quakes (Janev et al, 2021), organizational learning approaches for adaption to climate changes (Orsato et al, 2017) as well as knowledge management in energy data spaces (Rose et al, 1997). In addition to that, there is related work focusing on the energy industry itself, considering probabilistic risk assessment for preventing safety related disruptions (Blanco et al, 2019;Kosai and Unesaki, 2017), anticipation of power generation and outages (Kim et al, 2019;Moghavvemi and Faruque, 1999) as well as outage management approaches (He et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Anticipating Energy-driven Crises in Process Industry by AI-based Scenario Planning

Janzen,

Gdanitz,

Abdel Khaliq

et al. 2023

Proceedings of the Annual Hawaii International Conference on System Sciences

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Power outages and fluctuations represent serious crisis situations in energy-intensive process industry like glass and paper production, where substances such as oil, gas, wood fibers or chemicals are processed. Power disruptions can interrupt chemical reactions and produce tons of waste as well as damage of machine parts. But, despite of the obvious criticality, handling of outages in manufacturing focuses on commissioning of expensive proprietary power plants to protect against power outages and implicit gut feeling in anticipating potential disruptions. With AISOP, we introduce a model for AI-based scenario planning for predicting crisis situations. AISOP uses conceptual, well-defined scenario patterns to capture entities of crisis situations. Data streams are mapped onto these patterns for determining historic crisis scenarios and predicting future crisis scenarios by using inductive knowledge and machine learning. The model was exemplified within a proof of concept for energy-driven disruption prediction. We were able to evaluate the proposed approach by means of a set of data streams on weather and outages in Germany in terms of performance in predicting potential outages for manufacturers of paper industry with promising results.

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Building Trust in Business Ecosystems: The Interplay of Technology and Community in Governing Data Sharing

Järvihaavisto

Öhman

2022

IFIP Advances in Information and Communication Technology

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Managing Knowledge in Energy Data Spaces

Cited by 10 publications

References 12 publications

Responsible Knowledge Management in Energy Data Ecosystems

Responsible Knowledge Management in Energy Data Ecosystems

Anticipating Energy-driven Crises in Process Industry by AI-based Scenario Planning

Building Trust in Business Ecosystems: The Interplay of Technology and Community in Governing Data Sharing

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