Details
| Original language | English |
|---|---|
| Article number | 3973 |
| Number of pages | 17 |
| Journal | ENERGIES |
| Volume | 15 |
| Issue number | 11 |
| Early online date | 27 May 2022 |
| Publication status | Published - 1 Jun 2022 |
Abstract
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 process-ing. 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.
Keywords
- big data analytic, data exchange, data integration systems, energy big data, knowledge graphs, semantic interoperability
ASJC Scopus subject areas
- Energy(all)
- Renewable Energy, Sustainability and the Environment
- Engineering(all)
- Building and Construction
- Energy(all)
- Fuel Technology
- Engineering(all)
- Engineering (miscellaneous)
- Energy(all)
- Energy Engineering and Power Technology
- Energy(all)
- Energy (miscellaneous)
- Mathematics(all)
- Control and Optimization
- Engineering(all)
- Electrical and Electronic Engineering
Sustainable Development Goals
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In: ENERGIES, Vol. 15, No. 11, 3973, 01.06.2022.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Responsible Knowledge Management in Energy Data Ecosystems
AU - Janev, Valentina
AU - Vidal, Maria Esther
AU - Pujić, Dea
AU - Popadić, Dušan
AU - Iglesias, Enrique
AU - Sakor, Ahmad
AU - Čampa, Andrej
N1 - Funding Information: This work was partially supported by the EU H2020 funded projects PLATOON (GA No. 872592), the EU project LAMBDA (GA No. 809965), the EU project SINERGY (GA No. 952140) and partly by the Ministry of Science and Technological Development of the Republic of Serbia (No. 451-03-9/2021-14/200034) and the Science Fund of the Republic of Serbia (Artemis, No.6527051).
PY - 2022/6/1
Y1 - 2022/6/1
N2 - 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 process-ing. 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.
AB - 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 process-ing. 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.
KW - big data analytic
KW - data exchange
KW - data integration systems
KW - energy big data
KW - knowledge graphs
KW - semantic interoperability
UR - http://www.scopus.com/inward/record.url?scp=85131536662&partnerID=8YFLogxK
U2 - 10.3390/en15113973
DO - 10.3390/en15113973
M3 - Article
AN - SCOPUS:85131536662
VL - 15
JO - ENERGIES
JF - ENERGIES
SN - 1996-1073
IS - 11
M1 - 3973
ER -