<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Meeting the challenges of the energy sector relies on data – in particular sharing it internally and externally with a wide range of partners. Unfortunately, this valuable data often cannot be obtained from real objects due to location specifics or privacy concerns, although accurate, open-source data are a priority to provide researchers and energy experts with the information needed to accelerate the energy transition. In recent years, many studies have focused on the development of energy communities, using different methods to create data for case studies; however, these methods are often too broad and do not correlate with conditions in real locations.</p>
<p>This work aims to identify the challenges associated with creating realistic datasets for energy community studies, as well as highlight the methods of defining input data, considering the factors that make energy community studies a very complex task, and discuss the flaws of commonly used methods.</p>
</abstract>

Meeting the challenges of the energy sector relies on data – in particular sharing it internally and externally with a wide range of partners. Unfortunately, this valuable data often cannot be obtained from real objects due to location specifics or privacy concerns, although accurate, open-source data are a priority to provide researchers and energy experts with the information needed to accelerate the energy transition. In recent years, many studies have focused on the development of energy communities, using different methods to create data for case studies; however, these methods are often too broad and do not correlate with conditions in real locations.
This work aims to identify the challenges associated with creating realistic datasets for energy community studies, as well as highlight the methods of defining input data, considering the factors that make energy community studies a very complex task, and discuss the flaws of commonly used methods.

Reliable Data Profiling for Energy Communities – Review of Open-Source Approaches

Latvian Journal of Physics and Technical Sciences

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Meeting the challenges of the energy sector relies on data – in particular sharing it internally and externally with a wide range of partners. Unfortunately,...

Reliable Data Profiling for Energy Communities – Review of Open-Source Approaches

Online veröffentlicht: 15. Apr. 2023

Seitenbereich: 17 - 30

DOI: https://doi.org/10.2478/lpts-2023-0008

Schlüsselwörter
Energy community, electric load, renewable energy, profiling, modeling, policy, open-source data, open-source tools

© 2023 E. Kairisa et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Reliable Data Profiling for Energy Communities – Review of Open-Source Approaches

Online veröffentlicht: 15. Apr. 2023

Seitenbereich: 17 - 30

DOI: https://doi.org/10.2478/lpts-2023-0008

SchlüsselwörterEnergy community, electric load, renewable energy, profiling, modeling, policy, open-source data, open-source tools

© 2023 E. Kairisa et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
Energy community, electric load, renewable energy, profiling, modeling, policy, open-source data, open-source tools