Evaluation of spatial clustering methods for regionalisation of hydrogen ecosystems
Spatially resolved modelling of local hydrogen ecosystems can help to identify optimal sizing and locations for plants and infrastructure along the value chain. Spatial clustering to identify the subregions can lead to a better representation of important features compared to administrative units or...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-01-01
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| Series: | Energy Strategy Reviews |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2211467X24003365 |
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| author | Friedrich Mendler Barbara Koch Björn Meißner Christopher Voglstätter Tom Smolinka |
| author_facet | Friedrich Mendler Barbara Koch Björn Meißner Christopher Voglstätter Tom Smolinka |
| author_sort | Friedrich Mendler |
| collection | DOAJ |
| description | Spatially resolved modelling of local hydrogen ecosystems can help to identify optimal sizing and locations for plants and infrastructure along the value chain. Spatial clustering to identify the subregions can lead to a better representation of important features compared to administrative units or uniform grids. Several algorithms are available for regionalisation, but an evaluation of their suitability for hydrogen ecosystems or similar applications is missing in the literature. This paper presents a holistic evaluation of different spatial algorithms based on existing and newly developed statistical indicators. Although the best algorithm depends on the focus of the regionalisation process, the method REDCAP proved to be the best overall, especially with higher intra-cluster homogeneity compared to the widely used k-means algorithm. The developed indicators and their evaluation regarding different objectives are seen to be transferable to other clustering and regionalisation applications, like energy system analysis or general supply chain analysis. |
| format | Article |
| id | doaj-art-9006543077a24edaac33b7084a766294 |
| institution | Kabale University |
| issn | 2211-467X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy Strategy Reviews |
| spelling | doaj-art-9006543077a24edaac33b7084a7662942025-01-06T04:08:37ZengElsevierEnergy Strategy Reviews2211-467X2025-01-0157101627Evaluation of spatial clustering methods for regionalisation of hydrogen ecosystemsFriedrich Mendler0Barbara Koch1Björn Meißner2Christopher Voglstätter3Tom Smolinka4Hydrogen Division, Fraunhofer Institute for Solar Energy Systems, Freiburg, Germany; Remote Sensing and Landscape Information Systems, University of Freiburg, Freiburg, Germany; Corresponding author. Hydrogen Division, Fraunhofer Institute for Solar Energy Systems, Freiburg, Germany.Remote Sensing and Landscape Information Systems, University of Freiburg, Freiburg, GermanyHydrogen Division, Fraunhofer Institute for Solar Energy Systems, Freiburg, GermanyHydrogen Division, Fraunhofer Institute for Solar Energy Systems, Freiburg, GermanyHydrogen Division, Fraunhofer Institute for Solar Energy Systems, Freiburg, GermanySpatially resolved modelling of local hydrogen ecosystems can help to identify optimal sizing and locations for plants and infrastructure along the value chain. Spatial clustering to identify the subregions can lead to a better representation of important features compared to administrative units or uniform grids. Several algorithms are available for regionalisation, but an evaluation of their suitability for hydrogen ecosystems or similar applications is missing in the literature. This paper presents a holistic evaluation of different spatial algorithms based on existing and newly developed statistical indicators. Although the best algorithm depends on the focus of the regionalisation process, the method REDCAP proved to be the best overall, especially with higher intra-cluster homogeneity compared to the widely used k-means algorithm. The developed indicators and their evaluation regarding different objectives are seen to be transferable to other clustering and regionalisation applications, like energy system analysis or general supply chain analysis.http://www.sciencedirect.com/science/article/pii/S2211467X24003365Hydrogen systemsSpatial clusteringGeospatial analysisClustering evaluationSpatial resolution |
| spellingShingle | Friedrich Mendler Barbara Koch Björn Meißner Christopher Voglstätter Tom Smolinka Evaluation of spatial clustering methods for regionalisation of hydrogen ecosystems Energy Strategy Reviews Hydrogen systems Spatial clustering Geospatial analysis Clustering evaluation Spatial resolution |
| title | Evaluation of spatial clustering methods for regionalisation of hydrogen ecosystems |
| title_full | Evaluation of spatial clustering methods for regionalisation of hydrogen ecosystems |
| title_fullStr | Evaluation of spatial clustering methods for regionalisation of hydrogen ecosystems |
| title_full_unstemmed | Evaluation of spatial clustering methods for regionalisation of hydrogen ecosystems |
| title_short | Evaluation of spatial clustering methods for regionalisation of hydrogen ecosystems |
| title_sort | evaluation of spatial clustering methods for regionalisation of hydrogen ecosystems |
| topic | Hydrogen systems Spatial clustering Geospatial analysis Clustering evaluation Spatial resolution |
| url | http://www.sciencedirect.com/science/article/pii/S2211467X24003365 |
| work_keys_str_mv | AT friedrichmendler evaluationofspatialclusteringmethodsforregionalisationofhydrogenecosystems AT barbarakoch evaluationofspatialclusteringmethodsforregionalisationofhydrogenecosystems AT bjornmeißner evaluationofspatialclusteringmethodsforregionalisationofhydrogenecosystems AT christophervoglstatter evaluationofspatialclusteringmethodsforregionalisationofhydrogenecosystems AT tomsmolinka evaluationofspatialclusteringmethodsforregionalisationofhydrogenecosystems |