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Explorative spatial data mining for energy technology adoption and policy design analysis
| dc.contributor.author | Heymann, Fabian | es-ES |
| dc.contributor.author | Soares, Filipe Joel | es-ES |
| dc.contributor.author | Dueñas Martínez, Pablo | es-ES |
| dc.contributor.author | Miranda, Vladimiro | es-ES |
| dc.date.accessioned | 2025-09-26T18:06:45Z | |
| dc.date.available | 2025-09-26T18:06:45Z | |
| dc.date.issued | 2019-09-03 | es_ES |
| dc.identifier.uri | http://hdl.handle.net/11531/105390 | |
| dc.description | Capítulos en libros | es_ES |
| dc.description.abstract | es-ES | |
| dc.description.abstract | Spatial data mining aims at the discovery of unknown, useful patterns from large spatial datasets. This article presents a thorough analysis of the Portuguese adopters of distributed energy resources using explorative spatial data mining techniques. Results show clustering of distributed energy resources that currently passing the early adoption stage in Portugal. Furthermore, spatial adoption patterns are simulated over a 20 year horizon, analyzing technology concentration changes over time while comparing three different energy policy designs. Outcomes provide useful indication for both electrical network planning and energy policy design. | en-GB |
| dc.format.mimetype | application/octet-stream | es_ES |
| dc.language.iso | en-GB | es_ES |
| dc.publisher | Universidade de Trás-os-Montes e Alto Douro (Vila Real, Portugal) | es_ES |
| dc.source | Libro: 19th Conference on Artificial Intelligence - EPIA 2019, Página inicial: 427-437, Página final: | es_ES |
| dc.subject.other | Instituto de Investigación Tecnológica (IIT) | es_ES |
| dc.title | Explorative spatial data mining for energy technology adoption and policy design analysis | es_ES |
| dc.type | info:eu-repo/semantics/bookPart | es_ES |
| dc.description.version | info:eu-repo/semantics/publishedVersion | es_ES |
| dc.rights.holder | es_ES | |
| dc.rights.accessRights | info:eu-repo/semantics/openAccess | es_ES |
| dc.keywords | es-ES | |
| dc.keywords | Diffusion of Innovation, Renewable Energy, Spatial Data Mining | en-GB |
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