Towards data-driven electricity management: multi-region uniform data and knowledge graph
Abstract Due to growing population and technological advances, global electricity consumption is increasing. Although CO2 emissions are projected to plateau or slightly decrease by 2025 due to the adoption of clean energy sources, they are still not decreasing enough to mitigate climate change. The...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-024-04310-z |
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| _version_ | 1841544963582590976 |
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| author | Vid Hanžel Blaž Bertalanič Carolina Fortuna |
| author_facet | Vid Hanžel Blaž Bertalanič Carolina Fortuna |
| author_sort | Vid Hanžel |
| collection | DOAJ |
| description | Abstract Due to growing population and technological advances, global electricity consumption is increasing. Although CO2 emissions are projected to plateau or slightly decrease by 2025 due to the adoption of clean energy sources, they are still not decreasing enough to mitigate climate change. The residential sector makes up 25% of global electricity consumption and has potential to improve efficiency and reduce CO2 footprint without sacrificing comfort. However, a lack of uniform consumption data at the household level spanning multiple regions hinders large-scale studies and robust multi-region model development. This paper introduces a multi-region dataset compiled from publicly available sources and presented in a uniform format. This data enables machine learning tasks such as disaggregation, demand forecasting, appliance ON/OFF classification, etc. Furthermore, we develop an RDF knowledge graph that characterizes the electricity consumption of the households and contextualizes it with household-related properties enabling semantic queries and interoperability with other open knowledge bases like Wikidata and DBpedia. This structured data can be utilized to inform various stakeholders towards data-driven policy and business development. |
| format | Article |
| id | doaj-art-4604a9b062654feaaf15a5e92dee522f |
| institution | Kabale University |
| issn | 2052-4463 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj-art-4604a9b062654feaaf15a5e92dee522f2025-01-12T12:07:53ZengNature PortfolioScientific Data2052-44632025-01-0112112110.1038/s41597-024-04310-zTowards data-driven electricity management: multi-region uniform data and knowledge graphVid Hanžel0Blaž Bertalanič1Carolina Fortuna2Jozef Stefan InstituteJozef Stefan InstituteJozef Stefan InstituteAbstract Due to growing population and technological advances, global electricity consumption is increasing. Although CO2 emissions are projected to plateau or slightly decrease by 2025 due to the adoption of clean energy sources, they are still not decreasing enough to mitigate climate change. The residential sector makes up 25% of global electricity consumption and has potential to improve efficiency and reduce CO2 footprint without sacrificing comfort. However, a lack of uniform consumption data at the household level spanning multiple regions hinders large-scale studies and robust multi-region model development. This paper introduces a multi-region dataset compiled from publicly available sources and presented in a uniform format. This data enables machine learning tasks such as disaggregation, demand forecasting, appliance ON/OFF classification, etc. Furthermore, we develop an RDF knowledge graph that characterizes the electricity consumption of the households and contextualizes it with household-related properties enabling semantic queries and interoperability with other open knowledge bases like Wikidata and DBpedia. This structured data can be utilized to inform various stakeholders towards data-driven policy and business development.https://doi.org/10.1038/s41597-024-04310-z |
| spellingShingle | Vid Hanžel Blaž Bertalanič Carolina Fortuna Towards data-driven electricity management: multi-region uniform data and knowledge graph Scientific Data |
| title | Towards data-driven electricity management: multi-region uniform data and knowledge graph |
| title_full | Towards data-driven electricity management: multi-region uniform data and knowledge graph |
| title_fullStr | Towards data-driven electricity management: multi-region uniform data and knowledge graph |
| title_full_unstemmed | Towards data-driven electricity management: multi-region uniform data and knowledge graph |
| title_short | Towards data-driven electricity management: multi-region uniform data and knowledge graph |
| title_sort | towards data driven electricity management multi region uniform data and knowledge graph |
| url | https://doi.org/10.1038/s41597-024-04310-z |
| work_keys_str_mv | AT vidhanzel towardsdatadrivenelectricitymanagementmultiregionuniformdataandknowledgegraph AT blazbertalanic towardsdatadrivenelectricitymanagementmultiregionuniformdataandknowledgegraph AT carolinafortuna towardsdatadrivenelectricitymanagementmultiregionuniformdataandknowledgegraph |