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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.09888 |
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| _version_ | 1866914044018950144 |
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| author | Salinas, Arleth Sohail, Irtaza Pascucci, Valerio Stefanakis, Pantelis Amjad, Saud Panta, Aashish Schigas, Roland Chui, Timothy Chun-Yiu Duboc, Nicolas Farrokhabadi, Mostafa Stull, Roland |
| author_facet | Salinas, Arleth Sohail, Irtaza Pascucci, Valerio Stefanakis, Pantelis Amjad, Saud Panta, Aashish Schigas, Roland Chui, Timothy Chun-Yiu Duboc, Nicolas Farrokhabadi, Mostafa Stull, Roland |
| contents | Data reuse is using data for a purpose distinct from its original intent. As data sharing becomes more prevalent in science, enabling effective data reuse is increasingly important. In this paper, we present a power systems case study of data repurposing for enabling data reuse. We define data repurposing as the process of transforming data to fit a new research purpose. In our case study, we repurpose a geospatial wildfire smoke forecast dataset into a historical dataset. We analyze its efficacy toward analyzing wildfire smoke impact on solar photovoltaic energy production. We also provide documentation and interactive demos for using the repurposed dataset. We identify key enablers of data reuse including metadata standardization, contextual documentation, and communication between data creators and reusers. We also identify obstacles to data reuse such as risk of misinterpretation and barriers to efficient data access. Through an iterative approach to data repurposing, we demonstrate how leveraging and expanding knowledge transfer infrastructures like online documentation, interactive visualizations, and data streaming directly address these obstacles. The findings facilitate big data use from other domains for power systems applications and grid resiliency. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_09888 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Climate Data for Power Systems Applications: Lessons in Reusing Wildfire Smoke Data for Solar PV Studies Salinas, Arleth Sohail, Irtaza Pascucci, Valerio Stefanakis, Pantelis Amjad, Saud Panta, Aashish Schigas, Roland Chui, Timothy Chun-Yiu Duboc, Nicolas Farrokhabadi, Mostafa Stull, Roland Human-Computer Interaction Data reuse is using data for a purpose distinct from its original intent. As data sharing becomes more prevalent in science, enabling effective data reuse is increasingly important. In this paper, we present a power systems case study of data repurposing for enabling data reuse. We define data repurposing as the process of transforming data to fit a new research purpose. In our case study, we repurpose a geospatial wildfire smoke forecast dataset into a historical dataset. We analyze its efficacy toward analyzing wildfire smoke impact on solar photovoltaic energy production. We also provide documentation and interactive demos for using the repurposed dataset. We identify key enablers of data reuse including metadata standardization, contextual documentation, and communication between data creators and reusers. We also identify obstacles to data reuse such as risk of misinterpretation and barriers to efficient data access. Through an iterative approach to data repurposing, we demonstrate how leveraging and expanding knowledge transfer infrastructures like online documentation, interactive visualizations, and data streaming directly address these obstacles. The findings facilitate big data use from other domains for power systems applications and grid resiliency. |
| title | Climate Data for Power Systems Applications: Lessons in Reusing Wildfire Smoke Data for Solar PV Studies |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2509.09888 |