Climate Data for Power Systems Applications: Lessons in Reusing Wildfire Smoke Data for Solar PV Studies

2025-09-11

AI Summary

This study addresses the challenge of reusing wildfire smoke forecast data for solar photovoltaic (PV) impact analysis. We propose a “Data Reuse Transformation Framework” centered on three pillars: metadata standardization, contextual documentation enhancement, and creator–user collaborative communication—systematically transforming geospatial smoke forecasts into high-temporal-resolution historical datasets. Integrating geospatial analysis, interactive visualization, and data-streaming techniques, the framework enables automated data cleaning, spatiotemporal alignment, and open sharing. The resulting dataset has supported multiple empirical studies on PV power output degradation under smoke conditions, significantly improving the reusability of climate data in power system resilience assessment. It establishes a transferable methodological paradigm and practical workflow for cross-domain climate–energy data integration.

Technology Category

• Computer Vision: Remote Sensing / Geospatial AI
• Machine Learning: Time-Series/Data Streams
• Data Mining & Knowledge Management: Mining of Visual, Multimedia & Multimodal Data

Application Category

• Web Mining and Content Analysis: Web data integration and cleaning
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Security and Privacy: Data transparency and provenance

Abstract

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.

Problem

• Repurposing wildfire smoke data for solar energy studies
• Analyzing wildfire smoke impact on photovoltaic production
• Overcoming data reuse obstacles through knowledge transfer infrastructures

Innovation

• Repurposing wildfire smoke data for solar studies
• Creating historical datasets from forecast models
• Using interactive demos and documentation infrastructure