Evidence map›Paper›PMID 40846735›Full record

ArticleScientific reports2025

A multi-year analysis of the impact of heatwaves and compound weather events on power outages.

Shah Saki, Giulia Sofia, Bandana Kar, Emmanouil Anagnostou

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Shah SakiSchool of Civil and Environmental Engineering, University of Connecticut, 261 Glenbrook Road Unit, 3037, Storrs, CT, 06269- 3037, USA. Shah.saki@uconn.edu.
Giulia SofiaSchool of Civil and Environmental Engineering, University of Connecticut, 261 Glenbrook Road Unit, 3037, Storrs, CT, 06269- 3037, USA.
Bandana KarNational Renewable Energy Laboratory, 15013 Denver W Pkwy, Golden, CO, 80401, USA.
Emmanouil AnagnostouSchool of Civil and Environmental Engineering, University of Connecticut, 261 Glenbrook Road Unit, 3037, Storrs, CT, 06269- 3037, USA. Emmanouil.anagnostou@uconn.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Across the United States, power grids are increasingly under strain from extreme weather events, such as heatwaves, high winds, and heavy precipitation, that result in frequent, long-duration and widespread power outages. The strain is intensified when these events are compounded, which amplifies their impact and exacerbates the risk of disruptions. To identify weather variables driving outages and cluster regions based on these variables, we employed a self-organizing map (SOM) approach using county-level outage data from 2015 to 2022, obtained using the U.S. Department of Energy's EAGLE-I platform, and weather data from the ASOS weather station observations. The findings revealed substantial regional differences in outages resulting from variability in heat, wind, and precipitation. In California, for example, high heat coupled with strong wind gusts led to the most severe outages, while in Texas, the primary contributors were high heat followed by heavy precipitation. This research provides a nuanced understanding of weather-induced power outages, offering a data-driven approach for infrastructure planning and resilience efforts. This study underscores the crucial role of continental weather variability in shaping outage patterns and highlights the necessity for region-specific adaptation strategies to enhance grid resilience, considering the rise in heatwaves and compounding weather events.

Identifiers

PMID40846735
PMCPMC12373919

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.