Analysis of Correlation between Cold Weather Meteorological Variables and Electricity Outages

Melissa R. Allen-Dumas; Sangkeun Lee; Supriya Chinthavali

doi:10.1109/BigData55660.2022.10020733

Analysis of Correlation between Cold Weather Meteorological Variables and Electricity Outages

Melissa R. Allen-Dumas, Sangkeun Lee, Supriya Chinthavali

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Scopus citations

Abstract

The significance of the impact of weather on the electric grid has grown as climate change continues to increase the frequency and intensity of extreme weather events. In recent years (2021-2022) in particular, extreme winter weather has affected the grid in locations in the US rarely exposed to extreme low temperatures, snow and icing conditions. Here we analyze the correlation between cold weather meteorological variables and electricity outages during two large winter storm events, Uri (February 2021) and Landon (February 2022) using Random Forest machine learning and Pearson's correlation coefficient. Our geographical focus across the two storms is the state of Texas. Extrapolation of the method to winter weather impacts over other years and additional locations is proposed.

Original language	English
Title of host publication	Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022
Editors	Shusaku Tsumoto, Yukio Ohsawa, Lei Chen, Dirk Van den Poel, Xiaohua Hu, Yoichi Motomura, Takuya Takagi, Lingfei Wu, Ying Xie, Akihiro Abe, Vijay Raghavan
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	3398-3401
Number of pages	4
ISBN (Electronic)	9781665480451
DOIs	https://doi.org/10.1109/BigData55660.2022.10020733
State	Published - 2022
Event	2022 IEEE International Conference on Big Data, Big Data 2022 - Osaka, Japan Duration: Dec 17 2022 → Dec 20 2022

Publication series

Name	Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022

Conference

Conference	2022 IEEE International Conference on Big Data, Big Data 2022
Country/Territory	Japan
City	Osaka
Period	12/17/22 → 12/20/22

Funding

The authors wish to thank the North American Energy Resilience Model (NAERM) project for supporting this study This manuscript has been authored by UT-Battelle LLC under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes.DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).

Keywords

electric grid
extreme weather
machine learning
outages
winter storms

Access to Document

10.1109/BigData55660.2022.10020733

Cite this

Allen-Dumas, M. R., Lee, S., & Chinthavali, S. (2022). Analysis of Correlation between Cold Weather Meteorological Variables and Electricity Outages. In S. Tsumoto, Y. Ohsawa, L. Chen, D. Van den Poel, X. Hu, Y. Motomura, T. Takagi, L. Wu, Y. Xie, A. Abe, & V. Raghavan (Eds.), Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022 (pp. 3398-3401). (Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData55660.2022.10020733

Allen-Dumas, Melissa R. ; Lee, Sangkeun ; Chinthavali, Supriya. / Analysis of Correlation between Cold Weather Meteorological Variables and Electricity Outages. Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022. editor / Shusaku Tsumoto ; Yukio Ohsawa ; Lei Chen ; Dirk Van den Poel ; Xiaohua Hu ; Yoichi Motomura ; Takuya Takagi ; Lingfei Wu ; Ying Xie ; Akihiro Abe ; Vijay Raghavan. Institute of Electrical and Electronics Engineers Inc., 2022. pp. 3398-3401 (Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022).

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title = "Analysis of Correlation between Cold Weather Meteorological Variables and Electricity Outages",

abstract = "The significance of the impact of weather on the electric grid has grown as climate change continues to increase the frequency and intensity of extreme weather events. In recent years (2021-2022) in particular, extreme winter weather has affected the grid in locations in the US rarely exposed to extreme low temperatures, snow and icing conditions. Here we analyze the correlation between cold weather meteorological variables and electricity outages during two large winter storm events, Uri (February 2021) and Landon (February 2022) using Random Forest machine learning and Pearson's correlation coefficient. Our geographical focus across the two storms is the state of Texas. Extrapolation of the method to winter weather impacts over other years and additional locations is proposed.",

keywords = "electric grid, extreme weather, machine learning, outages, winter storms",

author = "Allen-Dumas, {Melissa R.} and Sangkeun Lee and Supriya Chinthavali",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE International Conference on Big Data, Big Data 2022 ; Conference date: 17-12-2022 Through 20-12-2022",

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doi = "10.1109/BigData55660.2022.10020733",

language = "English",

series = "Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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editor = "Shusaku Tsumoto and Yukio Ohsawa and Lei Chen and {Van den Poel}, Dirk and Xiaohua Hu and Yoichi Motomura and Takuya Takagi and Lingfei Wu and Ying Xie and Akihiro Abe and Vijay Raghavan",

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Allen-Dumas, MR , Lee, S & Chinthavali, S 2022, Analysis of Correlation between Cold Weather Meteorological Variables and Electricity Outages. in S Tsumoto, Y Ohsawa, L Chen, D Van den Poel, X Hu, Y Motomura, T Takagi, L Wu, Y Xie, A Abe & V Raghavan (eds), Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022. Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022, Institute of Electrical and Electronics Engineers Inc., pp. 3398-3401, 2022 IEEE International Conference on Big Data, Big Data 2022, Osaka, Japan, 12/17/22. https://doi.org/10.1109/BigData55660.2022.10020733

Analysis of Correlation between Cold Weather Meteorological Variables and Electricity Outages. / Allen-Dumas, Melissa R.; Lee, Sangkeun ; Chinthavali, Supriya.
Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022. ed. / Shusaku Tsumoto; Yukio Ohsawa; Lei Chen; Dirk Van den Poel; Xiaohua Hu; Yoichi Motomura; Takuya Takagi; Lingfei Wu; Ying Xie; Akihiro Abe; Vijay Raghavan. Institute of Electrical and Electronics Engineers Inc., 2022. p. 3398-3401 (Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Allen-Dumas, Melissa R.

AU - Lee, Sangkeun

AU - Chinthavali, Supriya

PY - 2022

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N2 - The significance of the impact of weather on the electric grid has grown as climate change continues to increase the frequency and intensity of extreme weather events. In recent years (2021-2022) in particular, extreme winter weather has affected the grid in locations in the US rarely exposed to extreme low temperatures, snow and icing conditions. Here we analyze the correlation between cold weather meteorological variables and electricity outages during two large winter storm events, Uri (February 2021) and Landon (February 2022) using Random Forest machine learning and Pearson's correlation coefficient. Our geographical focus across the two storms is the state of Texas. Extrapolation of the method to winter weather impacts over other years and additional locations is proposed.

AB - The significance of the impact of weather on the electric grid has grown as climate change continues to increase the frequency and intensity of extreme weather events. In recent years (2021-2022) in particular, extreme winter weather has affected the grid in locations in the US rarely exposed to extreme low temperatures, snow and icing conditions. Here we analyze the correlation between cold weather meteorological variables and electricity outages during two large winter storm events, Uri (February 2021) and Landon (February 2022) using Random Forest machine learning and Pearson's correlation coefficient. Our geographical focus across the two storms is the state of Texas. Extrapolation of the method to winter weather impacts over other years and additional locations is proposed.

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KW - extreme weather

KW - machine learning

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SP - 3398

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BT - Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022

A2 - Tsumoto, Shusaku

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A2 - Chen, Lei

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A2 - Motomura, Yoichi

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PB - Institute of Electrical and Electronics Engineers Inc.

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ER -

Allen-Dumas MR , Lee S , Chinthavali S. Analysis of Correlation between Cold Weather Meteorological Variables and Electricity Outages. In Tsumoto S, Ohsawa Y, Chen L, Van den Poel D, Hu X, Motomura Y, Takagi T, Wu L, Xie Y, Abe A, Raghavan V, editors, Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022. Institute of Electrical and Electronics Engineers Inc. 2022. p. 3398-3401. (Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022). doi: 10.1109/BigData55660.2022.10020733

Analysis of Correlation between Cold Weather Meteorological Variables and Electricity Outages

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