Evaluating penalized logistic regression models to predict Heat-Related Electric grid stress days
Abstract
Understanding the conditions associated with stress on the electricity grid is important in the development of contingency plans for maintaining reliability during periods when the grid is stressed. In this paper, heat-related grid stress and the relationship with weather conditions were examined using data from the eastern United States. Penalized logistic regression models were developed and applied to predict stress on the electric grid using weather data. The inclusion of other weather variables, such as precipitation, in addition to temperature improved model performance. Several candidate models and combinations of predictive variables were examined. A penalized logistic regression model which was fit at the operation-zone level was found to provide predictive value and interpretability. Additionally, the importance of different weather variables observed at various time scales were examined. Maximum temperature and precipitation were identified as important across all zones while the importance of other weather variables was zone specific. In conclusion, the methods presented in this work are extensible to other regions and can be used to aid in planning and development of the electrical grid.
- Authors:
-
- Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
- Smarter Decisions, LLC, Portland, OR (United States)
- Publication Date:
- Research Org.:
- Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1395358
- Alternate Identifier(s):
- OSTI ID: 1495528
- Report Number(s):
- PNNL-SA-126567
Journal ID: ISSN 0306-2619; PII: S0306261917313697
- Grant/Contract Number:
- AC05-76RL01830
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Applied Energy
- Additional Journal Information:
- Journal Volume: 205; Journal ID: ISSN 0306-2619
- Publisher:
- Elsevier
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION; Electrical grid; Grid stress; Heatwave; Statistical modeling
Citation Formats
Bramer, Lisa M., Rounds, J., Burleyson, C. D., Fortin, D., Hathaway, J., Rice, J., and Kraucunas, I. Evaluating penalized logistic regression models to predict Heat-Related Electric grid stress days. United States: N. p., 2017.
Web. doi:10.1016/J.APENERGY.2017.09.087.
Bramer, Lisa M., Rounds, J., Burleyson, C. D., Fortin, D., Hathaway, J., Rice, J., & Kraucunas, I. Evaluating penalized logistic regression models to predict Heat-Related Electric grid stress days. United States. https://doi.org/10.1016/J.APENERGY.2017.09.087
Bramer, Lisa M., Rounds, J., Burleyson, C. D., Fortin, D., Hathaway, J., Rice, J., and Kraucunas, I. Fri .
"Evaluating penalized logistic regression models to predict Heat-Related Electric grid stress days". United States. https://doi.org/10.1016/J.APENERGY.2017.09.087. https://www.osti.gov/servlets/purl/1395358.
@article{osti_1395358,
title = {Evaluating penalized logistic regression models to predict Heat-Related Electric grid stress days},
author = {Bramer, Lisa M. and Rounds, J. and Burleyson, C. D. and Fortin, D. and Hathaway, J. and Rice, J. and Kraucunas, I.},
abstractNote = {Understanding the conditions associated with stress on the electricity grid is important in the development of contingency plans for maintaining reliability during periods when the grid is stressed. In this paper, heat-related grid stress and the relationship with weather conditions were examined using data from the eastern United States. Penalized logistic regression models were developed and applied to predict stress on the electric grid using weather data. The inclusion of other weather variables, such as precipitation, in addition to temperature improved model performance. Several candidate models and combinations of predictive variables were examined. A penalized logistic regression model which was fit at the operation-zone level was found to provide predictive value and interpretability. Additionally, the importance of different weather variables observed at various time scales were examined. Maximum temperature and precipitation were identified as important across all zones while the importance of other weather variables was zone specific. In conclusion, the methods presented in this work are extensible to other regions and can be used to aid in planning and development of the electrical grid.},
doi = {10.1016/J.APENERGY.2017.09.087},
journal = {Applied Energy},
number = ,
volume = 205,
place = {United States},
year = {Fri Sep 22 00:00:00 EDT 2017},
month = {Fri Sep 22 00:00:00 EDT 2017}
}
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Works referenced in this record:
Energy sector vulnerability to climate change: A review
journal, February 2012
- Schaeffer, Roberto; Szklo, Alexandre Salem; Pereira de Lucena, André Frossard
- Energy, Vol. 38, Issue 1
Global observed changes in daily climate extremes of temperature and precipitation
journal, January 2006
- Alexander, L. V.; Zhang, X.; Peterson, T. C.
- Journal of Geophysical Research, Vol. 111, Issue D5
Quantifying the influence of global warming on unprecedented extreme climate events
journal, April 2017
- Diffenbaugh, Noah S.; Singh, Deepti; Mankin, Justin S.
- Proceedings of the National Academy of Sciences, Vol. 114, Issue 19
Impacts of climate change on electric power supply in the Western United States
journal, May 2015
- Bartos, Matthew D.; Chester, Mikhail V.
- Nature Climate Change, Vol. 5, Issue 8
The 2003 heat wave in Europe: A shape of things to come? An analysis based on Swiss climatological data and model simulations: THE 2003 HEAT WAVE IN EUROPE
journal, January 2004
- Beniston, Martin
- Geophysical Research Letters, Vol. 31, Issue 2
Vulnerability of US and European electricity supply to climate change
journal, June 2012
- van Vliet, Michelle T. H.; Yearsley, John R.; Ludwig, Fulco
- Nature Climate Change, Vol. 2, Issue 9
Investigating the nexus of climate, energy, water, and land at decision-relevant scales: the Platform for Regional Integrated Modeling and Analysis (PRIMA)
journal, February 2014
- Kraucunas, Ian; Clarke, Leon; Dirks, James
- Climatic Change, Vol. 129, Issue 3-4
Transforming the Electric Infrastructure
journal, December 2004
- Gellings, Clark W.; Yeager, Kurt E.
- Physics Today, Vol. 57, Issue 12
Modelling influence of temperature on daily peak electricity demand in South Africa
journal, November 2013
- Chikobvu, Delson; Sigauke, Caston
- Journal of Energy in Southern Africa, Vol. 24, Issue 4
Temperature and seasonality influences on Spanish electricity load
journal, January 2002
- Pardo, Angel; Meneu, Vicente; Valor, Enric
- Energy Economics, Vol. 24, Issue 1
Modeling Utility Load and Temperature Relationships for Use with Long-Lead Forecasts
journal, May 1997
- Robinson, Peter J.
- Journal of Applied Meteorology, Vol. 36, Issue 5
Greek long-term energy consumption prediction using artificial neural networks
journal, February 2010
- Ekonomou, L.
- Energy, Vol. 35, Issue 2
Electric load forecasting by seasonal recurrent SVR (support vector regression) with chaotic artificial bee colony algorithm
journal, September 2011
- Hong, Wei-Chiang
- Energy, Vol. 36, Issue 9
A hybrid dynamic and fuzzy time series model for mid-term power load forecasting
journal, January 2015
- Lee, Woo-Joo; Hong, Jinkyu
- International Journal of Electrical Power & Energy Systems, Vol. 64
A computational intelligence scheme for the prediction of the daily peak load
journal, December 2011
- Nagi, Jawad; Yap, Keem Siah; Nagi, Farrukh
- Applied Soft Computing, Vol. 11, Issue 8
Relationships between meteorological variables and monthly electricity demand
journal, October 2012
- Apadula, Francesco; Bassini, Alessandra; Elli, Alberto
- Applied Energy, Vol. 98
Models for mid-term electricity demand forecasting incorporating weather influences
journal, March 2005
- Mirasgedis, S.; Sarafidis, Y.; Georgopoulou, E.
- Energy, Vol. 31, Issue 2-3, p. 208-227
Modeling the effect of climate change on U.S. state-level buildings energy demands in an integrated assessment framework
journal, January 2014
- Zhou, Yuyu; Clarke, Leon; Eom, Jiyong
- Applied Energy, Vol. 113
Genome-wide association analysis by lasso penalized logistic regression
journal, January 2009
- Wu, Tong Tong; Chen, Yi Fang; Hastie, Trevor
- Bioinformatics, Vol. 25, Issue 6
The class imbalance problem: A systematic study1
journal, November 2002
- Japkowicz, Nathalie; Stephen, Shaju
- Intelligent Data Analysis, Vol. 6, Issue 5
Facing Imbalanced Data--Recommendations for the Use of Performance Metrics
conference, September 2013
- Jeni, Laszlo A.; Cohn, Jeffrey F.; De La Torre, Fernando
- 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction (ACII)
Ward’s Hierarchical Agglomerative Clustering Method: Which Algorithms Implement Ward’s Criterion?
journal, October 2014
- Murtagh, Fionn; Legendre, Pierre
- Journal of Classification, Vol. 31, Issue 3
Works referencing / citing this record:
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journal, January 2020
- Craig, Michael T.; Jaramillo, Paulina; Hodge, Bri-Mathias
- Environmental Research Letters, Vol. 15, Issue 2