IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v205y2017icp1408-1418.html
   My bibliography  Save this article

Evaluating penalized logistic regression models to predict Heat-Related Electric grid stress days

Author

Listed:
  • Bramer, L.M.
  • Rounds, J.
  • Burleyson, C.D.
  • Fortin, D.
  • Hathaway, J.
  • Rice, J.
  • Kraucunas, I.

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. 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.

Suggested Citation

  • Bramer, L.M. & Rounds, J. & Burleyson, C.D. & Fortin, D. & Hathaway, J. & Rice, J. & Kraucunas, I., 2017. "Evaluating penalized logistic regression models to predict Heat-Related Electric grid stress days," Applied Energy, Elsevier, vol. 205(C), pages 1408-1418.
    • Handle: RePEc:eee:appene:v:205:y:2017:i:c:p:1408-1418
    DOI: 10.1016/j.apenergy.2017.09.087
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261917313697
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2017.09.087?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Hong, Wei-Chiang, 2011. "Electric load forecasting by seasonal recurrent SVR (support vector regression) with chaotic artificial bee colony algorithm," Energy, Elsevier, vol. 36(9), pages 5568-5578.
    2. Mirasgedis, S. & Sarafidis, Y. & Georgopoulou, E. & Lalas, D.P. & Moschovits, M. & Karagiannis, F. & Papakonstantinou, D., 2006. "Models for mid-term electricity demand forecasting incorporating weather influences," Energy, Elsevier, vol. 31(2), pages 208-227.
    3. Zhou, Yuyu & Clarke, Leon & Eom, Jiyong & Kyle, Page & Patel, Pralit & Kim, Son H. & Dirks, James & Jensen, Erik & Liu, Ying & Rice, Jennie & Schmidt, Laurel & Seiple, Timothy, 2014. "Modeling the effect of climate change on U.S. state-level buildings energy demands in an integrated assessment framework," Applied Energy, Elsevier, vol. 113(C), pages 1077-1088.
    4. Ekonomou, L., 2010. "Greek long-term energy consumption prediction using artificial neural networks," Energy, Elsevier, vol. 35(2), pages 512-517.
    5. Pardo, Angel & Meneu, Vicente & Valor, Enric, 2002. "Temperature and seasonality influences on Spanish electricity load," Energy Economics, Elsevier, vol. 24(1), pages 55-70, January.
    6. Schaeffer, Roberto & Szklo, Alexandre Salem & Pereira de Lucena, André Frossard & Moreira Cesar Borba, Bruno Soares & Pupo Nogueira, Larissa Pinheiro & Fleming, Fernanda Pereira & Troccoli, Alberto & , 2012. "Energy sector vulnerability to climate change: A review," Energy, Elsevier, vol. 38(1), pages 1-12.
    7. Apadula, Francesco & Bassini, Alessandra & Elli, Alberto & Scapin, Simone, 2012. "Relationships between meteorological variables and monthly electricity demand," Applied Energy, Elsevier, vol. 98(C), pages 346-356.
    8. Ian Kraucunas & Leon Clarke & James Dirks & John Hathaway & Mohamad Hejazi & Kathy Hibbard & Maoyi Huang & Chunlian Jin & Michael Kintner-Meyer & Kerstin Dam & Ruby Leung & Hong-Yi Li & Richard Moss &, 2015. "Investigating the nexus of climate, energy, water, and land at decision-relevant scales: the Platform for Regional Integrated Modeling and Analysis (PRIMA)," Climatic Change, Springer, vol. 129(3), pages 573-588, April.
    9. Fionn Murtagh & Pierre Legendre, 2014. "Ward’s Hierarchical Agglomerative Clustering Method: Which Algorithms Implement Ward’s Criterion?," Journal of Classification, Springer;The Classification Society, vol. 31(3), pages 274-295, October.
    10. Matthew D. Bartos & Mikhail V. Chester, 2015. "Impacts of climate change on electric power supply in the Western United States," Nature Climate Change, Nature, vol. 5(8), pages 748-752, August.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Craig, Michael T. & Cohen, Stuart & Macknick, Jordan & Draxl, Caroline & Guerra, Omar J. & Sengupta, Manajit & Haupt, Sue Ellen & Hodge, Bri-Mathias & Brancucci, Carlo, 2018. "A review of the potential impacts of climate change on bulk power system planning and operations in the United States," Renewable and Sustainable Energy Reviews, Elsevier, vol. 98(C), pages 255-267.
    2. Murphy, Sinnott & Sowell, Fallaw & Apt, Jay, 2019. "A time-dependent model of generator failures and recoveries captures correlated events and quantifies temperature dependence," Applied Energy, Elsevier, vol. 253(C), pages 1-1.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Nnaemeka Vincent Emodi & Taha Chaiechi & ABM Rabiul Alam Beg, 2018. "The impact of climate change on electricity demand in Australia," Energy & Environment, , vol. 29(7), pages 1263-1297, November.
    2. Moral-Carcedo, Julián & Pérez-García, Julián, 2015. "Temperature effects on firms’ electricity demand: An analysis of sectorial differences in Spain," Applied Energy, Elsevier, vol. 142(C), pages 407-425.
    3. OrtizBeviá, M.J. & RuizdeElvira, A. & Alvarez-García, F.J., 2014. "The influence of meteorological variability on the mid-term evolution of the electricity load," Energy, Elsevier, vol. 76(C), pages 850-856.
    4. Chabouni, Naima & Belarbi, Yacine & Benhassine, Wassim, 2020. "Electricity load dynamics, temperature and seasonality Nexus in Algeria," Energy, Elsevier, vol. 200(C).
    5. Son, Hyojoo & Kim, Changwan, 2017. "Short-term forecasting of electricity demand for the residential sector using weather and social variables," Resources, Conservation & Recycling, Elsevier, vol. 123(C), pages 200-207.
    6. Trotter, Ian Michael & Féres, José Gustavo & Bolkesjø, Torjus Folsland & de Hollanda, Lavínia Rocha, 2015. "Simulating Brazilian Electricity Demand Under Climate Change Scenarios," Working Papers in Applied Economics 208689, Universidade Federal de Vicosa, Departamento de Economia Rural.
    7. Tyralis, Hristos & Karakatsanis, Georgios & Tzouka, Katerina & Mamassis, Nikos, 2017. "Exploratory data analysis of the electrical energy demand in the time domain in Greece," Energy, Elsevier, vol. 134(C), pages 902-918.
    8. Salisu, Afees A. & Ayinde, Taofeek O., 2016. "Modeling energy demand: Some emerging issues," Renewable and Sustainable Energy Reviews, Elsevier, vol. 54(C), pages 1470-1480.
    9. Craig, Michael T. & Cohen, Stuart & Macknick, Jordan & Draxl, Caroline & Guerra, Omar J. & Sengupta, Manajit & Haupt, Sue Ellen & Hodge, Bri-Mathias & Brancucci, Carlo, 2018. "A review of the potential impacts of climate change on bulk power system planning and operations in the United States," Renewable and Sustainable Energy Reviews, Elsevier, vol. 98(C), pages 255-267.
    10. Miller, J. Isaac & Nam, Kyungsik, 2022. "Modeling peak electricity demand: A semiparametric approach using weather-driven cross-temperature response functions," Energy Economics, Elsevier, vol. 114(C).
    11. Ozhegov, Evgeniy & Popova, Evgeniya, 2017. "Demand for electricity and weather conditions: Nonparametric analysis," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 46, pages 55-73.
    12. Voisin, N. & Kintner-Meyer, M. & Skaggs, R. & Nguyen, T. & Wu, D. & Dirks, J. & Xie, Y. & Hejazi, M., 2016. "Vulnerability of the US western electric grid to hydro-climatological conditions: How bad can it get?," Energy, Elsevier, vol. 115(P1), pages 1-12.
    13. Zhang, Wen Yu & Hong, Wei-Chiang & Dong, Yucheng & Tsai, Gary & Sung, Jing-Tian & Fan, Guo-feng, 2012. "Application of SVR with chaotic GASA algorithm in cyclic electric load forecasting," Energy, Elsevier, vol. 45(1), pages 850-858.
    14. Shao, Zhen & Chao, Fu & Yang, Shan-Lin & Zhou, Kai-Le, 2017. "A review of the decomposition methodology for extracting and identifying the fluctuation characteristics in electricity demand forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 123-136.
    15. Yau, Y.H. & Pean, H.L., 2011. "The climate change impact on air conditioner system and reliability in Malaysia—A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(9), pages 4939-4949.
    16. Harish, Santosh & Singh, Nishmeet & Tongia, Rahul, 2020. "Impact of temperature on electricity demand: Evidence from Delhi and Indian states," Energy Policy, Elsevier, vol. 140(C).
    17. Santágata, Daniela M. & Castesana, Paula & Rössler, Cristina E. & Gómez, Darío R., 2017. "Extreme temperature events affecting the electricity distribution system of the metropolitan area of Buenos Aires (1971–2013)," Energy Policy, Elsevier, vol. 106(C), pages 404-414.
    18. Barman, Mayur & Dev Choudhury, N.B. & Sutradhar, Suman, 2018. "A regional hybrid GOA-SVM model based on similar day approach for short-term load forecasting in Assam, India," Energy, Elsevier, vol. 145(C), pages 710-720.
    19. Salari, Mahmoud & Javid, Roxana J., 2016. "Residential energy demand in the United States: Analysis using static and dynamic approaches," Energy Policy, Elsevier, vol. 98(C), pages 637-649.
    20. Ke, Xinda & Wu, Di & Rice, Jennie & Kintner-Meyer, Michael & Lu, Ning, 2016. "Quantifying impacts of heat waves on power grid operation," Applied Energy, Elsevier, vol. 183(C), pages 504-512.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:205:y:2017:i:c:p:1408-1418. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.