IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v93y2008i6p897-912.html
   My bibliography  Save this article

Spatial generalized linear mixed models of electric power outages due to hurricanes and ice storms

Author

Listed:
  • Liu, Haibin
  • Davidson, Rachel A.
  • Apanasovich, Tatiyana V.

Abstract

This paper presents new statistical models that predict the number of hurricane- and ice storm-related electric power outages likely to occur in each 3km×3km grid cell in a region. The models are based on a large database of recent outages experienced by three major East Coast power companies in six hurricanes and eight ice storms. A spatial generalized linear mixed modeling (GLMM) approach was used in which spatial correlation is incorporated through random effects. Models were fitted using a composite likelihood approach and the covariance matrix was estimated empirically. A simulation study was conducted to test the model estimation procedure, and model training, validation, and testing were done to select the best models and assess their predictive power. The final hurricane model includes number of protective devices, maximum gust wind speed, hurricane indicator, and company indicator covariates. The final ice storm model includes number of protective devices, ice thickness, and ice storm indicator covariates. The models should be useful for power companies as they plan for future storms. The statistical modeling approach offers a new way to assess the reliability of electric power and other infrastructure systems in extreme events.

Suggested Citation

  • Liu, Haibin & Davidson, Rachel A. & Apanasovich, Tatiyana V., 2008. "Spatial generalized linear mixed models of electric power outages due to hurricanes and ice storms," Reliability Engineering and System Safety, Elsevier, vol. 93(6), pages 897-912.
    • Handle: RePEc:eee:reensy:v:93:y:2008:i:6:p:897-912
    DOI: 10.1016/j.ress.2007.03.038
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832007001305
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2007.03.038?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. T. Lumley & P. Heagerty, 1999. "Weighted empirical adaptive variance estimators for correlated data regression," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(2), pages 459-477, April.
    2. Varin, Cristiano & Host, Gudmund & Skare, Oivind, 2005. "Pairwise likelihood inference in spatial generalized linear mixed models," Computational Statistics & Data Analysis, Elsevier, vol. 49(4), pages 1173-1191, June.
    3. Cristiano Varin & Paolo Vidoni, 2005. "A note on composite likelihood inference and model selection," Biometrika, Biometrika Trust, vol. 92(3), pages 519-528, September.
    4. D. R. Cox, 2004. "A note on pseudolikelihood constructed from marginal densities," Biometrika, Biometrika Trust, vol. 91(3), pages 729-737, September.
    5. Zhang, Hao, 2004. "Inconsistent Estimation and Asymptotically Equal Interpolations in Model-Based Geostatistics," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 250-261, January.
    Full references (including those not matched with items on IDEAS)

    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. Joe, Harry & Lee, Youngjo, 2009. "On weighting of bivariate margins in pairwise likelihood," Journal of Multivariate Analysis, Elsevier, vol. 100(4), pages 670-685, April.
    2. Bhat, Chandra R. & Sener, Ipek N. & Eluru, Naveen, 2010. "A flexible spatially dependent discrete choice model: Formulation and application to teenagers' weekday recreational activity participation," Transportation Research Part B: Methodological, Elsevier, vol. 44(8-9), pages 903-921, September.
    3. Papageorgiou, Ioulia & Moustaki, Irini, 2019. "Sampling of pairs in pairwise likelihood estimation for latent variable models with categorical observed variables," LSE Research Online Documents on Economics 87592, London School of Economics and Political Science, LSE Library.
    4. Cristiano Varin, 2008. "On composite marginal likelihoods," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 92(1), pages 1-28, February.
    5. Chandra R. Bhat & Subodh K. Dubey & Mohammad Jobair Bin Alam & Waleed H. Khushefati, 2015. "A New Spatial Multiple Discrete-Continuous Modeling Approach To Land Use Change Analysis," Journal of Regional Science, Wiley Blackwell, vol. 55(5), pages 801-841, November.
    6. Hung‐pin Lai & Subal C. Kumbhakar, 2020. "Estimation of a dynamic stochastic frontier model using likelihood‐based approaches," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(2), pages 217-247, March.
    7. Deng Ling & Moore Dirk F., 2009. "Composite Likelihood Modeling of Neighboring Site Correlations of DNA Sequence Substitution Rates," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 8(1), pages 1-22, January.
    8. Varin, Cristiano & Host, Gudmund & Skare, Oivind, 2005. "Pairwise likelihood inference in spatial generalized linear mixed models," Computational Statistics & Data Analysis, Elsevier, vol. 49(4), pages 1173-1191, June.
    9. Bhat, Chandra R., 2011. "The maximum approximate composite marginal likelihood (MACML) estimation of multinomial probit-based unordered response choice models," Transportation Research Part B: Methodological, Elsevier, vol. 45(7), pages 923-939, August.
    10. Akim Adekpedjou & Sophie Dabo‐Niang, 2021. "Semiparametric estimation with spatially correlated recurrent events," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 48(4), pages 1097-1126, December.
    11. Richard A. Davis & Claudia Klüppelberg & Christina Steinkohl, 2013. "Statistical inference for max-stable processes in space and time," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 75(5), pages 791-819, November.
    12. Myrsini Katsikatsou & Irini Moustaki, 2016. "Pairwise Likelihood Ratio Tests and Model Selection Criteria for Structural Equation Models with Ordinal Variables," Psychometrika, Springer;The Psychometric Society, vol. 81(4), pages 1046-1068, December.
    13. Gourieroux, C. & Monfort, A., 2018. "Composite indirect inference with application to corporate risks," Econometrics and Statistics, Elsevier, vol. 7(C), pages 30-45.
    14. Nuo Xi & Michael W. Browne, 2014. "Contributions to the Underlying Bivariate Normal Method for Factor Analyzing Ordinal Data," Journal of Educational and Behavioral Statistics, , vol. 39(6), pages 583-611, December.
    15. Krupskii, Pavel & Joe, Harry & Lee, David & Genton, Marc G., 2018. "Extreme-value limit of the convolution of exponential and multivariate normal distributions: Link to the Hüsler–Reiß distribution," Journal of Multivariate Analysis, Elsevier, vol. 163(C), pages 80-95.
    16. Kenne Pagui, E.C. & Salvan, A. & Sartori, N., 2015. "On full efficiency of the maximum composite likelihood estimator," Statistics & Probability Letters, Elsevier, vol. 97(C), pages 120-124.
    17. Varin, Cristiano & Vidoni, Paolo, 2006. "Pairwise likelihood inference for ordinal categorical time series," Computational Statistics & Data Analysis, Elsevier, vol. 51(4), pages 2365-2373, December.
    18. Bennedsen, Mikkel & Lunde, Asger & Shephard, Neil & Veraart, Almut E.D., 2023. "Inference and forecasting for continuous-time integer-valued trawl processes," Journal of Econometrics, Elsevier, vol. 236(2).
    19. Bartolucci, Francesco & Lupparelli, Monia, 2012. "Nested hidden Markov chains for modeling dynamic unobserved heterogeneity in multilevel longitudinal data," MPRA Paper 40588, University Library of Munich, Germany.
    20. Büscher, Sebastian & Bauer, Dietmar, 2024. "Weighting strategies for pairwise composite marginal likelihood estimation in case of unbalanced panels and unaccounted autoregressive structure of the errors," Transportation Research Part B: Methodological, Elsevier, vol. 181(C).

    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:reensy:v:93:y:2008:i:6:p:897-912. 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: https://www.journals.elsevier.com/reliability-engineering-and-system-safety .

    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.