Publications

Refereed Articles  (Students supervised by me are denoted by *)

• Jeong, C.*, and Byon, E., 2024, Calibration of Building Energy Computer Models via Bias-Corrected Iteratively Reweighted Least Squares Method, Applied Energy, accepted.

• Lee, E., Ju, J., Byon, E., Ko, Y., 2024, Condition-based Selective Maintenance Optimization for Large-scale Systems Consisting of Many Homogeneous Units. To appear in IEEE Transactions on Reliability,.

• Jeong C.*, Xu, J.*, Berahas, A., Byon, E., and Cetin, K. 2023, Multi-block Parameter Calibration in Computer Models, INFORMS Journal on Data Science, vol. 2, No. 2, pp. 116-137 (code is also available on Code Ocean at https://codeocean.com/capsule/5557786/tree).
  • Best Paper Finalist in the 2021 IISE Data Analytics & Information Systems subdivision.
  • IOE Wilson Prize Winning Paper
  • IOE Bonder Fellowship Winning Research

• Jang, Y.*, Byon. E, Vanage, S., Cetin K., Gallus Jr., W., Manuel, L., Jahn., D. E., 2023, Spatiotemporal post-calibration in a Numerical Weather Prediction model for quantifying building energy consumptions, IEEE Transactions on Automation Science and Engineering, vol 20, no. 4, pp. 2732-2747 [code].

• Jain, P., Shashaani, S., Byon, E., 2023, Wake Effect Parameter Calibration with Large-Scale Field Operational Data using Stochastic Optimization, Applied Energy, vol. 347, 121426 [code]

• Kim, S, Byon,, and Ko, Y., 2023, Robust Importance Sampling for Stochastic Simulations with Uncertain Parametric Input Model. In Proceedings of the 2023 Winter Simulation Conference (WSC), San Antonio, TX.

• Liu, B.*, Yue, X., Byon, E., and Al Kontar, R. 2022, Parameter Calibration in wake effect simulation model with Stochastic Gradient Descent and stratified sampling, Annals of Applied Statistics, vol. 16, No. 3, 1795-1821 [code].
  • Best Student Paper Finalist in the 2021 IISE Data Analytics & Information Systems subdivision.
  • Best Paper Finalist in the 2021 IISE Data Analytics & Information Systems subdivision.

• Ko, Y. and Byon, E. 2022, Optimal budget allocation for stochastic simulation with importance sampling: exploration vs. replication, IISE Transactions, vol. 54, no. 9, pp. 881-893.
  • Editor’s Choice for Featured Article, IISE Transactions (2022).

• Li, S.*, Ko., Y., and Byon, E., 2021, Nonparametric importance sampling for wind turbine reliability analysis with stochastic computer models, Annals of Applied Statistics, vol. 15, pp. 1850-1871. [code]

• Pan, Q.*, Ko. Y, and Byon, E., 2021, Uncertainty Quantification for Extreme Quantile Estimation with stochastic computer models, IEEE Transactions on Reliability, vol.70, pp. 134-145.

• Pranav Jain, Sara Shashaani, Eunshin Byon, 2021, Wake Effect Calibration in Wind Power Systems with Adaptive Sampling based Optimization, In Proceedings of the 2021 Industrial Engineering Research Conference.

• Wang, J.*, AlShelahi, A.*, You, M.*, Byon, E. and Saigal, R., 2021, Integrative Density Forecast and Uncertainty Quantification of Wind Power Generation, IEEE Transactions on Sustainable Energy, vol. 12, pp. 1864-1875. [code]

• Wang, J.*, Chung, S., Alshelahi, A., Al Kontar, R., Byon, E., and Saigal, R., 2021, Look-ahead Planning for Renewable Energy: A Dynamic “Predict and Store” Approach, Applied Energy, vol. 296, 117068.

• Raed Al Kontar, Naichen Shi, Xubo Yue, Seokhyun Chung, Eunshin Byon, Mosharaf Chowdhury, Judy Jin, Wissam Kontar, Neda Masoud, Maher Noueihed, Chinedum E. Okwudire, Garvesh Raskutti, Romesh Saigal, Karandeep Singh, and Zhisheng Ye., 2021, The Internet of Federated Things (IoFT), IEEE Access, vol. 9, pp. 156071 – 156113.

• Kim, J., Jang, Y.*, Byon, Tilbury, D. M., Engoren, M. and Ramachandran, S. K., 2021, New unobtrusive respiration monitoring system using channel state information in Wi-Fi signal, IEEE Sensors, vol. 21, pp. 3810-3821.

• Pan, Q.*, Byon, E. and Lam, H. 2020, Adaptive Importance Sampling for Extreme Quantile Estimation with Stochastic Black Box Computer Models, Naval Research Logistics, 67, pp. 524-547 [code].
  • Best Student Paper Finalist in the INFORMS Quality, Statistics and Reliability (QSR) section

• Jang, Youngchan* and Byon, E. 2020, Probabilistic characterization of wind diurnal variability at non-observational locations for wind resource assessment, IEEE Transactions on Sustainable Energy, vol. 11, pp. 2535-2544. [code]
  • Related news article in Michigan Engineer News Center (https://news.engin.umich.edu/2020/04/making-wind-power-more-predictable-a-qa-with-eunshin-byon/)

• Li, D., Menassa, C. C., Kamat, V. R., and Byon, E., 2020, HEAT – Human Embodied Autonomous Thermostat, Building and Environment, vol. 178, pp. 106879.
  • Featured in Tech Xplore, Popular Mechanics, and Michigan Engineer News Center (<https://techxplore.com/news/2020-06-thermostats-autonomous-hvac-comfort-energy.html, https://www.popularmechanics.com/science/a32892303/autonomous-hvac-system, https://news.engin.umich.edu/2020/06/turning-faces-into-thermostats/)

• Jang, Y.*, Byon, E., Jahani, E. and Cetin, K. 2020, On the Long-term density prediction of peak electricity load with demand side management in buildings, Energy and Buildings, pp. 110450 [code].

• AlShelahi, A.*, Wang, J.*, You, M., Byon, E. and Saigal, R., 2020, Data-driven prediction for volatile processes based on real option theories, International Journal of Production Economics, vol. 226, pp. 107605.

• Parikh, N. D., W. J., Wang, J., Steuer, J., Tapper, E. B., Konerman, M., Singal, A. G., , Hutton, D. W., Byon, E., and Lavieri, M. S.  2019, Projected increase in obesity and non-alcoholic steatohepatitis-related liver transplantation waitlist additions in the United States, Hepatology, vol. 70, PP. 487-495.

• Jahn, D.E, Gallus, W.A, Nguyen, P.*, Pan, Q.*, Cetin, K.S., Byon, E., Manuel, L., Zhou, Y., Jahani, E., 2019, Projecting central U.S. most likely annual urban heat extremes through mid-century, Atmosphere, vol. 10, pp. 727.

• Choe, Y*., Lam, H. and Byon, E., 2018, Uncertainty quantification of importance sampling estimators with stochastic simulation models, Methodology and Computing in Applied Probability, 24, pp. 1155-1172 [supplement]. 
  • Mary G. and Joseph Natrella Scholarship Award Winning paper from the Quality and Productivity Section of American Statistical Association (ASA)

• Lin, K. Liu, E. Byon, X. Qian , S. Liu , and S. Huang, 2018, “A collaborative learning framework for estimating many individualized regression models in a heterogeneous population,” IEEE Transactions on Reliability, vol. 67, pp. 328 – 341.

• You, M.*, B. Liu*, E. Byon, S. Huang and J. Jin, 2018, Direction-dependent power curve modeling for multiple interacting wind turbines, IEEE Transactions on Power Systems, vol. 33, pp.1725-1733.

• You. M*, Byon, E. and Jin, J. and Lee, G., 2017, When Wind Travels Through Turbines: A New Statistical Approach for Characterizing Heterogeneous Wake Effects in Multi-turbine Wind Farms, IISE Transactions, Vol. 49, No. 1, pp. 84-95 [code].  
  • Best Student Paper Award in 2015 INFORMS Data Mining Section

• Ning, S., Byon, E., Wu, T. and Li, J., 2017, A sparse partitioned-regression model for nonlinear system-environment interactions, IISE Transactions on Quality and Reliability Engineering, vol. 49, pp. 1-13.
  • Honorable Mention in Best Paper Competition in the 2018 IISE Transactions Focus Issue on Quality and Reliability Engineering

• Ko. Y and Byon, E., 2017, Condition-based joint maintenance optimization for a large-scale system with homogeneous units, IISE Transactions on Quality and Reliability Engineering, Vol. 49, No. 5, pp. 493-504. 

• Pan, Q.* and Byon, E., 2017, Adaptive extreme load estimation in wind turbines, In Proceedings of the AIAA Science and Technology Forum and Exposition.

• Choe, Y.*, Byon, E., Jin, J., Guo, W., and Li, J., 2016, Change-point detection in solar panel performance analysis, In Proceeding of the Industrial Engineering Research Conference, Anaheim, California, May 21-24, 2016.
  • Best Paper Award winning paper in ISERC Energy Systems track.

• Byon, E., Choe, Y.* and Yampikulsakul N. *, 2016, Adaptive modeling and prediction in time-variant processes with application to wind power systems, IEEE Transactions on Automation Science and Engineering, Vol. 13, No.2, pp. 997-1007.

• Choe, Y.*, Guo, G. Byon, E. and Jin, 2016, Change-Point Detection on Solar Panel Performance Using Thresholded LASSO, Quality and Reliability Engineering International, Vol. 32, No. 8, pp. 2653-2665. 

• Choe, Y.*, Pan, Q. * and Byon, E., 2016, Computationally efficient uncertainty minimization in wind turbine extreme load assessments,  ASME Journal of Solar Energy Engineering: Including Wind Energy and Building Energy Conservation, Vol. 138, No. 4, pp. 0410121 [code].

• X. Hu, Y. Li, Byon, E. and F. B. Lawrence, 2015, Prioritizing regular orders while reserving capacity for emergency demand, European Journal of Operations Research, Vol. 247, No. 2, pp. 472-487. 

• Choe, Y.*, Byon, E., and Chen, N., 2015, Importance Sampling for the Reliability Evaluation with Stochastic Simulation Models, Technometrics, Vol. 57, No. 3, pp. 351-361[Supplement][code].
  • Selected for presentation in the Technometrics-sponsored session in the 2015 INFORMS conference; only two best papers selected by the journal editors are invited to present.  

• Ko. Y. M. and Byon. E., 2015, Reliability evaluation of large-scale systems with identical units, IEEE Transactions on Reliability, Vol. 64, No. 1, pp. 420-434. 

• Zhang, Y., Zhang, J., Kane, M. B., Häckell, M. Byon, E., Rolfes, R. and Lynch, J. P., 2015, Wireless Monitoring and Spectral Analysis of a 3 kW Wind Turbine for Condition Monitoring, In Proceedings of the 10th International Workshop on Structural Health Monitoring 2015, Stanford, CA, September 1-3.

• Lin, Y., Liu, K., Byon, E., Qian, X and Huang, 2015, S. Domain-knowledge driven cognitive degradation modeling of alzheimer’s disease, In Proceedings of SIAM (Society for industrial and Applied Mathematics) International Conference on Data Mining (SDM) conference (historical paper acceptance rate < 25%).

• Yampikulsakul N *., Byon, E., Huang S., Sheng S. and You. M*, 2014, Condition monitoring of wind turbine system with non-parametric regression-based analysis, IEEE Transactions on Energy Conversion, Vol. 29, No. 2, pp. 288-299.

• Lee, G., Byon, E., Ntaimo, L., and Ding. Y, 2013, Bayesian spline method for assessing extreme loads on wind turbines,  Annals of Applied Statistics, Vol. 7, No. 4, pp. 2034–2061.

• Byon, E. 2013, Wind turbine operations and maintenance: A tractable approximation of dynamic decision-making, IIE Transactions, Vol. 45, No. 11, pp. 1188-1201 
  • Best applications paper award in IIE Transactions Focused Issue on Quality & Reliability Engineering.
  • Finalist in 2014 INFORMS Energy, Natural Resources, and the Environment (ENRE) Young Researcher Prize
  • Featured by the Editor-in-Chief in the October Issue 2013 of IE Magazine.

• Byon, E., Ntaimo, L., Singh, C., and Ding, Y., 2013, Wind energy facility reliability and maintenance, Handbook of Wind Power Systems, Energy Systems, Springer-Verlag Berlin Heidelberg (edited by Pardalos, Pereira, Rebennack and Boyko, this book-chapter article was peer-reviewed). 

• Byon, E., Pérez, E., Ntaimo, L., and Ding, Y., 2011, Simulation of wind farm operations and maintenance using DEVS, Simulation, Vol. 87, No. 12, pp. 1091 – 1115.

• Byon, E. and Ding, Y., 2011, Integrating simulation and optimization for wind farm operations under stochastic conditions, Proceedings of the 2011 Industrial Engineering Research Conference.

• Byon, E., Shrivastava, A. K., and Ding, Y., 2010, A classification procedure for highly imbalanced class sizes, IIE Transactions, Vol. 42, No. 4, pp. 288-303
  • Featured by the Editor-in-Chief in the April Issue 2010 of IE Magazine.

• Byon, E., Ding, Y. and Ntaimo, L., 2010, Optimal maintenance strategies of wind turbine systems under stochastic weather conditions, IEEE Transactions on Reliability, Vol. 59, No. 2, pp. 393-404. [online supplement] [Correction]

• Byon, E. and Ding, Y. 2010, Season-dependent condition-based maintenance for a wind turbine using a partially observed Markov decision process, IEEE Transactions on Power Systems, Vol. 25, No. 4, pp. 1823-1834.

• Pérez, E., Ntaimo, L., Byon, E., and Ding, Y., 2010, A stochastic DEVS wind turbine component model for wind farm simulation, Symposium on Theory of Modeling and Simulation – DEVS Integrative M&S Symposium (DEVS 2010), Orlando, FL, April 12-15.

• Hu, X., Li, Y., Byon, E., and Lawrence, F. B., 2009, Prioritizing regular orders while reserving capacity for emergency demand, Manufacturing and Service Operations Management Society (MSOM) Annual Conference, Cambridge, MA, June 28-30).

• Byon, E., Ding, Y., and Ntaimo, L, 2009, Optimal maintenance strategies for wind turbine systems, The 15th ISSAT International Conference on Reliability and Quality in Design, San Francisco, CA, August 6-8.

• Park, C., Ding, Y., and Byon, E., 2008, Collaborative data reduction for energy efficient sensor networks, Annual IEEE Conference on Automation Science and Engineering (CASE 2008), Washington, D.C. August 23-27.

• Ding, Y., Byon, E., Park, C., Tang, J., Lu, Y., and Wang, X., 2007, Dynamic data-driven fault diagnosis of wind turbine systems, Lecture Note in Computer Science, Vol. 4487, 1197-1204.