Research

Student will be analyzing real estate data to gather insights about changes in housing markets. Required courses: basic statistics and linear algebra; EMGT6910 is preferred. Desired skills: forecasting; statistics; machine learning, programming (SAS, R, Python or Matlab). Contact faculty: Dr. Tao Hong. Posting date: 10/11/2019.

When a disaster hits the power grid causing damages to grid infrastructures and blackouts, utility companies must be able to restore power to customers as quickly as possible. We will do research to leverage advanced AI technology to dynamically form and control microgrids with a combination of distributed energy resources, energy storage, electric vehicles (EVs) and etc. to provide support restarting the grid from the bottom up. Dr. Linquan Bai. Posting date: 8/20/2019

Large amounts of distributed energy resources such as rooftop photovoltaics (PVs) are being integrated into the power grid. If appropriately controlled, a cluster of DERs can contribute to the bulk power grid as a thermal generator. We will develop novel optimization and control methods to enable DERs to provide frequency support in bulk power systems while meeting the operational constraints of the distribution power grid where DERs are physically located. Dr. Linquan Bai. Posting date: 8/20/2019.

Though the wholesale electricity markets have been established for almost 20 years, the electricity market in distribution systems is still in the concept stage. We will explore economic theories to design, model and analyze distribution-level electricity markets. We will design novel market mechanisms to be integrated with current and future system dispatch and controls. Block-chain based peer-to-peer trading will be developed under the distribution market framework. Dr. Linquan Bai. Posting date: 8/20/2019.

Student will be using NBA data to forecast outcomes of NBA games. Required courses: basic statistics and linear algebra; EMGT6910 is preferred. Desired skills: forecasting; statistics; machine learning, programming (SAS, R, Python or Matlab). Contact faculty: Dr. Tao Hong. Posting date: 7/8/2019.

Student will be doing research on novel decentralized optimization methods for PV inverter control in power distributin systems. Required courses: EMGT5201 or EMGT6952. Desired skills: convex optimization; distributed optimization; programming (Matlab/Python and CPLEX/GUROBI). Contact faculty: Dr. Linquan Bai. Posting date: 2/14/2019.

Student will be using realworld weather and electricity demand data to develop long-term load forecasts for about 50 small cities. Required courses: EMGT6910 or EMGT6965. Desired skills: forecasting; statistics; machine learning, programming (SAS, R, Python or Matlab). Contact faculty: Dr. Tao Hong. Posting date: 2/14/2019.

Student will be using real world weather and solar power data to forecast solar power generation for a solar farm. Required courses: EMGT6910 or EMGT6965. Desired skills: forecasting; statistics; machine learning, programming (SAS, R, Python or Matlab). Contact faculty: Dr. Tao Hong. Posting date: 2/14/2019.

Hong, T. & Hofmann, A. (2022) Data Integrity Attacks Against Outage Management Systems. IEEE Transactions on Engineering Management, 69(3), pp. 765-772. https://doi.org/10.1109/TEM.2021.3055139

• Chacra, S.A., Sireli, Y., Cali, U. (2021). A review of worldwide blockchain technology initiatives in the energy sector based on go-to-market strategies. International Journal of Energy Sector Management, https://doi.org/10.1108/IJESM-05-2019-0001 2021, 15(6), pp. 1050–1065.
• Huang, YL., Sikder, I. & Xu, G. (2021). Optimal override policy for chemotherapy scheduling template via mixed-integer linear programming. Optimization Letters. in press. https://doi.org/10.1007/s11590-021-01796-z
• Liu, Y., Jarvamardi, A., Zhang, Y., Liu, M., Hsiang, S. M., Yang, S., Yu, X.X., & Jiang, Z. (2021). Comparative Study on Perception of Causes for Construction Task Delay in China and the United States. Journal of Construction Engineering and Management, 147(3), 04020176.
• Mirzahossein, H., Gholampour, I., Sedghi, M., & Zhu, L. (2021). How realistic is static traffic assignment? Analyzing automatic number-plate recognition data and image processing of real-time traffic maps for investigation. Transportation Research Interdisciplinary Perspectives, 9, 100320. https://doi.org/10.1016/j.trip.2021.100320
• Rostami-Tabar, B., Ali, M., Hong, T., Hyndman, R.J.., Porter, M.D., & Syntetos, A. (2021). Forecasting for social good. International Journal of Forecasting, in press. https://doi.org/10.1016/j.ijforecast.2021.02.010
• Smith, M., Bader, S., Crotts, B., Sireli, Y. (2021). Conceptional Design of Additive Manufacturing Based Dissimilar Metal Embedded Sensors for TAD Canister Monitoring. Transactions of the American Nuclear Society, vol. 125, no. 1, pp. 214-217.
• Smith, M., Bader, S., Crotts, B., Sireli, Y. (2021). Data-based Optimization for Off-loading an SFP via a TAD Canister. Transactions of the American Nuclear Society, vol. 123, no. 1, pp. 211-214. https://doi.org/10.13182/T123-33063.
• Zenarosa, G.L., Prokopyev, O.A. & Pasiliao, E.L. (2021) On exact solution approaches for bilevel quadratic 0–1 knapsack problem. Ann Oper Res, 298, 555–572. https://doi.org/10.1007/s10479-018-2970-4
• Zhu, L., Zhao, Z., & Wu, G. (2021). Shared Automated Mobility with Demand-Side Cooperation: A Proof-of-Concept Microsimulation Study. Sustainability, 13(5), 2483. https://doi.org/10.3390/su13052483

• Adnan, Z. H., & Özelkan, E. C. (2020). Bullwhip effect in pricing under the revenue-sharing contract. Computers & Industrial Engineering, 145, 106528. https://doi.org/10.1016/j.cie.2020.106528
• Bracale, A., Caramia, P., De Falco, P., & Hong, T. (2020). A Multivariate Approach to Probabilistic Industrial Load Forecasting. Electric Power Systems Research, 187, 106430. https://doi.org/10.1016/j.epsr.2020.106430
• Bracale, A., Caramia, P., De Falco, P., & Hong, T. (2020). Multivariate quantile regression for short-term probabilistic load forecasting. IEEE Transactions on Power Systems, 35(1), 628 – 638. https://doi.org/10.1109/TPWRS.2019.2924224
• Xu, G., Semenov, A., & Rysz, M. (2020). An integer programming formulation of the key management problem in wireless sensor networks. Optimization Letters, 14(5), 1037-1051. https://doi.org/10.1007/s11590-019-01465-2
• Hong, T. (2020). Forecasting with high frequency data: M4 competition and beyond. International Journal of Forecasting, 36(1), 191-194. https://doi.org/10.1016/j.ijforecast.2019.03.013
• Hong, T., Pinson, P., Wang, Y., Weron, R., Yang, D. & Zareipour, H. (2020). Energy Forecasting: A Review and Outlook. IEEE Open Access Journal of Power and Energy, 7, 376-388. https://doi.org/10.1109/OAJPE.2020.3029979
• Javanmardi, A., Abbasian-Hosseini, S.A., Liu, M., & Hsiang, S.M. (2020). Improving Effectiveness of Constraints Removal in Construction Planning Meetings: Information-Theoretic Approach. Journal of Construction Engineering and Management, 146(4), https://dx.doi.org/10.1061/(ASCE)CO.1943-7862.0001790
• Kezunovic, M., Pinson, P., Obradovic, Z., Grijalva, S., Hong, T., & Bessa, R. (2020). Big data analytics for future electricity grids. Electric Power Systems Research, 189, 106788. https://doi.org/10.1016/j.epsr.2020.106788
• Lim, C., Teng, S. G., Al-Ghandour M., & Bowen, F. H. (2020). Let scheduling for funding scenario analysis of highway construction projects with a case of NCDOT. IEEE Transactions on Engineering Management, 67(2), 385 – 395. https://doi.org/10.1109/TEM.2018.2875615
• Panamtash, H., Zhou, Q., Hong, T., Qu Z., & Davis, K. (2020). A copula-based Bayesian method for probabilistic solar power forecasting. Solar Energy, 196, 336-345. https://doi.org/10.1016/j.solener.2019.11.079
• Sobhani, M., Hong, T., & Martin, C. (2020). Temperature data cleansing for electric load forecasting. International Journal of Forecasting, 36(2), 324-333. https://doi.org/10.1016/j.ijforecast.2019.04.022
• Wang, J., Zhou, H., Hong, T., Li, X., & Wang, S. (2020). A multi-granularity heterogeneous combination approach to crude oil price forecasting. Energy Economics, 91, 104790. https://doi.org/10.1016/j.eneco.2020.104790
• Yang, D. et al. (2020) Verification of deterministic solar forecasts. Solar Energy, 210, 20-37. https://doi.org/10.1016/j.solener.2020.04.019
• Zhang, Y. X., Javanmardi, A., Liu, Y. C., Yang, S. J., Yu, X. X., Hsiang, S. M., Jiang, Z., & Liu, M. (2020). How Does Experience with Delay Shape Managers’ Making-Do Decision: Random Forest Approach.Journal of Management in Engineering, 36(4). https://doi.org/10.1061/(ASCE)ME.1943-5479.0000776

• Abbsaian-Hosseini, S. A., Liu, M., & Hsiang, S. M. (2019). Social network analysis for construction crews. International Journal of Construction Management, 19(2), 113-127. https://doi.org/10.1080/15623599.2017.1389642
• Abuella, M., & Chowdhury, B. H. (2019). Forecasting of solar power ramp events: a post-processing approach. Renewable Energy, 133, 1380-1192. https://doi.org/10.1016/j.renene.2018.09.005
• Adnan, Z.H. & Özelkan, E. (2019). Bullwhip effect in pricing under different supply chain game structures. Journal of Revenue and Pricing Management, 18, 393–404. https://doi.org/10.1057/s41272-019-00203-8
• Arefi, M., & Chowdhury, B. (2019). Coherency detection of generators using recurrence quantification analysis. Electric Power Systems Research, 169, 162-173. https://doi.org/10.1016/j.epsr.2018.12.017
• Bracale, A., Carpinelli, G., De Falco, P., & Hong, T. (2019). Short-term industrial reactive power forecasting. International Journal of Electrical Power & Energy Systems, 107, 177-185. https://doi.org/10.1016/j.ijepes.2018.11.022
• Chen, S., Lanzas, C., Lee, C., Zenarosa, G. L., Arif, A. A., & Dulin, M. (2019). Metapopulation model from pathogen’s perspective: A versatile framework to quantify pathogen transfer and circulation between environment and hosts. Scientific reports, 9(1), 1694. https://doi.org/10.1038/s41598-018-37938-0
• Hong, T., & Pinson, P. (2019). Energy forecasting in the big data world. International Journal of Forecasting. 35(4), 1387-1388. https://doi.org/10.1016/j.ijforecast.2019.05.004
• Hong, T., Xie, J., & Black, J. (2019). Global Energy Forecasting Competition 2017: Hierarchical Probabilistic Load Forecasting. International Journal of Forecasting. 35(4), 1389-1399. https://doi.org/10.1016/j.ijforecast.2019.02.006
• Hossan, M.S., Chowdhury, B. (2019). Comparison of Time-Varying Load Models for Estimating CVR Factor and VSF Using Dual-Stage Adaptive Filter. IEEE Transactions on Power Delivery. 24(3). https://doi.org/10.1109/TPWRD.2019.2903167
• Hossan, S., & Chowdhury, B. H. (2019). Data-driven fault location scheme for advanced distribution management systems. IEEE Transactions on Smart Grid, 10(5), 5386 – 5396. https://doi.org/10.1109/TSG.2018.2881195
• Itiki, R., Di Santo, S.G., Itiki, C., Manjrekar, M., & Chowdhury, B. (2019). A comprehensive review and proposed architecture for offshore power system. 111, 79-92. https://doi.org/10.1016/j.ijepes.2019.04.008
• Luo, J., Hong, T., & Fang, S. (2019). Robust regression models for load forecasting. IEEE Transactions on Smart Grid. 10(5), 5397-5404 https://doi.org/10.1109/TSG.2018.2881562
• Meng, F., Chowdhury, B., & Hossan, M.S. (2019). Optimal integration of DER and SST in active distribution networks. International Journal of Electrical Power and Energy Systems, 104, 626-634. https://doi.org/10.1016/j.ijepes.2018.07.035
• Moghaddam, I. N., Chowdhury, B. H., & Doostan, M. (2019). Optimal sizing and operation of battery energy storage systems connected to wind farms participating in electricity markets. IEEE Transactions on Sustainable Energy, 10(3), 1184-1193. https://doi.org/10.1109/TSTE.2018.2863272
• Shuvra, M.A., & Chowdhury, B. (2019). Distributed dynamic grid support using smart PV inverters during unbalanced grid faults, IET renewable Power Generation, 13(4), 598-608. https://doi.org/10.1049/iet-rpg.2018.5761
• Sobhani, M., Campbell, A., Sangamwar, S., Li, C., & Hong, T. (2019) Combining weather stations for electric load forecasting. Energies, 12(8), 1510. https://doi.org/10.3390/en12081510
• Wang, Y., Chen, Q., Hong, T., & Kang, C. (2019). Review of smart meter data analytics: applications, methodologies, and challenges. IEEE Transactions on Smart Grid, 10(3), 3125-3148. https://doi.org/10.1109/TSG.2018.2818167
• Wang, Y., Zhang, N., Tan, Y., Hong, T., Kirschen, D.S., & Kang, C. (2019). Combining probabilistic load forecasts. IEEE Transactions on Smart Grid, 10(4), 3664-3674. https://doi.org/10.1109/TSG.2018.2833869
• Xiao, J., Lin, Q., Bai, L., Zhang, X., Zuo, L., Zhou, H., & Li, H. (2019) Security distance for distribution system: Definition, calculation, and application, International Transactions on Electrical Energy Systems, 29(5), e2838. https://doi.org/10.1002/2050-7038.2838
• Yue, M., Hong, T., & Wang, J. (2019), Descriptive analytics based anomaly detection for cybersecure load forecasting. IEEE Transactions on Smart Grid, 10(6), 5964 – 5974. https://doi.org/10.1109/TSG.2019.2894334
• Zhang, R., Jiang T., Bai, L., Guo, L., Chen, H., Li, X., Li, F. (2019) Adjustable robust power dispatch with combined wind-storage system and carbon capture power plants under low-carbon economy, International Journal of Electrical Power and Energy Systems, 113, 772-281. https://doi.org/10.1016/j.ijepes.2019.05.079

• Abuella, M., & Chowdhury, B. (2018). Improving combined solar power forecasts using estimated ramp rates: Data-driven post-processing approach. IET Renewable Power Generation, 12(10), 1127-1135. https://doi.org/10.1049/iet-rpg.2017.0447
• An, Q., Fang, S.-C., Li, H.-L., & Nie, T. (2018). Enhanced linear reformulation for engineering optimization models with discrete and bounded continuous variables. Applied Mathematical Modelling, 58, 140-157. https://doi.org/10.1016/j.apm.2017.09.047
• Chamana, M., & Chowdhury, B. H. (2018). Optimal voltage regulation of distribution networks with cascaded voltage regulators in the presence of high PV penetration. IEEE Transactions on Sustainable Energy, 9(3), 1427-1436. https://doi.org/10.1109/tste.2017.2788869
• Chamana, M., Chowdhury, B. H., & Jahanbakhsh, F. (2018). Distributed control of voltage regulating devices in the presence of high PV penetration to mitigate ramp-rate issues. IEEE Transactions on Smart Grid, 9(2), 1086-1095. https://doi.org/10.1109/tsg.2016.2576405
• Demirel, E., Özelkan, E. C., & Lim, C. (2018). Aggregate planning with flexibility requirements profile. International Journal of Production Economics, 202, 45-58. https://doi.org/10.1016/j.ijpe.2018.05.001
• He, Q., & Chen, Y. (2018). Dynamic pricing of electronic products with consumer reviews. Omega, 80, 123-134. https://doi.org/10.1016/j.omega.2017.08.014
• He, Q., & Chen, Y. (2018). Revenue-maximizing pricing and scheduling strategies in service systems with flexible customers. Operations Research Letters, 46(1), 134-137. https://doi.org/10.1016/j.orl.2017.11.012
• He, Q., & Hong, T. (2018). Integrated facility location and production scheduling in multi-generation energy systems. Operations Research Letters, 46(1), 153-157. https://doi.org/10.1016/j.orl.2017.12.001
• He, Q., Chen, Y., & Shen, Z. (2018). On the formation of producers’ information-sharing coalitions. Production and Operations Management, 27(5), 917-927. https://doi.org/10.1111/poms.12852
• Javanmardi, A., Abbasian-Hosseini, S. A., Liu, M., & Hsiang, S. M. (2018). Benefit of cooperation among subcontractors in performing high-reliable planning. Journal of Management in Engineering, 34(2), 04017062. https://doi.org/10.1061/(asce)me.1943-5479.0000578
• Liu, C., Hong, T., Li, H., & Wang, L. (2018). From club convergence of per capita industrial pollutant emissions to industrial transfer effects: An empirical study across 285 cities in China. Energy Policy, 121, 300-313. https://doi.org/10.1016/j.enpol.2018.06.039
• Luo, J., Hong, T., & Fang, S. (2018). Benchmarking robustness of load forecasting models under data integrity attacks. International Journal of Forecasting, 34(1), 89-104. https://doi.org/10.1016/j.ijforecast.2017.08.004
• Luo, J., Hong, T., & Yue, M. (2018). Real-time anomaly detection for very short-term load forecasting. Journal of Modern Power Systems and Clean Energy, 6(2), 235-243. https://doi.org/10.1007/s40565-017-0351-7
• Meng, F., Chowdhury, B., & Chamanamcha, M. (2018). Three-phase optimal power flow for market-based control and optimization of distributed generations. IEEE Transactions on Smart Grid, 9(4), 3691-3700. https://doi.org/10.1109/tsg.2016.2638963
• Mitra, B., Chowdhury, B., & Manjrekar, M. (2018). HVDC transmission for access to off-shore renewable energy: A review of technology and fault detection techniques. IET Renewable Power Generation, 12(13), 1563-1571. https://doi.org/10.1049/iet-rpg.2018.5274
• Moghaddam, I. N., Chowdhury, B. H., & Mohajeryami, S. (2018). Predictive operation and optimal sizing of battery energy storage with high wind energy penetration. IEEE Transactions on Industrial Electronics, 65(8), 6686-6695. https://doi.org/10.1109/tie.2017.2774732
• Okioga, I. T., Wu, J., Sireli, Y., & Hendren, H. (2018). Renewable energy policy formulation for electricity generation in the United States. Energy Strategy Reviews, 22, 365-384. https://doi.org/10.1016/j.esr.2018.08.008
• Özelkan, E. C., Lim, C., & Adnan, Z. H. (2018). Conditions of reverse bullwhip effect in pricing under joint decision of replenishment and pricing. International Journal of Production Economics, 200, 207-223. https://doi.org/10.1016/j.ijpe.2018.03.018
• Vatani, B., Chowdhury, B., & Lin, J. (2018). The role of demand response as an alternative transmission expansion solution in a capacity market. IEEE Transactions on Industry Applications, 54(2), 1039-1046 https://doi.org/10.1109/TIA.2017.2785761
• Vatani, B., Chowdhury, B., Dehghan, S., & Amjady, N. (2018). A critical review of robust self-scheduling for generation companies under electricity price uncertainty. International Journal of Electrical Power & Energy Systems, 97, 428-439. https://doi.org/10.1016/j.ijepes.2017.10.035
• Wang, J., Li, X., Hong, T., & Wang, S. (2018). A semi-heterogeneous approach to combining crude oil price forecasts. Information Sciences, 460-461, 279-292. https://doi.org/10.1016/j.ins.2018.05.026
• Xie, J., & Hong, T. (2018). Temperature scenario generation for probabilistic load forecasting. IEEE Transactions on Smart Grid, 9(3), 1680-1687. https://doi.org/10.1109/tsg.2016.2597178
• Xie, J., & Hong, T. (2018). Load forecasting using 24 solar terms. Journal of Modern Power Systems and Clean Energy, 6(2), 208-214. https://doi.org/10.1007/s40565-017-0374-0
• Xie, J., & Hong, T. (2018). Variable selection methods for probabilistic load forecasting: empirical evidence from seven states of the United States. IEEE Transactions on Smart Grid, 9(6), 6039-6046. https://doi.org/10.1109/tsg.2017.2702751
• Xie, J., Chen, Y., Hong, T., & Laing, T. D. (2018). Relative humidity for load forecasting models. IEEE Transactions on Smart Grid, 9(1), 191-198. https://doi.org/10.1109/tsg.2016.2547964