Anderson de Queiroz
Civil, Construction, and Environmental Engineering
- Phone: 919-515-2331
- Email: ardequei@ncsu.edu
- Office: Fitts-Woolard Hall 3181
- Website: https://arqueiroz.wordpress.ncsu.edu/category/ncsu/
Dr. Anderson Rodrigo de Queiroz is an assistant professor of Decision Sciences at the school of Business at North Carolina Central University (NCCU). He is also an adjunct research professor at the CCEE department at North Carolina State University (NCSU). He received his B.Sc. and M.Sc. degrees in Electrical Engineering both from Federal University at Itajubá, in Brazil, in 2005 and 2007, respectively. He has a Ph.D. in Operations Research and Industrial Engineering from the University of Texas at Austin, in 2011. Prior to joining NCCU he worked as a consultant/researcher in several projects related to power systems, and from 2013 to 2015 he was an assistant professor of electrical and computer engineering at the Federal University of Itajubá. During 2015-2016 he was a postdoctoral researcher at NCSU, where since 2016 he has been contributing to the CCEE department as a research professor. His interests include Operations Research and Energy Planning, with focus on large-scale stochastic optimization, analytics and decision-making techniques, as well as planning, operations, economics and design of electrical and energy systems.
Education
| Degree | Program | School | Year |
|---|---|---|---|
| Ph.D. | Doctor of Philosophy in Industrial Engineering | The University of Texas at Austin | 2011 |
| Ph.D. | Doctor of Philosophy in Operations Research | The University of Texas at Austin | 2011 |
| MEE | Master of Electrical Engineering | Federal University of Itajubá | 2007 |
| BSEPS | Bachelor of Science in Electrical Power Systems | Federal University of Itajubá | 2005 |
Publications
- Integrating Hydrological and Machine Learning Models for Enhanced Streamflow Forecasting via Bayesian Model Averaging in a Hydro-Dominant Power System
- Torres, F. L. R., Lima, L. M. M., Reboita, M. S., Queiroz, A. R., & Lima, J. W. M. (2024), WATER, 16(4). https://doi.org/10.3390/w16040586
- An Overview of Short-Term Load Forecasting for Electricity Systems Operational Planning: Machine Learning Methods and the Brazilian Experience
- Aquila, G., Scianni Morais, L. B., Faria, V. A., Marangon Lima, J. W., Marangon Lima, L. M., & Queiroz, A. R. (2023). [Review of , ]. ENERGIES, 16(21). https://doi.org/10.3390/en16217444
- Scenario generation and risk-averse stochastic portfolio optimization applied to offshore renewable energy technologies
- Faria, V. A. D., Queiroz, A. R., & DeCarolis, J. F. (2023), ENERGY, 270. https://doi.org/10.1016/j.energy.2023.126946
- Short-term load forecasting using neural networks and global climate models: An application to a large-scale electrical power system
- Morais, L. B. S., Aquila, G., Faria, V. A. D., Lima, L. M. M., Lima, J. W. M., & Queiroz, A. R. (2023), APPLIED ENERGY, 348. https://doi.org/10.1016/j.apenergy.2023.121439
- Co-Optimization of Reservoir and Power Systems (COREGS) for seasonal planning and operation
- Ford, L., Queiroz, A., DeCarolis, J., & Sankarasubramanian, A. (2022), ENERGY REPORTS, 8, 8061–8078. https://doi.org/10.1016/j.egyr.2022.06.017
- Efficiency analysis for performance evaluation of electric distribution companies
- Medeiros, G. O. S., Marangon-Lima, L. M., Queiroz, A. R., Marangon-Lima, J. W., Santos, L. C. B., Barbosa, M. A., & Alvares, J. E. (2022), INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, 134. https://doi.org/10.1016/j.ijepes.2021.107430
- Energy-Storage Modeling: State-of-the-Art and Future Research Directions
- Sioshansi, R., Denholm, P., Arteaga, J., Awara, S., Bhattacharjee, S., Botterud, A., … Zhang, Z. (2022), IEEE TRANSACTIONS ON POWER SYSTEMS, 37(2), 860–875. https://doi.org/10.1109/TPWRS.2021.3104768
- Optimizing offshore renewable portfolios under resource variability
- Faria, V. A. D., Queiroz, A. R., & De Carolis, J. F. (2022), APPLIED ENERGY, 326. https://doi.org/10.1016/j.apenergy.2022.120012
- Using robust optimization to inform US deep decarbonization planning
- Patankar, N., Eshraghi, H., Queiroz, A. R., & DeCarolis, J. F. (2022), ENERGY STRATEGY REVIEWS, 42. https://doi.org/10.1016/j.esr.2022.100892
- An assessment of multi-layer perceptron networks for streamflow forecasting in large-scale interconnected hydrosystems
- Faria, V. A. D., Queiroz, A. R., Lima, L. M., Lima, J. W. M., & Silva, B. C. (2021, July 23), INTERNATIONAL JOURNAL OF ENVIRONMENTAL SCIENCE AND TECHNOLOGY, Vol. 7. https://doi.org/10.1007/s13762-021-03565-y
