Bio

Anderson Rodrigo de Queiroz is an associate professor at the CCEE department at NC State University. He received his B.Sc. and M.Sc. degrees in Electrical Engineering from the Federal University of Itajubá, Brazil, in 2005 and 2007, respectively. He holds a Ph.D. in Operations Research and Industrial Engineering from the University of Texas at Austin, earned in 2011. Before joining NC Central University, he worked as a consultant/researcher on several projects related to power systems. From 2013 to 2015, he served as an assistant professor of electrical and computer engineering at the Federal University of Itajubá. Between 2015 and 2016, he was a postdoctoral researcher at NC State, where he has been a research professor in the CCEE department since 2016. His interests include Operations Research and Energy Planning, with a focus on large-scale stochastic optimization, analytics and decision-making techniques, as well as planning, operations, economics and design of electrical and energy systems.

Publications

• Evaluating Battery Degradation Models in Rolling-Horizon BESS Arbitrage Optimization , Energies (2026)
• Replication Data for "Power System Costs and Emissions Impacts of Data Center and Cryptocurrency Mining Expansion in the United States" , Zenodo (CERN European Organization for Nuclear Research) (2026)
• Routing Optimization Framework for Exploring Time-Varying Urban Road Network Vulnerability under Floods , Journal of Infrastructure Systems (2026)
• Spatio-Temporal Solar Power Forecasting Using GAN-Enhanced Irradiance Maps and Deep Neural Networks , SSRN Electronic Journal (2026)
• Spatio-Temporal Solar Power Forecasting Using GAN-Enhanced Irradiance Maps and Deep Neural Networks , SSRN Electronic Journal (2026)
• Spatio-Temporal Solar Power Forecasting Using GAN-Enhanced Irradiance Maps and Deep Neural Networks , SSRN Electronic Journal (2026)
• A framework for global sensitivity analysis in long-term energy systems planning using optimal transport , Energy (2025)
• Data-Driven Yield Improvement in Upstream Bioprocessing of Monoclonal Antibodies: A Machine Learning Case Study , Processes (2025)
• Day-ahead Solar Power Forecasting in a Large-scale System Using Statistical and Neural Network Models , (2025)
• Load Forecasting Using Recurrent and Transformer Neural Networks: A Comprehensive Analysis Across Multi-Time Scales , (2025)

Grants