About

Anderson de Queiroz is an Associate Professor in the Department of Civil, Construction and Environmental Engineering at NC State University. His contact email is ardequei@ncsu.edu. The page does not provide specific details about his research focus, background, or key contributions.

Research topics

• Computer Science
• Artificial Intelligence
• Engineering
• Computer Security
• Political Science
• Reliability engineering
• Electrical engineering
• Systems engineering
• Mathematical optimization
• Algorithm
• Business
• Real-time computing

Selected publications

• 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) · 2026-04-10

  datasetOpen access

  This dataset contains model inputs and outputs necessary to reproduce one scenario in the analysis presented in the manuscript “Power System Costs and Emissions Impacts of Data Center and Cryptocurrency Mining Expansion in the United States.” The scenario included reflects baseline digital demands, mid gas prices, and the inclusion of IRA subsidies as described in the manuscript. Datasets for other scenarios can be recreated using the references in the manuscript or requested from the corresponding author.

  PublisherDOI

• Two-Stage Robust Optimization with Model Predictive Control for Hydropower Scheduling under Hydrological and Market Uncertainty

  SSRN Electronic Journal · 2026-01-01

  preprintOpen access
  PublisherDOI

• Spatio-Temporal Solar Power Forecasting Using GAN-Enhanced Irradiance Maps and Deep Neural Networks

  SSRN Electronic Journal · 2026-01-01

  preprintOpen accessSenior author
  PublisherDOI

• Evaluating Battery Degradation Models in Rolling-Horizon BESS Arbitrage Optimization

  Energies · 2026-02-18

  articleOpen accessSenior authorCorresponding

  Battery Energy Storage Systems (BESS) can benefit from price volatility in electricity markets, but frequent cycling increases degradation and reduces long-term value. This study develops a rolling-horizon dispatch framework in which battery operation is fully price-driven, while degradation is evaluated separately to isolate the effect of degradation model choice. A 48 h look-ahead window is solved repeatedly and advanced by 24 h, with only the first 24 h of decisions implemented and remaining capacity carried forward. Degradation is assessed using three widely used model classes: Linear-Calendar (LC), Energy-Throughput (ET), and Cycle-Based rainflow (CB) models. The framework is applied to Electric Reliability Council of Texas (ERCOT) 15 min real-time prices for 2024 (Houston Zone). LC and ET result in limited annual capacity loss (≈2%) and modest economic impact, while the CB model predicts substantially higher degradation and large negative valuation. Sensitivity analysis shows that CB-based results are highly dependent on parameter calibration. Overall, the results highlight the strong influence of degradation modeling choices on BESS valuation under rolling-horizon operation.

  PublisherOA PDFDOI

• Spatio-Temporal Solar Power Forecasting Using GAN-Enhanced Irradiance Maps and Deep Neural Networks

  SSRN Electronic Journal · 2026-01-01

  preprintOpen accessSenior author
  PublisherDOI

• Routing Optimization Framework for Exploring Time-Varying Urban Road Network Vulnerability under Floods

  Journal of Infrastructure Systems · 2026-03-26

  article

  This work investigates the problem of vehicle routing and assessment of road network vulnerability during extreme flood conditions by considering inundation levels and their effects on road availability and travel speeds. A stormwater model is used to estimate street-level inundation during three major hurricanes that passed through Wilmington, North Carolina—Florence, Matthew, and Dorian—to determine how different storms can affect the road network system. Toward this, an optimization model was developed that uses flood versus speed reduction functions to integrate flood levels to estimate decreasing the number of available arcs and reducing the interconnectivity of different regions over time. A comparison of the road network analysis using flood estimates from the stormwater model and the 100-year flood maps from the Federal Emergency Management Agency of the United States was performed, and overall results indicate the importance of storm-specific flood estimates to assist emergency planners in the definition of critical roads and affected areas. The paper also explores how different regions are affected during various flood severity levels and identifies areas of vulnerability. It introduces severity scenarios based on route feasibility, shedding light on the dynamic nature of flood impact, and concludes by highlighting the influence of flood changes on the importance of specific road segments in network planning and operation.

  PublisherDOI

• Spatio-Temporal Solar Power Forecasting Using GAN-Enhanced Irradiance Maps and Deep Neural Networks

  SSRN Electronic Journal · 2026-01-01

  preprintOpen accessSenior author
  PublisherDOI

• Load Forecasting Using Recurrent and Transformer Neural Networks: A Comprehensive Analysis Across Multi-Time Scales

  2025-07-27

  articleSenior author

  Load forecasting plays a vital role in energy generation, efficient distribution and reliable operation of the grid. This paper examines the learning capabilities of five different machine learning architectures to provide load forecasts across various time scales (hourly, daily, weekly and monthly) using multistep, autoregressive and walk-forward prediction strategies. Time series energy data from California ISO and hourly weather data from the National Solar Radiation Database have been used as raw data for model training and validation. Maximum absolute percentage error (MAPE) is used as the accuracy metric while the pairwise statistical significance is compared using the Diebold Mariano test. It has been observed that bidirectional versions of long short-term memory and gated recurrent units perform better than their unidirectional counterparts. For hourly load forecasts, transformer neural networks outperform all the other models using a walk-forward prediction strategy (with a MAPE of 1.482%) while the bidirectional models perform better for larger prediction horizons.

  PublisherDOI

• Fused Portfolio Optimization for Harnessing Marine Renewable Energy Resources

  SSRN Electronic Journal · 2025-01-01

  preprintOpen accessSenior author
  PublisherDOI

• A Decision-Making Framework for Multi-Year Planning and Scalable Infrastructure in Sustainable Aviation Fuel Supply Chains

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access
  PublisherDOI

Frequent coauthors

• Joseph F. DeCarolis

  North Central State College

  79 shared

• Victor Augusto Durães de Faria

  45 shared

• Luana Medeiros Marangon Lima

  Duke University

  33 shared

• Hadi Eshraghi

  North Carolina State University

  32 shared

• J.W. Marangon Lima

  28 shared

• Giancarlo Áquila

  Universidade Federal de Itajubá

  25 shared

• A. Sankarasubramanian

  North Carolina State University

  23 shared

• Edson de Oliveira Pamplona

  Instituto Superior de Administração e Gestão

  17 shared

Education

• Operations Research and Industrial Engineering, Mechanical Engineering

  The University of Texas at Austin

  2011