1 citations · 1 across the 5 of their papers we have counts for
6 papers
Two-Stage Data-Driven Contextual Robust Optimization: An End-to-End Learning Approach for Online Energy Applications
Carlos Gamboa, Alexandre Street, Davi Valladão +1
Traditional end-to-end contextual robust optimization models are trained for specific contextual data, requiring complete retraining whenever new contextual information arrives. Th…
Assessing the Optimistic Bias in the Natural Inflow Forecasts: A Call for Model Monitoring in Brazil
Arthur Brigatto, Alexandre Street, Cristiano Fernandes +3
Hydroelectricity accounted for roughly 61.4% of Brazil's total generation in 2024 and addressed most of the intermittency of wind and solar generation. Thus, inflow forecasting pla…
Time Series Analysis by State Space Learning
André Ramos, Davi Valladão, Alexandre Street
Time series analysis by state-space models is widely used in forecasting and extracting unobservable components like level, slope, and seasonality, along with explanatory variables…
Efficiently Training Deep-Learning Parametric Policies using Lagrangian Duality
Andrew Rosemberg, Alexandre Street, Davi M. Valladão +1
Constrained Markov Decision Processes (CMDPs) are critical in many high-stakes applications, where decisions must optimize cumulative rewards while strictly adhering to complex non…
Assessing the Cost of Network Simplifications in Long-Term Hydrothermal Dispatch Planning Models
Andrew W. Rosemberg, Alexandre Street, Joaquim Dias Garcia +3
The sustainable utilization of hydro energy relies on accurate estimates of the opportunity cost of the water. This value is calculated through long-term hydrothermal dispatch prob…
Short-Term Covid-19 Forecast for Latecomers
Marcelo Medeiros, Alexandre Street, Davi Valladão +2
The number of Covid-19 cases is increasing dramatically worldwide. Therefore, the availability of reliable forecasts for the number of cases in the coming days is of fundamental im…