skip to main content

Explore Research Products in the NSF-PAR

  • Home
  • Search Results
  • Page 1 of 5

Search for: All records

Creators/Authors contains: "Fioretto, Ferdinando"

Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

  1. ; ( , Advances in neural information processing systems)
    Free, publicly-accessible full text available February 1, 2025
  2. ; ( , Advances in neural information processing systems)
    Free, publicly-accessible full text available February 1, 2025
  3. ; ; ( , arXivorg)
    Free, publicly-accessible full text available February 12, 2025
  4. ; ; ( , arXivorg)
    Free, publicly-accessible full text available February 6, 2025
  5. ; ; ( , arXivorg)
    Free, publicly-accessible full text available February 7, 2025
  6. ; ; ( , arXivorg)
    Free, publicly-accessible full text available February 5, 2025
  7. ; ; ; ; ( , Proceedings of the AAAI Conference on Artificial Intelligence)
    Free, publicly-accessible full text available February 18, 2025
  8. ; ; ; ( , arXivorg)
    Free, publicly-accessible full text available December 28, 2024
  9. ; ; ; ; ( , arXivorg)
    Free, publicly-accessible full text available December 6, 2024
  10. ; ; ; ( , IEEE PES Conference On Innovative Smart Grid Technologies Latin America)
    Optimal Power Flow (OPF) is a challenging problem in power systems, and recent research has explored the use of Deep Neural Networks (DNNs) to approximate OPF solutions with reduced computational times. While these approaches show promising accuracy and efficiency, there is a lack of analysis of their robustness. This paper addresses this gap by investigating the factors that lead to both successful and suboptimal predictions in DNN-based OPF solvers. It identifies power system features and DNN characteristics that contribute to higher prediction errors and offers insights on mitigating these challenges when designing deep learning models for OPF. 
    more » « less
    Free, publicly-accessible full text available November 6, 2024