arXiv (All)
open access
Stochastic Model Predictive Control under AC Power-Flow Constraints Using Generative Learning
This paper proposes an end-to-end generative framework for efficiently solving multi-period and multi-scenario stochastic model predictive control (SMPC) problems under nonlinear AC power-flow constraints. Conventional deterministic neural surrogates rely on a single-shot prediction, which makes rel
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Cyber Attack Prediction From Traditional Machine Learning to Generative Artificial Intelligence#186927
- Generative Model Unlearning: A Survey through Target Events, Unlearning Operators, and Evaluation Protocols#609804
- When the World Lies: Backdoor Attacks on Latent World Models for Downstream Control#1109740
- When the World Lies: Backdoor Attacks on Latent World Models for Downstream Control#919282
- Hybrid safe deep reinforcement learning and model predictive control for power distribution grids management system under uncertainties#643263
Record · ID 984357
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive