Predicting AC Optimal Power Flows: Combining Deep Learning and Lagrangian Dual Methods
Authors: Ferdinando Fioretto, Terrence W.K. Mak, Pascal Van Hentenryck630-637
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The proposed model is evaluated on a large collection of realistic mediumsized power systems. The experimental results show that its predictions are highly accurate with average errors as low as 0.2%. |
| Researcher Affiliation | Academia | 1Georgia Institute of Technology, 2Syracuse University ffiorett@syr.edu, wmak@gatech.edu, pvh@isye.gatech.edu |
| Pseudocode | Yes | Algorithm 1: Learning Step |
| Open Source Code | No | The paper does not provide a direct link to a code repository or explicitly state that the source code for the described methodology is being released. |
| Open Datasets | Yes | The experiments examine the proposed models on a variety of power networks from the NESTA library (Coffrin, Gordon, and Scott 2014). |
| Dataset Splits | Yes | The experiments use a 80{20 train-test split and report results on the test set. |
| Hardware Specification | No | No specific hardware details (like GPU/CPU models, memory) are mentioned for running the experiments. The paper only mentions 'GPU memory' in the conclusion, but not as part of the experimental setup. |
| Software Dependencies | No | The paper mentions using the Adam optimizer, but no specific software names with version numbers (e.g., PyTorch, TensorFlow, or specific solvers like CPLEX) are provided. |
| Experiment Setup | Yes | The training uses the Adam optimizer with learning rate (α 0.001) and β values p0.9, 0.999q and was performed for 80 epochs using batch sizes b 64. Finally, the Lagrangian step size ρ is set to 0.01. |