Name
Abstract | ||
|---|---|---|
ABSTRACTIn this work we address the problem of accelerating complex power-grid simulation through machine learning (ML). Specifically, we develop a framework, Smart-PGSim, which generates multitask-learning (MTL) neural network (NN) models to predict the initial values of variables critical to the problem convergence. MTL models allow information sharing when predicting multiple dependent variables while including customized layers to predict individual variables. We show that, to achieve the required accuracy, it is paramount to embed domain-specific constraints derived from the specific power-grid components in the MTL model. Smart-PGSim then employs the predicted initial values as a high-quality initial condition for the power-grid numerical solver (warm start), resulting in both higher performance compared to state-of-the-art solutions while maintaining the required accuracy. Smart-PGSim brings 2.60× speedup on average (up to 3.28×) computed over 10,000 problems, without losing solution optimality. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.5555/3433701.3433784 | The International Conference for High Performance Computing, Networking, Storage, and Analysis |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wenqian Dong | 1 | 0 | 0.34 |
| Zhen Xie | 2 | 0 | 0.34 |
| Gokcen Kestor | 3 | 148 | 14.25 |
| Li, Dong | 4 | 764 | 48.56 |