NP-NDS: A Nature-Powered Nonlinear Dynamical System for Power Grid Forecasting

Chunshu Wu, Ruibing Song, Chuan Liu, Yuqing Wang, Yousu Chen, Ang Li, Dongfang Liu, Ying Nian Wu, Michael Huang, Tong Geng

Keywords: dynamical system, nature-powered computing, power grid

TL;DR: Using an artificial, microscopic dynamical system to model and predict the behavior of power grid, which is considered as a macroscopic dynamical system in the real world.

Abstract: The power grid is a critical dynamical system that forms the backbone of modern society, powering everything from household appliances to complex industrial machinery. However, this essential system is not without vulnerabilities -- as electricity travels at lightspeed, unanticipated failures can cause catastrophic consequences such as country-wide blackouts in a cascading manner. In response to such threats, we introduce NP-NDS, a nature-powered nonlinear dynamical system designed to accurately and rapidly predict power grids as macroscopic dynamical systems in the real world. In particular, NP-NDS is established through a Hamiltonian-Hardware co-design: First, NP-NDS employs a hardware-friendly serial-additive Hamiltonian based on Chebyshev series for accurately capturing highly nonlinear interactions among power grid nodes, coupled with node-relation-aware training for high accuracy. Second, NP-NDS features a fully CMOS-based hardware dynamical system governed by the proposed Hamiltonian, facilitating inferences with "speed of electrons". Results show that NP-NDS achieves, on average, $2.3\times 10^3$ speedup and $10^5\times$ energy reduction with 23.6% and 28.2% decrease in MAE and RMSE compared to GNNs on power grid forecasting datasets.

Submission Type: Full paper proceedings track submission (max 9 main pages).

Submission Number: 74