# From Black Box to Clarity: AI-Powered Smart Grid Optimization with Kolmogorov-Arnold Networks

arXiv: https://arxiv.org/abs/2408.04063 · HTML: https://mlsanity.com/paper/2408.04063/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Aug 2024.

## Built on by (most cited first, 3 of 3)

- [The Unseen AI Disruptions for Power Grids: LLM-Induced Transients](https://mlsanity.com/paper/2409.11416/) (2024)
- [KAN versus MLP on Irregular or Noisy Functions](https://mlsanity.com/paper/2408.07906/) (2024)
- [Explainable fault and severity classification for rolling element bearings using Kolmogorov-Arnold networks](https://mlsanity.com/paper/2412.01322/) (2024)

## Abstract (arXiv)

This work is the first to adopt Kolmogorov-Arnold Networks (KAN), a recent breakthrough in artificial intelligence, for smart grid optimizations. To fully leverage KAN's interpretability, a general framework is proposed considering complex uncertainties. The stochastic optimal power flow problem in hybrid AC/DC systems is chosen as a particularly tough case study for demonstrating the effectiveness of this framework.