# Unlocking the Potential of Renewable Energy Through Curtailment Prediction

arXiv: https://arxiv.org/abs/2405.18526 · HTML: https://mlsanity.com/paper/2405.18526/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 May 2024.

## Built on by (most cited first, 2 of 2)

- [Beyond Efficiency: Scaling AI Sustainably](https://mlsanity.com/paper/2406.05303/) (2024)
- [Distributed LLM Pretraining During Renewable Curtailment Windows: A Feasibility Study](https://mlsanity.com/paper/2602.22760/) (2026)

## Abstract (arXiv)

A significant fraction (5-15%) of renewable energy generated goes into waste in the grids around the world today due to oversupply issues and transmission constraints. Being able to predict when and where renewable curtailment occurs would improve renewable utilization. The core of this work is to enable the machine learning community to help decarbonize electricity grids by unlocking the potential of renewable energy through curtailment prediction.