# AI Modelling and Time-series Forecasting Systems for Trading Energy Flexibility in Distribution Grids

arXiv: https://arxiv.org/abs/1909.10870 · HTML: https://mlsanity.com/paper/1909.10870/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Sep 2019.

## Built on by (most cited first, 1 of 1)

- [Knowledge- and Data-driven Services for Energy Systems using Graph Neural Networks](https://mlsanity.com/paper/2103.07248/) (2021)

## Abstract (arXiv)

We demonstrate progress on the deployment of two sets of technologies to support distribution grid operators integrating high shares of renewable energy sources, based on a market for trading local energy flexibilities. An artificial-intelligence (AI) grid modelling tool, based on probabilistic graphs, predicts congestions and estimates the amount and location of energy flexibility required to avoid such events. A scalable time-series forecasting system delivers large numbers of short-term predictions of distributed energy demand and generation. We discuss the deployment of the technologies at three trial demonstration sites across Europe, in the context of a research project carried out in a consortium with energy utilities, technology providers and research institutions.