# Knowledge- and Data-driven Services for Energy Systems using Graph Neural Networks

arXiv: https://arxiv.org/abs/2103.07248 · HTML: https://mlsanity.com/paper/2103.07248/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 25 references that are in the map.
- First posted on arXiv 12 Mar 2021.

## Builds on (most cited first, 10 of 10)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [Relational inductive biases, deep learning, and graph networks](https://mlsanity.com/paper/1806.01261/) (2018)
- [Spatially-Aware Graph Neural Networks for Relational Behavior Forecasting from Sensor Data](https://mlsanity.com/paper/1910.08233/) (2019)
- [Real-time Power System State Estimation and Forecasting via Deep Neural Networks](https://mlsanity.com/paper/1811.06146/) (2018)
- [Data-Driven Learning-Based Optimization for Distribution System State Estimation](https://mlsanity.com/paper/1807.01671/) (2018)
- [Scalable Deployment of AI Time-series Models for IoT](https://mlsanity.com/paper/2003.12141/) (2020)
- [Distributed Gauss-Newton Method for AC State Estimation: A Belief Propagation Approach](https://mlsanity.com/paper/1605.08336/) (2016)
- [Probabilistic Graphs for Sensor Data-driven Modelling of Power Systems at Scale](https://mlsanity.com/paper/1811.07267/) (2018)
- [AI Modelling and Time-series Forecasting Systems for Trading Energy Flexibility in Distribution Grids](https://mlsanity.com/paper/1909.10870/) (2019)

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

- [Graph Isomorphic Networks for Assessing Reliability of the Medium-Voltage Grid](https://mlsanity.com/paper/2310.01181/) (2023)

## Abstract (arXiv)

The transition away from carbon-based energy sources poses several challenges for the operation of electricity distribution systems. Increasing shares of distributed energy resources (e.g. renewable energy generators, electric vehicles) and internet-connected sensing and control devices (e.g. smart heating and cooling) require new tools to support accurate, datadriven decision making. Modelling the effect of such growing complexity in the electrical grid is possible in principle using state-of-the-art power-power flow models. In practice, the detailed information needed for these physical simulations may be unknown or prohibitively expensive to obtain. Hence, datadriven approaches to power systems modelling, including feedforward neural networks and auto-encoders, have been studied to leverage the increasing availability of sensor data, but have seen limited practical adoption due to lack of transparency and inefficiencies on large-scale problems. Our work addresses this gap by proposing a data- and knowledge-driven probabilistic graphical model for energy systems based on the framework of graph neural networks (GNNs). The model can explicitly factor in domain knowledge, in the form of grid topology or physics constraints, thus resulting in sparser architectures and much smaller parameters dimensionality when compared with traditional machine-learning models with similar accuracy. Results obtained from a real-world smart-grid demonstration project show how the GNN was used to inform grid congestion predictions and market bidding services for a distribution system operator participating in an energy flexibility market.