# ExARNN: An Environment-Driven Adaptive RNN for Learning Non-Stationary Power Dynamics

arXiv: https://arxiv.org/abs/2505.17488 · HTML: https://mlsanity.com/paper/2505.17488/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 31 references that are in the map.
- First posted on arXiv 23 May 2025.

## Builds on (most cited first, 7 of 7)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Neural Ordinary Differential Equations](https://mlsanity.com/paper/1806.07366/) (2018)
- [Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting](https://mlsanity.com/paper/1506.04214/) (2015)
- [HyperNetworks](https://mlsanity.com/paper/1609.09106/) (2016)
- [Recurrent Neural Networks for Multivariate Time Series with Missing Values](https://mlsanity.com/paper/1606.01865/) (2016)
- [Neural Controlled Differential Equations for Irregular Time Series](https://mlsanity.com/paper/2005.08926/) (2020)
- [Tackling Climate Change with Machine Learning](https://mlsanity.com/paper/1906.05433/) (2019)

## Built on by (most cited first, 8 of 8)

- [Latent Mixture of Symmetries for Sample-Efficient Dynamic Learning](https://mlsanity.com/paper/2510.03578/) (2025)
- [External Data-Enhanced Meta-Representation for Adaptive Probabilistic Load Forecasting](https://mlsanity.com/paper/2506.23201/) (2025)
- [Neural Predictive Control to Coordinate Discrete- and Continuous-Time Models for Time-Series Analysis with Control-Theoretical Improvements](https://mlsanity.com/paper/2508.01833/) (2025)
- [Modeling Time Series Dynamics with Fourier Ordinary Differential Equations](https://mlsanity.com/paper/2510.04133/) (2025)
- [M2OE2-GL: A Family of Probabilistic Load Forecasters That Scales to Massive Customers](https://mlsanity.com/paper/2511.17623/) (2025)
- [Cross-Domain Generalization of Multimodal LLMs for Global Photovoltaic Assessment](https://mlsanity.com/paper/2511.19537/) (2025)
- [Enabling Scalable Topology Inference in Distribution Systems via Constrained Multi-Source Inference](https://mlsanity.com/paper/2607.20480/) (2026)
- [From Shared Demand Patterns to Local Uncertainty: Probabilistic Load Forecasting by Mixing Compact Adaptations](https://mlsanity.com/paper/2610.08538/) (2026)

## Abstract (arXiv)

Non-stationary power system dynamics, influenced by renewable energy variability, evolving demand patterns, and climate change, are becoming increasingly complex. Accurately capturing these dynamics requires a model capable of adapting to environmental factors. Traditional models, including Recurrent Neural Networks (RNNs), lack efficient mechanisms to encode external factors, such as time or environmental data, for dynamic adaptation. To address this, we propose the External Adaptive RNN (ExARNN), a novel framework that integrates external data (e.g., weather, time) to continuously adjust the parameters of a base RNN. ExARNN achieves this through a hierarchical hypernetwork design, using Neural Controlled Differential Equations (NCDE) to process external data and generate RNN parameters adaptively. This approach enables ExARNN to handle inconsistent timestamps between power and external measurements, ensuring continuous adaptation. Extensive forecasting tests demonstrate ExARNN's superiority over established baseline models.