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

TL;DR

ExARNN introduces an environment-driven adaptive RNN framework that leverages external data and hierarchical hypernetworks with NCDEs to improve modeling of non-stationary power system dynamics.

Contribution

The paper presents a novel ExARNN model that adaptively adjusts RNN parameters using external environmental data via hierarchical hypernetworks and NCDEs.

Findings

ExARNN outperforms baseline models in power forecasting tasks.

The model effectively handles inconsistent timestamps in data.

ExARNN demonstrates superior adaptability to environmental changes.

Abstract

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.