ExARNN: An Environment-Driven Adaptive RNN for Learning Non-Stationary Power Dynamics
This addresses the challenge of accurate power system forecasting for grid operators and energy planners, though it appears incremental as it builds on existing RNN and hypernetwork methods.
The paper tackles the problem of modeling non-stationary power system dynamics influenced by renewable energy and environmental factors by proposing ExARNN, an adaptive RNN framework that integrates external data like weather and time to adjust parameters continuously, achieving superior forecasting performance over baseline models.
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.