Continual Learning Boosts Power Forecasting in Dynamic Energy Systems.

New research proposes a paradigm shift for power forecasting in energy markets, reframing it as a "continual learning" challenge rather than a static task. Traditional models struggle with the nonstationary nature of energy data, which constantly changes due to factors like weather, infrastructure, and consumption patterns. Furthermore, real-world deployments face strict limitations on historical data availability and the need for uninterrupted service. The study investigates the practical efficacy of six diverse continual learning approaches within an adaptive framework for regression. These methods were tested under various realistic data access and update policy scenarios using actual power datasets. The findings indicate that continual learning enables forecasting models to autonomously adapt to evolving data distributions, accumulate knowledge over time, and prevent "catastrophic forgetting" without requiring extensive historical data storage. This work offers valuable insights into integrating continual learning into industrial power forecasting pipelines, providing a scalable and sustainable solution for dynamic environments.