Towards Continuous Power Forecasting: Practical Continual Learning for Real-World Energy Systems in Nonstationary Time Series
arXiv:2606.24955v1 Announce Type: new Abstract: Power forecasting models deployed in real-world energy markets must operate under nonstationary conditions, where data distributions continually evolve due to weather variability, infrastructure upgrades, and changing consumption behaviors. In practice, these models face strict operational constraints: historical data may be limited or unavailable for repeated retraining, and uninterrupted long-term service is often required. This paper addresses these challenges by proposing the paradigm of Continuous Power Forecasting, which views power forecas
The increasing complexity of energy grids, coupled with the imperative for renewable energy integration and real-time market dynamics, demands more adaptive forecasting. This research reflects the growing urgency for AI capable of continuous learning in volatile environments.
Reliable and continuous power forecasting is crucial for grid stability, efficient energy markets, and the integration of intermittent renewable sources, directly impacting economic stability and climate goals.
Traditional batch learning models are becoming obsolete for critical infrastructure; the shift towards continuous learning models means energy forecasting will be more dynamic and resilient to change. This will accelerate more dynamic distribution of energy resources.
- · Renewable energy operators
- · Smart grid technology providers
- · AI/ML developers specializing in time series
- · Energy market participants
- · Legacy energy forecast providers
- · Energy systems reliant on static models
- · Fossil fuel generators that cannot react quickly
Improved power grid stability and efficiency as forecasting adapts to real-time conditions.
Accelerated adoption of distributed renewable energy sources due to better predictability and management.
Enhanced resilience of national infrastructure against climate variability and cyber threats to energy systems.
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