迈向连续功率预测：非平稳时间序列中实际能源系统的实用持续学习
Towards Continuous Power Forecasting: Practical Continual Learning for Real-World Energy Systems in Nonstationary Time Series
AI总结：
针对实际能源市场中功率预测模型面临的数据非平稳性和操作约束，提出连续功率预测范式，将预测视为持续学习问题，系统评估六种代表性持续学习方法，证明其能自适应分布漂移、积累知识并缓解灾难性遗忘。
摘要：
部署在真实能源市场中的功率预测模型必须在非平稳条件下运行，由于天气变化、基础设施升级和消费行为演变，数据分布持续变化。在实践中，这些模型面临严格的操作约束：历史数据可能有限或无法用于重复训练，且通常需要不间断的长期服务。本文通过提出连续功率预测范式来应对这些挑战，该范式将功率预测视为持续学习问题而非静态离线任务。基于自适应的回归持续学习框架，我们系统研究了来自三个方法论类别的六种代表性持续学习方法的实际有效性。这些方法在关于数据可访问性和更新策略的不同现实假设下进行评估。在真实功率数据集上的实验验证表明，持续学习使预测模型能够自适应分布漂移，随时间积累知识，并在不依赖大规模历史数据存储的情况下缓解灾难性遗忘。除了性能提升，我们的研究还提供了关于不同持续学习方法在实际操作约束下的稳定性和自适应行为的实用见解。总体而言，这项工作展示了如何将持续学习实际集成到工业功率预测管道中，为动态环境中的长期部署提供可扩展且可持续的解决方案。
英文摘要：
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 forecasting as a continual learning problem rather than a static offline task. Based on an adaptive continual learning framework for regression, we systematically investigate the practical effectiveness of six representative continual learning approaches from three methodological categories. These approaches are evaluated under different realistic assumptions regarding data accessibility and update policies. Experimental validation on real-world power datasets demonstrates that continual learning enables forecasting models to self-adapt to distributional drift, accumulate knowledge over time, and mitigate catastrophic forgetting without relying on large-scale historical data storage. Beyond performance gains, our study provides practical insights into the stability and adaptation behaviors of different continual learning approaches under realistic operational constraints. Overall, this work illustrates how continual learning can be pragmatically integrated into industrial power forecasting pipelines, offering a scalable and sustainable solution for long-term deployment in dynamic environments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
