结合天气预报聚合与状态空间模型的自适应概率电力负荷预测

中文摘要 AI 辅助

准确的电力负荷预测对于确保供需实时平衡至关重要，尤其是在受天气条件和可再生能源整合影响日益加深的系统中。本文提出了一种自适应概率预测框架，利用多个气象预报提供商来减轻天气预报误差对负荷预测的影响。我们的方法将自适应广义加性模型（GAM）与通过Viking算法估计的状态空间公式相结合，实现参数随时间的动态更新。为了改进不确定性量化，我们扩展了框架，增加了一个基于GAM的位置、尺度和形状（GAMLSS）概率层，使残差方差依赖于解释变量。我们进一步引入了一种短期修正机制，以提升不同预测时域的性能。本工作的一项关键贡献是聚合多个气象输入，其多样性被证明能改善点预测和概率预测，特别是在通过纳入预报离散度来捕捉极端分位数方面。该方法在法国国家电力消费数据上进行了评估，并显示出相对于单一提供商方法的持续增益，在概率预测准确性方面有显著提升。

英文摘要

Accurate electricity load forecasting is essential to ensure the real-time balance between supply and demand, especially in systems increasingly influenced by weather conditions and renewable energy integration. In this paper, we propose an adaptive probabilistic forecasting framework that leverages multiple meteorological forecast providers to mitigate the impact of weather prediction errors on load forecasts. Our approach combines an adaptive generalized additive model (GAM) with a state-space formulation estimated via the Viking algorithm, enabling dynamic parameter updates over time. To improve uncertainty quantification, we extend the framework with a probabilistic layer based on GAM for location, scale and shape (GAMLSS), allowing the residual variance to depend on explanatory variables. We further introduce a short-term correction mechanism to enhance performance across different forecast horizons. A key contribution of this work is the aggregation of multiple meteorological inputs, whose diversity is shown to improve both point and probabilistic forecasts, particularly in capturing extreme quantiles by incorporating forecast dispersion. The methodology is evaluated on French national electricity consumption data and demonstrates consistent gains over single-provider approaches, with significant improvements in probabilistic forecasting accuracy.

发表机构

• Viking Conseil
• MétéoFrance（法国气象局）

机构由 AI 辅助整理，请以论文原文为准。