Abstract
The increasing penetration of photovoltaic (PV) generation introduces significant uncertainty into power system operation, necessitating forecasting approaches that extend beyond deterministic point predictions. This paper proposes an any-quantile probabilistic forecasting framework for multi-regional PV power generation based on the Any-Quantile Recurrent Neural Network (AQ-RNN). The model integrates an any-quantile forecasting paradigm with a dual-track recurrent architecture that jointly processes series-specific and cross-regional contextual information, supported by dilated recurrent cells, patch-based temporal modeling, and a dynamic ensemble mechanism. The proposed framework enables the estimation of calibrated conditional quantiles at arbitrary probability levels within a single trained model and effectively exploits spatial dependencies to enhance robustness at the system level. The approach is evaluated using 30 years of hourly PV generation data from 259 European regions and compared against established statistical and neural probabilistic baselines. The results demonstrate consistent improvements in forecast accuracy, calibration, and prediction interval quality, underscoring the suitability of the proposed method for uncertainty-aware energy management and operational decision-making in renewable-dominated power systems.
Abstract (translated)
光伏（PV）发电渗透率的增加给电力系统操作带来了显著的不确定性，因此需要超越确定性点预测的方法来进行预报。本文提出了一种基于任意分位数递归神经网络（AQ-RNN）的多区域光伏发电概率预报框架。该模型结合了任意分位数预测方法和双轨递归架构，同时处理特定序列信息及跨地区背景信息，并通过膨胀递归细胞、基于块的时间建模以及动态集成机制来支持这一过程。所提出的框架能够在单一训练模型中估计任意概率水平下的校准条件分位数，并有效利用空间依赖性以提高系统的整体稳健性。 该方法使用来自欧洲259个区域的30年每小时光伏发电数据进行了评估，并与现有的统计和神经网络概率基线方法进行了比较。结果显示，在预报准确性、校准以及预测区间质量方面均有持续改进，这表明所提出的方法对于不确定性感知能源管理和可再生能源主导电力系统的运营决策具有适用性。
URL
https://arxiv.org/abs/2602.05660
