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.

翻译：光伏发电渗透率的不断提高给电力系统运行带来了显著的不确定性，这要求预测方法必须超越确定性点预测的范畴。本文提出了一种基于任意分位数循环神经网络（AQ-RNN）的多区域光伏发电概率预测框架。该模型将任意分位数预测范式与双通道循环架构相结合，通过空洞循环单元、基于分块的时间建模以及动态集成机制，共同处理序列特定信息和跨区域上下文信息。所提出的框架能够在单个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增强系统层面的鲁棒性。该方法使用来自欧洲259个区域、历时30年的小时级光伏发电数据进行评估，并与成熟的统计及神经概率基线模型进行比较。结果表明，该方法在预测精度、校准度和预测区间质量方面均取得持续改进，突显了所提方法在可再生能源主导的电力系统中进行不确定性感知能源管理与运行决策的适用性。