用单一模型实现多区域光伏发电任意分位数预测，提升电网调度精度。
Probabilistic Multi-Regional Solar Power Forecasting with Any-Quantile Recurrent Neural Networks
- 基于双轨循环结构融合区域特性和跨区域关联信息
- 在259个欧洲区域上30年小时级数据测试，分位数预测更准更稳
- 适合需要量化不确定性的可再生能源调度场景
光伏发电渗透率上升给电力系统运行带来显著不确定性，亟需超越确定性点预测的预报方法。本文提出一种基于任意分位数循环神经网络（AQ-RNN）的多区域光伏功率概率预测框架。该模型结合任意分位数预测范式与双轨循环架构，协同处理序列特异性与跨区域上下文信息，采用膨胀循环单元、基于块的时序建模及动态集成机制。所提框架可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提升系统级鲁棒性。基于259个欧洲区域30年小时级光伏出力数据进行评估，对比经典统计与神经概率基线模型，结果表明在预测准确性、校准度及预测区间质量上均有持续提升，验证了该方法在可再生能源主导电力系统中不确定性感知的能源管理与运行决策中的适用性。
原文摘要 · Abstract (English)
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.
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