Accurate intraday forecasts of the power output by PhotoVoltaic (PV) systems are critical to improve the operation of energy distribution grids. We describe a neural autoregressive model which aims at performing such intraday forecasts. We build upon a physical, deterministic PV performance model, the output of which being used as covariates in the context of the neural model. In addition, our application data relates to a geographically distributed set of PV systems. We address all PV sites with a single neural model, which embeds the information about the PV site in specific covariates. We use a scale-free approach which does rely on explicit modelling of seasonal effects. Our proposal repurposes a model initially used in the retail sector, and discloses a novel truncated Gaussian output distribution. An ablation study and a comparison to alternative architectures from the literature shows that the components in the best performing proposed model variant work synergistically to reach a skill score of 15.72% with respect to the physical model, used as a baseline.

翻译：准确预测光伏（PV）系统日内出力对改善配电网运行至关重要。本文描述了一种用于实现此类日内预测的神经自回归模型。该模型建立在确定性光伏物理性能模型基础上，将其输出作为神经模型的协变量使用。实验数据涉及地理分布式光伏系统群，我们采用单一神经网络模型处理所有光伏站点，通过特定协变量嵌入站点信息。本方法采用无尺度化策略，无需显式建模季节效应。该模型源自零售领域并经过改造，同时提出了一种新颖的截断高斯输出分布。消融实验及与文献中替代架构的比较表明，在最优性能模型中各组件协同作用，相较于作为基准的物理模型达到了15.72%的技能评分。