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Accurate intraday forecasts of the power output by PhotoVoltaic (PV) systemsare critical to improve the operation of energy distribution grids. We describea neural autoregressive model which aims at performing such intraday forecasts.We build upon a physical, deterministic PV performance model, the output ofwhich 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 theinformation about the PV site in specific covariates. We use a scale-freeapproach which does rely on explicit modelling of seasonal effects. Ourproposal repurposes a model initially used in the retail sector, and disclosesa novel truncated Gaussian output distribution. An ablation study and acomparison to alternative architectures from the literature shows that thecomponents in the best performing proposed model variant work synergisticallyto reach a skill score of 15.72% with respect to the physical model, used as abaseline.

Forecasting Intraday Power Output by a Set of PV Systems using Recurrent Neural Networks and Physical Covariates