Forecasting intraday power output by a set of PV systems using recurrent neural networks and physical covariates

Authors

Bruneau P., Fiorelli D., Braun C., Koster D.

Reference

Neural Computing and Applications, vol. 36, n° 31, pp. 19515-19529, 2024

Description

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 that aims to perform such intraday forecasts. We build upon a physical, deterministic PV performance model, the output of which is used as covariates in the context of the neural model. In addition, our application data relate 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 relies on the explicit modeling 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 show 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.

Link

doi:10.1007/s00521-024-10257-4