Forecasting Intraday Power Output by a Set of PV Systems using Recurrent Neural Networks and Physical Covariates
This work addresses the problem of improving energy grid operation through more accurate PV power forecasts, but it is incremental as it adapts an existing model from another sector with modifications.
The paper tackled intraday power output forecasting for a set of photovoltaic (PV) systems by developing a neural autoregressive model that integrates physical covariates and site-specific information, achieving a skill score of 15.72% relative to a baseline physical model.
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 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 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 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.