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

• URL: http://arxiv.org/abs/2303.08459v2
• Date: Tue, 12 Dec 2023 16:31:34 GMT
• Title: Forecasting Intraday Power Output by a Set of PV Systems using Recurrent Neural Networks and Physical Covariates
• Authors: Pierrick Bruneau, David Fiorelli, Christian Braun, Daniel Koster
• Abstract summary: Accurate 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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