Assessing Aerosol Impact on Digital Twin Solar Irradiance Forecasts and on PV Power Prediction

For many critical energy-related applications, such as reliable PV power production, accurate Global Horizontal Irradiance (GHI) short-term forecasts are crucial. Forecasts of GHI, as analyzed in this study, are produced by the state-of-the-art, high-resolution forecasts developed within the Destination Earth initiative, the ECMWF Digital Twin Engine using the ExtremeDT (Weather-Induced Extremes Digital Twin) dataset, that produces daily global simulations at resolutions of 4.4 km kilometres up to four days ahead. However the GHI ExtremesDT forecasts do not take into account the aerosol effects, which may introduce systematic biases, especially during periods of high aerosol load events. We investigated the impact of aerosols on DT GHI forecasts and the associated PV power predictions within the context of the DestinE Destination Renewable Energy (DRE) use case.
We analyze almost one year of data comprising two-day-ahead DT GHI forecasts and the corresponding aerosol optical depth (AOD) forecasts from the Copernicus Atmosphere Monitoring Service (CAMS). The DT GHI forecasts are corrected for aerosol effects using fast radiative transfer model techniques utilising the lidRadtran [1, 2] package.

Within the DRE project,  and based on co-design activities with the project's end user, site-specific PV power production forecasts tailored to the user’s needs and infrastructure were developed, using DT GHI forecasts and a machine learning (ML) model that was trained on the user’s historical data. The impact of aerosol correction was also evaluated by comparing PV power production forecasts derived from GHI forecasts with and without aerosol correction against the actual PV power production of the plant.

A machine learning (ML) model, trained on historical, site-specific production data from the QUEST PV park, was created within the DRE project to convert predicted solar irradiance into PV park power output in order to evaluate the impact on power production. The ML model propagates the original and aerosol-corrected GHI forecasts, which are then compared to actual production.

The results show how CAMS-based aerosol correction of GHI forecasts can reduce bias  and consistently improve PV power prediction. Results show the value of incorporating atmospheric composition data with ML-based power conversion models for operational energy applications, as well as the significance of aerosol representation in solar forecasting.

Acknowledgments

DRE project has received funding from the European Space Agency under the DESTINATION EARTH USE CASES – DESP USE CASES - ROUND 1. The duration of the project is 12 months (November 2023 - November 2024).

We would like also to acknowledge the COST Action HARMONIA (International network for harmonization of atmospheric aerosol retrievals from ground based photometers), CA21119.

Bibliography

(1) Mayer, B.; Kylling, A. Atmospheric Chemistry and Physics 2005, 5, 1855–1877.
(2) Emde, C.; Buras-Schnell, R.; Kylling, A.; Mayer, B.; Gasteiger, J.; Hamann, U.; Kylling, J.; Richter, B.; Pause, C.; Dowling, T.; Bugliaro, L. Geoscientific Model Development 2016, 9, 1647–1672.