Presentation Details
| Benchmarking of long-term degradation methods and their impact on accurate performance modeling for multiple PV technologies Juergen Sutterlueti1, Jesus Montes-Romero2, Nino Heinzle Heinzle1, Paris Kaimakis3, 4, Andreas Livera3, 4, George Makrides3, 4, George E.Georghiou3, 4. 1Gantner Instruments GmbH, Montafonerstraße 4, 6780 Schruns, Austria, Schruns, Austria.2Advances in Photovoltaic Technology (AdPVTech), University of Jaen (UJA), 23071 Jaen, Spain, Jaen, Spain.3PHAETHON Centre of Excellence (CoE) for Intelligent, Efficient and Sustainable Energy Solutions, De, Nicosia, Cyprus |
Abstract
Digital twin (DT) technologies offer a powerful paradigm for continuously modeling photovoltaic (PV) system performance and capturing long-term degradation effects under real operating conditions. Even though many physics-based and machine learning predictive models have been proposed, a key challenge remains the absence of accurate, transferable, and location-independent PV system short- and long-term performance predictive models. This work presents a digital twin–based performance modeling framework for short- and long-term assessment of PV systems, integrating data enrichment and hybrid mechanistic and time series analytics techniques to capture operational behavior and performance evolution from high-resolution monitoring data. The obtained results demonstrate that the proposed predictive DT model attains high accuracy, with the average root mean square error (RMSE), normalized to the system’s nominal capacity, remaining below 2%. The application of time-series analytical techniques to the degradation assessment revealed that the estimated degradation rates were in close agreement with the theoretical degradation behavior of the investigated systems over a 15-year period. Finally, the proposed approach supports the development of effective digital workflows for short- and long-term performance evaluation, forecasting and lifecycle management of utility-scale PV assets.
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No part of this publication may be reproduced, distributed, or transmitted in any form or by any means, including photocopying, recording, or other electronic or mechanical methods, without the prior written permission of the author.