https://doi.org/10.1051/epjpv/2023018
Regular Article
Long-term PV system modelling and degradation using neural networks
1
GreenPowerMonitor a DNV company, Gran Via de les Corts Catalanes, 130, Barcelona, Spain
2
DNV Denmark, Tuborg Parkvej 8, Hellerup, Denmark
* e-mail: gerardo.guerra@dnv.com
Received:
27
June
2023
Received in final form:
17
August
2023
Accepted:
25
August
2023
Published online: 23 October 2023
The power production of photovoltaic plants can be affected throughout its operational lifetime by multiple losses and degradation mechanisms. Although long-term degradation has been widely studied, most methodologies assume a specific degradation behaviour and require detailed metadata. This paper presents a methodology for the calculation of long-term degradation of a photovoltaic plant based on neural networks. The goal of the neural network is to model the photovoltaic plant's power production as a function of environmental conditions and time elapsed since the plant started operating. A big advantage of this method with respect to others is that it is completely data-driven, requires no additional information, and makes no assumptions related to degradation behaviour. Results show that the model can derive a long-term degradation trend without overfitting to shorter-term effects or abrupt changes in year-to-year operation.
Key words: Photovoltaic generation / long-term degradation / neural networks / machine learning / automatic differentiation
© G. Guerra et al., Published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
