Extracting and Generating PV Soiling Profiles for Analysis, Forecasting, and Cleaning Optimization

Matthew Muller, Leonardo Micheli, Eduardo Fernandez, Florencia Almonacid

Research output: Contribution to journalArticlepeer-review

33 Scopus Citations


The identification and prediction of the daily soiling profiles of a photovoltaic site is essential to plan the optimal cleaning schedule. In this article, we analyze and propose various methods to extract and generate photovoltaic soiling profiles, in order to improve the analysis and the forecast of the losses. New soiling rate extraction methods are proposed to reflect the seasonal variability of the soiling rates and, for this reason, are found to identify the most convenient cleaning day with the highest accuracy for the investigated sites. Also, we present an approach that could be used to predict future soiling losses through the implementation of stochastic weather generation algorithms whose ability to identify in advance the best cleaning schedule is also successfully tested. The methods presented in this article can optimize the operation and maintenance schedule and could make it possible, in the future, to predict soiling losses through analysis based only on environmental parameters, such as rainfall and particulate matter, without the need of long-term soiling data.

Original languageAmerican English
Article number8880477
Pages (from-to)197-205
Number of pages9
JournalIEEE Journal of Photovoltaics
Issue number1
StatePublished - Jan 2020

Bibliographical note

Publisher Copyright:
© 2019 IEEE.

NREL Publication Number

  • NREL/JA-5K00-75844


  • Field performance
  • optimization
  • photovoltaic (PV) systems
  • prediction methods
  • soiling
  • solar energy
  • stochastic processes
  • time series analysis


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