Abstract
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 language | American English |
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Article number | 8880477 |
Pages (from-to) | 197-205 |
Number of pages | 9 |
Journal | IEEE Journal of Photovoltaics |
Volume | 10 |
Issue number | 1 |
DOIs | |
State | Published - Jan 2020 |
Bibliographical note
Publisher Copyright:© 2019 IEEE.
NREL Publication Number
- NREL/JA-5K00-75844
Keywords
- Field performance
- optimization
- photovoltaic (PV) systems
- prediction methods
- soiling
- solar energy
- stochastic processes
- time series analysis