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

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.

Authors:
ORCiD logo [1]; ORCiD logo [1];  [2]; ORCiD logo [1]
  1. Univ. of Jaen (Spain)
  2. National Renewable Energy Lab. (NREL), Golden, CO (United States)
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE); European Union’s Horizon 2020 research and innovation programme
OSTI Identifier:
1593090
Report Number(s):
NREL/JA-5K00-75844
Journal ID: ISSN 2156-3381; MainId:12179;UUID:0c1e7f18-e137-ea11-9c2f-ac162d87dfe5;MainAdminID:659
Grant/Contract Number:  
AC36-08GO28308
Resource Type:
Accepted Manuscript
Journal Name:
IEEE Journal of Photovoltaics
Additional Journal Information:
Journal Volume: 10; Journal Issue: 1; Journal ID: ISSN 2156-3381
Publisher:
IEEE
Country of Publication:
United States
Language:
English
Subject:
14 SOLAR ENERGY; 47 OTHER INSTRUMENTATION; field performance; optimization; photovoltaic systems; prediction methods; soiling; solar energy; stochastic processes; time series analysis

Citation Formats

Micheli, Leonardo, Fernandez, Eduardo F., Muller, Matthew, and Almonacid, Florencia. Extracting and Generating PV Soiling Profiles for Analysis, Forecasting, and Cleaning Optimization. United States: N. p., 2020. Web. doi:10.1109/jphotov.2019.2943706.
Micheli, Leonardo, Fernandez, Eduardo F., Muller, Matthew, & Almonacid, Florencia. Extracting and Generating PV Soiling Profiles for Analysis, Forecasting, and Cleaning Optimization. United States. https://doi.org/10.1109/jphotov.2019.2943706
Micheli, Leonardo, Fernandez, Eduardo F., Muller, Matthew, and Almonacid, Florencia. Fri . "Extracting and Generating PV Soiling Profiles for Analysis, Forecasting, and Cleaning Optimization". United States. https://doi.org/10.1109/jphotov.2019.2943706. https://www.osti.gov/servlets/purl/1593090.
@article{osti_1593090,
title = {Extracting and Generating PV Soiling Profiles for Analysis, Forecasting, and Cleaning Optimization},
author = {Micheli, Leonardo and Fernandez, Eduardo F. and Muller, Matthew and Almonacid, Florencia},
abstractNote = {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.},
doi = {10.1109/jphotov.2019.2943706},
journal = {IEEE Journal of Photovoltaics},
number = 1,
volume = 10,
place = {United States},
year = {Fri Oct 23 00:00:00 EDT 2020},
month = {Fri Oct 23 00:00:00 EDT 2020}
}

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