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Title: Scalable Method for Extracting Soiling Rates from PV Production Data: Preprint

Abstract

We present a method for analyzing time series production data from photovoltaic systems to extract the rate at which energy yield is affected by the accumulation of dust, dirt, and other forms of soiling. We describe an approach that is based on prevailing methods which consider the change in energy production during dry periods. The method described here builds upon these methods by considering a statistical sample of soiling intervals from each site under consideration. The method enables straightforward application to a large number of sites with minimal parameterization of data-filtering requirements. Furthermore, it enables statistical confidence intervals and comparisons between sites.

Authors:
; ; ;
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Solar Energy Technologies Office (EE-4S)
OSTI Identifier:
1259447
Report Number(s):
NREL/CP-5J00-65763
DOE Contract Number:
AC36-08GO28308
Resource Type:
Conference
Resource Relation:
Conference: Presented at the 43rd IEEE Photovoltaic Specialists Conference, 5-10 June 2016, Portland, Oregon
Country of Publication:
United States
Language:
English
Subject:
14 SOLAR ENERGY; 36 MATERIALS SCIENCE; photovoltaics; soiling

Citation Formats

Deceglie, Michael G., Muller, Matthew, Kurtz, Sarah, and Defreitas, Zoe. Scalable Method for Extracting Soiling Rates from PV Production Data: Preprint. United States: N. p., 2016. Web. doi:10.1109/PVSC.2016.7749992.
Deceglie, Michael G., Muller, Matthew, Kurtz, Sarah, & Defreitas, Zoe. Scalable Method for Extracting Soiling Rates from PV Production Data: Preprint. United States. doi:10.1109/PVSC.2016.7749992.
Deceglie, Michael G., Muller, Matthew, Kurtz, Sarah, and Defreitas, Zoe. 2016. "Scalable Method for Extracting Soiling Rates from PV Production Data: Preprint". United States. doi:10.1109/PVSC.2016.7749992. https://www.osti.gov/servlets/purl/1259447.
@article{osti_1259447,
title = {Scalable Method for Extracting Soiling Rates from PV Production Data: Preprint},
author = {Deceglie, Michael G. and Muller, Matthew and Kurtz, Sarah and Defreitas, Zoe},
abstractNote = {We present a method for analyzing time series production data from photovoltaic systems to extract the rate at which energy yield is affected by the accumulation of dust, dirt, and other forms of soiling. We describe an approach that is based on prevailing methods which consider the change in energy production during dry periods. The method described here builds upon these methods by considering a statistical sample of soiling intervals from each site under consideration. The method enables straightforward application to a large number of sites with minimal parameterization of data-filtering requirements. Furthermore, it enables statistical confidence intervals and comparisons between sites.},
doi = {10.1109/PVSC.2016.7749992},
journal = {},
number = ,
volume = ,
place = {United States},
year = 2016,
month = 6
}

Conference:
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  • We present a method for analyzing time series production data from photovoltaic systems to extract the rate at which energy yield is affected by the accumulation of dust, dirt, and other forms of soiling. We describe an approach that is based on prevailing methods which consider the change in energy production during dry periods. The method described here builds upon these methods by considering a statistical sample of soiling intervals from each site under consideration. The method enables straightforward application to a large number of sites with minimal parameterization of data-filtering requirements. Furthermore, it enables statistical confidence intervals and comparisonsmore » between sites.« less
  • Empirical techniques for characterizing electrical energy use now play a key role in reducing electricity consumption, particularly miscellaneous electrical loads, in buildings. Identifying device operating modes (mode extraction) creates a better understanding of both device and system behaviors. Using clustering to extract operating modes from electrical load data can provide valuable insights into device behavior and identify opportunities for energy savings. We present a fast and effective heuristic clustering method to identify and extract operating modes in electrical load data.
  • The sheet resistance of three soil types (Arizona road dust, soot, and sea salt) on glass were measured by the transmission line method as a function of relative humidity (RH) between 39% and 95% at 60 degrees C. Sea salt yielded a 3.5 order of magnitude decrease in resistance on the glass surface when the RH was increased over this RH range. Arizona road dust showed reduced sheet resistance at lower RH, but with less humidity sensitivity over the range tested. The soot sample did not show significant resistivity change compared to the unsoiled control. Photovoltaic modules with sea saltmore » on their faces were step-stressed between 25% and 95% RH at 60 degrees C applying -1000 V bias to the active cell circuit. Leakage current from the cell circuit to ground ranged between two and ten times higher than that of the unsoiled controls. Degradation rate of modules with salt on the surface increased with increasing RH and time.« less