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Title: Signal Processing on PV Time-Series Data: Robust Degradation Analysis Without Physical Models

Abstract

A novel unsupervised machine learning approach for analyzing time-series data is applied to the topicofphotovoltaic system degradation rate estimation, sometimes referred to as energy yield degradation analysis. This approach only requires a measured power signal as an input-no irradiance data, temperature data, or system configuration information are required. We present results on a dataset that was previously analyzed and presented by National Renewable Energy Laboratory using RdTools, validating the accuracy of the new approach and showing increased robustness to data anomalies while reducing the data requirements to carry out the analysis.

Authors:
 [1];  [1];  [1];  [1]
  1. Stanford Univ., CA (United States); SLAC National Accelerator Lab., Menlo Park, CA (United States); National Renewable Energy Lab. (NREL), Golden, CO (United States)
Publication Date:
Research Org.:
SLAC National Accelerator Lab., Menlo Park, CA (United States); National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office
OSTI Identifier:
1575280
Alternate Identifier(s):
OSTI ID: 1660085
Report Number(s):
NREL/JA-5K00-75031
Journal ID: ISSN 2156-3381; TRN: US2001192
Grant/Contract Number:  
AC02-76SF00515; AC3608GO28308; AC36-08GO28308
Resource Type:
Accepted Manuscript
Journal Name:
IEEE Journal of Photovoltaics
Additional Journal Information:
Journal Volume: 10; Journal Issue: 2; Journal ID: ISSN 2156-3381
Publisher:
IEEE
Country of Publication:
United States
Language:
English
Subject:
14 SOLAR ENERGY; computer-aided analysis; data analysis; distributed power generation; photovoltaic (PV) systems; photovoltaic systems; computer aided analysis; statistical learning

Citation Formats

Meyers, Bennet, Deceglie, Michael, Deline, Chris, and Jordan, Dirk. Signal Processing on PV Time-Series Data: Robust Degradation Analysis Without Physical Models. United States: N. p., 2019. Web. doi:10.1109/JPHOTOV.2019.2957646.
Meyers, Bennet, Deceglie, Michael, Deline, Chris, & Jordan, Dirk. Signal Processing on PV Time-Series Data: Robust Degradation Analysis Without Physical Models. United States. doi:10.1109/JPHOTOV.2019.2957646.
Meyers, Bennet, Deceglie, Michael, Deline, Chris, and Jordan, Dirk. Mon . "Signal Processing on PV Time-Series Data: Robust Degradation Analysis Without Physical Models". United States. doi:10.1109/JPHOTOV.2019.2957646. https://www.osti.gov/servlets/purl/1575280.
@article{osti_1575280,
title = {Signal Processing on PV Time-Series Data: Robust Degradation Analysis Without Physical Models},
author = {Meyers, Bennet and Deceglie, Michael and Deline, Chris and Jordan, Dirk},
abstractNote = {A novel unsupervised machine learning approach for analyzing time-series data is applied to the topicofphotovoltaic system degradation rate estimation, sometimes referred to as energy yield degradation analysis. This approach only requires a measured power signal as an input-no irradiance data, temperature data, or system configuration information are required. We present results on a dataset that was previously analyzed and presented by National Renewable Energy Laboratory using RdTools, validating the accuracy of the new approach and showing increased robustness to data anomalies while reducing the data requirements to carry out the analysis.},
doi = {10.1109/JPHOTOV.2019.2957646},
journal = {IEEE Journal of Photovoltaics},
number = 2,
volume = 10,
place = {United States},
year = {2019},
month = {12}
}

Journal Article:
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