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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 topic of photovoltaic (PV) 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. We present results on a data set that was previously analyzed and presented by NREL 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)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Solar Energy Technologies Office (EE-4S)
OSTI Identifier:
1575280
Grant/Contract Number:  
AC02-76SF00515; AC3608GO28308
Resource Type:
Accepted Manuscript
Journal Name:
IEEE Journal of Photovoltaics
Additional Journal Information:
Journal Name: IEEE Journal of Photovoltaics; 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

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 topic of photovoltaic (PV) 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. We present results on a data set that was previously analyzed and presented by NREL 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 = ,
volume = ,
place = {United States},
year = {2019},
month = {12}
}

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