TY - JOUR
T1 - Transient Weighted Moving-Average Model of Photovoltaic Module Back-Surface Temperature
AU - Prilliman, Matthew
AU - Stein, Joshua S.
AU - Riley, Daniel
AU - Tamizhmani, Govindasamy
N1 - Funding Information:
Manuscript received December 9, 2019; revised March 10, 2020; accepted April 15, 2020. Date of publication May 18, 2020; date of current version June 19, 2020. This work was supported in part by the Sandia National Laboratories managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525 and in part by the U.S. Department of Energy’s Office of Energy Efficiency and Renewable Energy (EERE) under Solar Energy Technologies Office (SETO) Agreement 34366. (Corresponding author: Matthew Prilliman.) Matthew Prilliman and Govindasamy Tamizhmani are with Arizona State University, Mesa, AZ 85212 USA (e-mail: mprillim@asu.edu; manit@asu.edu).
Publisher Copyright:
© 2011-2012 IEEE.
PY - 2020/7
Y1 - 2020/7
N2 - Accurate modeling of photovoltaic (PV) performance requires the precise calculation of module temperature. Currently, most temperature models rely on steady-state assumptions that do not account for the transient climatic conditions and thermal mass of the module. On the other hand, complex physics-based transient models are computationally expensive and difficult to parameterize. In order to address this, a new approach to transient thermal modeling was developed, in which the steady-state predictions from previous timesteps are weighted and averaged to accurately predict the module temperature at finer time scales. This model is informed by 3-D finite-element analyses, which are used to calculate the effect of wind speed and module unit mass on module temperature. The model, in application, serves as an added filter over existing steady-state models that smooths out erroneous values that are a result of intermittency in solar resource. Validation of this moving-Average model has shown that it can improve the overall PV energy performance model accuracy by as much as 0.58% over steady-state models based on mean absolute error improvements and can significantly reduce the variability between the model predictions and measured temperature times series data.
AB - Accurate modeling of photovoltaic (PV) performance requires the precise calculation of module temperature. Currently, most temperature models rely on steady-state assumptions that do not account for the transient climatic conditions and thermal mass of the module. On the other hand, complex physics-based transient models are computationally expensive and difficult to parameterize. In order to address this, a new approach to transient thermal modeling was developed, in which the steady-state predictions from previous timesteps are weighted and averaged to accurately predict the module temperature at finer time scales. This model is informed by 3-D finite-element analyses, which are used to calculate the effect of wind speed and module unit mass on module temperature. The model, in application, serves as an added filter over existing steady-state models that smooths out erroneous values that are a result of intermittency in solar resource. Validation of this moving-Average model has shown that it can improve the overall PV energy performance model accuracy by as much as 0.58% over steady-state models based on mean absolute error improvements and can significantly reduce the variability between the model predictions and measured temperature times series data.
KW - Performance analysis
KW - photovoltaics (PV)
KW - renewable Energy
KW - thermal modeling
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U2 - 10.1109/JPHOTOV.2020.2992351
DO - 10.1109/JPHOTOV.2020.2992351
M3 - Article
AN - SCOPUS:85088010970
SN - 2156-3381
VL - 10
SP - 1053
EP - 1060
JO - IEEE Journal of Photovoltaics
JF - IEEE Journal of Photovoltaics
IS - 4
M1 - 9095219
ER -