Assessment of PV Module Temperature Models for Building-Integrated Photovoltaics (BIPV)

Nuria Martín-Chivelet, Jesús Polo, Carlos Sanz-Saiz, Lucy Tamara Núñez Benítez, Miguel Alonso-Abella and José Cuenca
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Nuria Martín-Chivelet: CIEMAT-Photovoltaic Unit, Avda. Complutense 40, 28040 Madrid, Spain
Jesús Polo: CIEMAT-Photovoltaic Unit, Avda. Complutense 40, 28040 Madrid, Spain
Carlos Sanz-Saiz: CIEMAT-Photovoltaic Unit, Avda. Complutense 40, 28040 Madrid, Spain
Lucy Tamara Núñez Benítez: UPM-Instituto de Energía Solar, Avda. Complutense 30, 28040 Madrid, Spain
Miguel Alonso-Abella: CIEMAT-Photovoltaic Unit, Avda. Complutense 40, 28040 Madrid, Spain
José Cuenca: CIEMAT-Photovoltaic Unit, Avda. Complutense 40, 28040 Madrid, Spain

Sustainability, 2022, vol. 14, issue 3, 1-15

Abstract: This paper assesses two steady-state photovoltaic (PV) module temperature models when applied to building integrated photovoltaic (BIPV) rainscreens and curtain walls. The models are the Ross and the Faiman models, both extensively used for PV modules mounted on open-rack support structures in PV plants. The experimental setups arrange the BIPV modules vertically and with different backside boundary conditions to cover the mounting configurations under study. Data monitoring over more than a year was the experimental basis to assess each model by comparing simulated and measured temperatures with the help of four different metrics: mean absolute error, root mean square error, mean bias error, and coefficient of determination. The performance ratio of each system without the temperature effect was calculated by comparing the experimental energy output with the energy output determined with the measured temperatures. This parameter allowed the estimation of the PV energy with the predicted temperatures to assess the suitability of each temperature model for energy-prediction purposes. The assessment showed that the Ross model is the most suitable for predicting the annual PV energy in rainscreen and curtain-wall applications. Highlighted is the importance of fitting the model coefficients with a representative set of in situ monitored data. The data set should preferably include the inner (backside) temperature, i.e., the air chamber temperature in ventilated façades or the indoor temperature in curtain walls and windows.

Keywords: building-integrated photovoltaics; BIPV; PV module temperature; steady-state temperature model; BIPV module; BIPV system; BIPV energy prediction (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:14:y:2022:i:3:p:1500-:d:736171

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