4.6 Article

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

Journal

SUSTAINABILITY
Volume 14, Issue 3, Pages -

Publisher

MDPI
DOI: 10.3390/su14031500

Keywords

building-integrated photovoltaics; BIPV; PVmodule temperature; steady-state temperature model; BIPV module; BIPV system; BIPV energy prediction

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This paper assesses the suitability of two steady-state photovoltaic module temperature models (Ross and Faiman) for building integrated photovoltaic rainscreens and curtain walls. The study shows that the Ross model is the most suitable for predicting annual PV energy output in these applications, highlighting the importance of fitting model coefficients with representative in situ data.
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 facades or the indoor temperature in curtain walls and windows.

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