A barrier to the widespread application of building integrated photovoltaics (BIPV) is the lack of validated predictive performance tools. Architects and building owners need these tools in order to determine if the potential energy savings realized from building integrated photovoltaics justifies the additional capital expenditure. The National Institute of Standards and Technology (NIST) seeks to provide high quality experimental data that can be used to develop and validate these predictive performance tools.
The temperature of a photovoltaic module affects its electrical output characteristics and efficiency. Traditionally, the temperature of solar cells has been characterized using the nominal operating cell temperature (NOCT), which can be used in conjunction with a calculation procedure to predict the module’s temperature for various environmental conditions. The NOCT procedure provides a representative prediction of the cell temperature, specifically for the ubiquitous rack-mounted installation. The procedure estimates the cell temperature based on the ambient temperature and the solar irradiance. It makes the approximation that the overall heat loss coefficient is constant. In other words, the temperature difference between the panel and the environment is linearly related to the heat flux on the panels (solar irradiance).
The heat transfer characteristics of a rack-mounted PV module and a BIPV module can be quite different. The manner in which the module is installed within the building envelope influences the cell’s operating temperature. Unlike rack-mounted modules, the two sides of the modules may be subjected to significantly different environmental conditions.
This paper presents a new technique to compute the operating temperature of cells within building integrated photovoltaic modules using a one-dimensional transient heat transfer model. The resulting predictions are compared to measured BIPV cell temperatures for two single crystalline BIPV panels (one insulated panel and one uninsulated panel). Finally, the results are compared to predictions using the NOCT technique.