The performance of photovoltaic (PV) arrays are affected by the operating temperature, which is influenced by thermal losses to the ambient environment. The factors affecting thermal losses include wind speed, wind direction, and ambient temperature. The purpose of this work is to analyze how the aforementioned factors affect array efficiency, temperature, and heat transfer coefficient/thermal loss factor. Data on ambient and array temperatures, wind speed and direction, solar irradiance, and electrical output were collected from a PV array mounted on a CanmetENERGY facility in Varennes, Canada, and analyzed. The results were compared with computational fluid dynamics (CFD) simulations and existing results from PVsyst. The findings can be summarized into three points. First, ambient temperature and wind speed are important factors in determining PV performance, while wind direction seems to play a minor role. Second, CFD simulations found that temperature variation on the PV array surface is greater at lower wind speeds, and decreases at higher wind speeds. Lastly, an empirical correlation of heat transfer coefficient/thermal loss factor has been developed.

For northern communities relying on solar photovoltaic (PV) systems to offset the cost of imported diesel fuel, there is an acute need to forecast energy losses due to snow. Two leading snow loss models, developed by Marion et al. and Townsend and Powers, are used widely in the PV industry. In order to test these models, this study compares modeled monthly and annual losses to a best-estimate reference based on PV power output and camera images from an array operating in Quebec, Canada. This study addresses two key areas: the development of an image-analysis algorithm to detect snow on a PV array and the validation of models estimating energy loss. For the PV array in this work, Townsend and Powers is a closer approximation of real annual energy losses than the Marion et al. model. However, to provide communities and PV system developers with accurate snow loss predictions, more work needs to be done to improve these models using arrays at different latitudes. The image analysis algorithm used in this work is 90% accurate in identifying snow. This leads to a difference of about 0.2% points in annual snow loss estimates when using the image analysis algorithm instead of visually inspected photos.

The output of solar photovoltaic (PV) cells is strongly dependent on the abundance of solar radiation. However, electrical performance of PV cells is affected by their operating temperatures, and that high temperatures lead to decreases in efficiency of a PV module. Thus, to maximize the performance of a PV system, a number of technologies such as sun tracking and active cooling