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Overheating in BIPV/T applications is a concern due to its negative impacts on the electrical conversion efficiency of the solar cells. Forced air cooling can be an effective thermal management strategy. However, its effectiveness is limited by the thermal resistance associated with the boundary layer formation on the walls of the air channel. The heat transfer effectiveness can be improved by appending transverse ribs to the BIPV/T air channel. This study numerically investigates the energy improvements associated with appending transverse ribs to a BIPV/T air channel using CFD. The impact of the varying the transverse rib roughness shape, pitch and height on the thermo-hydraulic performance parameter, electrical efficiency of the BIPV and building heating/cooling load is quantified. With the optimized transverse rib geometry, the heat removal rate is 2.73 times greater than with the smooth channel. This translated to a 30.5% reduction in the PV surface temperature and an increase in the electrical efficiency by 11.3% compared with the smooth channel under peak summer conditions for a mild oceanic climate. The wall heat gain during summer is also reduced by 45.2%.

The process of urbanization resulting from population growth is causing a transformation of natural landscapes into built environments, and contributing to a significant rise in air and surface temperatures in urban areas, resulting in what is known as the urban heat island (UHI). Ignoring the UHI effect and use of weather data from open fields and airport locations for energy and thermal comfort analysis can lead to over- and underestimation of heating and cooling loads, improper sizing of equipment, inefficiencies in the mechanical systems operation, and occupants’ thermal discomfort. There is a need for computationally efficient urban canopy temperature prediction models that account for the urban morphology and characteristics of the study area. This paper presents the development and application of an artificial neural network (ANN)-based method for generating hourly urban canopy temperature and local wind speed for energy simulation. It was used to predict the urban canopy temperature of a neighborhood in downtown Vancouver and the resulting building energy consumption and indoor temperature in a typical building in the area. The results showed that the UHI effect increased the total cooling energy demand by 23% and decreased the total heating energy consumption by 29%, resulting in an overall negative effect on the total energy demand of the building, which was 18% higher in the urban area. The UHI effect also increased the number of hours of indoor temperature above the cooling set point by 7.6%. The methodology can be applied to determine the urban canopy temperature of neighborhoods in different climate zones and determine the varying urban heat island effects associated with the locations.