Current and newly built buildings will inevitably experience the effects of climate change, therefore, the design and performance of these buildings should consider weather data that includes some of the effects of climate change, instead of only using historical weather data. However, climate change weather data suitable for buildings performance simulation are typically unavailable.
Building energy performance simulations are limited to typical meteorological weather conditions available in simulation software. Such simulations are insufficient for analysing energy performance sensitivity to a range of probable weather conditions. This research presents a method for developing robust meteorological weather data that can be used for energy performance sensitivity analysis without the need to access historical weather data. The method decomposes dry bulb temperature (DBT) and global horizontal solar radiation (H) into deterministic and stochastic components.
The shade effect of a rooftop photovoltaic (PV) collector on a roof is usually ignored in building energy simulation in Transient System Simulation (TRNSYS) software. This disregard is due to either the unavailability of a suitable shading component in the simulation software or to an assumption that the shade on opaque surfaces, such as roofs, has small impact on the indoor temperature and the subsequent heating and cooling energy usage. However, for a relatively large collector area, ignoring the collector shadow on the roof may produce inaccurate results.