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Building simulation is a powerful way to evaluate the performance of a building. The quality of simulation results however strongly depends on the accuracy of simulation input data. Especially for weather data files and occupant behaviour it is difficult to obtain accurate data. This paper evaluates the variability of building simulation results with regards to different weather data sets as well as different heating and cooling set points for a residential building in Victoria, Australia. Thermal comfort according to ASHRAE Standard 55, final energy consumption and peak cooling and heating loads are assessed. Simulations have been performed with EnergyPlus, and weather data for a multi-year approach have been generated with the software Meteonorm. The results show that different weather files for the same location as well as different conditioning set points can influence the results by approximately factor 2.

In 2011-12, greenhouse gas emissions from the Australian residential sector were 101.6 Mt and are expected to grow by 38% by 2050. In order to reduce these emissions, much emphasis has been placed on increasing the energy efficiency of buildings and appliances. Occupant behaviour, however, is probably the single most significant factor which determines energy use and emissions. This paper describes research undertaken to rank the most common occupant behaviours, based upon their impact on greenhouse gas emissions associated with residential energy use, in an architect-designed house in Australia. The occupant behaviours investigated were changing: the heating and cooling temperature set points, window openings, external blind use and lighting use. Simulations were carried out using Primero and EnergyPlus software. Based on the simulation results of greenhouse gas emissions, the following ranking of overall influence (from most influential to the least) has been determined: external blind use was one of the most effective measures to reduce emissions. Cooling set point temperature was similarly important with the magnitude of impact depending on the set point e.g. a 2°C increase had an impact comparable to the use of external blinds. The impact of the heating set point temperature was also dependent on the set point and overall slightly lower compared to the cooling set point temperature. Lighting use was the least influential parameter in the context of this study.