Effect of applied weather data sets in simulation of building energy demands: Comparison of design years with recent weather data

Abstract

Building energy demands in long-term studies are mostly calculated using the averaged weather data sets, reflecting the changes in environmental conditions over time, thus the different energy consumption each year. This paper reviews and discusses the coupled effects of warming trend in global mean surface temperatures, application of different design weather datasets, and utilization of different methods of building energy assessment on the calculated energy demands. The possible inaccuracies of building energy analyses caused by those three factors are investigated on the example of a residential house in Central Europe. For that purpose eight different weather data sets for Prague, Czech Republic are selected and simulations in two different scales are performed. The analysis of the effect of recent weather data is performed by an improved methodology. First, the simulation brings an increased precision as an advanced hygrothermal model is used for energy calculations. Second, the building performance is assessed, contrary to the Czech national standards, using both heating and cooling energy demands. The simulation results confirm the warming trend in the time period of 2013–2017 as the average heating demands are 3.95% lower and the average cooling demands 3.96% higher in a comparison with the Test Reference Year. In the extreme years, a 12–15% decrease of energy consumed for heating and up to 20% increase of energy necessary for cooling is found. This is in accordance with the presumed warming trend that has been widely discussed during the last few decades. Furthermore, the presented results verify the critical and positive design weather years as suitable for application in the simulation of heating and cooling energy demands in the Czech Republic.