Generation and assessment of local climatic data from numerical meteorological codes for calibration of building energy models

The assessment of building energy performance through dynamic simulations has been increasing significantly in recent years since it represents a key strategy for the correct design of highly efficient buildings. Results of dynamic energy simulations are affected by many uncertainties, and its reliability depends on the accuracy of the input variables. One of the most influential variables is the climate surrounding the building, a reason why the use of accurate weather data files is essential, but experimental datasets are not always available. In this context, this paper analyses numerical weather datasets obtained from different regional climate models by comparing them with real data; in addition, it evaluates their impact on the energy performance of a historical building in Asunción through dynamic simulations. The database of five different weather data sources is compared with observed meteorological data in order to assess their accuracy through statistical analyses. Moreover, some methodologies to estimate diffused and direct components of the global solar radiation are evaluated, with the objective of solving the problem of missing direct and diffused solar data components from the meteorological codes. Subsequently, weather data files are generated to quantify the influence of measured/simulated meteorological data on the evaluation of building energy performance. The results obtained in this paper show that the simulated meteorological data agree very well with real observations for the year under study. Also, the simulations of the building energy performance delivered similar values to those obtained using the real weather dataset. Therefore, the regional climate models can represent a reliable tool for building energy performance assessment, and mainly for the calibration of building energy models when measured weather data is not available. © 2019 Elsevier B.V.