# Weather Files for the Calibration of Building Energy Models
###### Abstract
The latter data source will produce the best results, as it will faithfully reproduce the microclimatic conditions of the building under study (heat islands, specific wind speeds, directions conditioned by adjacent buildings, solar radiation conditioned by the environment, etc.). Those coming from data sources from nearby locations (airports, specific places in the city, etc.) may have diametrically opposed microclimatic conditions that do not benefit the calibration process [31]. Finally, those that depend on third-party companies are at the mercy of the accuracy of the algorithms used to interpolate the values of the meteorological variables of the site under study. The general recommendation to reduce the \"energy performance gap\" involves the use of on-site weather station data [32; 33], but this is not always possible due to the high cost of the initial investment and its subsequent maintenance. There is the possibility of using a mixed weather file created from the optimal combination of meteorological variables from external companies and on-site weather stations since not all sensors have the same price, nor do they all have the same impacts in the simulation. This was the main objective of this research. This research is a continuation of a previous study conducted by the authors in which the impacts of weather files on simulation models were analyzed [34]. Using a previous calibrated model with different AMY weather files from the same location (from an on-site weather station, from a third-party weather company, and all their possible combinations), the impacts on the energy performance gap with respect to the different weather files were analyzed. In this case, the objective of the research was more demanding, since we analyzed the accuracy achieved in the calibration process of a building based on the weather data used, evaluating the use of data from both an on-site weather station and from a third-party weather company, as well as all of their possible combinations, in order to find the most cost-effective one. To make the results relevant, the degree of calibration of the selected simulation model was important. In this research, the Twin House models analyzed by the International Energy Agency--Energy in Buildings and Communities Programme, IEA EBC Annex 58 \"Reliable building energy performance characterization based on full-scale dynamic measurements\" [35] were used. The main objective of Annex 58 was to \"develop the necessary knowledge, tools, and networks to achieve reliable in situ dynamic testing and data analysis methods that can be used to characterize the actual energy performance of building components and whole buildings\". Different modeling teams (21 in total) were formed to receive the building details and boundary conditions to calibrate the Twin Houses using commercial and research simulation software [36]. The experiment lasted for two months, during which the interior conditions were modified to have different periods of load, free-floating, etc. Therefore, the calibrated model of the Twin Houses developed by the authors was chosen [37]. The calibration methodology used was based on the appropriate selection of capacitances, internal mass, infiltration, and thermal bridges of the model, which allowed obtaining a calibrated model that was one of the first in the ranking of the models obtained by the 21 teams of Annex 58. In this research, the calibrations were carried out using the same methodology, so that it was possible to establish the most cost-effective combination of the AMY weather files in relation to the on-site and third-party weather data used in the calibration process. The article is structured as follows. Section 2 explains the steps taken to achieve the objectives set out in this paper, the proper simulation and calibration process (Section 2.1), and the weather file creation for the simulations (Section 2.2). Section 2.3 explains the case study used, corresponding to the monitored houses of Annex 58, and the calibration and evaluation periods taken into account.