# Weather Files for the Calibration of Building Energy Models
###### Abstract
However, several studies report that the results of simulation models in the design phase rarely match the measured performance, calling this deviation, the \"energy performance gap\" [20]. The possible causes are manifold, ranging from deviations between actual user loads and their predictions, different characteristics of HVAC systems, startups and shutdowns of HVAC systems [21], etc. Regardless of the error of the simulation software itself, the general solution proposed by the researchers to reduce this gap is to use as much real data as possible, from the building characteristics of its envelopes, accuracy when modeling spaces and the environment, and from outside conditions of the building, e.g., the climate [22; 23]. To minimize these \"discrepancies\" and ensure the model's accuracy, it is advisable to perform a calibration of the energy model. There are numerous strategies used to obtain calibrated models [24] that ultimately attempt to find model parameters that give results that match the measurements obtained. The proper definition of calibration in ASHRAE Standard 14-2014 [25] reads, \"process of reducing the uncertainty of a model by comparing the predicted output of the model under a specific set of conditions to the actual measured data for the same set of conditions\". In both this standard and in the International Performance Measurement and Verification Protocol (IPMVP) [26] and in the Federal Energy Management Program (FEMP) [27], uncertainty limits are established, based on the coefficient of the variation of the root mean square error (CV(RMSE)) and the normalized mean bias error (NMBE). These indices limit what is considered a calibrated model and what is not. They are intended to measure the energy uncertainty of the building, but are sometimes also used to measure the deviation between the measured temperature and the temperature generated by the BEM [28; 29]. Since the calibration process must be performed taking into account the same indoor and outdoor conditions, the weather file plays an important role in the process. There are three main types of weather files: typical meteorological year (TMY), actual meteorological year (AMY), and weather forecasts. The latter is meaningless in terms of building energy calibration since the main purpose is to perform future predictions on such factors as consumption and comfort; therefore, they need calibrated models to reduce the uncertainty of their predictions, which depend on both the model and the accuracy of the weather forecast. Typical meteorological year (TMY) weather files are based on meteorological data from previous years--statistically processed--and are intended to represent the location climate. Their suitability depends mainly on the years used for their creation. They are used to obtain the estimated annual energy demand of the building, so that comparative studies, energy certifications, and even consumption calculations can be carried out when generic HVAC systems are used. They are the most common and there is a wide range of repositories of these types of files. These types of weather files do not represent site-specific meteorological conditions and are therefore not recommended for use in calibration processes, as in many cases energy use and peak loads are overestimated or underestimated [30]. Actual meteorological year (AMY) weather files are developed with data from real years without any statistical process. They are used to corroborate the solutions adopted under real climatic conditions. The weather files are necessary to perform calibrations since they provide information on the same \"set of conditions\" necessary to calibrate [25]. The suitability for the calibration process will largely depend on the origin of the data. There are three possible data sources for the creation of AMY weather files: nearby weather stations, weather data providers, and on-site weather stations.