# Weather Files for the Calibration of Building Energy Models
###### Abstract  
In the fight against climate change, energy modeling is a key tool used to analyze the performance of proposed energy conservation measures for buildings. Studies on the integration of photovoltaic energy in buildings must use calibrated building energy models, as only with them is the demand curve real, and the savings obtained at the self-consumption level, energy storage in the building, or feed into the grid are accurate. The adjustment process of a calibrated model depends on aspects inherent to the building properties (envelope parameters, internal loads, use schedules) as well as external to them (weather, ground properties, etc.). Naturally, the uncertainty of each is essential to obtaining good results. As for the meteorological data, it is preferable to use data from a weather station located in the building or its surroundings, although this is not always possible due to the cost of the initial investment and its maintenance. As a result, weather stations with public access to their data, such as those located at airports or specific locations in cities, are largely used to perform calibrations of building energy models, making it challenging to converge the simulated model with measured data. This research sheds light on how this obstacle can be overcome by using weather data provided by a third-party company, bridging the gap between reality and energy models. For this purpose, calibrations of the two buildings proposed in Annex 58 were performed with different weather configurations, using the mean absolute error (MAE) uncertainty index and Spearman's rank correlation coefficient (rho) as comparative measures. An optimal and cost-effective solution was found as an alternative to an on-site weather station, based on the use of a single outdoor temperature sensor in combination with third-party weather data, achieving a robust and reliable building energy model. building energy models (BEMs); weather file; weather station; calibration; EnergyPlus; energy simulation; Annex 58 +
Footnote †: journal: Applied  
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[PERSON] et al. analyzed the difference in the sizing of photovoltaic systems (fixed and tracking) when the energy balance was performed on a monthly or annual basis to obtain nZEB buildings [10]. The importance of the energy model is key to demonstrate that if the monthly balance is considered, sometimes the size of the PV plant does not fit the owner's financial criteria. However, it is not only the energy model that is important; weather forecasting errors can also seriously distort the expected results, leading to significant errors, such as \"different energy distributions between heating and cooling\" [11]. There are also other approaches for self-consumption installations in which the accuracy of the energy model is key to the improvement of the estimation of the building's energy demand coverage by renewables. There are approaches, such as power-to-heat (P2H), in which the building is used as a thermal battery for the storage of the energy generated hour-by-hour, which allows us to delay it in time so that when heating is needed, it is the building itself that gives it back to us thanks to the fact that it is stored in its thermal inertia [6; 12; 13; 14]; or in model predictive control (MPC) applications, whose objectives are to obtain the optimal heating curve of the building to decide which part of the energy generated is destined for self-consumption and which part of the grid, allowing the optimization of the design of PV self-consumption installations [15; 16; 17]. All of this can be managed individually or locally by managing the energy balances of the various buildings to make efficient use of the grid by sharing surplus electricity generated on-site [18]. In these cases, the energy model is essential to study both the grid limitation and the storage capacity of the facility [19].