# Weather Files for the Calibration of Building Energy Models
## 4 Conclusions  
The integration of renewable energies (particularly photovoltaic) in buildings for the study of complex energy-saving strategies, such as power-to-heat (P2H) or model predictive control (MPC), depends on the quality/accuracy of the energy models and weather forecasts. One of the key challenges in developing these realistic energy models is dealing with the many uncertainties involved in the process. Obtaining accurate building information is essential to developing a robust simulation model, but it is often a complex task. Weather data are part of this information and the quality will have a major influence on the results obtained. Figure 10: Results of the \(\rho\) index of the base and calibrated models with the different weather files created. Checking period, Houses N2 and O5. Figure 9: Results of the \(\rho\) index of the base and calibrated models with the different weather files created. Training period, Houses N2 and O5. An on-site weather station requires a heavy financial investment, not only in buying or renting the necessary sensors but also in processing and validating the generated data. They often have to be placed by expert personnel in order for these sensors to collect the data as realistically as possible, without interference from external factors. Despite these drawbacks, the data generated by these stations are optimal for input into an energy model calibration process. An alternative to the on-site weather station is the use of data from a third-party weather company. This is much cheaper and requires no effort to collect, but its major disadvantage is that it is generally much less accurate. BEMs simulated with the weather files provided by a third-party are much further away from the real building (when analyzing the behavior) than those simulated with the data provided by the on-site weather station. However, if these inputs are subjected to a calibration process, it is possible to obtain quality models that meet established objectives. This makes it feasible to use these files as a better alternative than using data from weather stations that do not take into account the characteristics of the building under study (weather stations located at airports or in specific places in the city). Although data provided by on-site weather station sensors continue to provide the best results in model fitting, this study has shown that third-party data are viable when the BEM is subjected to a calibration process, creating an alternative when choosing a weather file. In turn, the solution that could be the best (in terms of cost/effectiveness and efficiency) was found--the archive combining third-party weather data and outdoor temperature data measured in situ. This possibility is of great relevance as it allows obtaining calibrated models of buildings where a BMS with historical indoor and outdoor temperature data are available. Nowadays, these types of models are in great demand as they provide very useful information in optimization strategies that require training with data, such as machine learning, artificial neural networks, etc. One relevant finding obtained in this study is that it is possible to know in advance which sensors of the in situ weather station, when introduced into the data of a third-party weather file could improve the fit data of the calibrated model without being subjected to this adjustment process. The concordance results obtained with the simulation of the base model and the different weather files are proportionally similar to those obtained when the model is subjected to the calibration process. In the future, more case studies with different building characteristics, such as orientation, window wall ratio, thermal fabric properties, and weather conditions, should be explored to confirm these findings. Conceptualization, V.G.G., C.F.B. and G.R.R. ; methodology, V.G.G. and C.F.B. ; software, V.G.G. and G.R.R. ; validation, V.G.G., C.F.B. and G.R.R. ; formal analysis, V.G.G. ; investigation, V.G.G. ; resources, V.G.G. ; data curation, V.G.G.