Thermal modeling of building components plays a crucial role in designing energy efficiency measures, assessing living comfort, and preventing building damages. The accuracy of the modeling process strongly depends on the reliability of the physical models and the correct selection of input parameters, especially for historic buildings where uncertainties on wall composition and material properties are higher. This work evaluates the reliability of building thermal modeling and identifies the input parameters that most affect the simulation results. A monitoring system is applied to a historic building wall to measure the temperature profile. The long-term dataset is compared with the result of a simulation model. A sensitivity analysis is applied for the determination of the influential input parameters. A two-step optimization is performed to calibrate the numerical model: the first optimization step is based on an optimized selection of the database materials, while the second optimization step uses a particle swarm algorithm. The results indicate that the output of the simulation model is largely influenced by the coefficients describing the coupling with the boundary conditions and by the thermal conductivities of the materials. Very good results are obtained already after the first optimization step ($RMSE=0.75 °\mathrm{C})$ while the second optimization step improves further the agreement ($RMSE=0.48 °\mathrm{C})$. The parameter values reported in the datasheets do not match those found through optimization. Even with extensive optimization using an algorithm, starting with monitoring data is insufficient to identify material parameter values.

This data collection presents a comprehensive five-year monitoring record from a historic building located in Settequerce, South Tyrol (Italy). The 17th-century building, characterized by a stone masonry, was subject to a retrofit in 2017 that introduced a wood fiber insulation panel to the inner side of the building's walls. As part of the renovation process, a monitoring system was installed. This system incorporated internal, external, and in-wall sensors, which were strategically placed at three different points within the wall to evaluate the effects of the insulation on the stratigraphy. This setup ensured the continuous collection of climatic data, both from the exterior and interior environments, as well as from within the renovated wall's stratigraphy. This comprehensive dataset delivers new insights into the long-term performance of retrofit interventions. The significant potential for reuse of this dataset makes it a substantial foundation for future studies on energy-efficient retrofitting and the preservation of historical structures in comparable climate (Climate E Köppen). It is expected to inform data-driven decisions in future renovation and preservation projects of comparable historic buildings.