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The paper demonstrates the challenges in accurately measured heat transport through multilayer walls, emphasizing the need for careful sensor placement and analysis to address potential anomalies and ensure reliable data. The study was conducted on a building constructed in 2010 for agricultural purposes in rural region. The structure, with dimensions of 6×16.8 meters and a single-pitched roof, has partial basement and lacks insulation. The external wall, facing northeast, has internal dimensions of 5.5×3.0 meters and a peak height of 7.5 meters, and is made of 0.24 m thick aerated concrete blocks. The experiment involved placing thermocouples in the walls before installing thermal insulation, with one model coated with a reflective smoothing layer. In the first case, inside the building, the issue was identified as a failure to record data from one channel due to sensor damage, which was detected after the research was completed. In the second case, an error recorded outside was caused by the placement of a temperature sensor in close proximity to a thermal bridge, which distorted the readings. The diagnosis was made possible after processing the results. A large dataset, consisting of nearly 5E6 readings, was collected, which detailed analysis of the graphical and time-consuming interpretation of the results enabled proper interpretation and conclusions. Algorithms based on artificial intelligence could be successfully used to conduct such analyses and detect errors at an early stage of research. These algorithms can perform multicriteria analysis of recorded data in real-time which is of fundamental importance when carrying out research that is not dynamic and requires a long observation time, i.e. during changes in the external temperature.
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