Experimental validation and uncertainty analysis of an innovative IoT infrared sensor for in-situ wall thermal transmittance measurement

Abstract

This paper presents the development and experimental validation of an Internet of Things (IoT) thermography system for in-situ and real-time monitoring of wall thermal transmittance. The solution proposed has been derived from the upgrade of the Comfort Eye sensor, which is an infrared-based sensor adopted for non-intrusive indoor environmental quality monitoring in occupied buildings. In this work, the system has been used to detect potential building envelope inefficiencies and track building performance trends in a continuous way. The methodology is based on the ISO 9869-2 standard but it has been applied to an entire wall and during its normal functioning without the need of operators. The data management has been performed with a dedicated IoT architecture that allows the synchronised collection of quantities required for transmittance calculation, i.e. indoor and outdoor air temperatures together with the thermographic maps of the wall. The measurement technique has been validated in a real building through the comparison with the results obtained using a heat flux meter (HFM). An uncertainty analysis with Monte Carlo simulation has also been performed to evaluate the overall uncertainty of the method. The values obtained are coherent with those measured with the HFM and the infrared system has proved to be able to provide thermal transmittance measurements with an expanded uncertainty of ±0.038 W m−2K−1 with coverage factor k = 2. The innovative methodology described can be used for U-value estimation without the need for extra measuring tools.