Identifying optimal conditions for determining building heat transfer coefficients using on-board monitoring data

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Abstract
Assessing the overall Heat Transfer Coefficient (HTC) of a building received growing attention to gain insights into the energy performance gap of our building stock. So far, though, there is a lack of clear guidelines on optimal conditions for reliable in-use HTC assessment. The current study explores how in-use HTC-measurements vary with weather conditions, monitoring duration, and data granularity, focusing on two widely used methods: Multiple Linear Regression (MLR) and Auto-Regressive with Exogenous Input (ARX) modelling. Both techniques are applied to a comprehensive dataset of dwellings equipped with smart meters for gas and electricity, along with indoor temperature data from nearly all rooms, collected over one to three years. The extended monitoring period enabled analysis of HTC evolution and its standard deviation over time, helping identify optimal weather conditions, monitoring equipment and monitoring duration. The most reliable values were achieved in winter conditions under high indoor-outdoor temperature differences and low solar radiation. ARX methods demonstrated greater robustness throughout the year compared to MLR-methods. Reducing the number of indoor temperature sensors was found to significantly affect the estimates.

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