Impact of Measurement Uncertainty on Building Modeling and Retrofitting Decisions

Retrofitting buildings is essential for improving the existing global building stock. Innovations in wireless sensor networks have provided new means for scalable and potentially low-cost solutions for evaluating optimal retrofit measures in a building. Building models are used to explore different retrofit options and to find effective combinations of retrofit measures for a building in question. This paper departs outlining a novel grey-box modeling process for building retrofit based on measurement data. However, it is unknown if the measurement data and, as a consequence, the retrofit analysis is affected by uncertainties due to measurement accuracy and other factors. Quantifying these uncertainties during the analysis process is important in the context of making effective retrofit decisions. Consequently, this work examines the influence of measurement uncertainties on the generation of the retrofit models and the suggested retrofit measures. The results illustrate that measurement uncertainty is manageable for retrofit decisions, i.e., the measurement uncertainties rarely influence the ranking of retrofit measures. However, reduced measurement uncertainties are beneficial for adequately sizing the building retrofit interventions. It is shown that measurement uncertainty of flow meter measurements and indoor temperature measurements have the biggest impact on the heat loss coefficient estimation error, which ranges overall from 3 to 26%. Further, it is shown that some retrofit measures are more sensitive to uncertainty in the input data, such as district heating and wood pellets boilers, compared to measures that include heat pumps.

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