  Building Energy Rating (BER) stands as a pivotal metric, enabling building
owners, policymakers, and urban planners to understand the energy-saving
potential through improving building energy efficiency. As such, enhancing
buildings' BER levels is expected to directly contribute to the reduction of
carbon emissions and promote climate improvement. Nonetheless, the BER
assessment process is vulnerable to missing and inaccurate measurements. In
this study, we introduce \texttt{CLEAR}, a data-driven approach designed to
scrutinize the inconsistencies in BER assessments through self-supervised
contrastive learning. We validated the effectiveness of \texttt{CLEAR} using a
dataset representing Irish building stocks. Our experiments uncovered evidence
of inconsistent BER assessments, highlighting measurement data corruption
within this real-world dataset.
