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.

本研究提出了一种数据驱动的方法 	exttt {CLEAR}，用于审查建筑能效评估中的一致性问题，并通过自监督对比学习验证了其有效性。实验证据显示了不一致的建筑能效评估，并突出了这个真实数据集中的测量数据损坏问题。