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Model Failure or Data Corruption? Exploring Inconsistencies in Building Energy Ratings with Self-Supervised Contrastive Learning
April 22, 2024, 4:41 a.m. | Qian Xiao, Dan Liu, Kevin Credit
cs.LG updates on arXiv.org arxiv.org
Abstract: 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 …
abstract arxiv building buildings corruption cs.ai cs.lg data efficiency enabling energy energy efficiency failure improving pivotal ratings saving through type urban
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