1. Small Sample Building Energy Consumption Prediction Using Contrastive Transformer Networks.
- Author
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Ji, Wenxian, Cao, Zeyu, and Li, Xiaorun
- Subjects
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ENERGY consumption of buildings , *TRANSFORMER models , *ENERGY consumption , *FEATURE extraction , *EXHIBITION buildings - Abstract
Predicting energy consumption in large exposition centers presents a significant challenge, primarily due to the limited datasets and fluctuating electricity usage patterns. This study introduces a cutting-edge algorithm, the contrastive transformer network (CTN), to address these issues. By leveraging self-supervised learning, the CTN employs contrastive learning techniques across both temporal and contextual dimensions. Its transformer-based architecture, tailored for efficient feature extraction, allows the CTN to excel in predicting energy consumption in expansive structures, especially when data samples are scarce. Rigorous experiments on a proprietary dataset underscore the potency of the CTN in this domain. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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