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Imputing the long-term missing heating load data using a generative network
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DOI：10.1016/j.egyai.2025.100637.png)
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Abstract
En 中文
• A novel 2D generative network for imputing long-term missing heat load data. • Superior accuracy versus state-of-the-art models across diverse seasons. • Improved interpretability via analysis of key weather variable impacts. • Effective with limited data through multiple transfer learning strategies.
Keywords:
Generative network
Heating load data
Missing data imputation
Transfer learning
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