用迁移学习提升纺织厂能耗预测精度，少数据也能准。
Energy-Efficient Prediction in Textile Manufacturing: Enhancing Accuracy and Data Efficiency With Ensemble Deep Transfer Learning
- 多模型迁移+特征对齐，跨产线复用数据
- 数据少至20%-40%时，准确率提升5.66%
- 适合数据稀缺的智能工厂部署
传统纺织厂能耗高，优化生产需精准预测。深度神经网络虽有效，但依赖大量历史数据，而传感器部署和数据采集成本高昂。为此，本文提出集成式深度迁移学习（EDTL）框架，通过在数据丰富的产线（源域）预训练，并迁移到数据有限的产线（目标域），结合集成策略与特征对齐层，降低对大数据集的依赖。在真实纺织工厂数据集上的实验表明，相比传统DNN，EDTL在数据受限场景（20%-40%数据可用）下预测准确率提升5.66%，模型鲁棒性增强3.96%。该研究为节能纺织制造提供了高精度、低数据需求的可扩展解决方案。
原文摘要 · Abstract (English)
Traditional textile factories consume substantial energy, making energy-efficient production optimization crucial for sustainability and cost reduction. Meanwhile, deep neural networks (DNNs), which are effective for factory output prediction and operational optimization, require extensive historical data, posing challenges due to high sensor deployment and data collection costs. To address this, we propose Ensemble Deep Transfer Learning (EDTL), a novel framework that enhances prediction accuracy and data efficiency by integrating transfer learning with an ensemble strategy and a feature alignment layer. EDTL pretrains DNN models on data-rich production lines (source domain) and adapts them to data-limited lines (target domain), reducing dependency on large datasets. Experiments on real-world textile factory datasets show that EDTL improves prediction accuracy by 5.66% and enhances model robustness by 3.96% compared to conventional DNNs, particularly in data-limited scenarios (20%-40% data availability). This research contributes to energy-efficient textile manufacturing by enabling accurate predictions with fewer data requirements, providing a scalable and cost-effective solution for smart production systems.
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