基于数字双胞胎的深度学习框架,用于个性化的热舒适度预测和智能建筑中的节能运行
Ahmad Almadhor1, Nejib Ghazouani2, Belgacem Bouallegue3
1Department of Computer Engineering and Networks, Jouf University, Sakaka, 72388, Saudi Arabia. aaalmadhor@ju.edu.sa.
Scientific reports
|July 9, 2025
概括
本研究介绍了数字双胞胎和基于注意力的LSTM模型,用于在智能建筑中个性化预测热舒适度. 该系统达到83.8%的精度,提高了能源效率和乘客幸福感.
科学领域:
- 智能建筑技术 智能建筑技术
- 在HVAC中的人工智能
- 乘客舒适度建模使用者舒适度建模
背景情况:
- 传统的热舒适型号缺乏个性化和适应动态条件的能力.
- 智能建筑需要先进的解决方案,以提高能源效率和居民福祉.
- 个人偏好和实时环境变化挑战了现有的舒适度调节方法.
研究的目的:
- 开发一个数字双胞胎驱动的框架,以基于注意力的长短期记忆 (LSTM) 模型为个性化的热舒适度预测.
- 实现智能加热,通风和空调 (HVAC) 控制,以提高乘客舒适度和节能.
- 提高热舒适度预测模型的可解释性和透明度.
主要方法:
- 使用了ASHRAE全球热舒适度数据库II的子集,并进行了预处理 (归算,规范化).
- 开发了一个基于注意力的LSTM模型来预测热舒适度类别 (不舒服的冷,中性,不舒服的温暖).
- 集成了一个数字双胞胎用于实时模拟和可解释AI (XAI) 技术 (SHAP,LIME) 用于模型解释性.
主要成果:
- 拟议的模型实现了83.8%的测试准确性,超过了以前的先进方法.
- 注意力机制提高了预测性能,并专注于关键的时间特征.
- 基于场景的分析表明,通过优化HVAC控制,可以实现潜在的能源效率提升.
结论:
- 数字双胞胎驱动的基于注意力的LSTM框架为以乘客为中心的HVAC管理提供了强大的解决方案.
- 该模型在智能建筑中提供可解释和节能的热舒适度调节.
- 这种方法显著提升了个性化的舒适度预测和智能建筑控制.
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