深度学习和多目标优化,用于智能建筑中的基于占用率的实时能源控制
Shaoqi Zhang1,2, Cheng Kong3
1School of Education Department, Communication University of China, Nanjing, Nanjing, 211000, China. zsq18651881699@163.com.
Scientific reports
|November 11, 2025
概括
这项研究使用室内环境数据预测房间占用率,以提高能源效率和舒适度. 深度学习模型准确预测能源需求,优化建筑物管理.
科学领域:
- 建筑科学 建筑科学
- 人工智能的人工智能
- 能源管理 能源管理
背景情况:
- 室内环境条件可以预测房间占用率,影响能源效率和乘客舒适度.
- 传统的能源管理系统缺乏适应性,并与非线性关系作斗争.
研究的目的:
- 调查室内环境参数 (CO2,照明,湿度,温度) 对房间占用情况的预测能力.
- 为智能建筑引入一种新的深度学习增强的预测能量建模 (DL-PEM) 框架,并采用多目标粒子群集优化 (MOPSO).
- 通过将能源消耗和二氧化碳水平降至最低,同时最大限度地提高热舒适度来优化实时能源管理.
主要方法:
- 在DL-PEM中使用深度前神经网络 (DNN) 来建模环境变量和占用率之间的非线性关系.
- 集成的MOPSO为巴雷托最佳管理能源消耗,二氧化碳减少和热舒适之间的权衡.
- 根据占用预测实时调整自适应的HVAC和照明控制.
主要成果:
- 实现了99.8%的预测准确度和高达85%的优化效率.
- 超越基线模型,包括KNN,DT,AO-ANN和LSTM用于预测和控制任务.
- 在实证评估中与真实建筑数据相比,在实证评估中表现出与传统模型相比的显著改进.
结论:
- DL-PEM-MOPSO框架为智能建筑能源管理提供了一个可扩展,可适应和未来的解决方案.
- 提高决策透明度和改善占用数据分析.
- 成功优化了热舒适度,并实现了精确的电力需求预测,从而提高了整体能源利用率.
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