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Updated: Jan 7, 2026

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智能房间占用检测使用神经网络和puma优化算法.

El-Sayed M El-Kenawy1,2, Ahmed Mohamed Zaki3, Ebrahim A Mattar4

  • 1Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura, 35111, Egypt. sayed.kenawy@dhiet.edu.eg.

Scientific reports
|December 20, 2025
PubMed
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此摘要是机器生成的。

本研究介绍了一种优化的机器学习模型,用于使用环境数据准确检测房间占用情况. 这种新的方法提高了智能建筑自动化和能源效率.

科学领域:

  • 人工智能的人工智能
  • 建筑自动化 建筑自动化
  • 机器学习 机器学习

背景情况:

  • 传统的房间占用检测方法面临成本,可扩展性和适应性方面的挑战.
  • 精确的占用检测对于节能建筑,增强安全性和乘客舒适性至关重要.

研究的目的:

  • 为准确的房间占用检测提出一个优化的机器学习方法.
  • 用一种新的优化技术来解决现有方法的局限性.

主要方法:

  • 使用了一个神经网络 (NN) 模型,并通过Puma Optimizer 的Sine Cosine Optimizer (POSC) 进行优化.
  • 使用环境传感器数据,包括温度,湿度,光强度和二氧化碳水平,用于培训和评估.
  • 将POSC优化的NN模型与公共数据集上的遗传算法 (GA) 和灰狼优化 (GWO) 进行了比较.

主要成果:

  • 与传统方法相比,POSC优化的NN模型显示出更高的分类准确性.
  • 在精度,回忆和F1得分方面取得了显著的改进.
  • 优化技术通过平衡的勘探和开发,促进了更快的融合和更好的分类.

结论:

关键词:
能源效率 能源效率 能源效率机器学习 机器学习超启发式优化优化神经网络的神经网络的神经网络熊猫优化器可以优化.房间占用检测 房间占用检测参数和坐标优化器 参数和坐标优化器智能建筑智能建筑

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  • 将元启发式优化和深度学习结合在一起,为智能占用检测提供了一个实用的解决方案.
  • 这种方法可以为节能系统和智能建筑自动化做出重大贡献.
  • 拟议的模型显示了智能环境中的真实应用的潜力.