In building management, energy optimization is one of the main concern that needs to be automated. For automation, an intelligent system needs to be developed. However, an intelligent system needs to be trained in a large dataset before it can be used reliably. In this paper, we present a transfer learning scheme to develop an intelligent system for smart building management system. Specifically, the intelligent system is able to count human inside a room, which can be utilized to adaptively adjust energy usage in a room. The transfer learning scheme employs a deep learning model that is pretrained on ImageNet dataset. To enable the human counting capability, the model is trained on a dataset specifically collected for human counting case.

中文翻译：

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将迁移学习用于智能建筑管理系统

在建筑物管理中，能源优化是需要自动化的主要问题之一。为了自动化，需要开发一个智能系统。但是，智能系统需要在大型数据集中进行训练，然后才能可靠地使用。在本文中，我们提出了一种转移学习方案，以开发用于智能建筑管理系统的智能系统。具体地，该智能系统能够对房间内的人进行计数，这可以用来自适应地调节房间内的能源使用。转移学习方案采用在ImageNet数据集上预先训练的深度学习模型。为了启用人类计数功能，在专门为人类计数案例收集的数据集上训练模型。