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arXiv 2008.07414cs.CY
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迈向智慧可持续城市:使用判别模型解决建筑管理系统中的语义异质性

Towards Smart Sustainable Cities: Addressing semantic heterogeneity in building management systems using discriminative models

Chidubem Iddianozie, Paulito Palmes

摘要:

建筑管理系统(BMS)在推动智能可持续城市的发展中至关重要。这是因为它们在显著降低建筑物的能源消耗方面是有效的。典型的BMS由智能设备组成,这些智能设备相互通信以实现其目的。然而,这些设备及其相关元数据的异构性阻碍了依赖于这些设备之间的交互的解决方案的部署。尽管如此,使用数据驱动的方法自动推断这些设备的语义为这种异构性带来的问题提供了理想的解决方案。在本文中,我们进行了多维度的研究,以解决使用机器学习模型推断物联网设备的语义的问题。使用从物联网设备收集的超过6700万个数据点的两个数据集,我们开发了产生竞争结果的判别模型。特别是,我们的研究突出了图像编码时间序列(IETS)作为一个强大的替代统计特征为基础的推理方法的潜力。仅利用基于特征的方法所需的一小部分数据,我们的评估表明,这种编码在许多情况下与传统方法竞争,甚至优于传统方法。

英文摘要:

Building Management Systems (BMS) are crucial in the drive towards smart sustainable cities. This is due to the fact that they have been effective in significantly reducing the energy consumption of buildings. A typical BMS is composed of smart devices that communicate with one another in order to achieve their purpose. However, the heterogeneity of these devices and their associated meta-data impede the deployment of solutions that depend on the interactions among these devices. Nonetheless, automatically inferring the semantics of these devices using data-driven methods provides an ideal solution to the problems brought about by this heterogeneity. In this paper, we undertake a multi-dimensional study to address the problem of inferring the semantics of IoT devices using machine learning models. Using two datasets with over 67 million data points collected from IoT devices, we developed discriminative models that produced competitive results. Particularly, our study highlights the potential of Image Encoded Time Series (IETS) as a robust alternative to statistical feature-based inference methods. Leveraging just a fraction of the data required by feature-based methods, our evaluations show that this encoding competes with and even outperforms traditional methods in many cases.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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