论文标题
迈向智能可持续城市：使用歧视模型在建筑管理系统中解决语义异质性
Towards Smart Sustainable Cities: Addressing semantic heterogeneity in building management systems using discriminative models
论文作者
论文摘要
建筑管理系统（BMS）对于驱动智能可持续城市至关重要。这是由于他们已经有效地大大减少建筑物的能源消耗而有效。典型的BMS由智能设备组成，它们相互通信以实现其目的。但是，这些设备的异质性及其相关的元数据阻碍了取决于这些设备之间相互作用的解决方案的部署。但是，使用数据驱动方法自动推断这些设备的语义为这些异质性带来的问题提供了理想的解决方案。在本文中，我们进行了一项多维研究，以解决使用机器学习模型推断物联网设备语义的问题。使用从物联网设备收集超过6700万个数据点的两个数据集，我们开发了产生竞争结果的歧视模型。特别是，我们的研究突出了图像编码时间序列（IET）的潜力，作为基于统计特征的推理方法的强大替代方法。我们的评估仅利用基于功能方法所需的数据的一小部分，表明，在许多情况下，这种编码与传统方法的竞争甚至优于传统方法。
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.
