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.