An IoT-Based Thermal Model Learning Framework for Smart Buildings

Abstract

With the development of the Internet of Things (IoT) technologies, implementing intelligent controls in buildings to reduce energy consumption is becoming increasingly popular. Building climate control is of strong research interest due to high energy savings potentials associated with heating, ventilation, and air-conditioning (HVAC) controls. Because the operation of HVAC systems directly influences occupant thermal comfort, building-specific thermal models are needed for proper control. Such models describe how indoor temperature changes under different environmental conditions and HVAC status. Previous studies mainly developed thermal models for accurate building thermal simulation which are not practical for use in real-world applications as they require domain knowledge and lengthy building-by-building configuration. In this article, taking advantage of IoT technologies, a plug-and-play learning framework is proposed to automatically identify the thermal model of each thermal zone in a building without manual configuration. In contrast to the existing methods, a thermal model is learned using low-resolution temperature readings from IoT-based smart thermostats. The learning framework has been validated using the data collected from an IoT platform installed in a building over a summer. The performance of the learned model and the error of indoor temperature prediction based on this model have been evaluated and quantified. Findings demonstrate the learning process can be automated with either edge-computing or cloud-computing. This article shows the validity of the proposed IoT-based thermal model learning framework and offers a pragmatic solution for providing reliable thermal models to future smart building climate controls.