Smart Thermostats in Building Automation Systems and Smart Homes

Abstract

This article surveys the current achievements concerning smart thermostats for smart homes and smart buildings and observes the level of smartness of the microclimate control systems for buildings and rooms. The article shows the results of analyzes the advantages and disadvantages of the marketed solutions and sets requirements for smart thermostat as a component of building automation system. All solutions were classified at three "smartness levels", from zero to second, where zero level means simple manually adjusted temperature regulator, first level assumes working according the programmable schedule. The second level smart thermostat automatically generates and dynamically changes its schedule basing on the users' activity and presence, as well as on user-defined priorities. The article sets goals for further research in the field of self-learning algorithms for smart thermostat, because it seems actual to develop a self-learning smart thermostat capable of supporting a large buildings; it should analyze user behavior and effect control both at the level of a whole building and at the level of single rooms.

AI-Driven Urban Energy Solutions—From Individuals to Society: A Review

“…Its idea integrates supervised and reinforcement learning to address the challenge of meeting occupants' thermal comfort requirements while minimizing energy usage. Goman and Koroloev [30] present the newest achievements on smart thermostats in and smart buildings. These devices can contribute to energy savings and control.…”

Development of the temperature prediction algorithm for smart thermostat

“…Moreover, various systems that heat or cool a room have different performance and cost factor of energy that depend on external factors. An important task is to plan the operating modes, for example, consider the actual necessity in heating or cooling based on the number of people in rooms or on a schedule [6].…”

“…To conclude the aforesaid, the task of optimal temperature control in rooms can be formulated as follows: an optimal controller shall ensure the set quality of temperature control under a minimum power consumption or a minimum total cost of power consumed [6].…”

“…In connection with the mentioned above, the development of operating and learning algorithms for a smart thermostat is relevant. Solution of the following tasks was required for the development [6]:…”