Mechanical Engineering ETDs

Publication Date

Fall 12-14-2018


As part of an effort to achieve a better balance between the power demand and supply, load (demand) control simulation framework is previously developed. In the framework, HVAC load is used as a control resource, driven by the thermostat logic modeled within the framework. However, it has not been proved, whether a com- mercial thermostat is able to perform modeled thermostat’s features. Therefore, it is desired to integrate a commercial thermostat to the framework, to verify its capabil- ity of participating and performing the Demand Response (DR) scheme developed. A ‘Nest Learning Thermostat’ is selected as a commercial thermostat. Nest Application Programming Interface (API) is used to import response of the Nest thermostat, as well as to input desired settings on the thermostat. A PID control system is imple- mented to regulate the temperature of a physical chamber, where the Nest thermostat is installed. As a result, the Nest thermostat is verified to be capable of responding to the DR signal. Also, simulation of the Nest thermostat is obtained by modeling its inertial behavior and switching logics empirically observed. A few limitations of the current study is introduced, and future work to overcome the limitations is proposed.


Demand response, smart thermostat, smart grid

Degree Name

Mechanical Engineering

Level of Degree


Department Name

Mechanical Engineering

First Committee Member (Chair)

Andrea Mammoli

Second Committee Member

Peter Vorobieff

Third Committee Member

Ali Bidram

Document Type