Transfer Learning Model for Newly Setup Environment

A thermostatic device enhanced with a machine learning model that is pretrained from another environment, the device comprising:

• a temperature sensor;
• an interface to a communications network that is communicatively coupled to a server computer storing a plurality of pretrained thermostatic machine learning models;
• a memory configured to store a thermostatic machine learning model;
• a processor configured to:
• transmit, through the interface to the communications network, metadata corresponding to an environment of the thermostatic device;
• receive, through the interface, the thermostatic machine learning model for storage in the memory, wherein the thermostatic machine learning model is selected, by the server computer, based on the metadata, from among the plurality of pretrained thermostatic machine learning models stored at the server computer;
• detect, using the temperature sensor, a temperature of the environment of the thermostatic device to determine a temperature data;
• associate the temperature data with additional data comprising at least one of:
• an external temperature, external humidity, on-off status of a HVAC controlled by the thermostatic device, fan speed, and combination thereof;
• and
• wait a predetermined number of days before adjusting at least one of the fan speed, the temperature of the environment, or combination thereof, based on an output data from the thermostatic machine learning model.

A method involving a thermostatic system enhanced with a machine learning model that is pretrained from another environment, wherein the method comprises:

• transmitting, through an interface communicatively coupled to a server computer, metadata corresponding to an environment of a thermostatic device installed in a building;
• receiving, through the interface from the server computer, the thermostatic machine learning model for storage in a memory of the thermostatic device, wherein the thermostatic machine learning model is selected, by the server computer, based on the metadata, from among a plurality of pretrained thermostatic machine learning models stored at the server computer;
• detecting, using a temperature sensor of the thermostatic device, a temperature of the environment of the thermostatic device to determine a temperature data;
• associating the temperature data with additional data comprising at least one of:
• an external temperature, external humidity, on-off status of a HVAC controlled by the thermostatic device, fan speed, and combination thereof;
• and
• waiting a predetermined number of days before adjusting, by a processor of the thermostatic device, at least one of the fan speed, the temperature of the environment, or combination thereof, based on an output data from the thermostatic machine learning model.