Temperature preference learning by suggestion and user acceptance

A computing device configured for relative temperature preference learning, comprising:

• a processor;
• memory in electronic communication with the processor, wherein the memory stores computer executable instructions that when executed by the processor cause the processor to perform the steps of:
• identifying one or more current settings of a thermostat, wherein the thermostat is a component of a heating ventilation air conditioning (HVAC) system located at a premises, wherein the one or more current settings of the thermostat include at least one of a current target temperature, current runtime settings, and current airflow settings of the HVAC system;
• identifying historical temperature data stored in a database, the historical temperature data comprising a plurality of historical target temperatures, wherein each record of the plurality of historical target temperatures is associated with a historical indoor temperature and a historical outdoor temperature;
• calculating an average indoor differential, based at least in part on a set of calculated differentials between the identified historical target temperatures and associated historical indoor temperatures;
• calculating an average outdoor differential, based at least in part on a set of calculated differentials between the identified historical target temperatures and associated historical outdoor temperatures;
• generating one or more suggested target temperatures based at least in part on the average indoor differential and the average outdoor differential;
• calculating one or more real-time energy costs associated with each respective suggested target temperature of the one or more suggested target temperatures;
• communicating, over a time period, the one or more suggested target temperatures and each of the real-time energy costs associated with the one or more suggested target temperatures to an occupant of the premises;
• tracking, over the time period, user acceptance of the one or more suggested target temperatures;
• and
• learning temperature preferences for subsequent target temperatures based on an analysis of the tracked user acceptance of the one or more suggested target temperatures.

A non-transitory computer-readable storage medium storing computer executable instructions that when executed by a processor cause the processor to perform the steps of:

• identifying one or more current settings of a thermostat, wherein the thermostat is a component of a heating ventilation air conditioning (HVAC) system located at a premises, wherein the one or more current settings of the thermostat include at least one of a current target temperature, current runtime settings, and current airflow settings of the HVAC system;
• identifying historical temperature data stored in a database, the historical temperature data comprising a plurality of historical target temperatures, wherein each record of the plurality of historical target temperatures is associated with a historical indoor temperature and a historical outdoor temperature;
• calculating an average indoor differential, based at least in part on a set of calculated differentials between the identified historical target temperatures and associated historical indoor temperatures;
• calculating an average outdoor differential, based at least in part on a set of calculated differentials between the identified historical target temperatures and associated historical outdoor temperatures;
• generating one or more suggested target temperatures based at least in part on the average indoor differential and the average outdoor differential;
• calculating one or more real-time energy costs associated with each respective suggested target temperature of the one or more suggested target temperatures;
• communicating, over a time period, the one or more suggested target temperatures and each of the real-time energy costs associated with the one or more suggested target temperatures to an occupant of the premises;
• tracking, over the time period, user acceptance of the one or more suggested target temperatures;
• and
• learning temperature preferences for subsequent target temperatures based on an analysis of the tracked user acceptance of the one or more suggested target temperatures.