US 12,422,158 B2
Energy management and smart thermostat learning methods and control systems
Brian Richard Butler, Centerville, OH (US); Kevin Patrick Hallinan, Dayton, OH (US); Kefan Huang, Miamisburg, OH (US); Abdulrahman Alanezi, Al-Jubail (SA); David Alexander Alfano, Lancaster, PA (US); Andrew M. Welch, Franklin, OH (US); and Stuart Keith Morgan, West Chester, OH (US)
Assigned to Copeland Comfort Control LP, St. Louis, MO (US)
Filed by Copeland Comfort Control LP, St. Louis, MO (US)
Filed on Aug. 30, 2022, as Appl. No. 17/823,150.
Claims priority of provisional application 63/260,719, filed on Aug. 30, 2021.
Prior Publication US 2023/0063986 A1, Mar. 2, 2023
Int. Cl. G05B 13/02 (2006.01); F24F 11/30 (2018.01); F24F 11/63 (2018.01); F24F 130/10 (2018.01)
CPC F24F 11/63 (2018.01) [F24F 11/30 (2018.01); G05B 13/027 (2013.01); F24F 2130/10 (2018.01)] 17 Claims
OG exemplary drawing
 
1. A method of HVAC system performance monitoring using a computing device connected to at least one thermostat of an HVAC system in a building including a fan, the method comprising:
receiving thermostat data from the thermostat, the thermostat data including temperature setpoint data, measured building temperature data, and HVAC operation data for an initial time period, the HVAC operation data including usage data for the HVAC system fan;
receiving weather data from a weather service for the initial time period;
synchronizing the thermostat data with the weather data with respect to time;
determining, by interpolation, intermediate data between any data that is non-uniformly spaced with respect to time in the synchronized thermostat and weather data;
inserting the intermediate data into the synchronized thermostat and weather data to generate synchronized thermostat and weather data that is uniformly spaced;
training at least one machine learning model using the synchronized thermostat and weather data;
monitoring performance of the HVAC system over time using the trained machine learning model by repeatedly:
receiving weather data from the weather service for a future time period after the initial time period;
receiving additional thermostat data for the future time period from the thermostat, the additional thermostat data including temperature setpoint data, measured building temperature data, and HVAC operation data for the future time period;
determining, based on the received additional thermostat data, an actual amount of heating or cooling and an actual amount of fan usage for the future time period;
determining, using the trained machine learning model and based on the received weather data for the future time period, an expected amount of heating or cooling and an expected amount of fan usage for the future time period with the HVAC system set at the determined temperature setpoint;
comparing the actual amount of heating or cooling to the expected amount of heating or cooling;
comparing the actual amount of fan usage to the expected amount fan usage; and
determining that the performance of the HVAC system has decreased when the actual amount of heating or cooling differs from the expected amount of heating or cooling by more than a first threshold amount or the actual amount of fan usage differs from the expected amount of fan usage by more than a second threshold amount; and
outputting an alert when the monitored performance of the HVAC system is determined to have decreased.