Abstract: Disclosed is a system and method for remote performance monitoring of a ventilation system. The method is independent of measuring electric meter current consumed or thermostatic data. The method uses two sensors to measure temperature and humidity. A first sensor is placed on a return grille, and a second sensor is placed on the supply grille. Using only data from the sensors, an on-off duty cycle is calculated. The on-off duty cycle is calculated for an on-time period during which the ventilation system is operating and an off-time period during which the ventilation system is not operating between the air as measured by the first sensor and the air as measured by the second sensor on the supply air grille. Machine learning with two or more datasets of the sensor data and the on-off duty cycle to indicate a component of the ventilation system is faulty.

Abstract: Disclosed is a system and method for remote performance monitoring of a ventilation system. The method is independent of measuring electric meter current consumed or thermostatic data. The method uses two sensors to measure temperature and humidity. A first sensor is placed on a return grill and a second sensor is placed on the supply grill. Using only data from the sensors, an on-off duty cycle is calculated. The on-off duty cycle is calculated for an on-time period during which the ventilation system is operating and an off-time period during which the ventilation system is not operating between the air as measured by the first sensor and the air as measured by the second sensor on the supply air grill. Machine learning with two or more datasets of the sensor data and the on-off duty cycle to indicating a component of the ventilation system is faulty.