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AssigneeGrid4C
InventorsEran SAMUNI, Eran COHEN, Nathaniel SHIMONI, Noa RIMINI
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The present invention provides a method for monitoring a plurality of heating, ventilation, and air conditioning (HVAC) systems and predicting inefficient HVAC operation, implemented by one or more processors operatively coupled to a non-transitory computer readable storage device, on which are stored modules of instruction code that when executed cause the one or more processors to perform the following steps: Obtaining first training data for HVACs in a training set of households during a first period of spring weather; Obtaining second training data for HVACs in the training set of households during a period of summer weather, Preprocessing the training data to identify repeating patterns of HVAC coemption or generating additional derived parameters, in an aggregation process Calculating a “Household Efficiency Score”: the amount of energy required to change house temperature; Applying the first training data and the classification labels to train a supervised machine learning algorithm, to generate an HVAC classification model predictive of inefficiency during periods of summer weather conditions; Obtaining operational data pertaining to HVACs in an operational set of households during a second period of spring weather; and Applying the HVAC classification model to predict inefficiency of HVACs at individual households in the operational set, during periods of summer weather using only overall household power consumption.
A method for monitoring a plurality of heating, ventilation, and air conditioning (HVAC) systems and predicting inefficient HVAC operation, implemented by one or more processors operatively coupled to a non-transitory computer readable storage device, on which are stored modules of instruction code that when executed cause the one or more processors to perform the following steps:
A system for monitoring a plurality of heating, ventilation, and air conditioning (HVAC) systems and predicting inefficient HVAC operation, said system comprising a non-transitory storage device and one or more processing devices operatively coupled to the storage device on which are stored modules of instruction code executable by the one or more processors:
The system of claim 13 wherein the training data include:
The system of claim 13, wherein the calculating further include calculating HVAC Linear Coefficient and Breakpoint Temperature Difference.
The system of claim 13 further comprising classification module distinguishes between efficient and inefficient households, wherein inefficient is determined in case not being able to reach within predefined time period of the predetermined desired set temperature.
The system of claim 13 wherein a daily score for the HVAC efficiency is based on pattern recognition algorithms for identifying repeating pattern behavior of normal behavior of the HVAC.
The system of claim 13 wherein the power consumption measurement readings are measured granularity of a single reading each predefined interval from a smart household power meter.
The system of claim 13 wherein the HVAC efficiency classification are further based on additional classifiers, including at least one of:
The system of claim 13 wherein the training include acquire, during periods of second predefined period weather, data which indicates whether the HVAC has efficiently reached the thermostat setting temperature, this data serves as feedback for supervising the training of the HVAC efficiency classification model.
The method of claim 13 wherein the training process include generating additional derived parameters including the distribution of monthly HVAC efficiency scores over different temperature bins, which indicate HVAC responsiveness to temperature.
The system of claim 13 wherein in case of a suspected malfunction, calculates the excess in power consumption in comparison with similar households, to produce a malfunction “price tag”.
Dependencies are inferred from the claim text.