Method and system for automatic detection of malfunctions/inefficient household electronic heating device

Abstract

The present invention provides a method for automatic detection malfunction or inefficiency of electronic heating device, the method comprising: acquiring data related to each monitored household for generating a training set, including power consumption of each electronic heating device, power consumption of each household, household profile parameters, and household residents' profile parameters; training an electric Heating classification model for identifying existence of electronic heating based on load data; Determining the of existence of electronic heating and type of the device based on the Heating classification model; Training an insights model based on daily load pattern to identify activation pattern of the electronic heating devices using periodic household power consumption readings with no temperature; Prediction Detection and Identification of HVAC activation pattern using Periodic household power consumption readings with no temperature; Clustering aggregating in winter time activation pattern into Bins based on temperature for identifying malfunctioning or inefficiency based on HVAC behavior in different temp bins.

What is claimed is: 
     
         1 . A method for automatic detection malfunction or inefficiency of electronic heating device, implemented by a server module and a plurality of household client modules, wherein each of said server module and plurality of household client modules comprising one or more processors, operatively coupled to non-transitory computer readable storage devices, on which are stored modules of instruction code, wherein execution of said instruction code by said one or more processors implements the following actions:
 acquiring data related to each monitored household for generating a training set including at least one of, power consumption of each electronic heating device, power consumption of each household, household profile parameters, and household residents' profile parameters;   training an electric Heating classification model for identifying existence of electronic heating based on load data   Determining the of existence of electronic heating and type of the device based on the Heating classification model;   Training an insights model based on daily load pattern to identify activation and activation pattern of the electronic heating devices using Periodic secured household power consumption readings with no temperature not including summer period;   Prediction Detection and Identification of HVAC activation, activation pattern based on pattern insights model using Periodic secured household power consumption readings with no temperature of summer period; and   Clustering aggregating in winter time activation pattern into Bins based on temperature for identifying malfunctioning or inefficiency based on HVAC behavior in different temp bins.   
     
     
         2 . The method of  1  further comprising the step of determining malfunction or inefficiency based on how the electronic heating device/HVAC performs on different weather scenarios by identifying exaptational activation pattern gular temperature. 
     
     
         3 . The method of  claim 1  further comprising the step of training an ML model based on winter labels and the aggregated metrics in temp bin and Identifying deviations of the HVAC performance at each temperature bin) for determining if the HVAC has a malfunction or inefficient or not. 
     
     
         4 . The method of  claim 1  further comprising the step of training an electric Heating classification model to identify existence of steady load electronic heating based on load data 
     
     
         5 . The method of  claim 1 , wherein the acquired data further include Thermostats reading data 
     
     
         6 . The method of  claim 1  wherein the clarification model is further based on Checking Sub-hourly load patterns—identifying if the load pattern have similar pattern to know types of load pattern which indicates HVAC activity. 
     
     
         7 . The method of  claim 1  wherein the clarification model is further based on Checking correlation between the load signal and the temperature signal. 
     
     
         8 . The method of  claim 1  wherein insights model is using Multi-output convolutional network. 
     
     
         9 . The methods of  claim 1  wherein insights model determine activation pattern of HVAC including several metrics concerning the performance of the HVAC including at least one of: percent of time active, Count of on/off switches, Activations during the nighttime. 
     
     
         10 . A systems for automatic detection malfunction or inefficiency of electronic heating device, implemented by a server module and a plurality of household client modules, wherein each of said server module and plurality of household client modules comprising one or more processors, operatively coupled to non-transitory computer readable storage devices, comprising the following modules:
 acquisition module for aggregating and acquiring data related to each monitored household for generating a training set, including at least one of, power consumption of each electronic heating device, power consumption of each household, household profile parameters, and household residents' profile parameters;
 Electric Heating classification Module configured for training an electric Heating classification model for identifying existence of electronic heating based on load data 
 Prediction module for electrical heating configured to determine the of existence of electronic heating and type of the device based on the Heating classification model; 
 insights model module configured for training an insights model based Daily load pattern to identify activation and activation pattern of the electronic heating devices using Periodic secured household power consumption readings with no temperature not including summer period; 
 Prediction module of HVAC activation pattern configured for prediction, detection and Identification of HVAC activation, activation pattern based on pattern insights model using Periodic secured household power consumption readings with no temperature of summer period; 
 Aggregation module configured to clustering and aggregating in winter time activation pattern into Bins based on temperature for identifying malfunctioning or inefficiency based on HVAC behavior in different temp bins. 
   
     
     
         11 . The system of  claim 10  wherein the prediction module further comprising the step of determining malfunction or inefficiency based on how the electronic heating device/HVAC performs on different weather scenarios by identifying exaptational activation pattern in regular temperature. 
     
     
         12 . The system of  claim 10  prediction module further comprising the step of training an ML model based on winter labels and the aggregated metrics in temp bin and Identifying deviations of the HVAC performance at each temperature bin) for determining if the HVAC has a malfunction or inefficient or not. 
     
     
         13 . The system of  claim 10  wherein the insight module further comprising the step of training an electric Heating classification model to identify existence of steady load electronic heating based on load data 
     
     
         14 . The system of  claim 10 , wherein the acquired data further include Thermostats reading data 
     
     
         15 . The system of  claim 10  wherein the clarification model is further based on Checking Sub-hourly load patterns—identifying if the load pattern have similar pattern to know types of load pattern which indicates HVAC activity based 
     
     
         16 . The system of  claim 10  wherein the clarification model is further based on Checking correlation between the load signal and the temperature signal. 
     
     
         17 . The system of  claim 10  wherein insights model is using Multi-output convolutional network. 
     
     
         18 . The system of  claim 10  wherein insights model determine activation pattern of HVAC including several metrics concerning the performance of the HVAC including at least one of: percent of time active, Count of on/off switches, Activations during the nighttime.