US20210372647A1

US20210372647A1 - Method and system for automatic detection of malfunctions/inefficient household electronic heating device

Info

Publication number: US20210372647A1
Application number: US16/889,111
Authority: US
Inventors: Eran Cohen; Eran Samuni; Noa RUSCHIN RIMINI
Original assignee: Grid4C Ltd
Current assignee: Grid4C Ltd
Priority date: 2020-06-01
Filing date: 2020-06-01
Publication date: 2021-12-02

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.

Description

FIELD OF THE INVENTION

The present invention generally relates to the field of monitoring electronic appliances, and particularly to the field of automatic detection of household appliance malfunction of inefficiency.

BACKGROUND ART

Summary of the Present Invention

The present invention provides 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 se, 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.

According to some embodiments of the present invention the method 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.
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/inefficient or not.
According to some embodiments of the present invention the method further comprising the step of training an electric Heating classification model to identify existence of steady load electronic heating based on load data According to some embodiments of the present invention the acquired data further include Thermostats reading data
According to some embodiments of the present invention 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
According to some embodiments of the present invention the clarification model is further based on Checking correlation between the load signal and the temperature signal.
According to some embodiments of the present invention the insights model is using Multi-output convolutional network.
According to some embodiments of the present invention the 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.
The present invention provides a system 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 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;

According to some embodiments of the present invention 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.
According to some embodiments of the present invention 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.
According to some embodiments of the present invention the training modules further comprising the step of training an electric Heating classification model to identify existence of steady load electronic heating based on load data According to some embodiments of the present invention wherein the acquired data further include Thermostats reading data.
According to some embodiments of the present invention 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.
According to some embodiments of the present invention the clarification model is further based on Checking correlation between the load signal and the temperature signal.
According to some embodiments of the present invention the insights model is using Multi-output convolutional network.
According to some embodiments of the present invention the insights model determining 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; of switches, Activations during the nighttime.

BRIEF DESCRIPTION OF THE DRAWINGS

For a better understanding of various embodiments of the invention and to show how the same may be carried into effect, reference will now be made, purely by way of example, to the accompanying drawings in which like numerals designate corresponding elements or sections throughout.

With specific reference now to the drawings in detail, it is stressed that the particulars shown are by way of example and for purposes of illustrative discussion of the preferred embodiments of the present invention only, and are presented in the cause of providing what is believed to be the most useful and readily understood description of the principles and conceptual aspects of the invention. In this regard, no attempt is made to show structural details of the invention in more detail than is necessary for a fundamental understanding of the invention, the description taken with the drawings making apparent to those skilled in the art how the several forms of the invention may be embodied in practice. In the accompanying drawings:

FIG. 1 presents a block diagram depicting an overview of an electronic heating malfunction system in accordance with some embodiments of the present invention.

FIG. 2 present high level overviews of the models of FIG. 1 in accordance with some embodiments of the present invention.

FIG. 3 is a flow diagram, depicting the function of a data acquisition module according to some embodiments of the present invention.

FIG. 4 is a flow diagram, depicting the function of a Electric Heating classification model according to some embodiments of the present invention.

FIG. 5 is a flow diagram, depicting the function of Electric Heating prediction model according to some embodiments of the present invention.

FIG. 6 is a flow diagram, depicting the function of the insights model Training set Daily load pattern module according to some embodiments of the present invention.

FIG. 7 is a flow diagram, depicting the function of the insights model Prediction Detection and Identification of HVAC according to some embodiments of the present invention.

FIG. 8 is a flow diagram, depicting the function of the Clustering activation pattern into Bins based on temperature according to some embodiments of the present invention.

FIG. 9 is a flow diagram, depicting the functionality of the malfunction training and prediction module (ML), according to some embodiments of the present invention.

DETAILED DESCRIPTION OF THE DRAWINGS

FIG. 1 presents a block diagram depicting an overview of a electronic heating malfunction systems in accordance with some embodiments of the present invention.
The electronic heating malfunction system is comprised of two sub systems: subsystem 100A for identifying existence of an electronic heating device specifically the existence of HVAC activation and a the second subsystem 100B for identifying/prediction of malfunctioning HVAC.
The first subsystem A, is comprised of: Disaggregation Data acquisition module 1200 reading periodic household power consumption readings with temperature data on summer/winter time (100), for proving training data to Electric Heating classification model 1300A, training to identify existence of electronic heating based on load data.
Additionally or alternatively the system comprises an Electric Heating classification model 1300B, for training to identify existence of steady load electronic heating based on load data
Based on one of the classification modules using the activation on load data of current tested house holds after the training period, is determined the existence of the of an electronic heating appliance.
The second subsystem is comprised of: insights model 1500 for training daily load pattern to identify activation and activation pattern using training data 1600 of periodic secured household power consumption readings with no temperature of summer period and regardless of time in day and prediction module 1700 for Detection and Identification of HVAC Activation and activation pattern based on pattern insights model. The second subsystem activation is based on the identification data of the existence of HVAC appliance in the household.
The subsystem B is further comprised of Aggregation model For training Clustered/aggregated in winter time activation pattern into Bins based on temperature for identifying malfunctioning of the HVAC or inefficient working of the HVAC based on HVAC behavior in different temp bins. The malfunction or inefficiency determination is based on how the HVAC performs on different weather scenarios. (identifying exaptational activation pattern in “regular temperature).
Additionally or alternatively the system comprise malfunction or inefficiency training and prediction module (ML) 1900
FIG. 2 present high-level overviews of the models of FIG. 1 in accordance with some embodiments of the present invention.
Modules 200 a & 200 b are configured to interface the server module 100 using any type of wired or wireless data communication standard (e.g. LAN, WAN, WiFi, GSM, 3GPP, LTE etc.), and convey to the server 100 data pertaining to a specific household. This data includes at least one of: the household's properties, the household's overall power consumption, concurrent temperature measurements, Thermostats reading data and data relating to electronic heating appliance installed therein.
The training set household client 200 a is comprised of at least one of the following sub modules:

- electronic heating appliance agent module 2100,
- Household power meter interface 2200 a,
- Thermostats reader 2300, and
- Client alerts module 2400.

The global household client 200 b is comprised of the following sub module:

- Household power meter interface 2200 b

The electronic heating appliance agent module 2100, acquires data relating to at east one of: electronic heating appliance activation time; and
The Household power meter interface 2200 a, acquires the power consumption of the household over time. According to some embodiments, the Household power meter interface 2200 a obtains household power consumption readings every 15/30/60 minutes from a smart household power meter.
The client configuration module 2300 provides an interface for introducing household-specific parameters. These parameters include at least one of:

- the electronic heating appliance properties (e.g. make and model);
- residents' profile parameters (e.g. number of residents, household occupancy throughout the day).

The server 100 is a module implemented in software or hardware or any combination thereof, configured to interface a plurality of household client modules 200 a & 200 b, according to some embodiments. The server 100 obtains from each of the plurality of household client modules 200 a & 200 b data pertaining to each respective household, said data including at least part of:

- electronic heating appliance activation time;
- thermostat reading;
- Frequent regular household power consumption readings;
- Indoor temperature; and
- Resident profile parameters.

According to some embodiments, the server module 100 also communicates with an administrative client module (not shown), which provides an administrative interface for system configuration, real-time alerts and production of historical reports.
The server module 100 includes several sub modules for analyzing said obtained data, identifying electronic heating appliance as efficient or inefficient, and alerting against suspected conditions of inefficiency or malfunction.
In first model 1000A, the sub-modules include at least one of the following:

- The data acquisition module 1100,
- Electric Heating classification model 1300,
- Prediction module for electrical heating 1400
- insights model Training set Daily load pattern module 1500
- Prediction module for HVAC activation pattern 1700
- Aggregation model by temp bin module 1800
- malfunction inefficiency training and prediction module (ML) 1900;

The data acquisition module 1100 accumulates real-time data from multiple private client modules, and stores it in a database for further processing.
FIG. 3 is a flow diagram, depicting the function of a data acquisition module according to some embodiments of the present invention. This module resides within the server 100, and accumulates data pertaining to specific households, both within the household training group and beyond it. The data accumulation module 1100 data, as detailed bellow, in a database for further analysis:
Acquiring periodic power consumption of each electronic heating within the household training set by the electronic heating agent module 1052
Acquiring periodic household-level power consumption readings 1054
Optionally acquiring household-specific residents' profile parameters (e.g. number of inhabitants, household occupancy throughout the day etc. 1056
Optionally acquiring the day of week and month of the year 1058;
acquiring environmental data including temperature 1060;
FIG. 4 is a flow diagram, depicting the function of a Electric Heating classification model according to some embodiments of the present invention.
The process of preparing the Electric Heating classification model comprise the followings steps:
Obtaining at least part of the following data in respect to each household within the training set: (step 1310)

- Total household power consumption, from the household power meter interface
- Electric Heating power consumption, from the electronic heating agent module
- Environmental data and Day of week and month of the year from the data acquisition module
- thermostat data measurement for identifying if the electric heating work or not applying machine learning algorithm (step 1320) in relation to all households in the training set, according to the said Obtained data, thus creating the “Electric Heating classification model”, to determine exitances of electrical heating device based on at least one of the fooling parameters:

1. Checking daily load in various temperature bins—if colder days have significantly higher load beyond predefined threshold
2. Checking Sub-hourly load patterns—identifying if the load pattern have similar pattern to know types of load pattern which indicates HVAC activity based
3. Checking correlation between the load signal and the temperature signal,
FIG. 5 is a flow diagram, depicting the function of Electric Heating prediction model according to some embodiments of the present invention.
The process of predicting existence of electrical heating device using the Electric Heating classification model comprise the followings steps:
Obtaining at least part of the following data in respect to each household after the training set: (step 1410)

- Total household power consumption, from the household power meter interface
- Electric Heating power consumption, from the electronic heating agent module
- Environmental data and Day of week and month of the year from the data acquisition module

Using “Hvac classification model”, to determine exitances of electrical heating device based on obtained data (step 1420);
The existence of electronic heating device can be achieved in alternative technique using the method described in patent application no. us 20190034817, as summarized bellow
The present invention provides a method for determining the presence of an appliance such as electronic heating device within a surveyed household, based on periodic surveying of the household's electric meter.
The method comprising the steps of: pre-processing groups of households of historical consumption of appliances based actual measurement performed by sensors associated with said appliances in relation to profile of household including characteristics of the household and/or lifestyle of the occupant and environmental time dependent parameters, and
determining the probability of each appliance presence and specifically electronic heating device at the surveyed household based on identified profile parameters and actual behavior pattern of the analyzed household based on sampled measurement taken at predefined discrete time periods such 15 minutes in relation to actual time dependent environmental parameters of the relevant time period, by processing identified statistical correlations between presence of appliances at each household and 1) household profiles parameters, 2) household actual periodic consumption pattern 3) household actual periodic consumption pattern relation to environmental time dependent parameters.
FIG. 6 is a flow diagram, depicting the function of the insights model Training set Daily load pattern module according to some embodiments of the present invention.
The process of preparing the insights model for predicting activation and activation pattern of an electronic heating devices comprises the followings steps:
Obtaining at least part of the following data in respect to each household within the training set houses that have both smart meter and smart thermostat (step 1510)

- Total household power consumption, from the household power meter interface
- Load patterns of the household power meter interface
- thermostat data measurement

applying training a machine learning algorithm (step 1520) in relation to all households in the training set using Multi-output convolutional network, according to the said obtained data, thus creating the “Hvac classification model daily activation pattern”, to 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
FIG. 7 is a flow diagram, depicting the function of the insights model Prediction Detection and Identification of HVAC according to some embodiments of the present invention.
The process of predicting activation and activation pattern of an electronic heating devices using the insights model for, comprises the followings steps:
Obtaining at least part of the following data in respect to each household from beyond the training set: (step 1710);

- Household power consumption, from the household power meter interface
- Day of week and month of the year from the data acquisition module
- Cooling day and heating day indication from the data acquisition module.

Using insights model Training after the training stage, to determine activation pattern of the HVAC, using obtained total household power consumption and load pattern with no temperature of summer period and regardless of time in day (step 1720);
FIG. 8 is a flow diagram, depicting the function of the Clustering activation pattern into Bins based on temperature according to some embodiments of the present invention.
The process of clustering activation pattern comprises at least one of following steps:
Each of the metrics of the insights model are aggregated by different temperature bins (step 1810);
removed from the data Days that are extremely hot or cold are sufficient load, and of predicted on percent from the daily model (step 1820);
Checking the HVAC performs on different weather scenarios. at each bin (step 1830);
Determining malfunction or inefficiency based on how the HVAC performs on different weather scenarios. (identifying exaptational activation pattern in “regular temperature” (step 1840);
Identifying deviations of the HVAC performance at each day/period in comparison to behavior of other HVAC in the same weather scenarios (step 1850);
Identifying deviations of the performance at each temperature bin vs behavior of other HVAC in the same temperature bins and the performance of this HVAC in different temperature bins to establish if this HVAC is malfunction/inefficient or not. (step 1860);
FIG. 9 is a flow diagram, depicting the functionality of the malfunction inefficiency training and prediction module (ML), according to some embodiments of the present invention.
The process of predicting the malfunction or inefficiency of electrical heating device using prediction ML model comprise the followings steps:
Obtaining at least part of the following data in respect to each household from beyond for training set: (step 1910);

- Household power consumption, from the household power meter irate
- Day of week and month of the year from the data acquisition module
- Environmental data including temperature
- metrics of the insights model aggregated by different temperature bins

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/inefficient or not, (step 1920);
Obtaining at least part of the following data in respect to each household from beyond the training set: (step 1930);
Household power consumption, from the household power meter ace

- Day of week and month of the year from the data acquisition module
- Environmental data including temperature
- of the insights model aggregated by different temperature bins
- Identified deviations of the HVAC performance at each temperature bin

removed from the data Days that are extremely hot or cold are sufficient load, and of predicted on % from the daily model, (step 1940);
Determining malfunction or inefficiency of HVAC using the ML model using on the metrics of the previous model are aggregated by different temperature bins, For this aggregation we only consider days that have clear signs of HVAC activity (sufficient load, and predicted on % from the daily model)—this is mainly because HVAC is not turned on every day during the winter, and some houses may have two different HVACs (primary HVAC system is gas run, and only secondary is electric), step 1950)
These features are later used to train a classifier. The classifier can be implemented for example using a gradient boosted tree-based algorithm described in
The system of the present invention may include, according to certain embodiments of the invention, machine readable memory containing or otherwise storing a program of instructions which, when executed by the machine, implements some or all of the apparatus, methods, features and functionalities of the invention shown and described herein. Alternatively or in addition, the apparatus of the present invention ay include, according to certain embodiments of the invention, a program as above which may be written in any conventional programming language, and optionally a machine for executing the program such as but not limited to a general purpose computer which may optionally be configured or activated in accordance with the teachings of the present invention. Any of the teachings incorporated herein may wherever suitable operate on signals representative of physical objects or substances.
Unless specifically stated otherwise, as apparent from the following discussions, it is appreciated that throughout the specification discussions, utilizing terms such as, “processing”, “computing”, “estimating”, “selecting”, “ranking”, “grading”, “calculating”, “determining”, “generating”, “reassessing”, “classifying”, “generating”, “producing”, “stereo-matching”, “registering”, “detecting”, “associating”, “superimposing”, “obtaining” or the like, refer to the action and/or processes of a computer or computing system, or processor or similar electronic computing device, that manipulate and/or transform data represented as physical, such as electronic, quantities within the computing system's registers and/or memories, into other data similarly represented as physical quantities within the computing system's memories, registers or other such information storage, transmission or display devices. The term “computer” should be broadly construed to cover any kind of electronic device with data processing capabilities, including, by way of non-limiting example, personal computers, servers, computing system, communication devices, processors (e.g. digital signal processor (DSP), microcontrollers, field programmable gate array (FPGA), application specific integrated circuit (ASIC), etc.) and other electronic computing devices.
The present invention may be described, merely for clarity, in terms of terminology specific to particular programming languages, operating systems, browsers, system versions, individual products, and the like. It will be appreciated that this terminology is intended to convey general principles of operation clearly and briefly, by way of example, and is not intended to limit the scope of the invention to any particular programming language, operating system, browser, system version, or individual product.
It is appreciated that software components of the present invention including programs and data may, if desired, be implemented in ROM (read only memory) form including CD-ROMs, EPROMs and EEPROMs, or may be stored in any other suitable typically non-transitory computer-readable medium such as but not limited to disks of various kinds, cards of various kinds and RAMs. Components described herein as software ma y, alternatively, be implemented wholly or partly in hardware, if desired, using conventional techniques. Conversely, components described herein as hardware may, alternatively, be implemented wholly or partly in software, if desired, using conventional techniques.
Included in the scope of the present invention, inter alia, are electromagnetic signals carrying computer-readable instructions for performing any or all of the steps of any of the methods shown and described herein, in any suitable order; machine-readable instructions for performing any or all of the steps of any of the methods shown and described herein, in any suitable order; program storage devices readable by machine, tangibly embodying a program of instructions executable by the machine to perform any or all of the steps of any of the methods shown and described herein, in any suitable order; a computer program product comprising a computer useable medium having computer readable program code, such as executable code, having embodied therein, and/or including computer readable program code for performing, any or all of the steps of any of the methods shown and described herein, in any suitable order; any technical effects brought about by any or all of the steps of any of the methods shown and described herein, when performed in any suitable order; any suitable apparatus or device or combination of such, programmed to perform, alone or in combination, any or all of the steps of any of the methods shown and described herein, in any suitable order; electronic devices each including a process or/and a cooperating input device and/or output device and operative to perform in software any steps shown and described herein; information storage devices or physical records, such as disks or hard drives, causing a computer or other device to be configured so as to carry out any or all of the steps of any of the methods shown and described herein, in any suitable order; a program pre-stored e.g. in memory or on an information network such as the Internet, before or after being downloaded, which embodies any or all of the steps of any of the methods shown and described herein, in any suitable order, and the method of uploading or downloading such, and a system including server/s and/or client/s for using such; and hardware which performs any or all of the steps of any of the methods shown and described herein, in any suitable order, either alone or in conjunction with software. Any computer-readable or machine-readable media described herein is intended to include non-transitory computer- or machine-readable media.
Any computations or other forms of analysis described herein may be performed by a suitable computerized method. Any step described herein may be computer-implemented. The invention shown and described herein may include (a) using a computerized method to identify a solution to any of the problems or for any of the objectives described herein, the solution optionally include at least one of a decision, an action, a product, a service or any other information described herein that impacts, in a positive manner, a problem or objectives described herein; and (b) outputting the solution.
The scope of the present invention is not limited to structures and functions specifically described herein and is also intended to include devices which have the capacity to yield a structure, or perform a function, described herein, such that even though users of the device may not use the capacity, they are, if they so desire, able to modify the device to obtain the structure or function.
Features of the present invention which are described in the context of separate embodiments may also be provided in combination in a single embodiment.
For example, a system embodiments intended to include a corresponding process embodiment. Also, each system embodiment is intended to include a server-centered “view” or client centered “view”, or “view” from any other node of the system, of the entire functionality of the system, computer-readable medium, apparatus, including only those functionalities performed at that server or client or node.

Claims

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.