Abstract:
A method of identifying energy consumption associated with at least one appliance is provided. The method includes measuring an energy consumption signal, obtaining publicly available information of a location of the at least one appliance and estimating a plurality of probabilities of energized appliances based on the energy consumption signal and the publicly available information. The method further includes generating a new combination of the estimated plurality of probabilities of energized appliances and decomposing the at least one energy consumption signal into constituent individual loads and corresponding energy consumption.

Description:
BACKGROUND 
       [0001]    This invention relates generally to electric energy consumption measurement, and, more specifically, to load identification using cascaded cognitive learning. 
         [0002]    With the rising cost of energy/electricity, consumers are becoming more conscious of their consumption and more thoughtful in terms of sustainable energy planning. An itemized electricity bill indicating the energy consumption of each household appliance would provide useful information for consumers to consider. However, customers do not want to incur the expense of additional energy meters for measuring energy or power consumption of individual appliances. Non-intrusive appliance load monitoring (NIALM) has been attempted to identify electric appliances in a small building, such as a household, by monitoring a load profile signature of the whole household load at a single point with one recording device (that is, without individual meters on the appliances). 
         [0003]    One product that decomposes a signal measured at an incoming power meter into its constituent individual loads is known as Single Point End-use Energy Disaggregation (SPEED™), and is available from Enetics, Inc. of New York. The SPEED product uses an appliance template to describe the operating characteristics of appliances likely to be found in the home. If the appliance characteristics fall within the template parameters, the system can identify the appliances fairly well. Unfortunately, given the wide range of appliance parameters in the industry, the system has trouble identifying individual appliances in a high percentage of installations without modifying the template parameters. 
         [0004]    Another embodiment is described in commonly assigned US20090045804, which is herein incorporated by reference, wherein one embodiment is directed to an electric power meter comprising: at least one sensor configured to measure at least one desired energy consumption variable associated with a plurality of energy consumption devices and a decomposition module configured to decompose at least one output signal from the sensor into constituent individual loads and therefrom identify energy consumption corresponding to each energy consumption device. In one example, the power meter includes data fusion from multiple diverse sensors such as time, date, temperature, security systems, TVs, and computer networks to provide enhanced load definitions and does not require field training of parameters to generate desired results. The power meter, in one embodiment, is configured to communicate directly with smart appliances over a power line carrier, a wireless link, or other suitable communication means. 
         [0005]    Although several decomposition techniques have been proposed, a need still exists for a more comprehensive electric energy/power meter. 
       BRIEF DESCRIPTION 
       [0006]    In accordance with an exemplary embodiment of the present invention, an energy measurement system comprises: at least one sensor configured to measure at least one output signal associated with a plurality of appliances; an orientation module configured to gather publicly available information associated with a location of the appliances; a planning module configured to generate an appliance database based on an input signal from the orientation module; a decomposition module configured to decompose the at least one output signal into constituent individual loads and therefrom identify energy consumption corresponding to each appliance based on the appliance database; and a communication interface configured to transmit the decomposed output signal. 
         [0007]    In accordance with another exemplary embodiment of the present invention, a method of identifying energy consumption associated with at least one appliance is provided. The method includes measuring an energy consumption signal, obtaining publicly available information of a location of the at least one appliance and estimating a plurality of probabilities of energized appliances based on the energy consumption signal and the publicly available information. The method further includes generating a new combination of the estimated plurality of probabilities of energized appliances and decomposing the at least one energy consumption signal into constituent individual loads and corresponding energy consumption. 
         [0008]    In accordance with yet another exemplary embodiment of the present invention, an energy measurement system is provided. The system includes at least one sensor configured to measure at least one output signal associated with a plurality of appliances and a communication interface configured to transmit the at least one output signal to a remote utility station. The system further includes an orientation module configured to gather publicly available information associated with a location of the appliances and a planning module to generate an appliance database based on an input signal from the orientation module. A decomposition module is also provided in the system to decompose the at least one output signal into constituent individual loads and therefrom identify energy consumption corresponding to each appliance based on the appliance database. The orientation module, the planning module and the decomposition module are located at the remote utility station. 
     
    
     
       DRAWINGS 
         [0009]    These and other features, aspects, and advantages of the present invention will become better understood when the following detailed description is read with reference to the accompanying drawings in which like characters represent like parts throughout the drawings, wherein: 
           [0010]      FIG. 1  is a diagrammatical representation of an energy measurement system with a cognitive electric energy meter in accordance with an embodiment of the present invention; 
           [0011]      FIG. 2  is a diagrammatical representation an example itemized electric bill; 
           [0012]      FIG. 3  is a diagrammatical representation of a cognitive electric energy meter system broken into components in accordance with an embodiment of the present invention; 
           [0013]      FIG. 4  is a diagrammatical representation of a cognitive decomposition algorithm in accordance with an embodiment of the present invention; and 
           [0014]      FIG. 5  is a diagrammatical representation of an exemplary cascaded Bayesian network in accordance with an embodiment of the present invention. 
       
    
    
     DETAILED DESCRIPTION 
       [0015]    As discussed in detail below, embodiments of the present invention function to provide a system and a method that employs intelligence to decompose an energy signal measured at a meter into its constituent individual loads and to provide a usage summary to the consumer with no in home field installation cost and with no requirements for special sensors, interactions with the loads, or specifications on the loads. 
         [0016]      FIG. 1  shows an energy measurement system  10  with a cognitive electric energy meter  12  in accordance with an embodiment of the present invention.  FIG. 1  further illustrates various electrical loads  14  in a household. In one embodiment, the electric energy meter  12  includes voltage and current sensors or an energy sensor  16  and an intelligence unit  18  to decompose one measured load signal or energy consumption signal  20  into its constituents  22 . It should be noted here that the shown measured constituents  22  in  FIG. 1  are exemplary and that the measured constituents depend on the actual electrical load in the household. The cognitive electric energy meter  12  uses model based intelligence to decompose the load signal that is already measured at the incoming meter  16  into its constituent individual loads and may be used to provide a usage summary to the consumer with no in home installation of additional sensors. The intelligence unit  18  may be co-located with the energy meter  16  or may be at a location away from the energy meter  16 . It should be noted that the terms energy meter and the power meter have been used here interchangeably as energy can be determined by multiplying the power by the time. 
         [0017]    The cognitive electric power meter  12  allows a power utility provider to provide the consumer with a detailed electric bill showing individual loads usage, without requiring installation of invasive and expensive sensors on each of the branch circuit loads. This may be used to provide the consumer with a first order and persistent energy audit each month in order to help the consumer know how electricity is being used, and may drive conservation, maintenance, or appliance upgrade decisions. 
         [0018]    A typical consumer electric bill shows simply the difference between the meter reading at the beginning and end of the month to calculate total energy consumption, and then may provide a comparison to last year&#39;s bill for the similar period as well as previous monthly energy consumption statistics. 
         [0019]    As described in aforementioned US20090045804 and shown in  FIG. 2 , an exemplary itemized electric bill  40  may provide an estimate for each of the electric loads typically found in a home, a comparison to local peers for the same period, the national average, and the Department of Energy (DoE) goal. Such bills can serve as a first order energy audit to enable consumers to make better decisions about investing in new and more efficient technologies. 
         [0020]      FIG. 3  shows a cognitive electric energy measurement system  50  broken into components in accordance with an embodiment of the present invention. The system  50  includes a sensing module  52 , a database module  54 , an orientation module  56 , a planning module  57 , a decomposition module  58 , and an act module/communication interface  60 . The sensing module  52  typically includes energy sensors  62 , edge detector circuitry  64  for the energy sensors, and may further include environmental sensors  66 ; the database module  54  may include a local information database  68 , an appliance template database  69  and an Internet database  70 . The decomposition module  58  may include an appliance probability estimator  72  and appliance combinatorial estimator  74 . 
         [0021]    The energy sensors  62  include meters for measuring voltages, currents, admittances or impedances from any two phases and a neutral wire at the consumer location and may be used for computing instantaneous power and thus providing an energy consumption signal. Energy sensors  62  typically further include a timer to measure the time of day and date. In an alternative embodiment, the time and date information may be obtained from one or more sources such as a radio, wire, IP network, or other means. The date and time data can be used to help reduce error and to simplify the cognitive decomposition algorithms. In another embodiment, real and reactive admittance data or impedance data may be calculated from the measured voltages and currents, and significant step changes in admittance data or impedance data may then be identified. Real and reactive step changes, or edge data, refers to the change in admittance or impedance or power measured by the voltage and current sensors in the sensing module  52  every time an appliance turns ON or OFF. In one embodiment, the environmental sensors  66 , such as temperature and humidity sensors may be used, or such data may be obtained from database module  54 . The observed and estimated data from the sensing module  52  is supplied to the decomposition module  58 . In one embodiment, the decomposition module uses knowledge of installation location of the cognitive energy meter system to gather additional Meta data related to the customer site. Hence, in one embodiment, the location data is configured in the local information database  68  by the utility at installation time. In another embodiment, a global positioning system (GPS) module may be used along with the sensing module to detect the consumer location data. 
         [0022]    In the embodiment of  FIG. 3 , the decomposition module  58  further utilizes inputs from an appliance database  76 . The orientation module  56  collects data from the local database  68 , the Internet database  70  and the appliance template database  69  and provides it as an input signal to the planning module  57 , which then processes the data to generate the appliance database  76 . The local database  68 , when available, comprises information regarding types of appliances and number of appliances in the house and the house location. In one embodiment, the Internet databases  70  may include aerial imagery of a house from a Google Maps™ mapping service or the house details from Zillow.com® real estate service. The information obtained from Internet databases may be used to determine if the consumer location has a swimming pool in the backyard or to find out home details such as home value, square footage, number of stories, number of bedrooms and bathrooms, type of heating and cooling system, and year built, for example. If environmental sensors  66  (such as temperature and humidity sensors) are not present, environmental data may be obtained by internet databases  70 , if desired. The appliance template database  69  has information regarding the typical maximum and minimum power levels for appliances, and typical duty cycles of appliances. The planning module  57  uses the data received by orientation module  56  from the databases  68 ,  69 ,  70  to fill the appliance database with the probability that a particular appliance is installed in the house, and the probability it is on during a specific time (season, time-of-day, etc). In one embodiment, the probability is based on the size of the home, location, type of heating and cooling system, swimming pool availability, number of bedrooms and bathrooms, and whether or not city water exists or a well is required. It should be noted that the above parameters to build the appliance database are exemplary and any other such parameters may also be used to build the probability model. The appliance database is then fed as input to the decomposition module  58 . 
         [0023]    The decomposition module  58 , in an exemplary embodiment, comprises an Appliance Probability Estimator (APE)  72  and an Appliance Combinational Estimator (ACE)  74 . APE  72  is used for estimating the appliance state matrix (ON or OFF), which contains the estimated state for each possible appliance in the home, given data from the appliance database  76  and measured data from sensing module  52 . APE  72  also computes the confidence in the state matrix estimate and estimates the total power in the home based on the appliance state and the nominal power consumption of the appliance. The ACE  74  takes the outputs from the APE  72  and computes the difference between the total measured power in the home and the estimated power consumed in the home. If the residual power value is less than a power threshold, the current appliance state matrix is accepted. If not, a new combination of possible appliances in the home is generated by ACE  74 , and the new combination of appliances is fed back as input to APE  72 . In one embodiment, a genetic algorithm is used to generate a new combination of appliances from possible appliances in the appliance database. In this way, the decomposition module  58  generates an appliance state matrix providing information about number of appliances and their states, on or off. In one embodiment, the probabilities of the appliance states determined in the appliance state matrix are compared with a confidence threshold and if the probability values are higher than the confidence threshold, the appliance database is updated with the measured values for average power level, duty cycle, etc. using a feedback loop  77 . In one embodiment, the confidence threshold may have a value of 90% (in other words, the confidence of appliance state matrix being accurate is 90%). In this way, the cognitive meter  50  can learn the appliance parameters for the specific appliances in the home, instead of relying on the data from the appliance template database. The act module/communication interface  60  computes the energy usage of each appliance based in the appliance state and a time interval (nominally monthly), and communicates the results. In one embodiment, the communication is to the utility which then incorporates the information into the consumer&#39;s bill. In one embodiment, the communication interface  66  may include an RS232/USB/Firewire interface, an Ethernet interface, a Wifi interface, a wireless USB interface, or a cellular/WMAN interface. In one embodiment, the communication interface  66  may transmit the data from the sensing module  52  to a remote utility station and the database module  54 , the orientation module  56 , the planning module  57  and the decomposition module  58  may be installed at the remote utility station for decomposing the energy consumption signal into various appliance signals. In another embodiment, the processing is done within the meter itself with the communication interface being coupled from the meter to the utility or to the consumer, for example. 
         [0024]    A more detailed description of the APE  72  and ACE  74  is provided below.  FIG. 4  illustrates the cognitive decomposition algorithm  90  formed by the APE  72  and ACE  74  used in the decomposition module  58  of  FIG. 3  in accordance with an embodiment of the present invention. Module  94  forms the APE  72 , and Modules  96 ,  100 ,  102 ,  104  comprise the ACE  74 . APE  72  uses a priori knowledge about the residence or commercial establishment such as dwelling size, dwelling age, occupant demographic, temperature, humidity, time, power measurement etc. obtained in step  92 , as provided by Sensing Module  52  and Appliance Database  76 . The data in step  92  may be obtained from public/internet databases and various sensors as described earlier. In step  94 , the Appliance Probability Estimator (APE) (element  72  in  FIG. 3 ) is used to estimate an appliance probabilistic model representing appliances detected with varying degrees or rates of confidence. In one embodiment, the APE utilizes a Bayesian Network (BN) algorithm. In another embodiment, other probabilistic techniques such as Markov Chain or Hidden Markov Model may alternatively or additionally be employed. The appliance probabilistic model estimates the appliance status, ON or OFF. It is achieved by monitoring changes in power levels or admittance or impedance on one or two phases in the system and associating them with the knowledge about the residential or commercial establishments and the typical power levels of appliance as provided by the appliance database  76 . The APE determines the probability of an appliance being ON or being energized at the time of interest. It also determines the total power appliances may consume when ON. In one embodiment, the output of the APE may be a state matrix such as A=[1 0 1 0 1], wherein matrix A represents one appliance model and each element in the matrix represents a particular appliance. For example, first column of the matrix A may represent an Air Conditioner (AC) or the third column of the matrix A may represent a Pool Pump. Finally, the value of the matrix element represents the status of the particular appliance, with one example of 1 representing a corresponding appliance is ON and 0 representing the corresponding appliance is OFF. Thus, in one embodiment, the APE provides such a matrix with varying rates of confidence or probabilities. It also provides an estimate of the total power consumed in the house based on the appliance state and the nominal appliance power consumptions contained in the appliance database  76 . 
         [0025]    In step  96 , the estimated total power computed from step  94  is compared against the total measured power. If the difference between the estimated total power and the measured total power is less than a power threshold value then the estimated appliance state matrix is provided as output in step  98 . However, if the difference between estimated total power and the measured total power is higher than the power threshold, an Appliance Combinatorial Estimator (ACE) (element  74  in  FIG. 3 ) is used to estimate a new probability of appliances as shown by blocks  100 ,  102  and  104  and by providing a learning feedback loop  106 . In one embodiment, the ACE comprises a genetic algorithm (GA). As will be appreciated by those skilled in the art, a GA is a search technique used to find exact or approximate solutions to search problems, for example in this case, an appliance status matrix. In step  100 , a schema is determined based on a BN rate of confidence. As will be appreciated by those skilled in the art, a schema is a template that identifies a subset of strings with similarities at certain string positions. In one embodiment, the schema may look like B=[1 * 1 * *] for the earlier example of matrix A. In one example, the schema includes all those appliance matrices or appliance combinations where the AC and the Pool Pump are ON. 
         [0026]    In step  102 , the genetic algorithm is run on the schema determined in step  100 . In step  104 , the new probabilities are estimated by; taking BN confidence rates from step  94  into consideration and finding combinations of appliances that would need to be ON to match total power measured at the meter preserving these appliances. The GA output from step  104  is then fed back into the BN of step  94  in a form of an evidence node. The node would provide a TRUE if GA suggests a particular appliance is ON, and FALSE if the GA suggests that the appliance is OFF. APE of step  94  then uses this information to re-estimate appliances&#39; on/off status. The loop  106  continues until APE and ACE reach a stable set of appliances that are ON. 
         [0027]    As will be appreciated by those skilled in the art, a BN is a directed graphical model, and the heart of the BN algorithm lies in the celebrated inversion formula, 
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         [0000]    where, H and e are two events, while p(H|e) represents probability that event H will occur given event e. Similarly, p(e|H) represents probability of occurrence of event e given event H and p(H) and p(e) are general probabilities of events H and e respectively. In one embodiment, the event H may be that AC is ON and event e may be that the time is morning and outside temperature is low. Thus, in one embodiment, the probability of AC being ON given that the time is morning and the temperature is low may be computed by multiplying the previous belief of AC being ON p(H) by the likelihood events of time being morning and temperature being low p(e|H). The denominator in equation (1) is a normalizing constant that ensures the posterior adds up to 1. It should be noted here that the above events and the probabilities with given events are exemplary and other similar events and probabilities are in scope of the present algorithm. 
         [0028]      FIG. 5  illustrates an exemplary cascaded Bayesian network or APE  72  in accordance with an embodiment of the present invention. In step  132 , the Bayesian network  72  obtains input from appliance database (element  76  of  FIG. 3 ) containing temperature, power, time, and humidity etc. and provides this input to cascaded sub networks, which utilizes Bayesian statistics. In the first sub-network, a first classifier  134 , such as a temperature and time classifier, classifies some appliances from the household and filters out remaining appliances along with their probabilities by the first estimator  136 . The second network consists of a second classifier  138  for changes in power; such as a line voltage and load type (resistive, capacitive or inductive) classifier. The appliances from the first stage are then further filtered by a second estimator  140 . A third classifier  142  of geographic location follows this stage and leads to further strengthening of the belief in the probability of selected appliances and their status by utilizing a third estimator  144 . In another embodiment, all the classifiers may be combined into one classifier. The classifiers may also be referenced as nodes in one embodiment. The output of the third estimator  144  is then further provided to a step  96  of  FIG. 4  and further to GA if needed as described earlier. 
         [0029]    As will be appreciated by those skilled in the art, genetic algorithms use the principles of selection and evolution to solve a problem. The problem in the cognitive metering case is finding the best probabilistic model of appliances. In one embodiment, the genetic algorithm includes three steps: selection, crossover, and mutation. In the selection step, some elements from the Bayesian network are randomly selected based on the rate of confidence such that, the higher the rate of confidence, the higher the chance of being selected. The selected matrices are referred to as parent elements. For example, in one embodiment, the parent elements may be the matrices A=[1 0 1 0 1] and X=[0 0 0 1 1]. In the crossover step, a crossover point is selected for each of the parent elements, and new elements referred to as child elements are created from the parent elements. In one embodiment, the crossover may include a single point crossover, a multipoint crossover, or zero point crossover. In multipoint crossover many crossover points may be selected, whereas in zero point crossover no crossover point is selected and the parent element is selected as it is. As an example, if for the matrices A and X, a crossover point is selected as third column of the matrix then the child element may be a matrix Y=[A(3), X(2)]=[1 0 1 1 1]. In the mutation step, the parent elements as well as child elements are changed by a small amount. For example, in one embodiment, the matrix X=[0 0 0 1 1] may be replaced by a matrix Z=[0 0 1 1 1], i.e., the third column of matrix is changed to 1 from 0. Finally all the elements or solutions are fed back to the Bayesian network. The process continues until a suitable solution has been found or until difference between the total estimated power and the total measured power is not less than the power threshold. 
         [0030]    One advantage of the described cognitive energy meter is it reduces computation through cascaded Bayesian network in APE. It further enables close to real-time appliance identification of a household. Another advantage of the meter is enablement of unsupervised learning capabilities and better appliance identification for the given household. The meter also reduces dependence on having the appliance model for all homes and it does not require field training or manual intervention. 
         [0031]    While only certain features of the invention have been illustrated and described herein, many modifications and changes will occur to those skilled in the art. It is, therefore, to be understood that the appended claims are intended to cover all such modifications and changes as fall within the true spirit of the invention.