# US 2014/0207398 A1 · Transient Normalization for Appliance Classification, Disaggregation, and Power Estimation in Non-Intrusive Load Monitoring > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2014/0207398 A1 - Document ID: US20140207398A1 - Status: Published application - Kind: A1 - Application number: 14/097,011 - Filed: 2013-12-04 - Published: 2014-07-24 - Earliest claimed: 2013-01-23 - Assignees: Samsung Electronics Co., Ltd. - Applicants: Samsung Electronics Co., LTD - Inventors: Sean D. Lai; Suman Giri - Web page: https://patentgrep.com/patent/US20140207398A1 ## CPC - G01D 4/00: Tariff metering apparatus (in taximeters G07B13/00; apparatus actuated by coins, cards or the like with meter-controlled dispensing of liquid, gas, or electricity G07F15/00) - G01D 2204/14: Displaying of utility usage with respect to time, e.g. for monitoring evolution of usage or with respect to weather conditions - G01D 2204/24: Identification of individual loads, e.g. by analysing current/voltage waveforms - Y02B 90/20: Smart grids as enabling technology in buildings sector (smart grids supporting the management or operation of end-user stationary applications in general, or like technologies with no associated climate change mitigation effect Y04S20/00) - Y04S 20/30: Smart metering, e.g. specially adapted for remote reading - G01D: MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR - Y02B: CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS - Y04S: SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS ## Abstract Various apparatuses and methods are provided for monitoring individual appliance energy consumption using non-intrusive load monitoring (NILM). For example, one method includes detecting an ON event and an OFF event within a segment of streaming power input and extracting the streaming power input associated with the ON event in response to detecting the ON event. The extracted streaming power input includes a waveform associated with the ON event recorded over a time frame. The method also includes normalizing the waveform to reveal one or more unique variations and classifying the normalized waveform based on the one or more unique variations. The method further includes matching the ON event with a correlating OFF event based at least on the classification of the normalized waveform and calculating power consumed from a time of the ON event to a time of the OFF event. ## Claims ### Claim 1 An apparatus for monitoring individual appliance energy consumption using non-intrusive load monitoring (NILM), the apparatus comprising: an event detection unit configured to detect an ON event and an OFF event within a segment of streaming power input, the event detection unit configured to extract the streaming power input associated with the ON event in response to detecting the ON event, the extracted streaming power input comprising a waveform associated with the ON event recorded over a time frame; a signature normalization unit configured to normalize the waveform to reveal one or more unique variations; an ON event classification unit configured to classify the normalized waveform based on the one or more unique variations; a rules unit configured to match the ON event with a correlating OFF event based at least on the classification of the normalized waveform; and a power calculating unit configured to calculate power consumed from a time of the ON event to a time of the OFF event. ### Claim 2 (depends on 1) The apparatus of claim 1, wherein the event detection unit is configured to monitor a magnitude of the streaming power input over the time frame and determine if the magnitude of the streaming power input is maintained at or above a specified level. ### Claim 3 (depends on 1) The apparatus of claim 1, wherein the signature normalization unit is configured to normalize the waveform by smoothing the waveform, sorting a root mean square (RMS) of power values of the smoothed waveform, implementing a point wise minus a power level threshold, and dividing a maxima RMS value of the smoothed waveform. ### Claim 4 (depends on 1) The apparatus of claim 1, wherein the ON event classification unit is configured to classify the normalized waveform using a k-nearest neighbor (kNN) search between the normalized waveform and acquired training data to associate the normalized waveform with a particular appliance or an operational mode of a particular appliance. ### Claim 5 (depends on 4) The apparatus of claim 4, wherein the training data comprises waveforms previously identified by the apparatus. ### Claim 6 (depends on 1) The apparatus of claim 1, wherein the power calculating unit is configured to calculate the power consumed by multiplying power consumption at the time of the OFF event by a duration between the time of the ON event and the time of the OFF event. ### Claim 7 A method for monitoring individual appliance energy consumption using non-intrusive load monitoring (NILM), the method comprising: detecting an ON event and an OFF event within a segment of streaming power input and extracting the streaming power input associated with the ON event in response to detecting the ON event, the extracted streaming power input comprising a waveform associated with the ON event recorded over a time frame; normalizing the waveform to reveal one or more unique variations; classifying the normalized waveform based on the one or more unique variations; matching the ON event with a correlating OFF event based at least on the classification of the normalized waveform; and calculating power consumed from a time of the ON event to a time of the OFF event. ### Claim 8 (depends on 7) The method of claim 7, further comprising: monitoring a magnitude of the streaming power input over the time frame; and determining if the magnitude of the streaming power input is maintained at or above a specified level. ### Claim 9 (depends on 7) The method of claim 7, wherein normalizing the waveform comprises smoothing the waveform, sorting a root mean square (RMS) of power values of the smoothed waveform, implementing a point wise minus a power level threshold, and dividing a maxima RMS value of the smoothed waveform. ### Claim 10 (depends on 7) The method of claim 7, wherein classifying the normalized waveform comprising using a k-nearest neighbor (kNN) search between the normalized waveform and acquired training data to associate the normalized waveform with a particular appliance or an operational mode of a particular appliance. ### Claim 11 (depends on 10) The method of claim 10, wherein the acquired training data comprises previously classified waveforms. ### Claim 12 (depends on 7) The method of claim 7, wherein calculating the power consumed comprises multiplying a power consumption at the time of the OFF event by a duration between the time of the ON event and the time of the OFF event. ### Claim 13 An apparatus for monitoring individual appliance energy consumption using non-intrusive load monitoring (NILM), the apparatus comprising: an event detection unit configured to detect an ON event and an OFF event within a segment of streaming input data, the event detection unit configured to extract a raw waveform associated with the OFF event in response to detecting the OFF event, the raw waveform comprising a first raw waveform of a first period of time before the OFF event and a second raw wave form of a second period of time after the OFF event; an alignment unit configured to align the raw waveform with a sinusoid wave using cross-correlation and subtract the second raw waveform from the first raw waveform to produce a third raw waveform of the OFF event; a classification unit configured to classify the third raw waveform by subtracting a period of a sine wave from a single period of the third raw waveform; a rules unit configured to match the OFF event with a correlating ON event based at least on the classification of the third raw waveform; and a power calculating unit configured to calculate the power consumed from the time of the ON event to the time of the OFF event. ### Claim 14 (depends on 13) The apparatus of claim 13, wherein the classification unit is configured to identify a maximum value of a difference between the period of the sine wave and the single period of the third raw waveform and compare the maximum value with a threshold. ### Claim 15 (depends on 13) The apparatus of claim 13, wherein the classification unit is configured to classify the third raw waveform using a k-nearest neighbor (kNN) search between the third raw waveform and acquired training data to associate the third raw waveform with a particular appliance or an operational mode of a particular appliance. ### Claim 16 (depends on 15) The apparatus of claim 15, wherein the training data comprises waveforms previously identified by the apparatus. ### Claim 17 (depends on 13) The apparatus of claim 13, wherein the rules unit is configured to discard any unmatched ON and OFF events. ### Claim 18 A method for monitoring individual appliance energy consumption using non-intrusive load monitoring (NILM), the method comprising: detecting an ON event and an OFF event within a segment of streaming input data and extracting a raw waveform associated with the OFF event in response to detecting the OFF event, the raw waveform comprising a first raw waveform of a first period of time before the OFF event and a second raw wave form of a second period of time after the OFF event; aligning the raw waveform with a sinusoid wave using cross-correlation and subtracting the second raw waveform from the first raw waveform to produce a third raw waveform of the OFF event; classifying the third raw waveform by subtracting a period of a sine wave from a single period of the third raw waveform; matching the OFF event with a correlating ON event based at least on the classification of the third raw waveform; and calculating the power consumed from the time of the ON event to the time of the OFF event. ### Claim 19 (depends on 20) The method of claim 20, wherein classifying the third raw waveform comprises identifying a maximum value of a difference between the period of the sine wave and the single period of the raw waveform and comparing the maximum value with a threshold. ### Claim 20 (depends on 18) The method of claim 18, wherein classifying the third raw waveform comprises using a k-nearest neighbor (INN) search between the third raw waveform and acquired training data to associate the third raw waveform with a particular appliance or an operational mode of a particular appliance. ### Claim 21 (depends on 20) The method of claim 20, wherein the acquired training data comprises previously classified waveforms. ### Claim 22 (depends on 18) The method of claim 18, further comprising: discarding any unmatched ON and OFF events.