US2015012147A1PendingUtilityA1
System and method of compiling and organizing power consumption data and converting such data into one or more user actionable formats
Assignee: ENERGY AWARE TECHNOLOGY INCPriority: Jan 20, 2012Filed: Jan 21, 2013Published: Jan 8, 2015
Est. expiryJan 20, 2032(~5.5 yrs left)· nominal 20-yr term from priority
G06Q 30/02G06Q 50/06H04L 67/30G05B 19/048G05B 2219/2639
57
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Cited by
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Claims
Abstract
A method and system for use in creating a profile of, managing and understanding power consumption in a premise of a user, wherein said premise comprises two or more power consuming devices comprises measuring, via at least one sensor, aggregate energy consumption at the premise, receiving at a mobile computing device comprising a data processor, said aggregated signal from the sensor, collecting and recording the aggregate signal over a plurality of time resolutions and frequencies, therein to create a predicted aggregate signal for each time x and frequency y, detecting changes in the predicted aggregate signal at time x an frequency y (detected consumption pattern changes) and conveying to at least one of the user, a utility company, and other third party a notification of detected consumption pattern changes.
Claims
exact text as granted — not AI-modified1 - 30 . (canceled)
31 . A system for use in creating a profile of, managing and understanding power consumption in a home of a user, wherein said home comprises two or more power consuming devices which system comprises:
a) at least one sensor configured to measure aggregate energy consumption at the home; b) a mobile computing device comprising a data processor; c) computer readable memory including computer readable instructions which, when executed by the processor, cause the processor to perform the following steps: i) receive said aggregated signal from the sensor; ii) collect and record the aggregate signal over a plurality of time resolutions and frequencies, iii) create a predicted aggregate signal pattern for each time x and frequency y; vi) to detect changes in the predicted aggregate signal pattern at time x an frequency y (detected consumption pattern changes); and d) a communication interface operably connected to the mobile computing device and configured for conveying to a user notification of detected consumption pattern changes.
32 . A system for use in creating a profile of, managing and understanding power consumption in a home, wherein said home comprises two or more power consuming devices which system comprises:
a) at least one sensor configured to measure at least one energy consumption variable associated with at least one energy consumption device within the home (“the selected device”) and to generate at least one aggregated output signal therefrom; b) a mobile computing device comprising a data processor; c) computer readable memory comprising memory comprising a catalogue of a plurality of devices and one of a respective or estimated power draw of each such device, said memory including computer readable instructions which, when executed by the processor, cause the processor to perform the following steps: i) receive said aggregated signal from the sensor; ii) create and update a power profile for the selected device, iii) collect and analyze raw data in real time, iv) calculate a delta for each selected device (difference between an on state and an off state); v) calculate an estimated delta for the selected device, using ON-OFF sequences thereby acquiring a start value and end value, and vi) comparing the start value and end value to assess reliability of the estimated delta for the selected device; and d) a communication interface operably connected to the mobile computing device and configured for receiving user commands and queries, for requesting user input in respect to said devices and for transmitting information relating to the devices to the user.
33 . The system of claim 31 additionally comprising the features of claim 32 .
34 . (canceled)
35 . (canceled)
36 . The system of claim 32 wherein the data processor creates a power profile for a first selected device by instructing a user, via the interface, to independently switch said device between on-off positions (“switching set up protocol”), at least one time, to isolate a power draw for said device from the aggregated signal, and wherein data processor recognizes that the first device was selected and to isolate a differential in the aggregate signal based on differing switch positions during the switching set up protocol, said differential being the energy draw of the first device.
37 - 53 . (canceled)
54 . The system of claim 32 wherein the interface is configured to proactively convey notifications to a user, such notifications being generated by the processor in response to data analysis such notifications: a) proactively reminding users of a “left-on” device; b) providing a breakdown of any devices left on by accident; c) relaying consequences of “left-on” devices; and d) providing home security feedback to users.
55 . The system of claim 31 wherein the processor measures at least one energy consumption variable associated with at least one energy consumption device within the home automatically and without a user trigger/request.
56 . (canceled)
57 . (canceled)
58 . The system of claim 32 wherein a user creates a power profile for an energy consumption device by way of an application on a mobile processing device which application may be pre-installed on mobile devices during manufacture or can be downloaded by users/customers from various mobile software distribution platforms, or web applications delivered over, for example, HTTP which use server-side or client-side processing (for example, JavaScript) to provide an “application-like” experience within a Web browser.
59 . The system of claim 31 wherein the data processor monitors a users ‘away from the home’ hours based on usual power consumption patterns and stores data in memory in this regard, such monitoring being based upon at least one of the following: specific triggers in real-time power consumption indicative of whether a user is about to leave home; specific triggers in real-time power consumption indicative of whether a user has just left home; user input via interface; cues from a user's computing platform (including GPS signals); and external power signals (including Wi-Fi range and availability) and other metrics usable to gauge a user's proximity to the home.
60 . (canceled)
61 . (canceled)
62 . The system of claim 32 wherein the catalogue so created can be used for consumer analytics: a) defining and classifying user demographics; b) modeling user consumption behavior; c) forecasting utility bills; and d) forecasting consumption (collectively “user classification”).
63 - 69 . (canceled)
70 . The system of claim 31 which comprises a processor which enables energy consumption device to mobile computing device communications including a familiarity detector which identifies “habit” information of the user, said habit information being usable to perform device related tasks in the home without user input.
71 - 82 . (canceled)
83 . A method for use in creating a profile of, managing and understanding power consumption in a home of a user, wherein said home comprises two or more power consuming devices which comprises:
a) measuring, via at least one sensor, aggregate energy consumption at the home; b) receiving at a mobile computing device comprising a data processor, said aggregated signal from the sensor; c) collecting and recording the aggregate signal over a plurality of time resolutions and frequencies, therein to create a predicted aggregate signal for each time x and frequency y; d) detecting changes in the predicted aggregate signal at time x an frequency y (detected consumption pattern changes); and e) conveying to at least one of the user, a utility company, and other third party a notification of detected consumption pattern changes.
84 . The method of claim 83 wherein predicted aggregate signal is a power consumption forecast within the house for time x and frequency y and indicates behavioral patterns of the user (pattern of interest).
85 . The method of claim 83 wherein time is measured in an increment selected from the group consisting of second, minutes, hours, days, weeks, months, and years.
86 . The method of claim 83 wherein predicted aggregate signal is a forecast of aggregate power usage over a billing period (forecast bill) and wherein method comprises calculating a forecast bill based on said predicted aggregate signal; comparing an actual bill over the billing period, assessing performance by comparing forecast bill to actual bill as follows:
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where C is hourly consumption, F is hourly forecast, B R is real billing cost, B F is the forecast bill, P is billing period, and E p is forecast error of the billing period period.
87 . The method of claim 83 wherein patterns exist at different time intervals and frequencies and wherein consumption data provided in a resolution, is presented by C α :
C α ={C 1 α ,C 2 α ,C 3 α , . . . ,C N α }
a) resolve a correct time for a pattern of interest, β:
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b) calculating mean (μ) and deviation (s) of each β-sized time interval (t), within the period length P;
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wherein β≧α, since a desired pattern resolution is never smaller than an original data's resolution; and
c) forecasting consumption based on mean and standard deviation and wherein a low standard deviation (s t ) indicates a highly repetitive behavior in the given time resolution and offset, a high deviation indicates no significance pattern.
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where C is the hourly consumption, F is the hourly forecast, B R is the real billing cost, B F is the forecasted bill, P is the billing period, and E P is the forecast error of the given period.
88 . The method of claim 83 additionally comprising an analysis of consumption trends (predicted rate of change in consumption patterns) in the house which comprises:
wherein trends can be examined at different time-resolutions and polynomial orders and wherein a lower time-resolution (large β values) make the trend analysis less sensitive to noise (highly deviated data with insignificant forecasting value) and wherein higher polynomial orders are more responsive to change, but also more sensitive to noise,
a) adjusting the consumption data's resolution;
b) using linear regression is used to detect the trend:
n : polynomial order, c=a 0 ·x n +a 1 ·x n-1 + . . . +a n-1 ·x+a n
wherein x is the time and c is the consumption and wherein the least-squared solution to the above polynomial is:
m: data points,
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c) determining consumption at a given time (x)
first order: tr ( x )= a 0 ·x+a 1
n -th order: tr ( x )= a 0 ·x n +a 1 ·x n-1 + . . . +a n-1 ·x+a n
d) measuring accuracy of an estimated trend line
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89 . (canceled)
90 . (canceled)
91 . The method of claim 83 wherein the utility receives information relating to detected consumption pattern changes and then directs notification to the user of at least one of messages selected from the group consisting in whole or part of:
a grid within which home is located is experiencing an unusual over-consumption
a request to user to turn off at least one power consuming device.
92 . (canceled)
93 . (canceled)
94 . The method of claim 83 wherein the processor is configured within a mobile computing device selected from the group consisting of a Smartphone, tablet, netbook and laptop, an In-Home Display (IHD) platform and a home-energy management device
95 . (canceled)
96 . (canceled)
97 . The method of claim 83 wherein the notification of detected consumption pattern changes is conveyed via a communication interface selected from the group consisting of RS232, USB, Firewire, Ethernet, Zigbee, Wifi, Bluetooth, RFJID, wireless USB, cellular, and WMAN communication technologiesCited by (0)
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