U.S. patent application number 14/057561 was filed with the patent office on 2015-04-23 for real time energy consumption management of appliances, devices, and equipment used in high-touch and on-demand services and operations.
The applicant listed for this patent is GridPoint, Inc.. Invention is credited to Jill R. Goldschneider.
Application Number | 20150112763 14/057561 |
Document ID | / |
Family ID | 52826991 |
Filed Date | 2015-04-23 |
United States Patent
Application |
20150112763 |
Kind Code |
A1 |
Goldschneider; Jill R. |
April 23, 2015 |
REAL TIME ENERGY CONSUMPTION MANAGEMENT OF APPLIANCES, DEVICES, AND
EQUIPMENT USED IN HIGH-TOUCH AND ON-DEMAND SERVICES AND
OPERATIONS
Abstract
An embodiment models and predicts energy consumption and
provides recurring and realistic opportunities to reduce energy
consumption throughout the work day or process cycle using user
interfaces to convey positive and negative feedback in a controlled
manner; and user experiences that reward positive changes with
increased positive feedback and reduced negative feedback. Energy
consumption of categories of appliances, devices, and equipment is
considered a random variable. Using archived energy data, business
data, and other related data, statistical modeling is used to
create inverse cumulative probability distribution functions. An
energy budget (consumption prediction) is computed so that it meets
a probability p of the budget being exceeded during a given
interval. When the budget is exceeded the feedback is negative,
otherwise feedback is positive. Each budget is computed as the
value b of the random variable such that the probability that the
random variable will be less than or equal to b is 1-p.
Inventors: |
Goldschneider; Jill R.;
(Seattle, WA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
GridPoint, Inc. |
Arlington |
VA |
US |
|
|
Family ID: |
52826991 |
Appl. No.: |
14/057561 |
Filed: |
October 18, 2013 |
Current U.S.
Class: |
705/7.31 |
Current CPC
Class: |
G06Q 50/06 20130101;
G06Q 30/0202 20130101 |
Class at
Publication: |
705/7.31 |
International
Class: |
G06Q 30/02 20060101
G06Q030/02; G06Q 50/06 20060101 G06Q050/06 |
Claims
1-93. (canceled)
94. A method of controlling energy consumption at a facility having
energy consuming equipment and an on-site human equipment operator
capable of manually controlling the energy consumption of the
equipment, the method comprising: sub-metering the equipment at the
facility to produce time series data representing energy use during
each time interval of a plurality of successive non-overlapping
time intervals; repeatedly calculating, during and for each time
interval, the total energy consumption of the equipment using the
time series data; receiving a selected value indicative of a
probability P that the total energy consumption of the equipment
will exceed an energy budget B during each time interval;
calculating the energy budget B from a statistical model of energy
consumption for the equipment, wherein the energy budget B is a
function of the probability P; repeatedly comparing, during and for
each time interval, the total energy consumption of the equipment
to the energy budget B to determine the progress made towards
reaching the energy budget B for each time interval; and providing
real-time feedback to the human equipment operator at the facility
that indicates the progress made towards reaching the energy budget
B within each time interval, wherein the real time feedback
provides the human equipment operator with information that enables
the human equipment operator to make real-time decisions about how
to control the equipment in order to minimize energy
consumption.
95. The method of claim 94, further comprising: creating multiple
statistical models, each of the multiple statistical models
corresponding to one of multiple budgets B; and calculating the
multiple budgets B from their corresponding statistical models,
wherein each of the multiple budgets B corresponds to one or more
items of the energy consuming equipment.
96. The method of claim 95, wherein each of the multiple budgets B
corresponds to an equipment category.
97. The method of claim 94, further comprising: creating the
statistical model of energy consumption based on one or more
archived explanatory variables, archived consumption data, or
archived demand data, wherein the statistical model of energy
consumption includes an inverse cumulative probability distribution
function, and wherein the energy budget B is calculated by applying
the probability P to the inverse cumulative probability
distribution function.
98. The method of claim 97, further comprising: periodically
recalculating the energy budget B to reflect changes of the one or
more of explanatory variables, archived consumption data, or
archived demand data.
99. The method of claim 94, further comprising: periodically
updating the statistical model of energy consumption in order to
adjust the energy budget B.
100. The method of claim 94, further comprising: updating the
statistical model of energy consumption to include one or more
changes in business operation or equipment.
101. The method of claim 94, further comprising: creating the
statistical model of energy consumption based on a non-parametric
empirical quantile function.
102. The method of claim 94, further comprising: creating the
statistical model of energy consumption based on a parametric
quantile function.
103. The method of claim 94, wherein the real-time feedback
includes: displaying a graphical depletion gauge that indicates to
the human equipment operator how much of the energy budget B
remains with respect to the calculated energy budget B within each
time interval.
104. The method of claim 103, wherein the real-time feedback
includes: repeatedly displaying a color code that is indicative of
the total energy consumption of the equipment relative to the
energy budget B during each time interval.
105. The method of claim 104, wherein the color code is
superimposed over the graphical depletion gauge.
106. The method of claim 94, wherein the real-time feedback
includes: displaying a graphical accumulation gauge that indicates
to the human equipment operator how much of the energy budget B has
been consumed with respect to the calculated energy budget B.
107. The method of claim 106, wherein the real-time feedback
includes: repeatedly displaying a color code that is indicative of
the total energy consumption of the equipment relative to the
energy budget B during each time interval.
108. The method of claim 107, wherein the color code is
superimposed over the graphical accumulation gauge.
109. The method of claim 94, wherein providing the real-time
feedback includes: generating an audible sound that indicates to
the human equipment operator the total energy consumption of the
equipment relative to the energy budget B during each time
interval.
110. The method of claim 94, wherein multiple groups of the
equipment are sub-metered, each group having its own energy budget
B, further comprising: determining, for each group of equipment,
the number of groups that exceeded the corresponding energy budget
B for each time interval; and graphically displaying, on a time
scale delineating each of multiple time intervals, an indicator of
the number of equipment groups that exceeded the corresponding
energy budget B during each time interval.
111. The method of claim 110, wherein the time scale includes shift
labels.
112. The method of claim 94, wherein multiple groups of the
equipment are sub-metered, each group having its own energy budget
B, further comprising: determining, for each group of equipment,
the number of groups that met the corresponding energy budget B
over a fixed number of previous time intervals; and graphically
displaying one of a plurality of images indicative of the number of
groups of equipment that meet their corresponding budget B.
113. A computer program product for controlling energy consumption
at a facility having energy consuming equipment and an on-site
human equipment operator capable of manually controlling the energy
consumption of the equipment, wherein the equipment at a facility
is sub-metered to produce time series data representing energy use
during each time interval of a plurality of successive
non-overlapping time intervals, comprising: a computer usable
medium having computer readable program code embodied in the
computer usable medium for causing an application program to
execute on a computer system, the computer readable program code
means comprising: computer readable program code for repeatedly
calculating, during and for each time interval, the total energy
consumption of the equipment using the time series data; computer
readable program code for receiving a selected value indicative of
a probability P that the total energy consumption of the equipment
will exceed an energy budget B during each time interval; computer
readable program code for calculating the energy budget B from a
statistical model of energy consumption for the equipment, wherein
the energy budget B is a function of the probability P; computer
readable program code for repeatedly comparing, during and for each
time interval, the total energy consumption of the equipment to the
energy budget B to determine the progress made towards reaching the
energy budget B for each time interval; and computer readable
program code for providing real-time feedback to the human
equipment operator at the facility that indicates the progress made
towards reaching the energy budget B within each time interval,
wherein the real time feedback provides the human equipment
operator with information that enables the human equipment operator
to make real-time decisions about how to control the equipment in
order to minimize energy consumption.
114. The computer program product of claim 113, further comprising:
computer readable program code for creating multiple statistical
models, each of the multiple statistical models corresponding to
one of multiple budgets B; and computer readable program code for
calculating the multiple budgets B from their corresponding
statistical models, wherein each of the multiple budgets B
corresponds to one or more items of the energy consuming
equipment.
115. The computer program product of claim 114, wherein each of the
multiple budgets B corresponds to an equipment category.
116. The computer program product of claim 113, further comprising:
computer readable program code for creating the statistical model
of energy consumption based on one or more archived explanatory
variables, archived consumption data, or archived demand data,
wherein the statistical model of energy consumption includes an
inverse cumulative probability distribution function, and wherein
the energy budget B is calculated by applying the probability P to
the inverse cumulative probability distribution function.
117. The computer program product of claim 96, further comprising:
computer readable program code for periodically recalculating the
energy budget B to reflect changes of the one or more of
explanatory variables, archived consumption data, or archived
demand data.
118. The computer program product of claim 113, further comprising:
computer readable program code for periodically updating the
statistical model of energy consumption in order to adjust the
energy budget B.
119. The computer program product of claim 113, further comprising:
computer readable program code for updating the statistical model
of energy consumption to include one or more changes in business
operation or equipment.
120. The computer program product of claim 113, further comprising:
computer readable program code for creating the statistical model
of energy consumption based on a non-parametric empirical quantile
function.
121. The computer program product of claim 113, further comprising:
computer readable program code for creating the statistical model
of energy consumption based on a parametric quantile function.
122. The computer program product of claim 113, wherein the
real-time feedback includes: computer readable program code for
displaying a graphical depletion gauge that indicates to the human
equipment operator how much of the energy budget B remains with
respect to the calculated energy budget B within each time
interval.
123. The computer program product of claim 122, wherein the
real-time feedback includes: computer readable program code for
repeatedly displaying a color code that is indicative of the total
energy consumption of the equipment relative to the energy budget B
during each time interval.
124. The computer program product of claim 123, further comprising:
computer readable program code for superimposing the color code
over the graphical depletion gauge.
125. The computer program product of claim 113, wherein the
real-time feedback includes: computer readable program code for
displaying a graphical accumulation gauge that indicates to the
human equipment operator how much of the energy budget B has been
consumed with respect to the calculated energy budget B.
126. The computer program product of claim 125, wherein the
real-time feedback includes: computer readable program code for
repeatedly displaying a color code that is indicative of the total
energy consumption of the equipment relative to the energy budget B
during each time interval.
127. The computer program product of claim 126, further comprising:
computer readable program code for superimposing the color code
over the graphical accumulation gauge.
128. The computer program product of claim 113, wherein providing
the real-time feedback includes: computer readable program code for
generating an audible sound that indicates to the human equipment
operator the total energy consumption of the equipment relative to
the energy budget B during each time interval.
129. The computer program product of claim 113, wherein multiple
groups of the equipment are sub-metered, each group having its own
energy budget B, further comprising: computer readable program code
for determining, for each group of equipment, the number of groups
that exceeded the corresponding energy budget B for each time
interval; and computer readable program code for graphically
displaying, on a time scale delineating each of multiple time
intervals, an indicator of the number of equipment groups that
exceeded the corresponding energy budget B during each time
interval.
130. The computer program product of claim 129, wherein the time
scale includes shift labels.
131. The computer program product of 94, further comprising:
computer readable program code for sub-metering multiple groups of
the equipment, each group having its own energy budget B, further
comprising: computer readable program code for determining, for
each group of equipment, the number of groups that met the
corresponding energy budget B over a fixed number of previous time
intervals; and computer readable program code for graphically
displaying one of a plurality of images indicative of the number of
groups of equipment that meet their corresponding budget B.
132. A computer program product for controlling energy consumption
at a facility having multiple categories of energy consuming
equipment and an on-site human equipment operator capable of
manually controlling the energy consumption of the equipment, the
method comprising, wherein the multiple categories of equipment at
the facility are sub-metered to produce time series data
representing energy use during each time interval of a plurality of
successive non-overlapping time intervals, comprising: a computer
usable medium having computer readable program code embodied in the
computer usable medium for causing an application program to
execute on a computer system, the computer readable program code
means comprising: computer readable program code for repeatedly
calculating, during and for each time interval, the total energy
consumption of each category of the equipment using the time series
data; computer readable program code for receiving, for each of the
multiple categories of equipment, a value indicative of a
probability P that the total energy consumption of the equipment
category will exceed an energy budget B for the equipment category,
during each time interval; computer readable program code for
calculating, for each of the multiple categories of equipment, the
energy budget B, from a statistical model of energy consumption for
the equipment category where the energy budget B for each equipment
category is a function of the selected probability P for each
equipment category; computer readable program code for comparing,
during and for each time interval, the total energy consumption of
each equipment category to the energy budget B for each equipment
category to determine if the energy budget B for each category was
exceeded for each time interval; and computer readable program code
for providing real-time feedback to the human equipment operator at
the facility that indicates the number of equipment category
budgets B that were met within each time interval, wherein the real
time feedback provides the human equipment operator with
information that enables the human equipment operator to make
real-time decisions about how to control the equipment in order to
minimize energy consumption.
Description
BACKGROUND OF THE INVENTION
[0001] 1. Field of the Invention
[0002] The invention relates generally to systems and methods for
creating and implementing near real-time user interfaces
(dashboards, alerts, reports, and visual and audio cues) for the
management of appliances, devices, and other equipment used in
high-touch and on-demand services and operations in order to reduce
energy consumption while simultaneously meeting business goals.
More specifically, the invention relates to how energy use patterns
of appliances, devices and other equipment are collected, analyzed
and modeled, and how to provide to service and operative workers
real time feedback in a systematic and controlled manner to
positively affect the energy use of appliances, devices, and other
equipment used in their line of business while simultaneously
meeting business goals.
[0003] 2. Description of the Related Art
[0004] An energy management system (EMS) is used to instrument
(collect data), monitor, and report on power consuming devices,
appliances, and equipment as well as events and status conditions.
Examples of power consuming devices, appliances, and equipment
include refrigeration units, ovens, toasters, cash registers,
sewing machines, air compressors, conveyors, kilns, dryers,
extruders, LCD displays, lighting panels, HVAC units, sensors,
meters, controllers, and switches. Examples of events and status
conditions include door open, door closed, trash compactor full,
and trash compactor away. EMS data may be supplemented with
quantitative data including environmental and climate data such as
temperature, cloud cover, sun rise and set, and relative humidity;
non-energy utility use such as water, sewage, and
telecommunications; performance data such as uptime, runtime or
throughput; and business data such as purchases, orders, packaging,
and routing. In some cases, an EMS is also used to control devices
and appliances. For example, an HVAC may be controlled using
real-time temperature and humidity readings to achieve desired
comfort levels, and parking lights may be controlled by business
hours and local times of sun rise and sun set.
[0005] The EMS data are relayed to a data store or data center that
can be local to the collection device, building, business, or
remotely hosted or distributed in the cloud. The data are typically
accessed by a data processing and reporting system and presented to
a user who oversees facilities. The user would access the presented
data using a computer device such as a computer monitor, tablet, or
smart phone using an interface such as email, document viewer, web
browser client, or other hosted application that communicates to a
backend server and data store where the backend server may be
locally or remotely hosted and managed.
[0006] The facilities manager can look at total building energy use
trends, drill down to specific devices or appliances, examine or
identify certain unusual conditions that may be manually or
automatically detected, such as a malfunctioning HVAC unit, too low
or too high room temperatures, or an oven left on when the building
is unoccupied. A facilities manager can then take actions to
mitigate problems or prioritize retrofits and upgrades based on
energy use patterns of the various devices, appliances, and
equipment.
[0007] The facilities manager uses the EMS as both a strategic and
tactical tool. Interactions with the EMS may be sporadic or at
regular intervals such as daily, weekly, monthly, or quarterly
depending on the facilities manager's responsibilities and
priorities. However, employees working in high-touch and on-demand
services and operations and whose work involves the regular use of
multiple devices, appliances, and other equipment are not able to
use the EMS as part of their daily workflow for the benefit of the
business or activity in which they are involved.
[0008] In service and manufacturing-based industries, there are
often highly variable or erratic service and manufacturing requests
that arrive throughout the business day. EMS controls are usually
not employed in these lines of business, leaving operative and
service workers, such as food preparation and cooking machine
operators, furnace, kiln, and dryer operators, and first line
managers in charge of managing the devices, appliances, and
equipment of their trade on an ad hoc basis. Without feedback as to
the amount of energy used as a function of the business activity,
these workers have little guidance or incentive to make changes to
their workflow behavior that would reduce energy use.
[0009] To better manage the energy use of appliances, devices, and
other equipment, operative workers require real-time information
that is relayed with minimal detail, that is easy to see and
consume, and that does not distract them from their task at hand.
What information is needed and how it is conveyed will depend on
the work environment. Information may need to be relayed in visual
and/or audio forms. The level of details and types of information
may need to be a simple cue, prompt, or instruction for
"in-the-moment" feedback; simple summaries of energy use successes
or issues by shift or other period of time may need to be available
to line managers and operative workers to evaluate during and after
a shift or other period of time; scoreboards showing information
for multiple teams at one or at various business locations may be
needed to drive competitive behaviors; and richly detailed reports
of energy use trends and patterns at various levels of the
organization to enable better understanding of the business and
enable process improvements for reducing their carbon
footprint.
[0010] To make beneficial changes in their behavior, service
employees need a reasonable and manageable amount of in-the-moment
feedback that doesn't overwhelm them or discourage them from making
or continuing to make changes in their work flow that will lead to
energy savings while simultaneously meeting business goals. It is
important to incent positive changes made by a worker with positive
feedback and a reduction in negative feedback. Therefore, there
need to be methods and tools to define and control the level or
rate of feedback given to operative employees to prevent the "user
fatigue" or "backlash" that could result from too high a rate of
in-the-moment feedback as well as methods to demonstrate and reward
workflow changes that lead to energy savings.
[0011] The levels, or rates, of feedback may need to vary by any
number of factors including appliance, device, equipment, industry,
location, business unit, team, day of week, time of day, season,
weather, orders, customer, and more. Since energy use fluctuates
over time, there is inherent variability and unpredictability in
the tasks an operator may do at any given time. As the goal is to
drive improvements in energy use, robust statistical modeling and
machine learning techniques that can learn and adapt over time to
changing circumstances will be needed.
[0012] Furthermore, shift and line managers and executives will
need to see reports of the performance by shift, day, or other
periods of time that include longitudinal analysis to assess energy
savings improvements over time.
SUMMARY
[0013] Various embodiments of the invention solve the
above-mentioned problems by providing an energy management system
that submeters in near real-time the appliances, devices, and
equipment used by service and operative workers. The appliances,
devices, and equipment are organized into categories of which all
categories or a subset of categories may be used. To ensure that
the near real-time feedback and recent summaries of energy use are
relevant to the work at hand and that there are recurring and
realistic opportunities for workers to reduce energy consumption
throughout the work day or process cycle, discrete and independent
time intervals (such as 1 hour) are used in which the amount of
feedback of under and over use may be globally or independently set
and managed for each category and interval of time. An energy
budget for each category and for each time interval is provided
where the budget is defined so that it meets a specified
probability p of the budget being exceeded (alternatively, the
probability of the budget being met is 1-p) during that time
interval. When the budget is exceeded the feedback is said to be
negative, and when the budget is met the feedback is said to be
positive.
[0014] One method of providing energy budgets considers energy use
for each category and interval pair a random variable. Using
archived energy demand and consumption data, business data, and
other related data, statistical modeling is used to create inverse
cumulative probability distribution functions for each random
variable. Each budget can be computed as the value b of the random
variable such that the probability that the random variable will be
less than or equal to b is 1-p. The underlying statistical models
may be updated continuously as the volume of archived data expands
over time. Other methods for providing energy budgets may use
machine learning or other statistical techniques to predict or
compute the budget that would be or should be used.
[0015] Once the budgets are available, various user interfaces
(seen, heard, or otherwise perceived) can be used to convey: near
real-time feedback about the under and over use of energy by
category and interval so that service and operative workers may
make in-the-moment changes in their workflow to reduce energy
consumption; summary data regarding energy consumption of
categories for recent time intervals so that operative workers and
managers may understand short term performance or impact within a
shift or process cycle; a user experience that rewards and incents
sustained energy savings behaviors; and richly detailed, historical
reports by category or at the appliance, device, and equipment
levels over various windows of time to help the business better
manage delivery and operations.
BRIEF DESCRIPTION OF THE DRAWINGS
[0016] These and other features, aspects, and advantages of the
presently disclosed systems and methods will become better
understood with reference to the following description and appended
claims, and accompanying drawings where:
[0017] FIG. 1 is a block diagram illustrating an EMS that is using
data from multiple facilities as well as supplemental data for the
monitoring and control of the facilities and for providing near
real-time feedback.
[0018] FIG. 2 is a block diagram illustrating a portion of an EMS
submetering and control and real-time feedback solution within a
single facility or site.
[0019] FIG. 3 is a block diagram illustrating a simplified
configuration of controller and submetering hardware in a
facility.
[0020] FIG. 4 is a block diagram illustrating a server system
providing remote access for data and control and near real-time
data to an EMS provider, facilities manager, service or operative
worker, and business managers and business leaders.
[0021] FIG. 5 is a block diagram illustrating a system for
providing near real-time data for energy consumption management of
appliances, devices, and equipment used in high-touch and on-demand
services and operations.
[0022] FIG. 6 contains screen shots illustrating various near
real-time user interface visualizations of energy consumption under
and over use.
[0023] FIG. 7 and FIG. 8 contain screen shots illustrating various
user interface visualizations of summaries of energy consumption
for recent time intervals.
[0024] FIG. 9 contains tables that would be used to assist the
operator in configuring visualizations of summaries of energy
consumption for recent time intervals.
[0025] FIG. 10 contains tables illustrating a method for a user
experience and a concrete example of the method for rewarding and
incenting sustained energy savings behaviors.
[0026] FIG. 11 contains an illustration of a user interface that
incorporates near real-time visualizations of over and under use,
visualizations of summaries of energy consumption for recent time
intervals, and rewards imagery.
[0027] Some figures illustrate diagrams of the functional blocks of
various embodiments. The functional blocks are not necessarily
indicative of the division between hardware circuitry. Thus, for
example, one or more of the functional blocks (e.g., processors or
memories) may be implemented in a single piece of hardware (e.g., a
general purpose signal processor or a block or random access
memory, hard disk or the like). Similarly, the programs may be
standalone programs, may be incorporated as subroutines in an
operating system, may be functions in an installed software
package, and may reside in collocated or remotely located servers.
It should be understood that the various embodiments are not
limited to the arrangements and instrumentalities shown in the
drawings.
DETAILED DESCRIPTION OF THE INVENTION
[0028] The present invention may be understood more readily by
reference to the following detailed description of preferred
embodiments of the invention as well as to the examples included
therein. Embodiments of the invention provide systems and methods
for the modeling of energy use patterns and for the creation and
conveyance of near real-time feedback in a systematic and
controlled manner for in-the-moment energy consumption management
of appliances, devices, and equipment used in high-touch and
on-demand services and operations.
[0029] FIG. 1 is an illustration of an energy management system
(EMS) 100 for monitoring and controlling one or more facilities 101
which may be located in different geographic areas, and may receive
energy in one or more forms, for example electricity and natural
gas, and from one or more utilities. Utilities 102 use meters,
typically at the utility side of the interconnection point, to
monitor energy consumption and demand, while the EMS 100 uses
different meters to monitor energy consumption and demand,
typically at the facility side of the interconnection point. The
EMS solution may also include sub-metering within a facility; the
collection of other, non-energy specific, data within a facility;
and supplemental data from third party data providers 103. Data
streams are transmitted to a data store 104 and are processed into
consumable forms of data by backend, possibly distributed, servers
105 as designed and directed by the EMS provider 106. An EMS user
and operator, such as a facilities manager 107 accesses the
prepared data using an interface such as email, document viewer,
web browser client, or other hosted or native application. The
operator can take responsive or corrective action based on the
remotely received data provided by the remote servers 104. A near
real-time user interface 108 provides feedback to service or
operative workers 109 so that they can take immediate responsive or
corrective action to better manage energy use of the appliances,
devices and equipment used in their work flow. The EMS provider 106
also has access to the EMS 100 for the purpose of providing
support, maintenance, and additional services.
[0030] In an embodiment, each facility has submetering and possibly
control and user interface hardware installed in it that is part of
the energy management system and separate from utility-installed
meters. FIG. 2 is an illustration of the portion of an EMS that may
be found within a single facility 200. A facility may be equipped
with one or more meters 201 and one or more controllers 202. A
facility may be equipped with an interface 203 for monitoring and
control of the facility and with near real-time user interfaces 204
for appliances, devices and equipment used by businesses within the
facility. Typical measured data include, but are not limited to:
total electric 205, gas 206, and water 207 utility use; natural gas
or solar 208 and 209 power generation; facilities operations such
as irrigation 210, submetered utility use such as HVAC,
refrigeration, ovens and other appliances, devices and equipment
215-220; and status, events, and environmental data, 211-214. The
submetering equipment may include or interface with other devices
and sensors to collect status, events, and environment data such as
indoor and outdoor climate data, CO.sub.2, and door open/closed.
Total utility use metering 205-207 may be reconciled against
metering done by the utility on its side of the interconnection
point 221. Supplemental data (not shown in FIG. 2) may also be
collected and sent to the energy management system to be stored and
processed in support of specific EMS applications such as outdoor
equipment control, weather normalized energy modeling, bill and
rate verification, or order normalized energy modeling.
[0031] FIG. 3 is detailed schematic block diagram illustrating
typical energy management system control and submetering hardware
installed at a facility 300. A site controller 301 with embedded
control algorithms controls electrical loads on multiple circuits
314 via light control panels (LCPs) 315. The site controller 301 is
typically wired to common voltages at an electrical distribution
panel (not shown) of a building facility via a main line meter
(power submeter) 306. The site controller 301 includes memory 304
and CPU 305 for respectively storing and implementing energy
management algorithms. The algorithms accept real-time power and
environmental variable measurements (such as readings from
thermostats 317) as inputs and determine how to control the power
delivered on various circuits 314 and to control set points and
other configurable settings such as enabling/disabling compressor
stages on the thermostats 317. The site controller 301 may include
a power supply (not shown) and one or more wired or wireless local
communication and control interfaces 303 for controlling the
circuits 314 and thermostats 317. The thermostats 317 provide
temperature and humidity inputs to the site controller 301, and
output control signals to the HVAC units 316. A communication
interface 302 provides bi-directional communication with a
communication gateway 319, which in turn manages wired or wireless
communications from the EMS server.
[0032] In an embodiment, one or more submeters 306 are coupled to
the site controller 301 either via wired or wireless connection.
The submeter 306 includes hardware and firmware to provide sampling
functionality, including multiple analog-to-digital converters for
multi-channel fast waveform sampling of inputs such as current and
voltage to produce a suite of measurements including demand,
consumption, reactive power, power factor, and voltage. The
submeter 306 includes wired or wireless communication interfaces
307, current and voltage monitoring interfaces 308, memory 309, CPU
310, and may also include a power supply (not shown). The current
and voltage monitoring interfaces connect between the power
circuits being monitored and the A/D converter. Each channel may be
connected to a separate power circuit to monitor the flow of
current through the circuit. The connection is typically made with
a current transformer 313 at both a supply (i.e., hot) line and a
return (i.e., neutral) line of the power circuit, which provides a
waveform signal that is representative of the current flow at the
connection point. The submeter 306 can receive voltage and current
measurements from the main line 311 as well as measurements from
any of a number of devices 312 or groups of devices, as illustrated
in FIG. 2 and described herein. The controller 301 can also receive
data directly from other sensors 318. Sampled data flows from the
submetering devices 306 through the controllers 301 and on to the
remote EMS servers via a wired or wireless network.
[0033] The submetering and control equipment can collect near
real-time measurements (for example, every 1, 5, or 15 minutes, and
preferably 1 minute for this invention if used in a rapidly
changing service environment such as a quick service restaurant).
Each measurement has a time stamp, unit of measurement, and unique
source identifier associated with it. Data from the same unique
source comprise a time series which is univariate if only one unit
of measure is recorded or multivariate if multiple units of measure
are sampled. Preferably, sampling intervals are constant so that
the time variable is implicit. Event data such as door open and
door close may be irregularly spaced so that the time variable must
be explicit. The data collected by the submetering equipment is
sent to the energy management system via a wired or wireless
network to be stored and processed.
[0034] FIG. 4 is an illustration of a portion of an EMS system
external to a facility 400 with emphasis on server 401 and storage
405 systems used to provide remote data access and control to users
including EMS providers, facility managers, operators, service or
operative workers, managers, and business leaders.
[0035] The servers 401 include processors 402, memory 403, and one
or more I/O interfaces 404 for receiving data, transmitting control
and supporting end-user applications over a network 406 where
end-user applications include facility management or other end-user
application access and configuration 409, EMS service
administration 410, and near real-time data for in-the-moment
management of appliances, devices, and equipment used in a
particular line of business 411.
[0036] Storage abstractions 405 include one or more databases,
including a streaming data database 405a to store fresh data from
the facilities 407 and supplemental providers 408, a historic
database 405b to store archived data from the facilities 407 and
supplemental providers 408, a configuration and operating
parameters database 405c to store data required to run the EMS and
end-user applications, a facilities database 405d to store data
specific to each facility such as installation configuration and
assets and points of contact, a reports database 405e to store
static prepared data used in end-user applications, a statistical
models database 405f to store predictive models used in end-user
applications such as weather-normalized energy use prediction and
near real-time data for in-the-moment management of appliances,
devices, and other equipment used in high-touch and on-demand
services and operations, a database for other analytics 405g such
as alarms, and storage of other data 405h.
[0037] The memory 404 stores software (tangible data and programs)
for creating, editing, and executing data and instructions
necessary to operate the EMS and run end-user applications
including creation of data structures, statistical models, reports
and other data required to provide near real-time data for
in-the-moment management of appliances, devices, and other
equipment used in high-touch and on-demand services and
operations.
[0038] A business manager or operator 409 can access the user
application and configuration software and detailed reporting
software remotely or directly if the software is installed at the
user-operated control center. In an embodiment, the system is
configured such that the operator is able to configure near-real
time user interfaces for a single or multiple facilities and
business applications.
[0039] The measured time series data are typically conceptualized
and organized functionally. Business logic is applied to the time
series to create logical, hierarchical, nested or other forms of
structured data that support EMS applications including monitoring,
reporting, near real-time control, and facilities maintenance. User
interfaces to the EMS may be implemented and conceptualized at a
variety of levels including asset, appliance, device, and other
equipment, or as groups or cross-groups of assets, appliances,
devices, and other equipment. The software must be flexible to
support the wide variety of configurations that arise in the
installation of submetering and control hardware at a facility. For
example, if a facility has a single circuit dedicated to ovens,
submetering be done on the circuit only to save costs; however, if
there are additional ovens on different circuits, those ovens may
have been separately submetered. The software must accommodate
these variations when organizing the data.
[0040] To streamline and minimize the amount of feedback given to
operational workers, the time series data for the appliances,
devices, and equipment operated by them are organized into
categories of which all categories or a subset of categories may be
used. If a subset of categories is to be used, the business manager
or operator may, in an embodiment, manually select in the software
the categories to be used or select configurations in the software
so that the software will automatically select or recommend which
categories should be used, where the automated identification of
categories is done systematically using a method that includes
historic energy use and energy savings potential of each category.
In an embodiment, the operator can provide an ordering of the
selected categories for use in display or reporting and apply
unique display names and visual icons.
[0041] To ensure that the near real-time feedback and recent
summaries of energy use are relevant to the work at hand and that
there are recurring and realistic opportunities for operative
workers to reduce energy consumption throughout the work day or
process cycle, feedback is given in the context of discrete and
non-overlapping time intervals. The business manager or operator
may, in an embodiment, manually select in the software the time
interval or select configurations in the software so that the
software will automatically select or recommend the time interval,
where the automated identification of time interval is done
systematically using a method that includes business data such as
industry type and product specific to that facility and operator
team that has been surveyed from the business as well as archived
energy use and supplemental and business data.
[0042] Within each time interval, the amount of feedback of under
and over use (also referred to as positive and negative feedback,
respectively) may be globally or independently set and managed for
each category. The business manager or operator may, in an
embodiment, manually select in the software the feedback levels to
a global value or to specific values or select configurations in
the software so that the software automatically selects the
feedback levels. The automated selection is performed
systematically based on business data such as industry type,
product, work hours specific to that facility and operative team,
archived energy use and/or supplemental and other business
data.
[0043] In an embodiment, to provide the desired, configured
feedback regarding under and over use of energy for a category
within a given time interval, a budget is created where the budget
for a category is defined so that it meets a specified probability
p of the budget being exceeded in that time interval where p was
specified by the operator as the amount of feedback indicative of
over use. Alternatively, the budget may be defined so that it meets
a specified probability 1-p of the budget being met in that time
interval. In an embodiment, a budget is the mean, or average,
amount of energy consumed by a category for a given period of time
when p is set to be 0.5 (50% positive and 50% negative feedback).
To avoid user fatigue due to excessive negative feedback, the
operator may choose to use a smaller value of p such as 0.2
(20%).
[0044] FIG. 5 is an illustration showing a block diagram of a
system for creating near real-time data and feedback 500 including
creation of statistical models of energy consumption and demand,
energy budgets, and visualizations and reports and other user
interfaces for conveying near real time feedback. Available
explanatory variables 501 and 507 and response variables 502 and
508 are identified and stored in the Configuration and Operating
Parameters database 505. The Modeler 503 creates statistical models
using archived explanatory variable data 501 and archived response
variable data 502 and configuration and operating parameters 505
including category and time interval. The output of the Modeler 503
are stored in the Statistical Models database 504, where the output
includes the model, the selected explanatory variables, parameters
if the model is parametric, and other descriptors if the model is
non-parametric. The Predictor 506 creates real time predictions of
the response variables (energy demand and consumption) which
include computations of budgets which are predictions of energy
consumption, using real-time explanatory variable data 507, models
from the Statistical Model database 504, and configuration and
operating parameters including category, time interval, and
feedback parameter p. The predictions and budgets are used, with
archived data and real time data, to create near real time data 509
such as visualizations and reports and other user interfaces that
are provided to the end user 510 who may be a service or operative
employee, managers, or business leader.
[0045] In an embodiment of the Modeler 503 and Predictor 506,
energy consumption for each category and interval pair is
considered a random variable and the budget is derived from the
probability distribution function used to model the random
variable. Using archived energy demand and consumption data,
business data, and other related data, statistical modeling is used
to create inverse cumulative probability distribution functions
(also known as quantile functions) for each random variable. Each
budget is computed as the value b of the random variable such that
the probability that the random variable will be less than or equal
to b is 1-p. The underlying statistical models are updated,
preferably continuously as the volume of archived data expands over
time, to adapt to changes in the business and appliances, devices,
and other equipment. In an embodiment, the choice of the underlying
model depends on the data itself; when there is little archived
data and hence few data samples, it is preferable to use
non-parametric, empirical quantile functions such as those
elaborated in Hyndman and Fan, "Sample Quantiles in Statistical
Packages" American Statistician, 1996 and to recompute or update
the quantile functions with each newly acquired data sample. Other
embodiments for deriving energy budgets may use machine learning or
other statistical techniques to predict or compute the budget that
would be or should be used to achieve the desired levels of
negative and positive feedback.
[0046] Once the predictions (budgets) are available, various user
interfaces (seen, heard, or otherwise perceived) can be used to
convey: near real-time feedback about the under and over use of
energy by category and interval so that service and operative
workers may make in-the-moment changes in their workflow to reduce
energy consumption; summary data regarding energy consumption of
categories for recent time intervals so that operative workers and
managers may understand short term performance or impact within a
shift or process cycle; a user experience that rewards and incents
sustained energy savings behaviors; and richly detailed, historical
reports by category or at the appliance, device, and equipment
levels over various windows of time to help the business better
manage delivery and operations.
[0047] In an embodiment, the operational worker accesses near
real-time data interfaces in their work environment. What
information is needed and how it is conveyed will depend on the
work environment. It is crucial that data conveyed to operative
workers are not over detailed and that the mode or manner of
conveyance does not distract a worker from the task at hand.
Various means can be provided to convey in near real-time energy
over use for a given category. For example: if workers have line of
sight to dashboard or kiosk types of displays, an embodiment is a
gauge form of data visualization; if workers use visual displays
forms of information in an out of the way area, an embodiment is a
simple visual cue, such as a light, that turns on if energy is
being over used; if workers rely on audio signals, an embodiment
conveys an over-use message in an audible manner to prompt the
workers to make changes such as turning off an appliance.
[0048] One function of near-real time visualizations of energy over
and under consumption is to convey quickly and with minimal detail
which category is consuming or is at risk of consuming too much
energy. FIG. 6 shows two different embodiments of visualizations of
near-real time energy consumption against budget 600, both of which
use a gauge for each category where the real-time data are
normalized to each budget to eliminate variations of magnitude
across categories and where color overlays reinforce the current
state of over consumption (e.g. yellow or red) and under
consumption (e.g. green) as compared to predicted consumption.
[0049] The first visualization 601 uses a gas-tank like (depletion
paradigm) gauge for each category with a given "fuel budget" for
each time interval. At the beginning of each time interval, for
example the beginning of each hour for hourly intervals, the gauge
is reset to full (F) at the far right. Each gauge is continuously
updated to show consumption as a function of budget depletion. In
601, the dial will move from full towards empty (right to left)
over the time interval as energy is consumed. The gauges have color
overlays intended as prompts to users to reduce energy use. In 601,
assume that the time interval is one hour and the current time is
half-way through the current hour: the gauge is green in 601a
because energy consumption is at or below consumption expected
against the given budget; the gauge is yellow in 601b because
energy consumption is higher than expected against the given budget
and steps should be taken now to get energy use back on track; and
the gauge is red in 601c as energy consumption has exceeded the
entire budget allocated for that hour.
[0050] The second visualization 602 uses an accumulation paradigm
for the gauges for each category with a given budget "limit" for
each time interval. At the beginning of each time interval, for
example the beginning of each hour, the gauge is reset to 0% at the
far left. Each gauge is continuously updated to show consumption as
accumulation towards the limit. In 602, the dial will move from 0%
towards 100% (left to right) over the time interval as energy is
consumed. The gauges have color overlays intended as prompts to
users to reduce energy use. In 602, assume that the time interval
is one hour and the current time is half-way through the current
hour: the gauge is green in 602a because energy consumption is at
or below consumption expected against the given budget; the gauge
is yellow in 602b because energy consumption is higher than
expected against the given budget and steps should be taken now to
get energy use back on track; and the gauge is red in 602c as
energy consumption has exceeded the entire budget allocated for
that hour.
[0051] The a priori probability that a budget is exceeded for a
category in a given time period is p, where p is the very same
control parameter set by the operator to create the budgets. The
amount of yellow and red shown to the user is thus controlled by p.
If users respond to the feedback and make changes to their work
flow that reduce energy use, then in practice, then the users will
be rewarded with more positive feedback (green) and less negative
feedback (yellow and red).
[0052] One function of summary data visualizations of energy
consumption for recent time intervals is to help operative workers
and managers understand short term performance or impact within a
shift or process cycle. The visualizations are intended to convey
quickly and with minimal detail the aggregate over or under
consumption of energy of a set of categories over recent intervals.
In an embodiment, an operator can configure the visualization to
show I prior intervals over a fixed time period. For example, if
the interval size is one hour, the operator may select a summary of
all intervals during the past 24 hours or a summary of only the
completed intervals within the current calendar day (I=24). In an
embodiment the summary for the end state of one interval is
conveyed with a simple color overlay and/or by magnitude or other
cue. In an embodiment, there are N end states for the end of each
time interval, where an end state is the number of budgets not
exceeded at the end of the interval. The operator can configure
each interval end state to have a specific color overlay or other
cue. In an embodiment, the operator can configure the visualization
to exclude the data from specific categories, such as Total
Building or Main Load, as operative workers are unable to directly
control or be responsible for the energy consumption of some
categories but for which a near real-time gauge visualization
provides data of interest. If the number of budgets being tracked
in this visualization is B, then the number of end states is N=B+1.
In an embodiment, the operator can configure the visualization with
overlays that delineate shifts or process cycles.
[0053] FIG. 7 and FIG. 8 show two different embodiments of
visualizations, both of which use a colored tile for each past
interval to represent the state of energy consumption against
budget at the end of that interval. In both visualizations, the
time interval is one hour, 24 hours of past data are displayed, and
color only is used to convey state. Representations of state that
vary in color and magnitude or in magnitude only are not shown.
When data are not completely known (e.g. current interval or future
intervals) or state is otherwise unknown, a background color is
used for that tile.
[0054] In FIG. 7, linear visualizations are used to provide
summaries of energy consumption for recent intervals. In 701 four
colors are used to convey the ending state of one interval where
the end state is the number of budgets that were not exceeded and
there were three categories. If zero budgets were exceeded, then
green is shown; if any one budget was exceeded then yellow is
shown; if any two budgets were exceeded then orange is shown; if
all budgets were exceeded then red is shown. In 702, three colors
are used to convey the ending state of one interval where the end
state is the number of budgets that were not exceeded and there
were three categories. If zero budgets were exceeded, then green is
shown; if any one budget was exceeded then yellow is shown; if any
two budgets were exceeded then yellow is shown; if all budgets were
exceeded then red is shown. In 703 an overlay of shifts is applied
to 702 so that operative workers and line managers may better
understand short term performance and impact within a shift or
process cycle.
[0055] The embodiment shown in FIG. 8 conveys the data as a
circular, or pie chart visualization where 801 represents the same
data as 701, 802 represents the same data as 702, and in 803 and
overlay of shifts is applied to 801 so that that operative workers
and line managers may better understand short term performance and
impact within a shift or process cycle. When data samples are not
complete (e.g. current time interval or future intervals) or state
is otherwise unknown, a background color is used for that
slice.
[0056] The a priori probability that a budget is exceeded for a
category in a given time period is p, where p is the very same
control parameter set by the operator to create the budgets. The
choice of the number of categories and number of colors or cues to
use in the summaries of energy consumption for recent intervals has
a direct impact on the variability of the feedback provided to the
user. The probabilities of the states shown to the user are thus
controlled by p and the number of categories and number of states.
It is straightforward to compute the probability of a state given
the number of categories, the number of states, and the probability
p. An embodiment provides the operator information regarding what
to expect in the visualization so that the operator can make
informed decisions when configuring of the visualization for short
term energy consumption use.
[0057] FIG. 9 shows tables of data that would be used to assist the
operator in configuring color overlay visualizations of summaries
of energy consumption for recent time intervals. In example 901,
there are three categories and four end states (S1=green if all
budgets are met for the three categories; S2=yellow if two budgets
only are met for the three categories, S3=orange if one budget only
is met for the three categories; and S4=red if zero budgets are met
for the three categories). In an embodiment, the data provided to
the user would include the probabilities of each of the four end
states where the end state of each category is modeled as a
Bernoulli random variable that is parameterized by the probability
p of the budget being exceeded, and the three categories are
assumed to be statistically independent. In this example the random
variables are assumed to be statistically independent and
identically distributed (iid). In example 902, there are three
categories and three end states (S1=green if all budgets are met
for the three categories; S2=yellow if one or two budgets are met
for the three categories; and S3=red if zero budgets are met for
the three categories). As in example 901, the table shows the
probabilities of each of the three end states where the end state
of each category are modeled as iid Bernoulli random variables that
are parameterized by the probability p of the budget being
exceeded.
[0058] If users respond to the feedback and make changes to their
work flow that reduce energy use, then in practice, the users will
be rewarded in the summary visualization with more positive
feedback (more states where all budgets or all but one budgets were
met) and less negative feedback (states where two or more budgets
were not met).
[0059] To provide a user experience that rewards and incents
sustained energy savings behaviors, an embodiment uses changing
imagery on a dashboard to convey the cumulative impact of recent
time intervals. FIG. 10 illustrates a method for selecting reward
imagery based on recent past performance where recent past
performance is based on the set, or subset, of the number of time
intervals displayed in the summary of energy consumption for recent
time intervals.
[0060] In an embodiment, the operator can configure the number K of
most recent time intervals to be used in selecting and displaying
imagery on a dashboard where the maximum number for K should be the
maximum number I configured in the summary visualization. The state
of each interval displayed in the summary of energy consumption is
conveyed using a color overlay (or other cue). In an embodiment, a
numerical value is associated with each state, 1000, in addition to
the color associated with that state as used for the overlay in the
summary. The numerical values represent the "goodness" of the end
state. For example, in 1002, there are four end states S1-S4 for 3
categories where S1 is the state for no budgets exceeded, S2 is the
state for one budget only was exceeded, S3 is the state for two
budgets exceeded, and S4 is the state for all budgets exceeded. The
states S1-S4 are sorted by "goodness" where the best state is S1
and the worst state is S4 and numerical values are assigned to the
states in strictly decreasing order (or strictly increasing order)
so that the best state has the highest value (or lowest value) and
the worst state has the lowest value (or highest value).
[0061] In an embodiment, the operator configures the amount of
imagery (number of levels M) to be displayed and the imagery
itself. The range of levels that can be displayed can be determined
systematically and suggested to the operator based on the settings
of N and K. For the past K intervals, the values corresponding to
each interval state are summed and normalized to a range of 0 to 1
to create ratio R. Scalar quantization methodology is used to
systematically determine the bins associated with each level and
the imagery to be displayed. As shown in 1001, the M bins are
defined the range of the value R. Once a bin has been selected, the
Award Level is designates the imagery to be displayed. The award
level and imagery should be directly correlated to the overall
goodness of the recent history. That is, if the interval state
values are assigned so that the highest values are correlated to
the highest "goodness" of a state, then the ratios with the highest
values should correspond to the most rewarding imagery; if the
interval state values are assigned so that the highest values are
correlated to the least goodness of a state, then the ratios with
the highest values should be correspond to the least rewarding
imagery.
[0062] To walk through a complete example: in 1002, there are 3
budgets being tracked (B=3) and therefore four end states (N=4)
where S1 is the state for no budgets exceeded, S2 is the state for
one budget only was exceeded, S3 is the state for two budgets
exceeded, and S4 is the state for all budgets exceeded. The states
S1-S4 are sorted by "goodness" where the best state is S1 and the
worst state is S4 and numerical values are assigned to the states
in strictly decreasing order 3, 2, 1, 0 so that the best state has
the highest value. In 1004, K=6 of the most recent time intervals
are tracked. The values and colors for each of the six end states
are taken from 1002. In 1005, the sum of the values in 1004 is 11
and the maximum possible value is 3*6=18. The normalized value
R=0.61 is computed as the sum 11 divided by the maximum value 18.
In 1003, bins for M=8 levels are specified where the bins are
uniformly spaced (uniform scalar quantization is used to define the
bins assuming R is uniformly distributed). The award levels would
range from the least rewarding imagery assigned to the bin with the
lowest ratios R to the most rewarding imagery assigned to the bin
with the highest ratios R. The bin for R=0.61 in 1003 is Bin 4 as
0.61 is less than 0.625 and greater than or equal to 0.5. Therefore
the award level is L4 and the imagery to be displayed should be 3
levels "degraded" from the best imagery associated with award level
L7 and four levels "improved" from the worst imagery associated
with award level L0. In practice, the distribution of R is not
uniform, and an embodiment would use scalar quantization or
adaptive scalar quantization methodologies to compute the bins to
optimize the user experience so that all reward levels are conveyed
and feedback given appropriately.
[0063] In FIG. 11, an embodiment with all of the visualization
elements discussed above is illustrated where real-time gauges
(depletion paradigm) with color overlays are given prominent
display space for in the moment feedback, and summaries of recent
time intervals and reward imagery are given secondary and tertiary
placement shift related feedback.
[0064] To evaluate overall performance, shift and line managers and
executives have access to reports by shift, day, and other periods
as well as by organizational structure, to assess daily workflows
and energy savings improvements via an advanced reporting user
interface.
[0065] The present invention is described above with reference to
block diagrams and operational illustrations of methods and devices
for creating real time data for in-the-moment management of
appliances, devices, and equipment used in a particular line of
business. It is understood that each block of the block diagrams or
operational illustrations, and combinations of blocks in the block
diagrams or operational illustrations, may be implemented by means
of analog or digital hardware and computer program instructions.
These computer program instructions may be stored on
computer-readable media and provided to a processor of a general
purpose computer, special purpose computer, ASIC, or other
programmable data processing apparatus, such that the instructions,
which execute via the processor of the computer or other
programmable data processing apparatus, implements the
functions/acts specified in the block diagrams or operational block
or blocks. In some alternate implementations, the functions/acts
noted in the blocks may occur out of the order noted in the
operational illustrations. For example, two blocks shown in
succession may in fact be executed substantially concurrently or
the blocks may sometimes be executed in the reverse order,
depending upon the functionality/acts involved.
[0066] At least some aspects disclosed can be embodied, at least in
part, in software. That is, the techniques may be carried out in a
special purpose or general purpose computer system or other data
processing system in response to its processor, such as a
microprocessor, executing sequences of instructions contained in a
memory, such as ROM, volatile RAM, non-volatile memory, cache or a
remote storage device.
[0067] Routines executed to implement the embodiments may be
implemented as part of an operating system, firmware, ROM,
middleware, service delivery platform, SDK (Software Development
Kit) component, web services, or other specific application,
component, program, object, module or sequence of instructions
referred to as "computer programs." Invocation interfaces to these
routines can be exposed to a software development community as an
API (Application Programming Interface). The computer programs
typically comprise one or more instructions set at various times in
various memory and storage devices in a computer, and that, when
read and executed by one or more processors in a computer, cause
the computer to perform operations necessary to execute elements
involving the various aspects.
[0068] A machine-readable medium can be used to store software and
data which when executed by a data processing system causes the
system to perform various methods. The executable software and data
may be stored in various places including, for example, ROM,
volatile RAM, non-volatile memory and/or cache. Portions of this
software and/or data may be stored in any one of these storage
devices. Further, the data and instructions can be obtained from
centralized servers or peer-to-peer networks. Different portions of
the data and instructions can be obtained from different
centralized servers and/or peer-to-peer networks at different times
and in different communication sessions or in a same communication
session. The data and instructions can be obtained in entirety
prior to the execution of the applications. Alternatively, portions
of the data and instructions can be obtained dynamically, just in
time, when needed for execution. Thus, it is not required that the
data and instructions be on a machine-readable medium in entirety
at a particular instance of time.
[0069] Examples of computer-readable media include but are not
limited to recordable and non- recordable type media such as
volatile and non-volatile memory devices, read only memory (ROM),
random access memory (RAM), flash memory devices, floppy and other
removable disks, magnetic disk storage media, optical storage media
(e.g., Compact Disk Read-Only Memory (CD ROMS), Digital Versatile
Disks (DVDs), etc.), among others.
[0070] In general, a machine readable medium includes any mechanism
that provides (e.g., stores) information in a form accessible by a
machine (e.g., a computer, network device, personal digital
assistant, manufacturing tool, any device with a set of one or more
processors, etc.).
[0071] In various embodiments, hardwired circuitry may be used in
combination with software instructions to implement the techniques.
Thus, the techniques are neither limited to any specific
combination of hardware circuitry and software nor to any
particular source for the instructions executed by the data
processing system.
[0072] Although the present invention has been described in
considerable detail with reference to certain preferred versions
thereof, other versions are possible. Therefore, the spirit and
scope of the appended claims should not be limited to the
description of the preferred versions contained herein. The
reader's attention is directed to all papers and documents which
are filed concurrently with this specification and which are open
to public inspection with this specification, and the contents of
all such papers and documents are incorporated herein by
reference.
[0073] All the features disclosed in this specification (including
any accompanying claims, abstract, and drawings) may be replaced by
alternative features serving the same, equivalent or similar
purpose, unless expressly stated otherwise. Thus, unless expressly
stated otherwise, each feature disclosed is one example only of a
generic series of equivalent or similar features.
[0074] Any element in a claim that does not explicitly state "means
for" performing a specified function, or "step for" performing a
specific function, is not to be interpreted as a "means" or "step"
clause as specified in 35 U.S.C .sctn.112, sixth paragraph. In
particular, the use of "step of" in the claims herein is not
intended to invoke the provisions of 35 U.S.C .sctn.112, sixth
paragraph.
* * * * *