U.S. patent application number 10/942773 was filed with the patent office on 2005-02-17 for automatic energy management and energy consumption reduction, especially in commercial and multi-building systems.
Invention is credited to Brickfield, Peter J., Mahling, Dirk, Noyes, Mark, Weaver, David.
Application Number | 20050038571 10/942773 |
Document ID | / |
Family ID | 29248142 |
Filed Date | 2005-02-17 |
United States Patent
Application |
20050038571 |
Kind Code |
A1 |
Brickfield, Peter J. ; et
al. |
February 17, 2005 |
Automatic energy management and energy consumption reduction,
especially in commercial and multi-building systems
Abstract
Automatic energy management is provided, in even the most
complex multi-building system. The necessity of a human operator
for managing energy in a complex, multi-building system is reduced
and even eliminated. Computer-based monitoring and computer-based
recognition of adverse energy events (such as the approach of a new
energy peak) is highly advantageous in energy management. Immediate
automatic querying of energy users within a system of buildings for
energy curtailment possibilities is provided. Such immediate,
automatic querying may be answered by the energy users through
artificial intelligence and/or neural network technology provided
to or programmed into the energy users, and the queried energy
users may respond in real-time. Those real-time computerized
responses with energy curtailment possibilities may be received
automatically by a data processing facility, and processed in
real-time. Advantageously, the responses from queried energy users
with energy curtailment possibilities may be automatically
processed into a round-robin curtailment rotation which may be
implemented by a computer-based control system. Thus, impact on
occupants is minimized, and energy use and energy cost may be
beneficially reduced in an intelligent, real-time manner. The
invention also provides for early-recognition of impending adverse
energy events, optimal response to a particular energy situation,
real-time analysis of energy-related data, etc.
Inventors: |
Brickfield, Peter J.;
(Cambridge, MA) ; Mahling, Dirk; (Chelmsford,
MS) ; Noyes, Mark; (North Andover, MA) ;
Weaver, David; (Coral Gables, FL) |
Correspondence
Address: |
MCGUIREWOODS, LLP
1750 TYSONS BLVD
SUITE 1800
MCLEAN
VA
22102
US
|
Family ID: |
29248142 |
Appl. No.: |
10/942773 |
Filed: |
September 17, 2004 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
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10942773 |
Sep 17, 2004 |
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10092507 |
Mar 8, 2002 |
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Current U.S.
Class: |
700/295 ;
307/38 |
Current CPC
Class: |
Y02E 40/70 20130101;
Y02P 80/10 20151101; H02J 3/003 20200101; H02J 3/14 20130101; Y04S
40/20 20130101; Y04S 50/10 20130101; H02J 2203/20 20200101; Y04S
20/222 20130101; Y02E 60/00 20130101; H02J 2310/60 20200101; Y02B
70/3225 20130101; H02J 3/008 20130101; Y04S 10/50 20130101; H02J
2310/64 20200101 |
Class at
Publication: |
700/295 ;
307/038 |
International
Class: |
H02J 003/14; H02J
001/00 |
Claims
1. An energy management system comprising: computer-based
monitoring for an adverse energy event in a building system;
computer-based recognition of an adverse energy event in the
building system; immediate automatic querying of energy users
within the building system for energy curtailment possibilities;
automatic receipt of responses from queried energy users with
energy curtailment possibilities; automatic processing of energy
curtailment possibilities into a round-robin curtailment
rotation.
2. The system of claim 1, wherein the adverse energy event is
selected from the group consisting of a new peak demand; a
human-given directive to curtail a certain amount of energy
consumption; and an excess increase of energy price in a
deregulated market.
3. The system of claim 1, wherein the monitoring occurs in a
context selected from a business-as-usual context; 24.times.7
permanent load reduction context; and an emergency context.
4. The system of claim 1, including 24.times.7 permanent load
reduction.
5. The system of claim 1, including minimization of energy
consumption in ongoing business-as-usual energy consumption.
6. The system of claim 1, wherein the building system is selected
from the group consisting of a single building and at least two
buildings.
7. A system of claim 1, wherein the adverse energy event is a surge
or a steady increase towards a new peak demand.
8. A system of claim 1, wherein the system comprises at least two
buildings.
9. A system of claim 1, wherein the system comprises load balancing
between buildings.
10. The system of claim 1, wherein served by the system is a
building or are buildings selected from the group consisting of at
least one university building; at least one hotel building; at
least one hospital building; at least one car dealership building;
at least one shopping mall; at lease one government building; at
least one chemical processing plant; at least one manufacturing
facility; and any combination thereof of buildings.
11. The system of claim 1, wherein at least two buildings are under
management and are geographically dispersed.
12. The system of claim 1, wherein a human operator is not
needed.
13. The system of claim 12, wherein a human operator has an
optional override right.
14. The system of claim 1, including at least two buildings, said
buildings being commonly owned or not commonly owned.
15. The system of claim 1, including automatic documentation of
energy savings attributable to any automatic intervention(s) by the
energy management system.
16-37. (cancelled).
38. A computer-based energy management system, comprising: (A)
non-human, computerized processing of obtained energy data, wherein
the obtained energy data is for at least one energy user in a
building system, said processing including automatic determination
of whether at least one energy-relevant event is present; and (B)
upon recognition of an automatic determination that at least one
energy-relevant event, a non-human, computerized response thereto
based upon artificial intelligence reasoning.
39. The system of claim 38, wherein the non-human, computerized
response is formulated after processing of more information than
could be accomplished by a human in whatever processing time has
been expended.
40. The system of claim 38, including artificial intelligence
reasoning based on one or more of: (A) knowledge about a building
or buildings in the building system; (B) knowledge about an energy
using device; (C) knowledge about the building system; and (D) data
outside the building system.
41. The system of claim 38, including automatic querying of energy
users.
42. The system of claim 41, including receiving responses from
queried energy users and automatically processing the received
responses.
43. The system of claim 42, including automatic formulation of an
optimal energy-saving command decision and/or strategy.
44. The system of claim 43, including executing the optimal
energy-saving command decision or strategy.
45. The system of claim 44, wherein the optimal energy-saving
command decision comprises a rotation of energy curtailment that
minimizes impact over energy users in the system.
46. The system of claim 38, wherein the computerized response
includes at least one determination based on one or more of: (A)
air quality, humidity, pollutants, air flow speed, temperature, and
other descriptors of physical properties of air; (B) light
direction, light color, ambient temperature, foot candle, kw
consumption of light producing equipment, smell of light, and other
descriptors of physical properties of light; (C) plug load; motion
sensed by motion sensors; carbon dioxide levels; brightness; sound
levels; automated device for sensing human presence; motion
detectors; light-sensing apparatus; habitation-sensor; (D) chemical
or biological warfare agent sensing device.
47. The system of claim 46, wherein the chemical or biological
warfare agent sensing device is selected from the group consisting
of a mustard gas sensor, an anthrax sensor, a carbon monoxide
sensor, a carbon dioxide sensor, a chlorine gas sensor and a nerve
gas sensor.
48. The system of claim 38, wherein the artificial intelligence
reasoning comprises at least one artificial intelligent agent and
at any given time, what the artificial intelligence agent is doing
may be monitored.
49. The system of claim 47, including monitoring is by a human
viewing what the artificial intelligence agent is doing.
50. The system of claim 47, including generation of a log of
historical activity by one or more artificial intelligent agents
performing the artificial intelligence reasoning.
51. The system of claim 38, including machine-based detection of
presence of a chemical or biological warfare agent, to which a
machine-based response is determined.
52. The system of claim 48, wherein the machine-based response
includes release of an anti-agent and/or adjustment of one or more
energy users.
53. The system of claim 38, including at least one machine-based
determination of at least one parameter of interest to a building
manager, said parameter being measurable and controllable.
54. The system of claim 38, including automatic monitoring of the
computerized response.
55. The system of claim 38, including communication over the
Internet.
56. The system of claim 38, wherein a human operator may enter a
query.
57. The system of claim 56, wherein the human operator query is a
query as to current state of one or more devices in a specified
building.
58. The system of claim 56, wherein the human operator query is a
query requesting a prediction of effect of proposed control
action(s) on an energy bill and/or on comfort.
59. A computer-based round-robin rotation system for energy users,
wherein the energy users are under computer-based control and are
present in a building system, the round-robin rotation system
comprising a series of computer-based energy curtailment commands
to each of a plurality of energy users in the building system,
wherein (1) each computer-based energy curtailment command in the
series of energy curtailment commands (a) is specific to the energy
user to which the curtailment command is directed; (b) has been
derived from an energy curtailment offer provided by the energy
user; and/or (c) is based on continually learned and observed
characteristics of the energy user; and/or (2) an energy user in
the plurality of energy users is grouped with other energy users
based on similarity with regard to a certain parameter or
parameters.
60. The round-robin rotation system of claim 59, wherein the
building system includes at least two buildings.
61. The round-robin rotation system of claim 59, wherein the
round-robin system is formulated in response to a human request for
energy curtailment.
62. The round-robin rotation system of claim 59, wherein the
round-robin system is implemented under business-as-usual
circumstances.
63. The round-robin rotation system of claim 62, wherein the system
has learned by artificial intelligence that a desired target
parameter in each area served by the system can be maintained by a
round-robin rotation.
64. The round-robin rotation system of claim 63, wherein the target
parameter is room temperature.
65-73. (cancelled).
74. An energy curtailment system comprising an automatically
managed round-robin rotation of a plurality of energy curtailment
interventions.
75. The energy curtailment system of claim 74, wherein each
respective energy curtailment intervention within the plurality of
energy curtailment interventions is derived from an energy
curtailment offer from a to-be-curtailed energy user.
76. The energy curtailment system of claim 75, including a
plurality of to-be-curtailed energy users in a multi-building
system.
77. The energy curtailment system of claim 76, wherein the
multi-building system includes buildings geographically dispersed
at least a state's distance apart.
78. The energy curtailment system of claim 75, wherein new energy
peaks are avoided without human operator intervention.
79. The energy curtailment system of claim 75, wherein automatic
documentation is automatically generated of avoidance of a new
energy peak, with said automatic documentation being (a) stored in
an accessible computer file and/or (b) printed and/or stored in a
human operator-friendly format.
80. The energy management system of claim 1, wherein monthly energy
consumption is reduced for the building system and/or peak load
demand charges for the building system are lowered.
81. The energy management system of claim 1, including one
revenue-grade virtual meter which is an aggregation of
revenue-grade meters.
82. The energy management system of claim 1, wherein energy use is
constantly monitored and/or adjusted, said constant monitoring
and/or adjustment being non-human, wherein business-as-usual
constant adjustment, 24.times.7 load reduction is provided.
83. The energy management system of claim 82, wherein the non-human
constant monitoring and/or adjustment is by artificial
intelligence.
84. The energy management system of claim 82, wherein the non-human
constant monitoring and/or adjustment is to monitor and/or adjust
at least one factor that influences energy consumption.
85. The energy management system of claim 84, wherein the at least
one factor that influences energy consumption is selected from the
group consisting of current weather conditions at and/or
approaching an energy-user; occupancy levels of a facility served
by an energy-user; market price of energy; weather forecasts;
market price forecasts; air quality; air quality forecasts;
lighting quality; lighting quality forecasts; plug load patterns;
and plug load pattern forecasts.
86. The energy management system of claim 85, including monitoring
and adjusting based on all of current weather conditions at and/or
approaching an energy-user; occupancy levels of a facility served
by an energy-user; market price of energy; weather forecasts;
market price forecasts; air quality; air quality forecasts;
lighting quality; lighting quality forecasts; plug load patterns;
and plug load pattern forecasts.
87. The energy management system of claim 1, wherein the adverse
energy event being monitored-for is at least one recognizable
pattern of data that has been learned via artificial intelligence
by a computer system doing the monitoring.
88. The energy management system of claim 87, wherein the computer
doing the recognition of an adverse energy event, for each
recognized pattern of data that is an adverse energy event, reacts
with an automatic response based upon reasoning.
89. The system of claim 88, wherein the reasoning-based response is
a querying response to be executed.
90. The energy management system of claim 1, wherein responses from
queried energy users with energy curtailment possibilities are
automatically processed by a computer with a set of instructions
for evaluating how to enact each respective curtailment possibility
of each respective energy user offering a curtailment
possibility.
91. The energy management system of claim 90, wherein the computer
automatically processing the responses from queried users totals
the respective curtailment possibilities from the queried energy
users amounts, determines whether the total of respective
curtailment possibilities is sufficiently large, and, if so,
proceeds to schedule a round-robin energy curtailment rotation
pursuant to criteria.
92. The energy management system of claim 90, wherein the computer
automatically processing the responses from queried users totals
the respective curtailment possibilities from the queried energy
users amounts, determines whether the total of respective
curtailment possibilities is sufficiently large, and, if not,
notifies a human user.
93. The energy management system of claim 1, including a
preliminary step of functional testing for obtaining data and
formulating applicable rules, and a continuous process of learning
embedded in a neural net of a modeling agent associated with an
energy-using device.
94-99. (cancelled).
100. An energy management system for automatically achieving energy
curtailment in a multi-building system, comprising: immediate
automatic querying of energy users within the building system for
energy curtailment possibilities; automatic receipt of responses
from queried energy users with energy curtailment possibilities;
automatic processing of energy curtailment possibilities into a
round-robin curtailment rotation.
101. The energy management system of claim 100, wherein the
immediate automatic querying is directly or indirectly activated
based on a request by a local independent system operator, a power
authority or a utility supplier.
102. The energy management system of claim 100, wherein the
round-robin curtailment rotation is executed and achieves energy
consumption reduction.
103. The energy management system of claim 102, wherein the energy
consumption reduction occurs during an energy emergency.
104. The energy management system of claim 103, wherein the energy
emergency is declared by a local independent system operator, a
power authority, a utility supplier, or a governmental
authority.
105. The energy management system of claim 100, wherein no human is
controlling.
106. The energy management system of claim 100, wherein the
round-robin curtailment rotation has been called in order that
energy may be sold back into the grid.
107. The energy management system of claim 100, wherein maximum
energy curtailment is achieved with minimal impact to occupants of
buildings in the building system.
108. The energy management system of claim 100, wherein maximum
energy curtailment is achieved with no greater than a certain
defined level of impact to occupants of buildings in the building
system.
109. The energy management system of claim 100, wherein the
multi-building system is owned by an owner selected from the group
consisting of a commercial entity, a university and a
government.
110. The system of claim 1, wherein each energy user has associated
therewith a dedicated neural network that continuously learns
operating characteristics of said energy user associated with the
dedicated neural network, wherein forward and backward reasoning
and forecastability are provided.
111. The system of claim 1, including at least one modeling agent
and/or at least one forecasting agent.
112. The system of claim 1, wherein the system is autonomous,
artificial-intelligence based, real-time, over the Internet.
Description
BACKGROUND OF THE INVENTION
[0001] This invention relates generally to systems and methods for
managing use of energy, and especially to systems and methods for
managing energy use in a complex multi-building context.
[0002] A number of factors have combined in recent years to create
an electrical energy crisis in many regions of the United States.
These include: a shortage of generating capacity; lack of capital
investment in new transmission capacity; fuel volatility; and
increased demand. The result is a power shortage and difficulties
in the energy infrastructure.
[0003] Multiple-building systems, such as commonly owned systems of
30, 60 or more buildings, exist throughout the world today.
Examples of such building systems include, e.g., university
systems. Multiple building systems may be geographically dispersed.
Controlling energy consumption, and costs of energy consumption, in
such wide-spread building systems presents challenges. See, e.g.,
U.S. Pat. No. 6,178,362 issued in 2001 to Woolard et al. (assigned
to Silicon Energy Corp.), discussing some of the problems of energy
management and energy cost management for commercial users who
operate large physical plants.
[0004] Conventionally, if it was desired to reduce energy
consumption by a particular amount (such as a 40 KW reduction in
the next two hours) in a multi-building system, which typically use
procedure-based systems (such as conventional building management
systems, current-generation energy management software, or
SCADA-type systems), the building manager was required to conduct
all the steps and tasks necessary to accomplish the goal manually.
Thus, the question of how to accomplish a specified energy
consumption reduction has been heavily human-dependent.
[0005] Another question is how to know what specific energy
consumption reduction to even want to accomplish. That question,
too, has been heavily human-dependent. For example, conventionally,
as in U.S. Pat. No. 6,178,362, various meters and data-taking
devices have been included in multi-building systems, but the
obtained energy data still must be reviewed by a human operator.
The necessary inclusion of a human operator in conventional systems
has posed certain substantial disadvantages. A human operator may
fail to recognize one or more energy-relevant events (such as the
threat of a new maximum peak). The diligence, accuracy, speed, and
foresight of a human operator necessarily may be limited,
contributing to likely missed recognition of such energy relevant
events. Human operators may have other duties, so that they not be
reviewing relevant energy data at what would be a critical time.
Human operators may review data yet fail to appreciate its
significance. Human operators may review data, appreciate its
significance, and decide on a course of action that may be less
than optimal in terms of cost or convenience or comfort.
[0006] In any energy management system, reaching a new maximum of
peak usage will be expensive and is acknowledged as something to be
recognized--and avoided. In a human-based energy management system,
the human operator may, or may not, be looking at energy data
output at a time when the data is surging towards a new peak. Human
operators come in a variety of diligence, attentiveness, and
ability levels. Human operators tasked with recognizing surges
towards new peaks tend to have other tasks, such that they cannot
provide a sufficient level of attention and monitoring to recognize
every surge towards a new peak.
[0007] Recognition of an energy-relevant event such as a surge
towards a new peak is only one aspect of energy management. After
recognition that an undesirable energy-relevant event is in
progress, there remains the question of what response to take.
There is only so much information and so many permutations that a
human operator possibly can take into account in a fixed amount of
time. The human operator is called upon to decide and act quickly,
to avoid the new peak toward which the system is surging. When a
human operator recognizes that a new energy peak is being
approached, he or she will want to act quickly to avoid reaching
the peak and will make a decision to reduce power to one or more
power consumers in the system. The human operator is essentially
incapable in a limited amount of time of consulting or studying the
many different energy users (such as energy-using devices or
apparatuses such as air-conditioners, etc.) to ascertain the status
of each. A human operator practically speaking can do no more than,
at best, execute one or more energy-reducing commands--for at least
the reason that the luxury of time is not present.
[0008] Software systems that reduce energy consumption in building
have been available for many years. These systems work by
connecting various pieces of energy-consuming equipment to a
computer, which allows the building manager to monitor consumption,
and, if necessary, manually reduce it. More sophisticated systems
allow third party "service bureaus" to provide these functions for
building owners, but they still rely on intensive human
intervention to be effective. Heretofore, the analysis and
management of energy consumption has been a manual process.
Computers and software systems have been able to collect data on
energy consumption in particular facilities or on individual pieces
of equipment for years. But human beings have had to analyze that
information, and decide what action to take to reduce energy
consumption. And because many factors affect energy consumption at
any given moment--the weather outside, the number of people inside,
etc.--it has never been possible to accurately and precisely adjust
energy consumption in real time. For example, the Woolard et al.
system seeks to use three dimensional facilities navigation tools,
energy consumption analysis processes, TCP/IP communication and a
World Wide Web (WWW)-based interface, but it is based on
sub-systems each of which "performs operations which permit an
employee of the entity to control and manage its facilities
including its energy consumption." Id., column 2, lines 26-29
(emphasis added).
[0009] The electricity crisis in California in 2001 provides a
vivid illustration. Although many buildings and factories in the
state have energy management systems, the only option available to
power suppliers and commercial consumers trying to prevent
wholesale network collapse was literally to turn out the lights in
"brownouts" and rolling blackouts. The energy management systems in
place and the people who monitor them on a daily basis were simply
not capable of analyzing all of the potential alternative for
reducing energy consumption and doing so quickly. The only choice
was to shut down whole systems and businesses. Power outages, even
planned power outages, have highly disruptive effects, such as
disrupting telephone and computer network equipment, data
inaccessibility, etc.
[0010] The various government and quasi-government entities charged
with ensuring energy availability will continue to push users to
curtail their electric power usage in order to avoid the
devastating impact of blackouts, either actual or threatened.
Avoidance of power outages by large users of power is sought, as
having many benefits. Businesses need to have reliable sources of
energy. Governments face social and political consequences of
chronic energy shortages. Power suppliers cannot meet the demand
for electricity in their areas, without building large
power-generating reserves, which is not an optimal solution. Thus,
it will be appreciated that there are many challenges in the areas
of energy consumption, energy shortages, and energy management that
remain to be addressed.
SUMMARY OF THE INVENTION
[0011] In the present invention, a system comprising artificial
intelligence is connected to energy-using devices (such as pieces
of equipment). Energy consumption advantageously may be monitored
and/or manipulated in real time. Artificial intelligence (such as
intelligent agents) may be used to evaluate, forecast and/or
control energy consumption patterns. From the system comprising
artificial intelligence, control signals may be sent to deploy
agreed-upon energy-saving strategies at the building and/or device
(energy user) level. Advantageously, energy management can be
autonomous, artificial-intelligence based, real-time, over the
Internet.
[0012] A significant advantage of the invention is to provide
maximum energy curtailment with minimal impact to occupants of
buildings in the building system. Maximum energy curtailment may be
achieved with no greater than a certain defined level of impact to
occupants of buildings in the building system.
[0013] The invention in a first preferred embodiment provides an
energy management system comprising: computer-based monitoring for
an adverse energy event in a building system; computer-based
recognition of an adverse energy event in the building system;
immediate automatic querying of energy users within the building
system for energy curtailment possibilities; automatic receipt of
responses from queried energy users with energy curtailment
possibilities; automatic processing of energy curtailment
possibilities into a round-robin curtailment rotation. Preferably,
responses from queried energy users with energy curtailment
possibilities are automatically processed by a computer with a set
of instructions for evaluating how to enact each respective
curtailment possibility of each respective energy user offering a
curtailment possibility.
[0014] In another preferred embodiment, the invention provides a
method for minimizing and/or eliminating need for human operator
attention in energy management of a building system, comprising:
non-human, computerized processing of obtained energy data, wherein
the obtained energy data is for at least one energy user in the
building system, said processing including (A) automatic
determination of whether at least one energy-relevant event is
present or (B) continual optimization of a setting of the at least
one energy user. Optionally, when a energy-relevant event is
automatically determined to be present, the invention provides
immediately activating an automatic response to the energy-relevant
event. Another preferred but optional example is mentioned, wherein
at least one intelligent agent, from the obtained energy data,
actually forecasts the peak; wherein the energy-relevant event is a
threat of a new maximum peak, and the immediately activated
automatic response includes energy reduction interventions to avoid
the new maximum peak.
[0015] In a further preferred embodiment, the invention provides a
computer-based energy management system, comprising: non-human,
computerized processing of obtained energy data, wherein the
obtained energy data is for at least one energy user in a building
system, said processing including automatic determination of
whether at least one energy-relevant event is present; and upon
recognition of an automatic determination that at least one
energy-relevant event, a non-human, computerized response thereto
based upon artificial intelligence reasoning.
[0016] Additionally, in another preferred embodiment the invention
provides a computer-based round-robin rotation system for energy
users, wherein the energy users are under computer-based control
and are present in a building system, the round-robin rotation
system comprising: a series of computer-based energy curtailment
commands to each of a plurality of energy users in the building
system, wherein (1) each computer-based energy curtailment command
in the series of energy curtailment commands; (a) is specific to
the energy user to which the curtailment command is directed; (b)
has been derived from an energy curtailment offer provided by the
energy user; and/or (c) is based on continually learned and
observed characteristics of the energy user; and/or (2) an energy
user in the plurality of energy users is grouped with other energy
users based on similarity with regard to a certain parameter or
parameters.
[0017] The invention, in another preferred embodiment, provides a
computer based method of avoiding a new energy peak, comprising:
priming a computer-based system with data as to energy peak(s)
already reached in a building system; for current energy usage in
the building system, obtaining, in real-time, computer-readable
data from which to automatically forecast if a new energy peak is
approaching; and real-time automatic processing the obtained
computer-readable data to forecast whether or not a new energy peak
is approaching. Preferably, if the real-time automatic processing
of the obtained computer-readable data provides a forecast that a
new energy peak is approaching, an immediate, real-time, automatic
response is initiated.
[0018] In a further preferred embodiment, the invention provides an
energy curtailment system comprising an automatically managed
round-robin rotation of a plurality of energy curtailment
interventions. Each respective energy curtailment intervention
within the plurality of energy curtailment interventions may
derived from an energy curtailment offer from a to-be-curtailed
energy user. A plurality of to-be-curtailed energy users may be
included in a single building or in a multi-building system.
[0019] Additionally, the invention in yet another embodiment
provides a compilation of energy-relevant data, comprising: a
stream of energy-related data for at least one individual energy
user within a plurality of energy users (such as where the at least
one individual energy user is within a multi-building system and
separate streams of data are provided for other individual energy
users within the multi-building system.) The invention also
provides a data analysis method, comprising leveraging a stream of
energy-related data for at least one individual energy user within
a plurality of energy users, wherein the leveraging includes a
comparison against historic data for the device. The leveraging may
include computer-based searching for rapid deviation from a
historic pattern.
[0020] Another embodiment of the invention provides a method of
determining whether to repair or replace an individual energy user,
comprising: reviewing a stream of energy-related data for the
individual energy user, wherein the individual energy user is
contained within a plurality of energy users.
[0021] The invention in an additional embodiment provides an energy
management system for automatically achieving energy curtailment in
a multi-building system, comprising: immediate automatic querying
of energy users within the building system for energy curtailment
possibilities; automatic receipt of responses from queried energy
users with energy curtailment possibilities; automatic processing
of energy curtailment possibilities into a round-robin curtailment
rotation.
[0022] Some perfecting details of the inventive systems, methods,
etc. are mentioned as follows, without the invention being limited
thereto.
[0023] Preferably, each energy user has associated therewith a
dedicated neural network, such as a dedicated neural network that
continuously learns operating characteristics of said energy user
associated with the dedicated neural network, wherein forward and
backward reasoning and forecastability are provided.
[0024] Where an adverse energy event or energy-relevant event is
mentioned, examples may be a new peak demand or threat thereof; a
human-given directive to curtail a certain amount of energy
consumption; and/or an excess increase of energy price in a
deregulated market. The adverse or energy-relevant energy event may
be a surge or a steady increase towards a new peak demand; at least
one recognizable pattern of data that has been learned via
artificial intelligence by a computer system doing the monitoring;
etc. Preferably, the computer doing the recognition of an adverse
energy event, for each recognized pattern of data that is an
adverse energy event, reacts with an automatic response based upon
reasoning (such as a a querying response to be executed).
[0025] Where monitoring is mentioned, the monitoring may occur in a
context selected from a business-as-usual context; 24.times.7
permanent load reduction context; and an emergency context. Energy
use may be constantly monitored and/or adjusted, said constant
monitoring and/or adjustment being non-human, wherein
business-as-usual constant adjustment, 24.times.7 load reduction is
provided. The non-human constant monitoring and/or adjustment
preferably is by artificial intelligence; and, preferably is to
monitor and/or adjust at least one factor that influences energy
consumption (such as current weather conditions at and/or
approaching an energy-user; occupancy levels of a facility served
by an energy-user; market price of energy; weather forecasts;
market price forecasts; air quality; air quality forecasts;
lighting quality; lighting quality forecasts; plug load patterns;
plug load pattern forecasts; etc.).
[0026] The invention may include and/or provide one or more of the
following:
[0027] at least one modeling agent and/or at least one forecasting
agent;
[0028] 24.times.7 permanent load reduction;
[0029] minimization of energy consumption in ongoing
business-as-usual energy consumption;
[0030] load balancing between buildings;
[0031] automatic documentation of energy savings attributable to
any automatic intervention(s) by the energy management system;
[0032] machine-based learning from the obtained data and/or
machined-based constructing a model from the obtained data;
[0033] automatic documentation of energy savings attributable to
any said automatic intervention(s);
[0034] machine-based reasoning to select between at least two
conflicting goals (such as machine-based reasoning is to select
between a market price goal and a comfort-maintenance goal);
[0035] a computerized display of energy data and/or device;
[0036] on human demand, computerized forecasting, computerized
simulation of an effect or effects of a proposed control action,
and/or computerized reporting on simulation at various levels of
aggregation;
[0037] artificial intelligence reasoning based on one or more of:
(A) knowledge about a building or buildings in the building system,
(B) knowledge about an energy using device, (C) knowledge about the
building system, and (D) data outside the building system;
[0038] automatic querying of energy users;
[0039] receiving responses from queried energy users and
automatically processing the received responses;
[0040] automatic formulation of an optimal energy-saving command
decision and/or strategy;
[0041] executing the optimal energy-saving command decision or
strategy;
[0042] generation of a log of historical activity by one or more
artificial intelligent agents performing the artificial
intelligence reasoning;
[0043] machine-based detection of presence of a chemical or
biological warfare agent, to which is determined a machine-based
response (such as release of an anti-agent and/or adjustment of one
or more energy users); at least one machine-based determination of
at least one parameter of interest to a building manager, said
parameter being measurable and controllable;
[0044] automatic monitoring of the computerized response;
[0045] communication over the Internet;
[0046] learning by artificial intelligence that a desired target
parameter (such as room temperature) in each area served by the
system can be maintained by a round-robin rotation;
[0047] compiling a complete array of historical data in
computer-readable form, determining one or more patterns thereform,
and comparing therewith current real-time data to forecast if a new
peak is going to be reached;
[0048] neural network based prediction;
[0049] one revenue-grade virtual meter which is an aggregation of
revenue-grade meters;
[0050] monitoring and adjusting based on all of current weather
conditions at and/or approaching an energy-user; occupancy levels
of a facility served by an energy-user; market price of energy;
weather forecasts; market price forecasts; air quality; air quality
forecasts; lighting quality; lighting quality forecasts; plug load
patterns; and plug load pattern forecasts;
[0051] preliminary functional testing for obtaining data and
formulating applicable rules, and a continuous process of learning
embedded in a neural net of a modeling agent associated with an
energy-using device.
[0052] Where a building system is mentioned, the building system
may be a single building or at least two buildings. The building or
buildings may be, for example, at least one university building; at
least one hotel building; at least one hospital building; at least
one car dealership building; at least one shopping mall; at lease
one government building; at least one chemical processing plant; at
least one manufacturing facility; and any combination thereof of
buildings.
[0053] When at least two buildings are provided, the at least two
buildings under management may be geographically dispersed (such as
a state's difference apart); and/or commonly owned or not commonly
owned. Ownership may be, for example, by a commercial entity, a
university, a government, etc.
[0054] Where a peak is mentioned, examples of a peak include a kW
demand peak, a lighting peak, a carbon dioxide peak, a pollutant
peak, etc.
[0055] Where an automatic response is mentioned, preferably the
automatic response is non-determinative.
[0056] There has been mentioned automatic determination of whether
at least one energy-relevant event is present, and preferably such
automatic determination comprises application of artificial
intelligence.
[0057] When artificial intelligence is mentioned, preferably the
artificial intelligence is that of neural networks; rule-based
expert systems; and/or goal-based planning systems. The artificial
intelligence reasoning may comprise at least one artificial
intelligent agent and, optionally, at any given time, what the
artificial intelligence agent is doing may be monitored (such as
monitoring by a human viewing what the artificial intelligence
agent is doing.).
[0058] One or more of the following may be provided and/or
included: more obtained energy data is processed in a given time
period than could be processed by a human being; a non-human,
computerized response may be formulated after processing of more
information than could be accomplished by a human in whatever
processing time has been expended; monthly energy consumption may
be reduced for the building system and/or peak load demand charges
for the building system are lowered. Where computerized reporting
has been mentioned, the aggregation level for the computerized
reporting may be at an individual device, at everything in a
building, at a set of buildings, or everything commonly owned. The
optimal energy-saving command decision may comprise a rotation of
energy curtailment that minimizes impact over energy users in the
system.
[0059] Where a computerized response has been mentioned, the
computerized response may include at least one determination based
on one or more of: (A) air quality, humidity, pollutants, air flow
speed, temperature, and other descriptors of physical properties of
air; (B) light direction, light color, ambient temperature, foot
candle, kw consumption of light producing equipment, smell of
light, and other descriptors of physical properties of light; (C)
plug load; motion sensed by motion sensors; carbon dioxide levels;
brightness; sound levels; automated device for sensing human
presence; motion detectors; light-sensing apparatus;
habitation-sensor; (D) chemical or biological warfare agent sensing
device (such as a mustard gas sensor, an anthrax sensor, a carbon
monoxide sensor, a carbon dioxide sensor, a chlorine gas sensor, a
nerve gas sensor, etc.).
[0060] Where a round-robin rotation system has been mentioned,
examples may be a round-robin system formulated in response to a
human request for energy curtailment; a round-robin system
implemented under business-as-usual circumstances, etc. By way of
example, a computer may automatically process the responses from
queried users, total the respective curtailment possibilities from
the queried energy users amounts, determine whether the total of
respective curtailment possibilities is sufficiently large, and,
(A) if so, proceed to schedule a round-robin energy curtailment
rotation pursuant to criteria; and, (B) if not, notify a human
user. The round-robin curtailment rotation may be executed and
achieve energy consumption reduction. The energy consumption
reduction may occur during an energy emergency (such as, e.g., an
energy emergency declared by a local independent system operator, a
power authority, a utility supplier, or a governmental authority,
etc.). A round-robin curtailment rotation may be called in order
that energy may be sold back into the grid.
[0061] Where computer-readable data has been mentioned, the
computer-readable data may comprise data from the energy users in
the building system; from a source selected from sensing devices,
electric meters used for billing, and information from individual
devices; etc.
[0062] Demand for each individual device may be forecast based on
temperature forecasts; patterns historically observed and learned
via artificial intelligence and under continual update; and
occupancy where the individual device is provided.
[0063] Where immediate automatic querying has been mentioned, such
querying may be directly or indirectly activated such as querying
based on, for example, a request by a local independent system
operator, a power authority or a utility supplier.
[0064] The invention provides optional documentation, such
automatic documentation automatically generated of avoidance of a
new energy peak, with said automatic documentation being (a) stored
in an accessible computer file and/or (b) printed and/or stored in
a human operator-friendly format.
[0065] The invention advantageously makes possible that, if
desired, a human operator is not needed. If desired, a human
operator may have an optional override right. Also optionally, a
human operator may enter a query (such as a query as to current
state of one or more devices in a specified building, a query
requesting a prediction of effect of proposed control action(s) on
an energy bill and/or on comfort, etc.). The invention includes an
embodiment wherein no human operator intervention is involved in
either the automatic processing to forecast whether or not a new
energy peak is approaching nor the immediate, real-time, automatic
response to the forecast that a new energy peak is approaching. By
applying the invention, new energy peaks may be avoided without
human operator intervention. Advantageous results (such as energy
consumption reduction) mentioned herein may be achieved even when
no human is controlling.
BRIEF DESCRIPTION OF THE DRAWINGS
[0066] FIG. 1 is a flow chart of an exemplary inventive energy
management system which is machine-based and may operate
human-free.
[0067] FIG. 2 is a flow chart of exemplary machine-based energy
data receipt and processing, including automatically identifying
and responding to an adverse energy event, according to the
invention.
[0068] FIG. 3 is a flow chart of an exemplary machine-based energy
curtailment response to an adverse energy event, according to the
invention.
[0069] FIGS. 4A and 4B are examples of schematic diagrams of the
relationship of user-set goals to be effectuated by higher level
agents and the higher level agents, according to the invention.
[0070] FIG. 5 is a diagram of an exemplary Internet-based energy
management system of three buildings, according to the
invention.
[0071] FIG. 6 is flow chart for an exemplary round robin algorithm
for load rotation, according to the invention.
[0072] FIG. 7 is a chart of an exemplary rotation schedule/matrix
example according to the invention, with a load rotation "round
robin" approach being shown.
[0073] FIG. 8 is a graph of Peak Load: exemplary Virtual Meter
according to the invention versus Real Meters.
DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS
[0074] As may be seen with reference to FIG. 1, in one preferred
embodiment, the invention is a machine-based energy management
system, which may be human-free in operation. Although a human
operator is not needed, a human operator is not necessarily
precluded from acting in the energy management system. In the
energy management system of FIG. 1, for data received
electronically from a plurality of energy users, an adverse energy
event is monitored-for electronically (100). If the electronic
monitoring 100 detects no adverse energy event, the electronic
monitoring for an adverse energy event continues (100A) as more
to-be-monitored data is electronically received. If the electronic
monitoring 100 electronically detects an adverse energy event 110,
the adverse energy event that has been electronically detected 110
is electronically acted-upon 120 by adjustment of at least some of
the plurality of energy users. (Herein, reference is made,
variously, to electronically-done steps, machine-based activity,
computer functioning, electronic activity, automatic activity, and
the like, in each case, to make clear that the step is performed in
a non-human manner rather than to limit reference to a particular
machine or device.) The monitored-for adverse energy event may be
any energy-usage and/or energy-cost related event, for which the
received to-be-monitored data may be electronically monitored. As a
preferred example of an adverse energy event for which the received
energy data may be monitored is mentioned the surge towards a new
energy peak. It will be recognized and appreciated that a machine,
such as computer, may more rapidly calculate and compare numerical
information than could a human operator. Presented with the same
electronic energy data, a machine-based system can more rapidly and
accurately arrive at a faster conclusion as to the direction being
taken by the energy use in the entire system. The greater the
number of energy users, the greater the number of buildings in the
system, and the greater the dispersion of energy users over
different buildings, the more difficult it is for a human operator
or for several human operators to make decisions that minimize
energy usage and cost of energy usage without adversely affecting
operations and occupants.
[0075] The invention may be used in any system including a
plurality of energy users, most preferably a system in which the
plurality of energy users are dispersed in multiple commercial
buildings. The invention provides particular advantage in a
multi-commercial building system, because of the difficulties
otherwise posed by energy management and energy cost control in
such multi-commercial building systems. As examples of commercial
buildings may be mentioned, e.g., university buildings, factories,
hospitals, office buildings, etc. It will be appreciated that not
necessarily all energy users in a building are required to
participate in the energy management system of the invention.
[0076] As examples of an "energy user" in the present invention may
be mentioned any device that requires energy to operate, including
but not limited to, e.g., air conditioners, chillers, heating and
ventilation, fans, fountain pumps, elevators, other equipment,
lighting, etc.
[0077] As examples of the to-be-monitored energy data received
electronically from the energy users are any data that are
receivable from an energy user in real-time (i.e., embedded)
communication with a data-receiving device. As examples of such
data movement may be mentioned an electronic means of real-time
data movement such as a network (such as the Internet (i.e., the
World Wide Web (WWW), an intranet, etc.), most preferably, the
Internet. As examples of the sources of the machine-readable data
that is received and subjected to machine-based monitoring may be
mentioned any metering device, measuring device, etc. that measures
energy use (including actual use and scheduled upcoming use) of an
energy user.
[0078] With reference to FIG. 1, the electronic-acting upon 120 a
detected adverse energy event may be any response or adjustment
that reduces energy cost and/or energy usage, most preferably an
energy cost reduction and energy usage reduction approach with
minimal impact on occupant comfort and normal operations. An
exemplary electronic response 120 to a detected adverse energy
event may be seen with respect to FIG. 2, in which the energy
management system provides electronic receipt of data from a
plurality of energy users (200). The received data is
electronically processed (210) vis--vis whether an adverse energy
event (such as approach of a new peak) may exist, and when an
adverse energy event is detected, the system electronically
requests energy curtailment possibilities (220) from some or all of
the plurality of energy users. Energy curtailment possibilities
from energy users are electronically received (230), and received
energy curtailment possibilities are automatically processed
(240).
[0079] In the electronic request for energy curtailment
possibilities (220), it is not required that all energy users be
queried for energy curtailment possibilities. For example, certain
energy users that are deemed essential may be excluded from being
part of an automatic query for energy curtailment possibilities.
The requests for energy curtailment possibilities are directed to
such energy users that have the ability to consider their energy
curtailment possibilities and to formulate an energy curtailment
response (such as an offer of kilowatt hours to forego). Thus, the
energy users to be queried are supplied with such artificial
intelligence, neural network technology, or other computer- or
machine-based technology and programming such that they can
compute, in real-time, what energy curtailment they can offer based
on certain preset rules applicable to the respective energy user,
such as rules relating to current weather conditions, comfort, etc.
Thus, it will be appreciated that respective energy users will have
programming suitable for the context in which the energy user
operates. For example, an air conditioning energy user and a
multi-elevator energy user will be programmed to consider different
factors for evaluating whether each can use less energy. An air
conditioning energy user may be programmed to consider outside
temperature and time of day and other factors, while a
multi-elevator energy user may consider time of day and not be
programmed to consider outside temperature. The multi-elevator
energy user may place much different emphasis on time-of-day than
the air conditioning energy user. For example, because shutting
down elevators at certain high-traffic times of day may achieve an
energy savings but be unacceptable from a building management
viewpoint, the elevator energy user's formulation of an energy
curtailment possibility response may heavily depend on the time of
day.
[0080] Each to-be-queried energy user thus is provided with a means
to intelligently respond in real-time with an appropriate response
that is minimally-invasive or bothersome to the building occupants
and those being served by the queried energy user.
[0081] With reference to FIG. 2, it is particularly mentioned that
the energy curtailment possibilities from the energy users are
electronically received (230) and automatically processed (240).
That is, reliance on a human operator advantageously is not
needed.
[0082] It will be appreciated that, in a preferred embodiment, the
invention provides for the energy management system of FIG. 3, in
which the system provides real-time machine-based evaluation of
data for energy curtailment possibilities (340), from which an
automatic round-robin energy curtailment rotation is established
(350). Each affected energy user is automatically advised of the
energy curtailment that the affected energy user is to implement
(360) as its part in the round-robin energy curtailment rotation.
It will be appreciated that a human operator or operators
(especially in a multi-building system) cannot formulate an optimal
round-robin energy curtailment rotation in the short time that a
machine-based system can. Moreover, even once a machine-based
energy curtailment round-robin rotation is established, it will be
appreciated that it is much preferred for a machine-based system to
electronically implement the established rotation, compared to
human involvement in controlling the to-be-curtailed energy users.
While human involvement in the inventive energy management system
is not prohibited (such as the ability of a human operator to
override or command that a certain feature of the established
rotation not be implemented), preferably human operator involvement
is limited or none.
[0083] With reference to FIGS. 4A and 4B, the relationship of
trigger agent(s) 1 (such as user-set goals or peak load) for higher
level agents to accomplish, higher level agents that are to
accomplish those user-set goals, and devices 2, 2A, 2B may be seen
in two exemplary uses of the invention. As examples of trigger
agent 1 when the trigger agent 1 comprises user-set goals for
higher level agents to accomplish may be mentioned: impact on
occupants expressed in computer-based terms; price sensitivity
expressed in computer-based terms, etc.
[0084] Energy-using devices 2, 2A, 2B are shown, but more sets of
devices may be incorporated into the system. In the case of the
three sets of devices 2, 2A, 2B, three are shown for manageability
of illustration, and not to indicate any limitation of the
invention in that regard. The same comment applies to other
features shown herein, such as buildings 10, 10A, 10B.
[0085] Device 2 is provided with a forecasting agent 3, a modeling
agent 4 and a control agent 5. Respectively, device 2A is provided
with a forecasting agent 3A, a modeling agent 4A and a control
agent 5A and device 2B is provided with a forecasting agent 3B, a
modeling agent 4B and a control agent 5B. Preferably, each device
(energy user) has a dedicated neural network that continuously
learns the operating characteristics of that energy user, and
allows forward and backward reasoning, thereby making forecasts.
For example, the neural network may learn that if the temperature
setting of an air conditioning unit is bumped by 2 degrees, that a
certain drop in kW consumption results. The neural network may
learn that if kW consumption is dropped by 3 kW, a certain
temperature effect is observed. The neural network may learn that
if a certain event or device setting adjustment occurs, the time
elapsed before an OSHA level is reached is a certain amount. These
are only a few examples of reasoning by the neural network.
[0086] As a modeling agent 4 may be used any system configurable as
a neural network that may be disposed with respect to an energy
user (device) to learn (preferably, to continuously learn).
Examples of what may be learned about an associated device by a
modeling agent include, e.g., energy consumption (kW), temperature,
degradation time, fan speed, vane position, etc. A modeling agent
preferably continually learns the operating characteristics of the
device with which it is associated, thus understanding, for
example, the connections between energy consumption (kW) and room
temperature for an air conditioner. A forecasting agent predicts
energy consumption of the device associated therewith under various
conditions, allowing simulation and curtailment decision making. A
device control agent takes control of the device.
[0087] As a forecasting agent 3 may be used any system that, upon
receiving a question, returns to the modeling agent 4 and runs the
question. Preferably, the forecasting agent 3 neither over- nor
under-generalizes. For reducing over- and under-generalization by
the forecasting agent 4, it is preferred for the modeling agent 4
to have been in continual learning for as long a time period as
possible, with relatively longer times of continual learning being
more preferable. For example, a modeling agent that has been in
continual learning for a one-day period has only a certain limited
number of data points and if a query is posed to the forecasting
agent and the forecasting agent cannot find an exact match of data
points in the modeling agent, the forecasting agent will need to
generalize (i.e., extrapolate) and is relatively likely to over- or
under-generalize. If the modeling agent has been in continual
learning for a five-year period, if the same query is posed to the
forecasting agent, the forecasting agent is relatively more likely
to find a match, or at least a closer match, of data points in the
modeling agent, and thus the forecasting agent is relatively less
likely to over- or under-generalize.
[0088] Higher-level control agents run on the portfolio or building
level, controlling many devices (via their control agents). Based
on user-set goals and environmental input (such as price of energy,
temperature, occupancy, etc.), the higher level agents devise a
strategy to achieve the user-set goals and accomplish the user-set
goals by controlling the device agents. Higher-level agents reason
via artificial intelligence to find a suitable balance between two
or more goals, such as between savings and comfort.
[0089] A higher level control agent (for load rotation) 6 is
provided in FIGS. 4A and 4B. In FIG. 4B, a second higher level
control agent (for load rotation) 6' also is provided, showing a
situation where load rotation may have two different portfolios. An
example of a context represented by FIG. 4B may be where different
strategies are in place. Examples of a strategy include, e.g., a
permanent 24.times.7 load rotation; a curtailment load rotation. A
strategy or strategies is or are embodied by one or more artificial
intelligent agents.
[0090] An artificial intelligent agent may be concerned with a
strategy such as price sensitivity; air supply; temperature, etc.
Intelligent agents can be used to reduce energy cost better than
automated building management systems and/or human experts. Herein,
examples are given showing how intelligent agents using continuous
learning and reasoning can manage energy better than conventional
building management systems. Both, the cost of energy for large
buildings and the comfort for tenants are taken into account. The
scenarios of the Examples herein highlight major differences
between knowledge based energy management and conventional,
schedule-driven energy management. From the Examples and
Comparative Examples herein, it can be seen that continuous
learning is more accurate than one-time settings; accurate
forecasting enables smart planning; flexible responses to
curtailment requests are provided; a wider range of information
inputs means better building intelligence; rigid scheduling cannot
accomplish the results of knowledge-based reasoning; feedback loops
are too simplistic for today's sophisticated energy management
concepts; trial and error methods are too costly
[0091] Referring to FIGS. 4A and 4B, a higher level control agent 7
that is price sensitive is provided. Control agents 6, 6' (in FIG.
4B) and 7 are intelligent agents.
[0092] For connecting to buildings in which the devices 2, 2A, 2B
are located, suitable hardware and software (such as platform
hardware and software provided by Engage Networks and Silicon
Energy) are used. A building equipped with a building management
system (BMS) provides the easiest connection to the system
comprising the intelligent agents (i.e., the higher-level control
agents), as well as to individual energy-consuming devices 2, 2A,
2B. When BMS is used, as a platform may be mentioned any
communications protocol that allows quick and seamless
communication with the BMS and the devices it monitors. As a most
preferred example may be mentioned BMS platforms with open
communications protocols such as BacNet via UDP, which allow quick
and seamless communication between the system comprising the
intelligent agents with the BMS and the devices it monitors. If the
BMS is not open (i.e., does not adhere to an open communications
standard, such as BacNet or OPC), appropriate drivers may be
obtained and used to communicate. For example, the BMS manufacturer
may be contacted to buy or otherwise obtain the driver or at least
the specifications for the driver to talk through the Internet to
the BMS through them. Examples of such BMS drivers include, e.g.,
control drivers by Johnson, Invensys, Honeywell, etc. As software
useable in the invention may be any software that allows
communication with a BMS such that remote control can be
achieved.
[0093] Advantageously, the present invention is installable in
conjunction with certain existing equipment and software. For
example, hardware devices may be installed that can translate
between protocols and conduct simple data buffer or transfer tasks.
Existing monitoring systems (such as those provided by Engage
Networks and Silicon Energy) may be leveraged, for connecting
portfolios of buildings. Such existing monitoring systems which
allow end-users to manually control a BMS are lacking in any
artificial intelligence capability, and that artificial
intelligence capability is thus supplied by the present invention.
The present invention, by operating in connection with an existing
monitoring system, can connect to an installed, in-place customer
base quickly, with minimal local installation.
[0094] Referring to FIG. 5, an exemplary Internet-based energy
management system of three buildings 10, 10A, 10B, according to the
invention, may be seen. It will be appreciated that the invention
may be used with more or less than three buildings. Each respective
building 10, 10A, 10B has located on-site respective energy-using
devices 11, 11A, 11B. A preferred embodiment is discussed in which
a building such as building 10 has multiple energy-using devices
11.
[0095] Each respective building 10, 10A, 10B has associated
therewith respective meters 12, 12A, 12B. A preferred embodiment is
discussed in which a building such as building 10 has multiple
meters 12, but it is possible for a building to have only one
meter. As a meter may mentioned any metering device that measures
energy-relevant information, such as air temperature, air quality,
humidity, etc. Meters 12, 12A, 12B and devices 11, 11A, 11B are
connected through a building management system or energy management
system (such as an existing building management system) and a
network 15 (such as the Internet) to at least one intelligent
agent, most preferably to a system including intelligent agents.
Each respective building 10, 10A, 10B has associated therewith a
respective building management system or energy management system
13, 13A, 13B (such as a conventional building management system, a
conventional energy management system, etc.). In FIG. 5, a three
layered architecture (user interface, business logic, and data
layer) is shown.
[0096] Each respective building 10, 10A, 10B has associated
therewith a respective protocol driver 14, 14A, 14B. Each
respective protocol driver 14, 14A, 14B is in communication with a
network 15 (such as the Internet). The network 15, in addition to
receiving data from protocol drivers 14, 14A, 14B, also receives
other energy-relevant data 16 (such as a price feed (in $/MWh)
and/or a NOAA weather feed, etc.).
[0097] In this example, the network 15 (such as the Internet)
further is in communication with a communication layer (such as a
communication layer comprising AEM/DCOM (or other Engage Data
Server driven by Active Server Page Technology through the firewall
generating HTML pages) 17, FTP (File Transfer Protocol) 18,
BacNet/UDP 19, and expandable protocol slots 20). BacNet/UDP is an
open standard, an example of an open building intercommunications
protocol, put forward by the BacNet consortium. BacNet via UDP
takes that protocol and transports it in datagrams (UDP) over the
Internet. The UDP is the envelope; the BacNet message is the
content. A communication layer other than a communication layer
comprising AEM/DCOM 17, FTP 18, BacNet/(UDP 19, and expandable
protocol slots 20 may be used in the invention. It will be
appreciated that AEM/DCOM, FTP, BacNET/UDP and expandable protocol
slots are shown as examples and their use is not required, with
communications tools being useable in the invention.
[0098] AEM/DCOM 17, FTP 18, BacNet/UDP 19, and expandable protocol
slots 20 are included in data processing system or computer system
25. Data processing or computer system 25 thus is able provide a
real-time database 26. The real-time database 26 advantageously
includes real-time energy-relevant information specific to the
buildings 10, 10A, 10B as well as real-time energy-relevant
information "from the world," i.e., the energy-relevant information
16 (such as price feed in $/MWH and NOAA weather feed).
[0099] Data processing or computer system 25 further includes
intelligent agents 21, optional but particularly preferred
financial engine 22, optional but particularly preferred
notification workflow system 23 and optional but particularly
preferred energy monitoring system 24, which receive, process
and/or act on information communicated via the network 15 (such as
the Internet). Advantageously, real-time receipt is made possible,
as well as real-time processing and/or acting on received
information. The intelligent agents 21 are the heart of the
intelligent use of energy system of FIG. 5. The intelligent agents
21 preferably function in neural networks, which monitor each piece
of equipment, forming a non-parametric model of its behavior,
allowing accurate predictions of the impact that specific energy
control actions will have on the building environment. Also, energy
savings predictions can be accomplished based on environmental
changes (temperature, air-quality, etc.). These "device agents" are
used by higher-level agents to pursue a number of strategies, such
as "minimum disturbance load rotation" or "supply air reset". These
intelligent agents function like highly specialized, 24.times.7
staff members, and can be switched on or off, or given different
goals to accomplish. The intelligent agents 21 monitor and control
the devices to maximize energy savings, while minimizing impact on
environmental quality.
[0100] The data processing or computer system 25 thus monitors and
processes the real-time database 26, based on rules and/or
parameters, and formulates real-time queries (such as queries for
energy curtailment possibilities from energy-using devices 11
within building 10) and/or commands (such as an energy curtailment
round-robin rotation to be imposed on devices 11, 11A, 11B). The
real-time queries and/or commands formulated by the data processing
or computer system 25 are communicated in real-time via the network
15 (such as the Internet) to the respective protocol drivers 14,
14A, 14B which leads to devices 11, 11A, 11B being controlled in an
overall energy use reducing manner but with minimized discomfort or
inconvenience to occupants or users of buildings 10, 10A, 10B.
Discomfort or inconvenience to occupants or users of buildings 10,
10A, 10B is considered and included in the data processing or
computer system 25 so that a particular energy-using device in the
plurality of devices 11, 11A, 11B will not be curtailed in its
energy use in a manner that would cause discomfort or negative
impact. Thus, certain energy-using devices (such as computer
equipment, hospital equipment, etc.) are treated differentially and
intelligently so as not to be subjected to energy curtailment in
the same manner as other energy-using devices, while other
energy-using devices that are otherwise identical but in different
buildings may be subjected to different energy curtailment based on
time of day and occupancy or the like in the respective buildings.
Thus, if building 10 and building 10A are in different time zones
but otherwise have a similar set of respective devices 11, 11A,
they may be controlled appropriately and in a maximally
energy-intelligent manner.
[0101] It will be appreciated that the data processing or computer
system 25 depends on rules and/or expressions and/or logic which
are expressed in terms of variables and/or input which are
manipulable and evaluable. For example, there may be used rules,
expressions, variables and/or input suitable for aggregating energy
use for the entire system of buildings 10, 10A, 10B and monitoring
whether movement towards a new energy peak is occurring.
[0102] Thus, it will be appreciated that, in operation, the
computer or data processing system 25 with the real-time database
26, the network 15 (such as the Internet) and the buildings 10,
10A, 10B essentially run themselves without necessity of a human
operator. It will be appreciated that the computer or data
processing system 25 can be far more effective at computational
operations than can a human operator, and also can process the
available data and real-time information, using the rules, far more
quickly and accurately than a human operator could in the same
amount of time. Thus, the invention advantageously provides
machine-based operations in areas where reliance on human operators
conventionally meant responses that now can be seen as relatively
slow, inadequate or non-optimal.
[0103] Artificial intelligence and neural network technology are
used so that a controller for an energy using device such as
protocol driver 14, for example, may have a basis for responding to
a query for energy curtailment possibilities. A set of rules is put
into place for the protocol driver 14 and any energy-using devices
11 associated therewith. The set of rules is any set of rules
appropriate to the energy-using device, the building in which the
energy-using device is situated, and the building occupants or
those served by the building. For example, the set of rules may
take into account outside temperature, inside temperature, etc. and
based on the differential therebetween may characterize the comfort
level, with certain differential ranges being assigned to certain
comfort level characterizations. While the set of rules is fixed in
operation, the set of rules may be subjected to overhaul and
change, such as if it is decided that a colder or warmer
temperature range is now to be considered acceptable than in the
past. While in a preferred embodiment the variables and rules
operate so as to minimize any need or desire for human operator
intervention, optionally, a manual human operator override may be
provided, in which a human operator would be permitted to override
computer-based control of one or more energy-using devices.
[0104] Referring to FIG. 5, it will be appreciated that the
invention as discussed above advantageously permits systems of
buildings 10, 10A, 10B to "run themselves" without the necessity of
intervention of a human operator (on-site of buildings 10, 10A, 10B
or elsewhere such as at a monitoring facility). A human operator is
not needed for making energy curtailment and energy use decisions
and optimizing energy use in real-time.
[0105] For the intelligent use of energy system of FIG. 5,
buildings 10, 10A, 10B and users (such as building managers, energy
managers, financial managers, etc.) are connected over a network 15
(such as the Internet). The intelligent use of energy system may
include modules dedicated to meeting respective needs of users with
different responsibilities and concerns, such as a financial engine
module, a notification workflow module, an energy monitoring
module, etc.
[0106] Moreover, valuable information is provided that building
managers, financial managers and/or energy managers may observe how
the computer-based system is performing, via browser-based user
interface 27. For example, in a preferred embodiment, users access
the intelligent use of energy system through a web-browner that
connects to an ASP hosting site of intelligent use of energy
software, which in turn, connects through the Internet, either
directly or indirectly, to the buildings 10, 10A, 10B managed by
the system. To accommodate the diversity of building management
systems and associated protocols that are commercially in use,
there may be used a communications module, preferably one that is
an expandable communications bus architecture that can easily
accommodate new communication protocols as plug-ins; also,
preferably the communications module is one that can communicate
with existing bidding management systems, "monitoring" systems and
associated protocols currently available in the marketplace as well
as able to communicate with new systems being developed and
developed in the future. A particularly preferred communications
module to use is IUE-Comm, developed by the present applicant
[0107] Building managers, financial managers, energy managers,
and/or others via browser-based user interface 27 may view
information that would be of interest to them. For example, a
building manager may use the system of FIG. 5 to monitor the
current state of devices 11, 11A, 11B in the buildings 10, 10A,
10B. Building managers can see the temperature setting of
air-conditioners, the consumption of chillers, the speed of fans,
etc. The building managers can also optionally simulate one or more
"what if" scenarios, using the intelligent agents, to predict the
effect of control actions on the energy bill and the comfort in the
building. Building managers optionally may manipulate the
parameters of the intelligent agents, such as by constraining the
temperature band used by a "supply air rest" agent. The building
manager no longer needs to control individual devices (as he would
conventionally do) because the intelligent use of energy system of
FIG. 5 is "goal based". The manager gives the system a goal (such
as to save 40 KW in the next two hours) and the intelligent use of
energy system of FIG. 5 determines how to best achieve the goal. A
building manager can rely on and use the intelligent agents like
highly specialized, 24.times.7 staff members, switching them on or
off, or giving them different goals to accomplish.
[0108] The energy manager refers to a human responsible for the
optimal use of energy across facilities, such as across buildings
10, 10A, 10B. Issuing curtailment requests, for instance, is one of
the major tasks of an energy manager. Using the system of FIG. 5,
issuance of a curtailment request optionally can be accomplished
manually, or automatically by pre-instructing the intelligent
agents.
[0109] The financial manager is a human. In a preferred embodiment
of the invention, the financial manager generally is interested in
showing the savings that have been produced by using an intelligent
use of energy system such as that according to FIG. 5. Finance
modules in the system draw on a data warehouse that is created
based on the system's real-time data base, and support the
financial manager in analyzing energy consumption, identifying peak
demands, pin-pointing inefficient equipment or operations, and
demonstrating the overall effect of the agents saving energy costs.
While such mentioned finance-related activities may not be
necessary, they are particularly preferred for using the invention
in a commercial context.
[0110] The real-time database 26 will be understood as a database
continuously changing to reflect current data. The data in the
real-time database 26 preferably is saved in a data warehouse (not
depicted on FIG. 5), and from the data warehouse is usable such as
for energy analysis and financial reporting.
[0111] A particularly preferred example of an energy curtailment
regiment that may be automatically devised and implemented
according to the invention is a round robin load rotation. The flow
chart of FIG. 6 shows a preferred round robin algorithm for load
rotation. For simplicity, not all checks (e.g., equipment status,
manual override, etc.) that may preferably be performed are shown
on FIG. 6. In FIG. 6, the round robin algorithm begins with a
curtailment call 600 for a specific amount "X" KW (such as 30 KW).
In response to the curtailment call 600, the rotation counter is
set 602 to group curtailment duration, from which the system
increments group selection counter 603. Also, based on the rotation
counter being set 602, the system shuts group equipment down 604.
After group equipment shut down 604, the system turns on previous
equipment group 606. The system then asks 608 whether the
curtailment requirement has been met, and if not, shuts the next
(group+1) equipment down 609, and loops to re-ask 608 whether the
curtailment requirement has been met. The loop continues until the
question 608 of whether the curtailment requirement has been met is
answered affirmatively, and then the system asks 610 if the
rotation duration is complete; if not complete, the loop to the
question 608 of whether the curtailment requirement has been met
continues. When the question whether the rotation duration is
complete 610 can be answered affirmatively, the system then asks
612 whether all groups have been rotated through. If all groups
have not been rotated through, return is provided to incrementing
group selection counter 603.
[0112] When the question 612 whether all groups have been rotated
through is answered affirmatively, the system next resets group
selection counter 614, and asks 616 whether the curtailment call is
complete. If the curtailment call is not complete, i.e., if not
enough energy curtailment can be achieved, the algorithm ends (END)
on FIG. 6.
[0113] The invention has many practical and industrial uses. For
example, the invention advantageously combines the power of
artificial intelligence with the Internet to enable energy-using
customers (such as building systems) to dramatically cut building
energy costs in real time. Customers are able to do so by reducing
the energy they consume each month, lowering their "peak load"
demand charges, and by aggregating multiple electric meters into
one "virtual meter." Also, the neural network and artificial
intelligence used in the invention permit many factors that
influence consumption (such as current weather conditions,
occupancy levels and market price of energy) to be taken into
account, as the system constantly monitors and adjusts energy
use.
[0114] It is calculated that certain installations using the
invention are expected to be able to reduce energy costs by more
than 15% when all major energy-consuming devices are connected and
full control over device settings is given to a software system
according to the invention.
[0115] In another embodiment, the invention also provides for
quantification of economic and energy savings, and for revenue
generation. Particularly, the "curtailable" capacity and energy
that may be generated from using the invention may be sold to
regional control authorities and/or energy service providers.
[0116] The invention takes energy management to a new level, by
applying the power of artificial intelligence, and by drastically
reducing or removing the human element altogether (except when
building managers choose to over-ride the system). Neural network
and intelligence agent technology is used to monitor, analyze and
adjust energy consumption in real-time. A number of factors,
including energy prices, current and forecasted weather conditions,
current and scheduled occupancy levels, space air temperature and
space air quality, etc., may be taken into consideration. These
factors are applied to the selection of strategies for reducing
energy consumption at any given moment. The inventive energy
management system is much more "intelligent" on a real-time basis
than a human operator, who could not possibly analyze all of the
constantly-changing factors affecting energy consumption and make
adjustments quickly. Furthermore, the system provides a wealth of
new data to building owners and managers, who can then make
informed decisions for further energy reductions and future
equipment purchase decisions.
[0117] The invention thus may be used to provide one or more of the
following advantages: permanent load reduction; peak load
avoidance; aggregation of multiple meters under a single "virtual
meter"; automated curtailment response to Independent System
Operator (ISO)/supplier requests; extensive baseline analysis,
reporting and financial control; real-time reading of meters and
devices; meter equipment trending; alarming; reporting; intelligent
use of energy financing. These advantages and uses of the invention
are discussed as follows.
[0118] As for permanent load reduction, it will be appreciated that
the invention permits customers (such as industrial, commercial,
university, hospital and other customers) to reduce their energy
consumption on an ongoing basis by making thousands of minor
adjustments hour-by-hour, twenty-four hours a day, to every piece
of equipment attached to the system. When hundreds of
energy-consuming devices such as air handlers, chillers and
lighting systems are covered by the system, minor adjustments to
each one can have a significant impact on overall energy
consumption. For example, the system can meet energy reduction
goals by raising the temperature in unoccupied rooms from 70
degrees to 75 degrees. Or, in response to an unusually cool summer
day, the system might decide that starting the air conditioning
before employees arrive for work would not be necessary or
economic. Over the course of a month or a year, these minor
adjustments add up to significant reductions in energy consumption
and costs, without any discernable impact on operations or
people.
[0119] Peak load avoidance is also advantageously provided by the
invention. The energy bills for commercial customers consist of two
parts--the cost of total energy consumption for the month, and a
charge for the "peak" energy consumed during that month. This "peak
load" charge can account for as much as 50% of the electric bill.
The invention provides for intelligent use of energy, by applying
artificial intelligence to achieve on-going peak load reduction
guidelines, pre-set by the customer. In a typical situation, the
customer would want to insure that peak loads will not exceed a
previously set maximum or, more aggressively, might decide to
reduce peak loads each month (such as by 10%). At any point in the
month when peak loads approach the preset threshold, the
intelligent agents in the invention can choose from a wide variety
of available strategies to prevent crossing the line, such as
raising (or lowering) thermostats throughout a building(s); dimming
lights; etc. However, if executing a certain strategy would violate
another parameter or other parameters set by the customer (e.g.,
that temperature must never go beyond a certain threshold or that
lights cannot be dimmed below defined lumens, etc.), then the
intelligent agents of the invention will either employ another
strategy for reducing peak load demand, or notify the customer that
the goal cannot be achieved. All of this analysis, action and/or
notification occurs within minutes, and permits customers
(including commercial customers in multi-building systems) to truly
control their peak load charges.
[0120] The ability to produce a virtual meter or virtual meters is
another advantage of the invention. Many commercial energy
consumers receive a bill for every meter in their building or
portfolio of buildings. It is not unusual for a single building to
have multiple electric meters, and major office complexes or
building portfolios in a given area may have many meters. Beyond
the inefficiency inherent in receiving and paying numerous electric
bills each month, electricity consumers are also charged for
multiple, separate peak loads. The total of these peak load charges
can be significantly greater than the actual peak that a single
consumer reached at a particular point in a given month.
Intelligent use of energy according to the present invention can
resolve this problem by aggregating all of a customer's meters into
one "virtual meter." This virtual meter can encompass hundreds of
meters in dozens of buildings within a single Electricity (Energy)
Pool. (The United States is divided into ten Pools which have very
different tariff structures and regulations. As a result, a virtual
meter cannot aggregate meters in different pools, under the present
framework in the United States.) Customers could receive one bill,
not hundreds, and the peak load charge would be calculated against
the combined meters, not against each individual meter. This can
lead to significant savings.
[0121] Another advantage of the invention is the provision of
automated curtailment. Solving the long-term energy problem in the
United States (and elsewhere) will require a multi-dimensional
approach. New construction of energy plants and transmission
facilities alone will not solve the problem, particularly in the
short term where California and other areas face the potential for
a California-size crisis. The present invention can play a
significant role in mitigating energy shortages, and over the long
term, substantially reduce the need for and cost of additional
energy infrastructure. Under the terms of many commercial
contracts, energy suppliers can ask customers to reduce consumption
an agreed-upon number of times each year. During the electricity
crisis in California in the spring of 2001, these provisions were
invoked a number of times. To encourage commercial consumers to
reduce consumption, and thereby avoid a crisis like that in
California, many energy suppliers offered incentives to users to
voluntarily curtail power during peak load events, such as
unusually hot summer days. These incentives can include significant
reservation charges for agreeing to be curtailed, discounts on
tariffs, or even payments to companies that "sell back" unused
power or capacity when requested to do so. In many instances,
building managers used conventional energy management systems to
manually or non-analytically turn off equipment in response to
requests to cut consumption. In many cases, this led to the
shutdown of businesses for hours or days, as happened in
California. The invention can prevent this undesirable business
shutdown situation, by automatically curtailing equipment in
response to a request by the energy supplier, and can do so in a
manner that minimizes disruptions. The intelligent agents of the
invention, for example, may be able to achieve the curtailment by
slight adjustments in equipment, or by selectively shutting down
non-essential devices first. Or, the system in the invention may be
set to shut down only non-essential buildings. The invention
provides the ability to take into account many factors before
taking action, and to do so within mere minutes of a request to
curtail consumption, something otherwise beyond the capability of
any human operator or business manager or conventional energy
management.
[0122] The invention also provides advantageous analyses, reporting
and financial control. When a customer initially determines to
proceed with start-up of an inventive computer-based energy
management system according to the invention, the customer's data
may be entered in the computer-based system and provide the
baseline for future analysis of the customer's energy consumption.
Thus the impact of the computer-based system on the customer's
energy consumption may be seen. Once a computer-based energy
management system according to the invention is fully operational,
a customer is able to monitor and analyze its energy consumption in
real-time. Optionally a customer may customize an energy management
system so that that it provides information in a manner and format
suited to the customer needs. A customer can monitor and analyze
the customer's energy consumption at a given moment or over any
specified period of time.
[0123] Another use and advantage of the invention is regarding
real-time reading of meters and devices. The energy consumption of
every meter and device connected to the system may be monitored and
evaluated, if desired. Equipment that is not performing at peak
efficiency can be repaired or replaced, further lowering the
overall energy costs. The invention thus provides a data stream
relating to individual efficiency of energy-using equipment.
[0124] The invention also is useful in meter equipment trending,
including permitting energy-using customers to undertake trend
analysis on a meter-by-meter basis in real time. Building managers
can access screens at any time that show the current usage trend on
a given meter, and provide a forecast for future consumption if the
trend does not change.
[0125] The invention further provides for alarming, including
appropriate notification when any situation occurs outside
specified parameters. For example, if a peak load threshold is
about to be exceeded, the system provides notification immediately
so that remedial action can be taken. However, the system also
provides notification for less critical problems, such as
malfunction of a particular piece of equipment, or sudden changes
in energy consumption patterns.
[0126] In the invention, reporting may be provided. A full suite of
reports may be provided, which can be accessed at any time or on a
regular basis. These reports may include billing rates and
differential billing, load shaping and profiling, and virtually any
other report that a client may specify.
[0127] In the present invention, advantages related to finance also
may be provided. Conventionally, the bill that an average
commercial customer receives from its energy provider is immensely
complex and frequently incorrect. By using the invention, the
customer may compare actual real time data collected against
detailed baseline data and against rate and tariff structures that
apply to the customer's energy consumption. Such a comparison will
show a customer the level of savings and revenue achieved and help
the customer to ascertain whether or not the bill provided by the
energy provider is correct.
[0128] Thus, the present invention provides the mentioned
advantages, with a rapidity of evaluation and of execution,
accuracy, and precision well beyond that possible by a human
operator or team of human operators. Also, advantageously, the
energy management systems of the present invention are intelligent
and "learn," i.e., the systems learn from prior experience to
improve results over time.
EXAMPLE 1
[0129] Initial deployment of energy load reduction according to the
invention is accomplished by a fixed rotation schedule of equipment
that is stepped through in a serial fashion. System attributes,
such as allowable curtailment duration and electrical demand, is
determined through functional testing and pre-programmed in a fixed
matrix. A rotation script is then deployed to systematically cycle
each piece (or group) of equipment off and on at a fixed duration.
This `round robin` rotation approach offers a
less-than-fully-optinized rotation cycle but the system responses
obtained from this method is used for training of the programmable
intelligent agent (PIA) for optimal load rotation.
[0130] Ultimately, a programmable intelligent agent optimizes the
load rotation of curtailable loads, using a combination of
intelligent agents which operate the device level, portfolio level,
and pool level as follows:
[0131] 1) Device Level Programmable Intelligent Agent--utilizes a
forward artificial neural network (FANN) to predict the load
rotation period for equipment (device level IA).
[0132] 2) Portfolio Level Programmable Intelligent Agent--optimizes
resource leveling based on the predicted load rotation periods
derived in the device level PIA. Initially the portfolio is defined
as the building revenue meter.
[0133] 3) Pool Level Intelligent Agent--optimizes the load rotation
using pre-conditioning strategies based on the timing of the market
order. Once the PIA algorithms are trained, the `round robin`
algorithm is withdrawn and the Intelligent Agent takes over
schedule load rotation.
[0134] The system to which this example is applied is as follows.
The equipment targeted for load rotation includes: electrical space
heat, air conditioning compressors, fan motors, package unitary
HVAC equipment, and process motors.
[0135] The Intelligent Agent interface requirements include:
[0136] 1) Internet enabled on/off control. Curtailable loads are
grouped into "banks" of equivalent electrical demand. Multiple
matrices with alternative demand sizes may be deployed.
[0137] 2) Optional Internet enabled monitoring of process limit(s)
(required for resource leveling IAs). Where group on/off control is
used, an average process measurement is to be provided, for
example, the average space temperature or common return air
temperature. If process limit monitoring is not deployed, then an
acceptable period of the rotation schedule is determined through
functional testing of process drift.
[0138] 3) Optional Internet enabled control of process setpoint
(required for preconditioning strategies IAs). If process setpoint
is not deployed, then preconditioning strategies cannot be
implemented.
[0139] 4) Internet enabled monitoring of energy consumption through
any of the following methods:
[0140] a) Kw meter located either at the equipment level or in the
electrical service to the electric heaters.
[0141] b) Virtual Kw meter based on field verified rated kW and
equipment on/off status. Calculation of virtual Kw can reside
either within the customer's Building Control System or at an
Application Server.
[0142] A Rotation Schedule/Matrix example is shown in FIG. 7, of a
round robin approach to load rotation. The rotation matrix in FIG.
6 provides an example grouping for a 30 KW curtailment rotation
schedule. Curtailable demand is determined by continuous
measurement using the Internet enabled kW meter.
[0143] A brief description of each data entry is as follows. Data
fields in bold italic are temporary values which are updated once
the PIAs for Load Duration and Resource Leveling Optimization are
deployed.
[0144] Rotation Group--Numerical group assignment for the purpose
of prioritization and counting. The rotation groups are fixed
during the initial deployment. The Load Rotation IAs optimize
groups into equal load/equal duration.
[0145] Equipment ID--Alphanumeric equipment descriptor.
[0146] Controlled Device ID--Alphanumeric description of energy
device.
[0147] Manual/Auto Indicator Address--The address of the
Manual/Auto Status. (The term "address" is used to refer to the
location assigned by the enabling platform (e.g., Silicon Energy
"PtID", Engagenet, etc.) to be written to or read from.) Reading
this point gives a "digital state" (0 or 1) indication the
equipment has been placed in local override and is therefore not
accessible for curtailment. If equipment status is unavailable,
then the application "remembers" the outcome from the remote
control event. For example, if equipment did not change kW during
prior remote control event, then digital state is set to off.
("0").
[0148] Equipment Status Address--(Optional) A read only point that
indicates the operational status (state output) of a piece of
equipment. Operational status includes: normal, alarm, alarm
code.
[0149] Curtailable Demand Setpoint--The curtailable electrical
demand (kW). The setpoint for resettable devices is derived by
means of direct measurement and approved by customer. For on/off
devices, the curtailable demand setpoint is equal to zero.
[0150] Curtailable Demand--Curtailable demand initially is fixed,
derived by means of direct measurement and review of normal kW
process range. Once the Load Rotation IAs are enabled, the
curtailable electrical demand is calculated as:
[0151] Electrical Meter (prior time interval)--Curtailable Demand
Setpoint.
[0152] The calculated curtailable Demand is used within the Load
Rotation IAs to optimize groups into equal load/equal
intervals.
[0153] On/Off Control Address--The primary control address to which
curtailment on/off commands are written. The command is as detailed
in the on/off Control Command entry.
[0154] On/Off Control Command--The command signal to be written to
the primary control address to place the equipment into curtailment
mode. In most cases, this is a digital state command (0 or 1). The
complement of the state command is used to disable curtailment. In
those cases that require two cases, this typically is the "Internet
control override" signal.
[0155] Reset Control Address--The (optional) secondary address to
which curtailment commands are written.
[0156] Reset Control Command--The (optional) secondary command
required to place a piece of equipment into curtailment mode. In
most cases this is an analog reset variable (%, mA, V), for
example, a reduced speed input into a variable speed drive. This
input need not be reset to disable the curtailment mode, and is
ignored when Control Command 1 indicates disablement.
[0157] Minimum Equipment Off Time--The minimum duration in which a
piece of equipment may be sent into curtailment mode. Required to
prevent short-cycling of equipment.
[0158] Maximum Curtailment Duration--The maximum duration a piece
of equipment may be sent into curtailment mode. This period is
initially determined through testing and is typically the worse
case time interval before a process limit (space temperature,
CO.sub.2, etc.) served by the equipment falls out of range. Once
the Device Level IAs are trained, the maximum duration will be fed
into the Load Rotation IA for sorting into load groups.
[0159] Settling Duration--The time delay in which two equipment
groups are required to overlap in curtailment mode (both groups in
curtailment mode) before the prior group is brought out of
curtailment mode. This is used to accommodate an overshoot
electrical demand as the prior group's equipment is brought back to
non-curtailment mode. Initially the settling duration will be
fixed. Once the FANN IAs are trained, the settling duration will be
fed into the Load Rotation IA for determining the group
overlap.
[0160] Revenue Meter Address--The address of the utility meter
serving the equipment to be placed in curtailment mode. The net
desired kW curtailment must be seen at the meters or additional
groups must be rotated into curtailment mode. Reasons for
shortfalls in curtailment may include equipment off or in local
override mode, or other equipment brought online during the
previous group rotation. A range is established for the need for
additional curtailment.
[0161] Process Variable 1 Address--The address of the first process
variable to be used to constrain the magnitude and duration of the
curtailment. For example, an AHU load rotation may be constrained
by space temperature. This is typically be a zone temperature for
AHU type curtailment. This information may not be required for the
simplest form of the Round Robin approach. Up to three process
variables are available for use; while not required, at least
preferably one is be used (example for no process variables:
fountain pumps).
[0162] Process Variable 1 Min Range--This is the lower allowable
limit for the primary process variable. Note that a curtailment
range is typically more extreme than for normal allowable operating
conditions.
[0163] Process Variable 1 Max Range--This is the maximum allowable
limit for the primary process variable. Note that a curtailment
range is typically more extreme than for normal allowable operating
conditions.
[0164] Process Variable 2 Address--The address of the second
process variable to be used to constrain the magnitude and duration
of the curtailment. This information may not be required for the
simplest form of the Round Robin approach. Two process variables
are available for use; while not required, at least one preferably
is used (example for no process variables: fountain pumps).
[0165] Process Variable 2 Min Range--This is the lower allowable
limit for the secondary process variable. Note that a curtailment
range is typically more extreme than for normal allowable operating
conditions.
[0166] Process Variable 2 Max Range--This is the maximum allowable
limit for the secondary process variable. Note that a curtailment
range is typically more extreme than for normal allowable operating
conditions.
[0167] AND Inclusions--This entry lists equipment that must be
placed into curtailment mode simultaneously with the listed
equipment entry. Circumstances for this include matched Supply
Air/Return Air fan sets.
[0168] OR Exclusions--This entry lists equipment that cannot be
placed into curtailment mode simultaneously with the equipment
entry. Circumstances for this include shutting down all elevators
simultaneously.
[0169] In this example, data sources and grouping rules are as
follows. Curtailment duration and impact are determined through
functional testing; the equipment is disabled and the spaces served
by the units is monitored to determine the maximum duration of
curtailment before space conditions fall out of acceptable range.
Equipment is grouped under the following rules:
[0170] Equipment must be controllable and curtailable.
[0171] Equipment with fixed and coinciding operational schedules
(during possible periods of curtailment) are required.
[0172] Curtailment durations are determined by monitoring space
conditions during functional testing. Space temperature is
monitored at minimum; other conditions such as IAQ (CO.sub.2
levels) or relative humidity levels may also be observed to
determine curtailment durations.
[0173] Equipment is grouped such that the curtailable demand for
each group is approximately equal.
[0174] The functional curtailment duration for each group is the
smallest duration for any individual piece of equipment within that
group.
[0175] Minimal occupant impact is the basis for group priority.
Example: fountains first, alternating elevators next, AHUs
after.
[0176] Simultaneous, multiple group curtailments are spread across
the building portfolio; concentrations of curtailment groups within
one building are not permitted.
[0177] Groups may require exclusivity. Example: all elevators may
not be in the same group.
[0178] Groups may require inclusivity. Example: return and supply
fan operation for the same space may be interlinked.
[0179] The flow chart of FIG. 6 is applicable to the above rotation
schedule example, for a 30 kW curtailment call. Additional
equipment status checks, manual override checks, etc. are performed
that are not shown on FIG. 6.
EXAMPLE 2
[0180] An example of Peak Load: Virtual Meter according to the
invention versus Real Meters is shown in FIG. 8. In this
hypothetical example, the combined total energy usage recorded by
four meters A, B, C and D was 95 kW. However, Meter A reached its
peak at 4:00 p.m. on the third day of the month, Meter B's peak
occurred at 10:00 a.m. on the 12.sup.th, Meter C recorded its
highest usage at noon on the 16.sup.th and Meter D recorded its
peak at 6:00 p.m. on the 29.sup.th. Despite the fact that none of
these peaks occurred at the same time, or even on the same day, the
customer was charged for the combined total of the four.
[0181] With a virtual meter, however, there is only one recorded
peak--the single point in time during the month when the customer's
total aggregate usage was highest. In this example, that peak was
only 80 kW, and could have occurred at any time during the billing
cycle. This functionality can provide the customer the ability to
negotiate with its energy supplier for a different rate or tariff
and hence significantly lower the "peak load" bill. (It is noted
that relations between commercial consumers and energy suppliers
vary greatly, depending on whether a given market is still
regulated or deregulated. The ability of a customer to win lower
rates through negotiations will be highly dependent on the nature
of the market and the players involved.)
EXAMPLE 3
[0182] In this Example, there is provided an energy management
system according to the invention in which are used five integrated
products or features:
[0183] 1) Permanent Load Reduction--software intelligent agents
that continually make and implement complex multi-input,
device-setting decisions, and permanently reduce the amount of
energy consumed by a customer.
[0184] 2) Peak Load Avoidance--The use of a neural network to
forecast, identify and minimize peak load events, reducing the
portion of a customer's energy bill related to its peak energy
usage each month. These peak load events can account for up to 50%
of annual energy costs and thus their reduction is highly
advantageous.
[0185] 3) Virtual Meter Data Aggregation--The integration of
multiple buildings and electrical meters into one virtual meter,
which can eliminate multiple billings, consolidate billable peak
loads and give the customer greater flexibility in managing its
energy consumption. This, in turn, can create a new, reduced peak
load for the aggregated portfolio that will allow for negotiations
of better rates from the customer's energy supplier, thus notably
reducing demand charges.
[0186] 4) Capacity Savings and Emergency Curtailment--The system
has the ability to rapidly reduce a customer's immediate energy
usage at its request in response to short-term curtailments in
energy supply. Energy consumption may be rapidly curtailed in
response to requests by authorities or the energy supplier. This
capability also makes it possible for a customer to sell into
available markets the kilowatts of capacity it can curtail and the
kilowatt-hours of energy it is able to provide back to the market
during an emergency. Upon request by the local ISO, power authority
or utility supplier, the IUE system will place buildings in peak
curtailment operation. Non-essential loads will be de-energized and
HVAC equipment will be rotated in and out of service to maintain a
consistent load reduction through the curtailment period.
[0187] 5) Finance--Tools for comparing actual energy usage with
energy bills, which frequently overstate the amount of energy
consumed by commercial customers.
[0188] In the following Examples and Comparative Examples, a number
of scenarios illustrate the major issues in which ways to manage
energy conventionally and according to the invention differ, and
show the advantage of using intelligent agents to manage
energy.
COMPARATIVE EXAMPLE C1
[0189] An automated building management system or energy management
system
COMPARATIVE EXAMPLE C2
[0190] A building management system operated by an expert human
engineer.
INVENTIVE EXAMPLE 4
[0191] Intelligent agents are provided according to the invention.
The Intelligent agents continually learn. A "modeling neural net"
is connected to each device controlled. This net has one job: learn
all there is to know about this device. All parameters of this
device are followed by the neural net. Minute changes in operating
characteristics, due to wear and tear, aging, weather, new
parameter constellations etc. are immediately picked up and become
part of the "model" that the agents have of this device.
[0192] For an air conditioner, for example, the neural network
knows how much power it consumes at a certain temperature setting
at a certain outside temperature with a certain occupancy of the
building. The net also knows this connection from other
perspectives, it knows at which temperature setting, for a given
occupancy and outside temperature what the power consumption would
be.
[0193] The relationships between these parameters is neither linear
nor easily expressable in an algebraic formula or differential
equation (which are the standard ways in engineering to model
systems). The neural networks work like the human mind, creating
connections between concepts, which are either reinforced or
weakened, depending on observation--herein called "learning", as in
how children learn language or a ball game.
[0194] Continuous Learning
[0195] By knowing about every detail of operational
characteristics, the intelligent agents can run the devices in the
optimal fashion for the way the equipment operates right that
moment. This characteristic is contrasted this to the way a
conventional, automated building management system (BMS) works.
Usually, the BMS does not even have a model of the device. The BMS
may give fixed instructions to the device, regardless of the
current efficiency or price of energy or interaction between
temperature and occupancy. The BMS has put the device on a
schedule, e.g go to 71 F by 7 am, and that what it will do until it
is given a new schedule. At best, the BMS has resets that are based
on more than one variable, e.g., outside air temperature or space
air temperature. Yet the BMS still does not make predictions about
future energy use, which would inform its energy management
decisions. BMSs are typically set for worst case ranges, to avoid
trouble calls--clearly not the smartest or most energy efficient
way to run a building. It is easy to see how energy is wasted by
such a rigid operation as the Comparative Example 1, that does not
take changes and idiosyncrasies at the equipment level into effect.
Also the case (Comparative Example 2) where a human expert controls
is building management system is not much better. It is impossible
for a human to monitor hundreds of devices with tens of points on
every device. Imagine a building engineer in front of a monitoring
console who has to track 230 air conditioners, the energy
consumption of those devices, their temperature setting, the time
it takes each device to change by 1 degree Fahrenheit, do that at
different exterior temperatures, factor in different occupancy
loads, and do that 24 hours a day. Clearly the human operator is
overloaded with information.
[0196] Accuracy of Forecasting
[0197] Building management system such as Comparative Examples 1
and 2 are not used to forecast energy use. At best, these systems
can look up yesterday's energy use and report that to a human user.
They cannot factor this information into their own control actions.
At best, Comparative Examples 1 and 2 need a human to do this. The
human building managers will base their actions usually on
"experience", meaning that they will look at the weather forecast
and err on the save side, either overcooling a building in the
summer, or overheating it in the summer. Building managers are
mostly striving to please patrons, not financial managers. Even a
cost-conscious building manager is lacking the inputs and the
modeling power to create an accurate forecast for their buildings,
device by device, floor by floor, building by building, campus by
campus.
[0198] The intelligent agents of Inventive Example 4, on the other
hand, leverage the learning that has occurred over time in the
neural network and use it to predict energy usage exactly in that
fashion. Predictions are made short term or long range device by
device, building by building, portfolio by portfolio. This allows
the agents to pre-cool just to the right amount. During a
curtailment it allows agents to predict degradation of room
temperature and gently rotate the equipment that is either being
shut off or reduced. Thus the impact on building comfort is
maintained, while energy cost is kept at its lowest.
[0199] Flexibility of Responses to Curtailment Requests
[0200] The conventional way as in Conventional Examples 1 and 2 to
implement a curtailment response is to devise a number of
curtailment stages (e.g Normal Stage, Yellow Alert Stage, Red Alert
Stage). As energy becomes more scarce the possibility of a rolling
black-out occurs, and higher curtailment stages are called into
action. Each such curtailment stage specifies exactly how energy
must be conserved. Detailed plans exist that require certain air
conditioners to be set to higher temperatures or to be switched off
completely. Instructions for switching off certain banks of lights
or pumps or other such measures may be part of curtailment plans.
One can easily see that these plans as in Comparative Examples 1
and 2 are very rigid. Clearly energy shortages do not come in three
or four flavors. Yet the responses are patterned that way. The
reason for this is obvious: in order to deal with the vast
complexity of energy consuming devices, certain simplifications
must be made to react quickly. Coarse tools, such as block-based
building management systems and human operators who can only
execute a limited number of operations until the curtailment level
is to be met, inherently provide such limitations.
[0201] Greater flexibility is provided by the intelligent agents as
used in Inventive Example 4. The agents can monitor energy price
and scarcity of energy in the grid. This can happen on a extremely
fine grained scale, not just green-yellow-red. Due to the agents
ability to reason, they can react most appropriately even in early
stages of an energy crunch. As the crunch gets more severe, agents
can adopt their curtailment measures too. These curtailment
measures are not the coarse measures taken be switching blocks of
equipment off but try to minimize the impact on comfort and quality
in the building. The agents can do this by using their knowledge
about the operating characteristics of the devices and by
forecasting energy need and consumption. Then the agents can take
gentle control actions. While each control action may only save a
minute amount of energy, compared to the sledge-hammer method of
completely switching of full banks of equipment, the sum of these
many minute savings equals the coarse action savings taken today by
less sophisticated control systems and overworked building
managers.
[0202] Wider Range of Information Inputs
[0203] Building management systems (BMS) as in the Comparative
Example use simple feedback loops to control temperature or other
such variables. The feedback/control variable in these loops is
mostly a single, internal parameter to the system. Such an approach
is too myopic to manage a system intelligently. The controller does
not take a sufficiently wide range of variables into consideration.
There are additional internal variables from the BMS itself (such
as carbon dioxide or other air quality measures). Connected to this
are occupancy date, influencing variables such as air flow or fan
speed or heating in winter. Humans rarely monitor global variables
from outside the building that influence major decisions which can
impact cost immensely. Building managers usually do not have a
display of the current price of energy. On those extreme days where
the price of energy in unregulated markets shoots over the $200/kWh
mark, neither the BMS on a schedule, nor the human building manager
will react to this fact. While the price of energy in a deregulated
market is mostly of concern to the ESCO and not the end-consumer,
not preparing for flexible pricing falls short of being prudent
energy management for every participant.
[0204] Intelligent agents as in Inventive Example 4 on the other
hand take many global variables into consideration. The agents thus
have the ability to aggressively conserve energy when it becomes
extremely expensive. Thus the agents sacrifice a little building
comfort when it pays heavy dividends, yet keep the building
comfortable when it is cheap to do so. Knowing about events in the
world of energy, and not just the local building, thus pays a
return to the building owner.
[0205] Rigid Scheduling Versus Knowledge-Based Reasoning
[0206] Previously, the dynamic reasoning and decisions based on
knowledge as in Inventive Example 4 have been said to be superior
to fixed schedules or simple loops according to Comparative
Examples 1 and 2. Yet all conventional building management systems
employ exactly these basic concepts. The schedule is the preferred
way to control a device in a conventional system. Even though this
schedule may be very sophisticated (such as being able to
distinguish weekdays from weekends, to recognize holidays, to set
repeats, very much like the calendar function in Mircosoft Outlook,
for example), still the conventional schedule does not ascertain
whether the currently scheduled course of action makes sense under
the current environmental conditions. The conventional BMS will do
what is instructed to do, even if it has long gone off-course. In
the scenario of energy costs skyrocketing on a particularly hot
summer's day in California, the BMS will do what it is scheduled to
do. It will cool the space to 68 F, even if that runs up an energy
bill that is in the hundreds of thousands of dollars just for that
one day.
[0207] The agents of Inventive Example 4, on the other hand,
knowing the energy price, will start reasoning to find a compromise
between cost and comfort. First the agents will most likely set the
temperature to 71 F, then they will rotate among various zones in
the building to distribute "discomfort" equally, and only when that
is exhausted go to an even higher temperature to keep costs under
control.
[0208] Feedback Loops
[0209] From a cybernetic point of view, current BMSs such as the
Comparative Example use either simple feedback loops (keep
temperature a set level) or they use triggers from simple sensors
to control an action (light sensor switches on parking lot lights).
Such cybernetic constructs cannot deal with diametrically opposed
objectives, such as how to save as much energy as possible while
keeping building comfort as high as possible.
[0210] The Inventive Example 4 has the artificial intelligence
tools to balance opposed objectives, such as energy saving and
building comfort.
[0211] Blocks
[0212] The Comparative Example building management system is
created on the metaphor of its predecessor--electromechanical
systems. End-users are presented with "blocks" which may represent
relays and other such physical entities. On a higher level control
blocks represent blocks of panels or devices. While this metaphor
is initially helpful for a building manager to make the transition
from the physical world to systems controlled by microprocessors,
it does eventually limits what the system can do.
[0213] Inventive Example 4 is not subject to such eventual
limitations.
[0214] Trial and Error
[0215] Some BMSs and even some domestic thermostats claim they can
"learn". What most of these devices are actually doing is a
stochastic approximation approach--in other words: they are
guessing. Guessing a value can be very wasteful, taking many
"stabs" until a somewhat satisfactory value is found. Guessing also
allows no transfer from one learning event to the next. The
Comparative Example suffers from the costs of trial and error.
[0216] In Inventive Example 1, true learning (observation and the
creation of a knowledge base) occurs, such as by neural networks
and rule-based expert systems. Due to such true learning, the
system in Inventive Example 1 therefore come up with the right
answer faster, more accurately and in a wider range of learning
situations than the Comparative Examples.
[0217] The differences between the Comparative Examples 1 and 2 and
Inventive Example 4 are summarized as follows. Inventive Example 1
using intelligent agents surpasses both, automated building
management systems (Comparative Example 1) and human experts
(Comparative Example 2) in delivering better building comfort at
less cost. The table below summarizes these findings.
1 Intelligent Agents Human Expert driving Concept driving a BMS a
BMS Automatic BMS Learning operational Constant observation of
Common sense of At best a fixed parametric specifics of devices
every single device updates operational characteristics;
description of a device; no [how to run air the neural network for
that macro level only, can not learning or updating. coniditioners,
pumps etc. device continually; up-to- follow all points on all
Usually no modeling of most efficiently] date representation of the
devices in detail for 24 device parameters. device hours per day.
Forecasting energy usage Neural nets can make Human can make
educated At best lookup in a [saving money by doing accurate
predictions based guess based on experience; database, very
inaccurate; necessary cooling/heating on historic observations.
usually can not take all usually no forecasting. actions when
energy is datapoints into account due cheap] to information
overload and non-linear nature of forecasting formulas. Dealing
with curtailments Infinitely small levels of Brute force; using
None; needs human [keeping good building automatic response to
predefined groups of operator. comfort while responding tightening
energy supply; devices to switch off in to curtailments] increasing
stages of severity Day to day operation of Constant observation of
Operates BMS on macro According to a fixed devices every single
device updates level; can not dedicate full schedule (e.g. set A/C
to [Running the building in the neural network for that 8 hours to
drive system; 70.degree. F. at 6 am) or a single the most energy
efficient device continually; up-to- unable to deal with flow of
event (e.g. parking lots manner] date representation of the
information (up to light go on when it is device 150 Mb/minute)
dark); no tactical optimization. Context for decision Use all local
feeds from Not all global information Use local sensor readings
making building (e.g temperature, feeds accessible to building only
for simple feedback [being pound-wise, not humidity, CO2, etc.);
use manager; decisions made loop penny foolish] weather forecasts
from with incomplete data on a NOAA; use price feeds guesswork
basis from ISOs; Decision making methods Forward and backward
Intelligent decision making Simple feedback loops reasoning using
neural using reasoning and with single control networks, rule-based
"common sense" parameter systems, and plan-based systems;
Granularity of Control Every device has an agent Limited by the
control Controlling via "blocks" attached which in turn can level
provided by the BMS and other electro- be leveraged by higher
mechanical metaphors that level strategy agents; this do not
utilize the full allows to implement many flexibility of
computerized different strategies, systems independent of physical
"control blocks" Automatic determination Neural networks Based on
experience and Trial and error to deduce of parameters continually
use monitoring observations; prone to pre-cooling or pre-heating as
input to learning; system misjudgements and effects times wastes
energy and never guesses but uses of exponential effects does not
adapt to changes reasoning based on historic (humans can only
estimate in the building or the facts. linear systems well),
environment (weather) Control over multiple Agents can leverage
Restricted to a single Restricted to a single buildings multiple
devices and building via the current building. If umbrella
therefore have more BMS. system exist than only for degrees of
freedom in monitoring. finding the best compromise between savings
and comfort
[0218] The basis of comparison is both, the cost of energy consumed
in the scenario and the comfort level achieved for the tenants.
[0219] The scenarios show that agents can run a building more
effectively and efficiently due to the following reasons:
[0220] Continuous learning: Intelligent agents operate equipment
more efficiently and effectively by automatically learning the
operating characteristics of devices via a neural network approach
is. This is faster, more accurate, and more representative of the
current state of the device than preprogramming a building
management system with static, manufacturer supplied parameters or
specs for the device, which may not represent the current condition
of the actual device.
[0221] Accurate forecasting: Intelligent agents save money by using
their device knowledge to make knowledgeable energy forecasts which
result in savings rather than operating on a fixed schedule
regardless of temperature forecasts or changing energy prices.
[0222] Flexible response to curtailment requests: Intelligent
agents can achieve higher comfort levels for tenants during
curtailment events since they do not rely on predetermined "shut
down" groups or sequences, which are the only way building
management systems or humans can handle these complex requests for
curbing energy use.
[0223] Wider information context: Since agents have access to a
wider context to base their decisions on, they can save more money
than a building management system. The building management system
is constrained to data that comes from the building itself, while
the agents can leverage subscriptions to the NOAA (national
oceanographic and atmospheric administration) or to various price
feeds from ISOs.
[0224] Reasoning wins over scheduling: Agents can run the equipment
more effectively and efficiently on a day to day basis. The agents
can do this, since they have the ability to reason about causes and
trade-offs react flexibly to events in the building itself
(temperature, CO.sub.2, occupancy, etc.) and to global changes
(weather data, price of energy). Building management systems are
usually on a schedule, where they take control actions, regardless
of occupancy or the price of energy. This makes building management
systems less efficient.
[0225] Reasoning is more powerful than feedback loops: From a
cybernetic point of view, current BMSs use either simple feedback
loops (keep temperature a set level) or they use triggers from
simple sensors to control an action (light sensor switches on
parking lot lights). Such cybernetic constructs can not deal with
diametrically opposed objectives, namely to save as much energy as
possible while keeping building comfort as high as possible. It
takes domain knowledge, learning, and decision making intelligence
in a system to accomplish this.
[0226] Conventional block control is cumbersome: Most building
management systems have been created on the metaphor of their
predecessors--electromechanical systems. End-users are presented
with "blocks" which may represent relays and other such physical
entities. On a higher level control blocks represent blocks of
panels or devices. While this metaphor is initially helpful for a
building manager to make the transition from the physical world to
systems controlled by microprocessors, it does eventually limits
what the system can do.
[0227] Intelligent learning is more cost effective than
trial-and-error: While some conventional BMSs and even some
domestic thermostats claim they can "learn", what most of these
devices actually are doing is a stochastic approximation
approach--in other words: they are guessing. Guessing a value can
be very wasteful, taking many "stabs" until a somewhat satisfactory
value is found. Guessing also allows no transfer from one learning
event to the next. True learning on the other hand requires
observation and the creation of a knowledge base. Neural networks
and rule-based expert systems can do this and therefore come up
with the right answer faster, more accurately and in a wider range
of learning situations.
[0228] Agents control more than one building: While traditional
BMSs merely schedule and monitor the actions in a single building,
our agents control devices, such as HVAC or lighting across a whole
portfolio of buildings. This prevents the agents from merely
finding local maxima but allow them to globally optimize. It also
equips the agents with increased degrees of freedom in balancing
energy savings requirements with tenant comfort across
buildings.
[0229] While various embodiments of the present invention have been
shown and described, it should be understood that other
modifications, substitutions and alternatives may be made by one of
ordinary skill in the art without departing from the spirit and
scope of the invention, which should be determined from the
appended claims.
* * * * *