U.S. patent application number 16/224806 was filed with the patent office on 2020-03-12 for method for creating demand response determination model for hvac system and method for implementing demand response.
The applicant listed for this patent is SEOKYOUNG SYSTEMS. Invention is credited to Ye Eun JANG, Youngjin KIM, YoungSeok SOHN.
Application Number | 20200080744 16/224806 |
Document ID | / |
Family ID | 69720693 |
Filed Date | 2020-03-12 |
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United States Patent
Application |
20200080744 |
Kind Code |
A1 |
SOHN; YoungSeok ; et
al. |
March 12, 2020 |
METHOD FOR CREATING DEMAND RESPONSE DETERMINATION MODEL FOR HVAC
SYSTEM AND METHOD FOR IMPLEMENTING DEMAND RESPONSE
Abstract
A method for implementing a demand response (DR) for a HVAC
system in a building is provided. The method comprises: creating a
zone temperature determination model that outputs temperatures of
the building by considering an input power provided to the HVAC
system and a thermal state of the building; generating objective
functions for a power supply schedule in which optimal solutions
vary with electricity prices and the thermal state, wherein the
power supply schedule includes linear equations for emulating the
zone temperature determination model; determining the optimal
solutions to the objective functions based on a plurality of
electricity price profiles and thermal state profiles; and creating
a demand response determination model for taking the electricity
price profiles and the thermal state profiles as input and
producing a power supply schedule for the HVAC system as
output.
Inventors: |
SOHN; YoungSeok;
(Seongnam-si, KR) ; KIM; Youngjin; (Pohang-si,
KR) ; JANG; Ye Eun; (Sejong-si, KR) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
SEOKYOUNG SYSTEMS |
Seoul |
|
KR |
|
|
Family ID: |
69720693 |
Appl. No.: |
16/224806 |
Filed: |
December 19, 2018 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
F24F 2110/20 20180101;
F24F 2110/10 20180101; F24F 11/47 20180101; F24F 2140/60 20180101;
F24F 2110/40 20180101; F24F 11/61 20180101; F24F 11/63 20180101;
F24F 2140/50 20180101 |
International
Class: |
F24F 11/47 20060101
F24F011/47; F24F 11/61 20060101 F24F011/61 |
Foreign Application Data
Date |
Code |
Application Number |
Sep 12, 2018 |
KR |
10-2018-0109099 |
Claims
1. A method for implementing a demand response (DR) for a heating,
ventilation, and air-conditioning (HVAC) system in a building, the
method comprising: creating a zone temperature determination model
that outputs temperatures of the building by considering an input
power provided to the HVAC system and a thermal state of the
building, wherein the zone temperature determination model is
created by training a first artificial neural network based on a
plurality of first training data sets; generating objective
functions for a power supply schedule in which optimal solutions
vary with electricity prices and the thermal state, wherein the
power supply schedule includes linear equations for emulating the
zone temperature determination model; determining the optimal
solutions to the objective functions based on a plurality of
electricity price profiles and thermal state profiles; and creating
a demand response determination model that outputs the power supply
schedule for the HVAC system by considering the electricity price
profiles and the thermal state profiles, wherein the demand
response determination model is created by training a second
artificial neural network based on a plurality of second training
data sets each including the electricity price profiles, thermal
state profiles, and determined optimal solutions.
2. The method of claim 1, wherein the building comprises multiple
zones, and the zone temperature determination model outputs
temperatures for each of the multiple zones.
3. The method of claim 2, wherein each of the plurality of first
training data sets includes information related to an existing
input power provided to the HVAC system and the thermal state of
the building and information related to temperatures for the
multiple zones dependent on the existing input power provided to
the HVAC system and the thermal state of the building.
4. The method of claim 1, wherein the thermal state of the building
comprises at least one of an atmospheric temperature, daylight
hours, a wind force, a humidity, a thermal load on the building,
and/or building usage schedules.
5. The method of claim 3, wherein the information related to the
existing input power provided to the HVAC system and the thermal
state of the building is obtained from a building energy management
system (BEMS).
6. The method of claim 2, wherein an input layer of the first
artificial neural network comprises the input power provided to the
HVAC system from a predetermined first time to the present time,
the thermal state from a predetermined second time to the present
time, and the temperatures for the multiple zones from a third
predetermined time to the present time.
7. The method of claim 6, wherein the first artificial neural
network model is implemented as a deep nonlinear auto-regressive
network (D-NARX).
8. The method of claim 1, wherein the first artificial neural
network comprises a plurality of hidden layers, wherein one or more
of the hidden layers uses a sigmoid function or rectified linear
unit (ReLU) function as an activation function.
9. The method of claim 1, wherein the first artificial neural
network comprises a pre-processor for normalizing input data and a
post-processor for de-normalizing output data.
10. The method of claim 2, wherein the first artificial neural
network comprises one or more weight coefficients and one or more
bias values, wherein the weight coefficients and the bias values
are determined based on normalized mean squared errors (NMSE).
11. The method of claim 10, wherein the weight coefficients and
bias values are determined for each zone.
12. The method of claim 1, wherein the linear equations for
emulating the zone temperature determination model comprise
piecewise linear equations which are generated by locally
linearizing the activation functions respectively corresponding to
the hidden layers included in the first artificial neural
network.
13. The method of claim 2, wherein the power supply schedule is
determined in such a way as to maintain the temperatures of the
multiple zones within a predetermined range and minimize the total
electricity cost according to time-varying electricity prices.
14. The method of claim 13, wherein the objective functions are
used to determine the power supply schedule for the HVAC system and
the temperatures for the multiple zones as the optimal solutions,
in order to minimize the total electricity cost and the sum of
surpluses from a predetermined boundary temperature.
15. The method of claim 2, wherein the temperature boundary
condition for each of the multiple zones allows a first offset for
the lower limit of the boundary temperature and a second offset for
the upper limit of the boundary temperature, and the first and
second offsets are set differently for each of the multiple
zones.
16. The method of claim 15, wherein the first offset does not
exceed a first reinforced offset, and the second offset does not
exceed a second reinforced offset.
17. The method of claim 1, wherein, in the objective function, the
HVAC system input power is set to zero for predetermined hours that
there are no people in the building.
18. The method of claim 1, wherein the electricity price profiles
include information on time-varying electricity prices, and the
thermal state profiles include information on the time-varying
thermal state of the building.
19. The method of claim 1, wherein, in the determining of optimal
solutions to objective functions for a power supply schedule,
optimal solutions to objective functions for a power supply
schedule are determined by using mixed-integer linear programing
(MILP).
20. A method for implementing a demand response (DR) for a heating,
ventilation, and air-conditioning (HVAC) system in a building, the
method comprising: obtaining an electricity price prediction
profile including information related to time-varying electricity
prices and a thermal state prediction profile including information
related to an time-varying thermal state of the building; and
determining a power supply schedule for the HVAC system in the
building based on the information related to the time-varying
electricity prices and the information related to the time-varying
thermal state of the building.
Description
CROSS-REFERENCE TO RELATED APPLICATION
[0001] This application is based on and claims the benefit of
priority to Korean Patent Application No. 10-2018-0109099, filed on
Sep. 12, 2018, the disclosure of which is incorporated herein in
its entirety by reference.
BACKGROUND OF THE INVENTION
Field of the Invention
[0002] The present invention relates to implement a demand response
for a heating, ventilation, and air-conditioning (HVAC) system in a
building, and more particularly, to a method of utilizing a
supervised learning for an improved demand response for the HVAC
system in the building.
Related Art
[0003] Buildings have high thermal capacity, and thus heating,
ventilation, and air-conditioning (HVAC) systems in the buildings
can be used for a demand response (DR). However, it is particularly
difficult to employ the demand response in the HVAC system in a
multi-zone building, because using an HVAC system as a demand
response resource requires different temperature models for each
zone dependent on the thermal conditions of the building and
facility management in the building and these models should reflect
the physical characteristics in detail.
SUMMARY OF THE INVENTION
[0004] An aspect of the present invention is to provide a method
for creating a demand response determination model for an HVAC
system, that can easily and quickly produce an optimal schedule of
input power to the HVAC system by training an artificial neural
network based on data built up through machine learning under
normal building management conditions, emulating the trained
artificial neural network into a mathematical formula using a
piecewise linear equation, and applying this mathematical formula
for a price-based demand response scheduling optimization
problem.
[0005] Another aspect of the present invention is to provide a
method for implementing demand response that can easily and quickly
produce an optimal schedule of input power to the HVAC system by
training an artificial neural network based on data built up
through machine learning under normal building management
conditions, emulating the trained artificial neural network into a
mathematical formula using a piecewise linear equation, and
applying this mathematical formula for a price-based demand
response scheduling optimization problem.
[0006] However, the problems to be solved by the present inventive
concept are not limited to the above, and it may be variously
extended without departing from the spirit and scope of the present
inventive concept.
[0007] An exemplary embodiment of the present invention provides a
method for implementing a demand response (DR) for a heating,
ventilation, and air-conditioning (HVAC) system in a building, the
method comprising: creating a zone temperature determination model
that outputs temperatures of the building by considering an input
power provided to the HVAC system and a thermal state of the
building, wherein the zone temperature determination model is
created by training a first artificial neural network based on a
plurality of first training data sets; generating objective
functions for a power supply schedule in which optimal solutions
vary with electricity prices and the thermal state, wherein the
power supply schedule includes linear equations for emulating the
zone temperature determination model; determining the optimal
solutions to the objective functions based on a plurality of
electricity price profiles and thermal state profiles; and creating
a demand response determination model that outputs the power supply
schedule for the HVAC system by considering the electricity price
profiles and the thermal state profiles, wherein the demand
response determination model is created by training a second
artificial neural network based on a plurality of second training
data sets each including the electricity price profiles, thermal
state profiles, and determined optimal solutions.
[0008] In an aspect, the building comprises multiple zones, and the
zone temperature determination model outputs temperatures for each
of the multiple zones.
[0009] In an aspect, each of the plurality of first training data
sets includes information related to an existing input power
provided to the HVAC system and the thermal state of the building
and information related to temperatures for the multiple zones
dependent on the existing input power provided to the HVAC system
and the thermal state of the building.
[0010] In an aspect, the thermal state of the building comprises at
least one of an atmospheric temperature, daylight hours, a wind
force, a humidity, a thermal load on the building, and/or building
usage schedules.
[0011] In an aspect, the information related to the existing input
power provided to the HVAC system and the thermal state of the
building is obtained from a building energy management system
(BEMS).
[0012] In an aspect, an input layer of the first artificial neural
network comprises the input power provided to the HVAC system from
a predetermined first time to the present time, the thermal state
from a predetermined second time to the present time, and the
temperatures for the multiple zones from a third predetermined time
to the present time.
[0013] In an aspect, the first artificial neural network model is
implemented as a deep nonlinear auto-regressive network
(D-NARX).
[0014] In an aspect, the first artificial neural network comprises
a plurality of hidden layers, wherein one of more of the hidden
layers uses a sigmoid function or rectified linear unit (ReLU)
function as an activation function.
[0015] In an aspect, the first artificial neural network comprises
a pre-processor for normalizing input data and a post-processor for
de-normalizing output data.
[0016] In an aspect, the first artificial neural network comprises
one or more weight coefficients and one or more bias values,
wherein the weight coefficients and the bias values are determined
based on normalized mean squared errors (NMSE).
[0017] In an aspect, the weight coefficients and bias values are
determined for each zone.
[0018] In an aspect, the linear equations for emulating the zone
temperature determination model comprise piecewise linear equations
which are generated by locally linearizing the activation functions
respectively corresponding to the hidden layers included in the
first artificial neural network.
[0019] In an aspect, the power supply schedule is determined in
such a way as to maintain the temperatures of the multiple zones
within a predetermined range and minimize the total electricity
cost according to time-varying electricity prices.
[0020] In an aspect, the objective functions are used to determine
the power supply schedule for the HVAC system and the temperatures
for the multiple zones as the optimal solutions, in order to
minimize the total electricity cost and the sum of surpluses from a
predetermined boundary temperature.
[0021] In an aspect, the temperature boundary condition for each of
the multiple zones allows a first offset for the lower limit of the
boundary temperature and a second offset for the upper limit of the
boundary temperature, and the first and second offsets are set
differently for each of the multiple zones.
[0022] In an aspect, the first offset does not exceed a first
reinforced offset, and the second offset does not exceed a second
reinforced offset.
[0023] In an aspect, in the objective function, the HVAC system
input power is set to zero for predetermined hours that there are
no people in the building.
[0024] In an aspect, the electricity price profiles include
information on time-varying electricity prices, and the thermal
state profiles include information on the time-varying thermal
state of the building.
[0025] In an aspect, in the determining of optimal solutions to
objective functions for a power supply schedule, optimal solutions
to objective functions for a power supply schedule are determined
by using mixed-integer linear programing (MILP).
[0026] Another exemplary embodiment of the present invention
provides a method for implementing a demand response (DR) for a
heating, ventilation, and air-conditioning (HVAC) system in a
building, the method comprising: obtaining an electricity price
prediction profile including information related to time-varying
electricity prices and a thermal state prediction profile including
information related to an time-varying thermal state of the
building; and determining a power supply schedule for the HVAC
system in the building based on the information related to the
time-varying electricity prices and the information related to the
time-varying thermal state of the building.
BRIEF DESCRIPTION OF THE DRAWINGS
[0027] FIG. 1 is a flowchart of an algorithm for optimal demand
response scheduling according to an exemplary embodiment of the
present invention.
[0028] FIG. 2 is a flowchart of a method for creating a demand
response determination model for a heating, ventilation, and
air-conditioning (HVAC) system according to an exemplary embodiment
of the present invention.
[0029] FIG. 3 is a flowchart of a method for implementing a demand
response for the HVAC system according to an exemplary embodiment
of the present invention.
[0030] FIG. 4 shows an architecture of a first artificial neural
network according to an exemplary embodiment of the present
invention.
[0031] FIG. 5 is a detail view of a J-th hidden layer in FIG.
4.
[0032] FIG. 6 is a detail view of a K-th hidden layer in FIG.
4.
[0033] FIG. 7 is a detail view of an L-th hidden layer in FIG.
4.
[0034] FIG. 8 is an exemplary view of a piecewise linear
approximation of a sigmoid function as an activation function.
[0035] FIG. 9 is an exemplary view of a piecewise linear
approximation of an ReLU function as an activation function.
[0036] FIG. 10 shows the processing of a data set for optimal
solutions to objective functions.
[0037] FIG. 11 shows the processing of a training data set used to
train a second artificial neural network.
[0038] FIG. 12 shows the determination of an optimal demand
response schedule using the trained second artificial neural
network.
[0039] FIG. 13 shows an optimal demand response algorithm according
to an exemplary embodiment of the present invention.
DETAILED DESCRIPTION OF THE INVENTION
[0040] Examples of various embodiments are illustrated and
described further below. It will be understood that the description
herein is not intended to limit the claims to the specific
embodiments described. On the contrary, it is intended to cover
alternatives, modifications, and equivalents as may be included
within the spirit and scope of the present disclosure as defined by
the appended claims.
[0041] It will be understood that, although the terms "first",
"second", "third", and so on may be used herein to describe various
elements, components, regions, layers and/or sections, these
elements, components, regions, layers and/or sections should not be
limited by these terms. These terms are used to distinguish one
element, component, region, layer or section from another element,
component, region, layer or section. Thus, a first element,
component, region, layer or section described below could be termed
a second element, component, region, layer or section, without
departing from the spirit and scope of the present disclosure.
[0042] It will be understood that when an element or layer is
referred to as being "connected to", or "coupled to" another
element or layer, it can be directly on, connected to, or coupled
to the other element or layer, or one or more intervening elements
or layers may be present. In addition, it will also be understood
that when an element or layer is referred to as being "between" two
elements or layers, it can be the only element or layer between the
two elements or layers, or one or more intervening elements or
layers may also be present.
[0043] The terminology used herein is for the purpose of describing
particular embodiments only and is not intended to be limiting of
the present disclosure. As used herein, the singular forms "a" and
"an" are intended to include the plural forms as well, unless the
context clearly indicates otherwise. It will be further understood
that the terms "comprises", "comprising", "includes", and
"including" when used in this specification, specify the presence
of the stated features, integers, operations, elements, and/or
components, but do not preclude the presence or addition of one or
more other features, integers, operations, elements, components,
and/or portions thereof. As used herein, the term "and/or" includes
any and all combinations of one or more of the associated listed
items. Expression such as "at least one of" when preceding a list
of elements may modify the entire list of elements and may not
modify the individual elements of the list.
[0044] Unless otherwise defined, all terms including technical and
scientific terms used herein have the same meaning as commonly
understood by one of ordinary skill in the art to which this
inventive concept belongs. It will be further understood that
terms, such as those defined in commonly used dictionaries, should
be interpreted as having a meaning that is consistent with their
meaning in the context of the relevant art and will not be
interpreted in an idealized or overly formal sense unless expressly
so defined herein.
[0045] In the following description, numerous specific details are
set forth in order to provide a thorough understanding of the
present disclosure. The present disclosure may be practiced without
some or all of these specific details. In other instances,
well-known process structures and/or processes have not been
described in detail in order not to unnecessarily obscure the
present disclosure.
[0046] Overview
[0047] According to an aspect of the present invention, there is
provided a method of using a heating, ventilation, and
air-conditioning (HVAC) system in a multi-zone building for an
optimal demand response through a machine learning.
[0048] In the method disclosed according to an aspect of the
present invention, a first artificial neural network may be trained
based on data built up under normal building management conditions,
the trained first artificial neural network may be emulated into a
mathematical formula using a piecewise linear equation, and this
mathematical formula may be used for a price-based demand response
scheduling optimization problem. A scheduling optimization problem
may be solved for a variety of electricity prices and the thermal
conditions of the building, and a resultant optimal solution to the
objective function may be used to train a second artificial neural
network (e.g., a deep neural network) which will be used for
determining an optimal demand response schedule. According to an
aspect of the present invention, this algorithm may be called a
supervised-learning-aided meta-prediction (SLAMP). An machine
learning-based strategies according to an exemplary embodiment of
the present invention may be actually applied without sacrificing
residents' thermal preferences and cost-effective operation.
[0049] More specifically, according to an aspect of the present
invention, a novel method that allows a heating, ventilation, and
air-conditioning (HVAC) system in a multi-zone commercial building
to optimally participate in a demand response through a machine
learning. This method involves continuous scheduling of in input
power for the HVAC system with respect to time, by which the
temperatures of the zones are maintained within a certain range and
optimally determined with respect to time-varying electricity
prices.
[0050] To predict how the temperatures in the zones change with
changes in the input power for the HVAC system, a first artificial
neural network (ANN) model using a feedback loop, time-delayed
input, and multiple hidden layers may be implemented. Using a
supervised learning algorithm, the above first artificial neural
network model is trained on building management data for the
building under normal conditions, and then emulated by a set of
piecewise linear equations. For optimal demand response scheduling,
an optimization problem may be formulated as a linear equation, and
a globally optimal solution may be found using mixed-integer linear
programming (MILP). Optimal solutions found on a profile of a
variety of electricity prices and the thermal state of the building
is used to train a second artificial neural network (e.g., a deep
neural network) model, and afterwards the second artificial neural
network is used to instantly produce an optimal demand response
schedule without solving any optimization problem. This may be
collectively called a supervised-learning-aided meta-prediction
(SLAMP) algorithm.
[0051] According to the SLAMP algorithm according to an aspect of
the present invention, the proposed algorithm is advantageous in
that it may be instantly applied to various types of HVAC systems
and commercial buildings because it uses a machine learning
technique, not a physics-based modeling method. Moreover, emulation
of the first artificial neural network with a feedback loop, pre-
and post-processors of data, and hidden layers may be included as a
primary technical characteristic of the algorithm, whereby an
optimization problem may be formulated as a linear equation, and a
globally optimal solution may be found using mixed-integer linear
programming. Furthermore, a method of supervised-learning-aided
meta-prediction according to an aspect of the present invention
allows for instantly producing an optimal schedule of input power
to an HVAC system dependent on the thermal state of the building
for 24 hours and varying electricity prices. This may significantly
reduce calculation time and help a distribution system manager by
controlling the overall load demand on the HVAC system within a
distribution network through electricity price signals.
Supervised Learning of Thermal Response from Multi-Zone Building
for an HVAC System Operation
[0052] FIG. 1 is a flowchart of an algorithm for an optimal demand
response scheduling according to an exemplary embodiment of the
present invention. That is, FIG. 1 is a schematic view of a machine
learning-based demand response strategic algorithm according to an
aspect of the present invention. The algorithm according to an
aspect of the present invention may be largely divided into three
stages. To begin with, a first artificial neural network is
implemented for modeling the operation of a heating, ventilation,
and air-conditioning (HVAC) system and the thermal state of a
building. Afterwards, by emulating the first artificial neural
network by a linear equation, an optimization problem is
formulated, and an optimal solution is found. Next, a
supervised-learning-aided meta-prediction algorithm is implemented
using the found optimal solution in order to produce an optimal
demand response schedule.
[0053] Referring to FIG. 1, in order to implement the algorithm
according to an aspect of the present invention, data from a
building energy management system (BEMS) concerning the HVAC system
operation and thermal conditions (weather, zone temperatures, etc.)
of a target multi-zone building (step 10).
[0054] For instance, a first artificial neural network model (see
FIG. 4) may be trained for estimating zone temperatures under given
operating conditions (e.g., a profile of power supply) of the HVAC
system and thermal conditions (e.g., the environmental values of
the building, including atmospheric temperature, daylight hours,
etc.) which are obtained from the BEMS (step 20). Afterwards, the
first artificial neural network model may be emulated by linear and
piecewise linear equations (by using pre- and post-processors; see
Mathematical Formulas 2 to 11) (step 30). Using this, an
optimization problem for a price-based demand response scheduling
may be formulated (see Mathematical Formulas 5 to 25) (step 40).
Optimal solutions to Mathematical Formulas 5 to 25 may be found
using mixed-integer linear programming (MILP) (step 50). The above
steps 20 to 50 may be called as an artificial neural network
emulation method 1 for estimating zone temperatures based on the
operation of the HVAC system and thermal conditions.
[0055] Referring again to FIG. 1, optimal solutions to objective
functions may be found for a plurality of thermal conditions and
electricity price profiles. Concretely speaking, optimal solutions
to formulated optimization objective functions for price-based
demand response scheduling in Mathematical Formulas 5 to 25 (step
50) may be iteratively found for the thermal conditions of the
building and various profiles of electricity prices (step 60) (see
Part 1 of Algorithm 1 in FIG. 13). Based on the thermal conditions
of the building, the various profiles of electricity prices, and
the found optimal solutions, a second artificial neural network
(e.g., deep neural network) for optimal demand response scheduling
of the HVAC system may be trained (step 70) (see Part 2 of
Algorithm 1 in FIG. 13). The above steps 50 to 70 may be called a
supervised-learning-aided meta-prediction (SLAMP) method.
[0056] Afterwards, based on the trained second artificial neural
network, an optimal demand response schedule for the HVAC system
reflecting time-varying electricity prices and the thermal
conditions of the building may be quickly and easily produced (step
80).
[0057] According to another aspect of the present invention, the
above-described algorithm may be defined in two separate methods:
one is creating a demand response determination model for the HVAC
system and the other is implementing demand response for the HVAC
system based on the created model.
[0058] FIG. 2 is a flowchart of a method for creating a demand
response determination model for a heating, ventilation, and
air-conditioning (HVAC) system according to an exemplary embodiment
of the present invention. As shown in FIG. 2, the method for
creating a demand response determination model for the HVAC system
according to an exemplary embodiment of the present invention is a
method for creating a demand response (DR) determination model for
the HVAC system in a building, and the building may have multiple
zones. Still, the method according to an exemplary embodiment of
the present invention may also be applied to a building with a
single zone.
[0059] To begin with, a first artificial neural network may be
trained based on a plurality of first training data sets, whereby a
zone temperature determination model for taking the input power to
the HVAC system and the thermal state of the building as input and
producing output temperatures for the building may be created (step
210). That is, the zone temperature determination model may be
created by considering an input power provided to the HVAC system
and a thermal state of the building, where the zone temperature
determination model may be created by training a first artificial
neural network based on a plurality of first training data
sets.
[0060] Afterwards, objective functions for a power supply schedule
including linear equations for emulating the above-created zone
temperature determination model may be generated whose optimal
solutions vary with electricity prices and thermal state (step
220), and the optimal solutions to the above-generated objective
functions may be determined based on a plurality of electricity
price profiles and thermal state profiles (step 230). Here, the
power supply schedule may include linear equations for emulating
the zone temperature determination model.
[0061] Next, a second artificial neural network may be trained
based on a plurality of second training data sets each including
the electricity price profiles, thermal state profiles, and
determined optimal solutions, whereby a demand response
determination model for taking the electricity price profiles and
the thermal state profiles as input and producing a power supply
schedule for the HVAC system as output may be created (step 240).
That is, the demand response determination model may be created by
considering the electricity price profiles and the thermal state
profiles, where the demand response determination model may be
created by training a second artificial neural network based on a
plurality of second training data sets each including the
electricity price profiles, thermal state profiles, and determined
optimal solutions.
[0062] FIG. 3 is a flowchart of a method for implementing demand
response for a heating, ventilation, and air-conditioning (HVAC)
system according to an exemplary embodiment of the present
invention. As shown in FIG. 3, the method for implementing demand
response for the HVAC system according to an exemplary embodiment
of the present invention is a method for implementing demand
response (DR) for the HVAC system in a building, and the building
may have multiple zones. Still, the method according to an
exemplary embodiment of the present invention may also be applied
to a building with a single zone.
[0063] To begin with, an electricity price prediction profile
including information related to time-varying electricity prices
and a thermal state prediction profile including information
related to an time-varying thermal state of the building may be
obtained (step 310), and a power supply schedule for the HVAC
system in the building may be determined based on the information
related to the time-varying electricity prices and the information
related to the time-varying thermal state of the building (step
320). This determination may be made based on the above-explained
demand response determination model.
[0064] Now, each of the steps of the above-described algorithm
and/or method according to the exemplary embodiments of the present
invention will be described in more detail.
Artificial Neural Network Architecture and Training
[0065] As stated above with reference to FIG. 2, in the method for
creating a demand response determination model for a heating,
ventilation, and air-conditioning (HVAC) system according to an
exemplary embodiment of the present invention, a zone temperature
determination model for taking the input power to the HVAC system
and the thermal state of the building as input and producing output
temperatures for the building may be created first by training a
first artificial neural network based on a plurality of first
training data sets (step 210). If the building has multiple zones,
the zone temperature determination model may output temperatures
for the multiple zones.
[0066] Regarding this, the architecture and training of the first
artificial neural network for creating a zone temperature
determination model will be described in detail first. FIG. 4 shows
an architecture of a first artificial neural network according to
an exemplary embodiment of the present invention. FIG. 5 is a
detail view of a J-th hidden layer in FIG. 4. FIG. 6 is a detail
view of a K-th hidden layer in FIG. 4. FIG. 7 is a detail view of
an L-th hidden layer in FIG. 4.
[0067] The indoor temperature T.sub.z.sup.t in each zone z at a
certain time t may be determined by the thermal state E.sup.t of
the multi-zone building and the input power P.sup.t to the HVAC
system for a period from the previous time t-.tau. to the present
time t. If the building's thermal state E.sup.t and the input power
P.sup.t to the facilities are the same, the indoor temperature
T.sub.z.sup.t-1 at the previous time directly affects the current
temperature T.sub.z.sup.t. That is, the current temperature
T.sub.z.sup.t is an output from a state-space model for the
thermodynamic design of the building and at the same time is used
as a state variable. From this, it can be seen that an artificial
neural network model is suitable for thermodynamic design because
it takes the input power and thermal state in the past as inputs
and has a feedback loop of the indoor temperature.
[0068] Regarding this, each of the first training data sets for
training the first artificial neural network may include
information related to an existing input power provided to the HVAC
system and the thermal state of the building and information
related to temperatures for the multiple zones dependent on the
existing input power provided to the HVAC system and the thermal
state of the building.
[0069] More specifically, as shown in FIGS. 4 to 7, an input layer
of the first artificial neural network may include, as input
neurons, the input power to the HVAC system from a predetermined
first time until the present time, the thermal state from a
predetermined second time to the present time, and the temperatures
for the multiple zones from a third predetermined time until the
present time.
[0070] Moreover, to increase the accuracy of the model, multiple
hidden layers using a sigmoid function or rectified linear unit
(ReLU) function as an activation function may be used. As a result,
as shown in FIGS. 4 to 7, the first artificial neural network model
may be implemented as a deep nonlinear auto-regressive network
(D-NARX) with exogenous inputs. Also, other types of multilayer
networks may be partially modified and applied to the
algorithm.
[0071] According to an aspect, the first artificial neural network
model may include a pre-processor for normalizing input data and a
post-processor for de-normalizing output data.
[0072] Specifically, the first artificial neural network model may
include a pre-processor for normalizing an input data set
X.sub.z.sup.t. This prevents the learning rate of the artificial
neural network from slowing down due to the vanishingly small
gradient in the learning algorithm. Based on the validity of
building management data from the building energy management system
(BEMS), the thermal state E.sup.t of the building may include at
least one of an atmospheric temperature, daylight hours, a wind
force, a humidity, a thermal load on the building, and/or building
usage schedules. These factors have different value ranges. The
post-processor is used to transform the normalized indoor
temperature y.sub.z.sup.t back to the range of the original
temperature T.sub.z.sup.t.
[0073] As shown in FIGS. 4 to 7, the first artificial neural
network may include one or more weight coefficients and one or more
bias values. During a learning process, the weight coefficients
IW.sub.jiz, HW.sub.kjz, and LW.sub.lz, and the bias values
b.sub.j(k)z and o.sub.z may be determined for all neurons (input,
hidden, and output neurons) based on normalized mean squared errors
(NMSE).
e ( T z , T z ' ) = 1 - t = 1 N T ( T z t - T z t ' ) 2 t = 1 N T (
T z t - T z , avg ) 2 where T z , avg = 1 N T t = 1 N T T z t [
Mathematical Formula 1 ] ##EQU00001##
[0074] where T.sub.z.sup.t' is the model-predicted value of the
indoor temperature T.sub.z.sup.t, and N.sub.T is the total number
of training (or test) data sets. The weight values and the bias
values may be set constant during a scheduling period
(1h.ltoreq.t.ltoreq.N.sub.H). The weight coefficients and bias
values for the first artificial neural network may be determined
for each of the multiple zones included in the building, and
therefore the first artificial neural network model may have
different weight coefficients and bias values for each zone. All
zones may have the same input (except the previous indoor
temperature T.sub.z.sup.t-1), the same activation functions, and
the same network architecture.
Piecewise Linear Emulation of Trained Artificial Neural Network
[0075] Referring again to FIG. 2, in the method for creating a
demand response determination model for an HVAC system according to
an exemplary embodiment of the present invention, linear equations
for emulating the above-described zone temperature determination
model created by training the first artificial neural network may
be obtained (step 220).
[0076] As explained above with reference to FIGS. 4 to 7, the first
artificial neural network according to an aspect of the present
invention includes a plurality of hidden layers, and at least one
of the hidden layers may use a sigmoid function or rectified linear
unit (ReLU) function as an activation function. According to an
aspect, the linear equations for emulating the zone temperature
determination model may include piecewise linear equations which
are generated by locally linearizing the activation functions
respectively corresponding to the hidden layers included in the
first artificial neural network.
[0077] Concretely speaking, the zone temperature determination
model of the trained first artificial neural network is emulated by
linear equations and piecewise linear equations and applied to an
optimization problem dealt with in the step 220 of FIG. 2.
Referring to FIGS. 4 to 7, the output n.sub.jz.sup.t of a j-th
hidden neuron in the first hidden layer may be calculated by the
following Mathematical Formula 2, where x.sub.iz.sup.t denotes
normalized input data of an i-th input neuron at a specific time t
in a specific zone Z.
n jz t = i = 1 N I IW jiz x iz t + b jz , .A-inverted. j .di-elect
cons. H 1 z , .A-inverted. t , .A-inverted. z , [ Mathematical
Formula 2 ] ##EQU00002##
[0078] Similarly, the output n.sub.kz.sup.t of a k-th hidden neuron
may be calculated by the following Mathematical Formula 3 by using
the output m.sub.jz.sup.t of the activation function of the
previous hidden neuron (j, j=k-1).
n kz t = j = 1 N J HW kjz m jz t + b kz , .A-inverted. k .di-elect
cons. H Kz , .A-inverted. t , .A-inverted. z . [ Mathematical
Formula 3 ] ##EQU00003##
[0079] where the output m.sub.jz.sup.t of the j-th hidden neuron in
Mathematical Formula 3 may be represented as n.sub.jz.sup.t, and
may be calculated by the first or second column of the following
Mathematical Formula 4 depending on the type of the activation
function. A sigmoid function and an ReLu function may be
respectively represented by the first and second lines of
Mathematical Formula 4.
F J : m jz t = 2 1 + exp ( - 2 n jz t ) - 1 , .A-inverted. j ,
.A-inverted. t , .A-inverted. z , F J : m jz t = max ( 0 , n jz t )
, .A-inverted. j , .A-inverted. t , .A-inverted. z . [ Mathematical
Formula 4 ] ##EQU00004##
[0080] FIG. 8 is an exemplary view of a piecewise linear
approximation of a sigmoid function as an activation function. FIG.
9 is an exemplary view of a piecewise linear approximation of an
ReLU function as an activation function. As shown in FIGS. 8 and 9,
the activation functions of all hidden neurons may be represented
by local linearization by the following Mathematical Formulas 5 to
8.
m jz t - s = 1 N S l s q sjz t = F J , min , .A-inverted. j ,
.A-inverted. t , .A-inverted. z , [ Mathematical Formula 5 ] - q 1
jz t + ( r 1 - r 0 ) w 1 jz t .ltoreq. 0 , .A-inverted. j ,
.A-inverted. t , .A-inverted. z , q 1 jz t .ltoreq. ( r 1 - r 0 ) ,
.A-inverted. j , .A-inverted. t , .A-inverted. z , [ Mathematical
Formula 6 ] - q sjz t + ( r s - r ( s - 1 ) ) w sjz t .ltoreq. 0 ,
.A-inverted. s .di-elect cons. [ 2 , , ( N S - 1 ) ] , .A-inverted.
j , .A-inverted. t , .A-inverted. z , q sjz t - ( r s - r ( s - 1 )
) w ( s - 1 ) jz t .ltoreq. 0 , .A-inverted. s .di-elect cons. [ 2
, , ( N S - 1 ) ] , .A-inverted. j , .A-inverted. t , .A-inverted.
z , [ Mathematical Formula 7 ] - q N s jz t .ltoreq. 0 ,
.A-inverted. j , .A-inverted. t , .A-inverted. z , q N s jz t - ( r
N s - r ( N s - 1 ) ) w ( N s - 1 ) jz t .ltoreq. 0 , .A-inverted.
j , .A-inverted. t , .A-inverted. z . [ Mathematical Formula 8 ]
##EQU00005##
[0081] In Mathematical Formula 5, N.sub.S denotes the number of
piecewise linear blocks, F.sub.J,min is the minimum value of
F.sub.J, and l.sub.S is the local linearization of the gradient of
the output m.sub.jz.sup.t of the activation function for an s-th
linear segment of n.sub.jz.sup.t. In Mathematical Formulas 6 to 8,
q.sub.sjz.sup.t denotes the input of the activation function for
the s-th linear segment of n.sub.jz.sup.t, and w.sub.sjz.sup.t
denotes a binary variable for completing local linearization.
Similarly, the output of the output neuron is as shown in the
following Mathematical Formula 9. Here, N.sub.L is the number of
neurons in the last hidden layer.
y z t = l = 1 N L LW lz m lz t + o z , .A-inverted. t ,
.A-inverted. z , [ Mathematical Formula 9 ] ##EQU00006##
[0082] To put it simply, the activation function F.sub.0 for the
output layer may be set as a linear identity function. Here, it is
assumed that the first artificial neural network successfully
reflects the thermodynamic design of the building by multiple
hidden layers.
[0083] Mathematical Formulas 10 and 11 are formulated that
represent the aforementioned pre- and post-processors. Here,
X.sub.iz and X.sub.iz represent the maximum and minimum values of
training data X.sub.iz.sup.t, and T.sub.z and T.sub.z denote the
maximum and minimum values of the indoor temperature T.sub.z.sup.t
in the training data. Mathematical Formulas 2 to 11 may be applied
to other types of artificial neural networks or activation
functions without loss of universality.
- 2 ( X iz _ - X iz _ ) X iz t + x iz t = - 2 X iz _ ( X iz _ - X
iz _ ) - 1 , .A-inverted. i , .A-inverted. t , .A-inverted. z , [
Mathematical Formula 10 ] - ( T z _ - T z _ ) 2 y z t + T z t = ( T
z _ - T z _ ) 2 + T z _ , .A-inverted. t , .A-inverted. z , [
Mathematical Formula 11 ] ##EQU00007##
Supervised Learning-Aided Demand Response for HVAC System in
Multi-Zone Building
Formulation of Optimization Problem Using Trained Artificial Neural
Network
[0084] Referring again to FIG. 2, in the method for creating a
demand response determination model for an HVAC system according to
an exemplary embodiment of the present invention, objective
functions for a power supply schedule including linear equations
for emulating the above-described zone temperature determination
model created by training the first artificial neural network may
be generated whose optimal solutions vary with electricity prices
and thermal state (step 220). That is, an optimization problem may
be formulated by using the trained artificial neural network.
[0085] In the above-described algorithm and/or method according to
the exemplary embodiments of the present invention, the optimal
solution to the objective function represented by the following
Mathematical Formula 12 may be found by using the emulated first
artificial neural network model, in order to optimally manage the
HVAC system in the multi-zone building using price-based demand
response.
arg min P t , T z t J DR = t = 1 N H C E t P t + z = 1 N Z t = 1 N
H C V t ( .DELTA. T z tH + .DELTA. T z tL ) [ Mathematical Formula
12 ] ##EQU00008##
[0086] Here, constraints to consider may be set, and examples of
these constraints are as follows.
[0087] Constraints on Indoor Temperature T.sub.z.sup.t
(Mathematical Formulas 13 to 15)
T.sub.z.sup.t-.DELTA.T.sub.z.sup.tH.ltoreq.T.sub.z,max.sup.t,
.A-inverted.t, .A-inverted.z, [Mathematical Formula 13]
-T.sub.z.sup.t-.DELTA.T.sub.z.sup.tL.ltoreq.-T.sub.z,min.sup.t,
.A-inverted.t, .A-inverted.z, [Mathematical Formula 14]
HT.sub.z,min.sup.t.ltoreq.T.sub.z.sup.t.ltoreq.HT.sub.z,max.sup.t,
.A-inverted.t, .A-inverted.z, [Mathematical Formula 15]
[0088] Constraints on the Relationship Between Indoor Temperature
T.sub.z.sup.t and HVAC System Input Power Pt
[0089] Along with Mathematical Formulas 5 to 11, the constraints
for the following Mathematical Formulas 16 to 22 are
presupposed.
n jz t - i .di-elect cons. X Cz N Cz IW jiz x iz t - i .di-elect
cons. X Fz N Fz IW jiz x iz t = i .di-elect cons. X Ez N Ez IW jiz
x iz t + b jz , .A-inverted. j .di-elect cons. H 1 z , .A-inverted.
t , .A-inverted. z , [ Mathematical Formula 16 ] n jz t = r 0 + s =
1 N S q sjz t , .A-inverted. j .di-elect cons. H 1 z , .A-inverted.
t , .A-inverted. z , [ Mathematical Formula 17 ] - j = 1 N J HW kjz
m jz ' + s = 1 N S q skz ' = b kz - r o , .A-inverted. k .di-elect
cons. H K ( = J + 1 .gtoreq. 2 ) z , .A-inverted. t , .A-inverted.
z , [ Mathematical Formula 18 ] X iz t - T z ( t - i .DELTA. t ) =
0 , .A-inverted. i .di-elect cons. X Fz , .A-inverted. t .gtoreq. (
i + 1 ) .DELTA. t , .A-inverted. z , [ Mathematical Formula 19 ] X
iz t = T z , pre ( N H + ( t - i .DELTA. t ) ) , .A-inverted. i
.di-elect cons. X Fz , .A-inverted. t .ltoreq. i .DELTA. t ,
.A-inverted. z , [ Mathematical Formula 20 ] - 1 .ltoreq. x iz t
.ltoreq. 1 , .A-inverted. i .di-elect cons. { X Cz , X Fz } ,
.A-inverted. t , .A-inverted. z [ Mathematical Formula 21 ] - 1
.ltoreq. y z t .ltoreq. 1 , .A-inverted. t , .A-inverted. z , [
Mathematical Formula 22 ] ##EQU00009##
[0090] Constraints on Time-Delayed Input Power (Mathematical
Formulas 23 to 25)
X.sub.iz.sup.t=P.sup.(t-(i-1).DELTA.t), .A-inverted.i .di-elect
cons. X.sub.Cz, .A-inverted.t, .A-inverted.z, [Mathematical Formula
23]
X.sub.iz.sup.(t+(i-1).DELTA.t)-X.sub.(i+1)z.sup.(t+i.DELTA.t)=0,
.A-inverted.i .di-elect cons. X.sub.Cz, 1h.ltoreq.t<t.sub.e,
.A-inverted.z, [Mathematical Formula 24]
X.sub.iz.sup.t=0, .A-inverted.i .di-elect cons. X.sub.Cz,
t.ltoreq.(i-1).DELTA.t,
N.sub.H-(i-1).DELTA.t.ltoreq.t.ltoreq.N.sub.H, .A-inverted.z.
[Mathematical Formula 25]
[0091] A power supply schedule produced according to the algorithm
and/or method according to one exemplary embodiment of the present
invention may be determined in such a way as to maintain the
temperatures of the multiple zones in the building within a
predetermined range and minimize the total electricity cost
relative to time-varying electricity prices. Therefore, according
to an aspect, the objective functions acting as criteria for
achieving optimal demand response may be used to determine the
power supply schedule for the HVAC system and the temperatures for
the multiple zones as the optimal solutions, in order to minimize
the total electricity cost and the sum of surpluses from a
predetermined boundary temperature.
[0092] More specifically, referring to FIG. 12, the objective
function in Mathematical Formula 12 may be largely divided into two
terms. The first term is the sum of time-varying electricity prices
C.sub.E.sup.t multiplied by the input power P.sup.t to the HVAC
system for 24 hours, the goal of which is to lower facility
management costs.
[0093] The second term in Mathematical Formula 12 represents the
penalty for the temperature difference .DELTA.T.sub.z.sup.tH or
.DELTA.T.sub.z.sup.tL which is the surplus generated when the
indoor temperature T.sub.z.sup.t corresponding to the input power
exceeds the maximum limit T.sub.z,max.sup.t or minimum limit
T.sub.z,min.sup.t. The first and second terms respectively
correspond to Mathematical Formulas 13 and 14. In other words, the
temperature boundary condition may be alleviated by including the
temperature difference .DELTA.T.sub.z.sup.tH or
.DELTA.T.sub.z.sup.tL in Mathematical Formula 12, by which this
optimization problem can be solved reliably. To put it another way,
in an objective function for an algorithm and/or method according
to an aspect of the present invention, the temperature boundary
condition for each of the multiple zones is designed to allow a
first offset against the lower limit of the boundary temperature
and a second offset for the upper limit of the boundary
temperature, whereby the temperature boundary condition may be
alleviated.
[0094] Here, the maximum and minimum limits or the first and second
offsets may be set differently for each of the multiple zones in
the building, based on the thermal preferences of residents in each
zone. However, the boundary condition may be reinforced as in
Mathematical Formula 15, in order to prevent an excessive increase
or decrease in temperature. Thus, the first offset may be set to
not exceed a first reinforced offset, and the second offset may be
set to not exceed a second reinforced offset. The values of the
first and second reinforced offsets may be set the same as in
Mathematical Formula 15, or may be set different from each
other.
[0095] The second constraint for Mathematical Formulas 16 to 22
describes the relationship between the input power P.sup.t, which
is one of the input variables for the trained first artificial
neural network model, and the temperature T.sub.z.sup.t of each
zone, which is the output variable. Particularly, Mathematical
Formula 16 is an equivalent expression of Mathematical Formula 2,
in which the input variables x.sub.z.sup.t may include a
controllable variable P.sup.t, a feedback variable T.sub.z.sup.t-1,
and an environmental input variable E.sup.t. Moreover, Mathematical
Formula 17 shows that the activation function input n.sub.jz.sup.t
in Mathematical Formula 16 is equivalent to the sum of
q.sub.sjz.sup.t: i.e., the value assigned in the linear blocks,
where r.sub.0 is an arbitrarily large negative number, as shown in
FIGS. 8 and 9. Analogously, it can be seen that Mathematical
Formula 3 is equivalently expressed as Mathematical Formula 18
using the linear expression n.sub.kz.sup.t=r.sub.0+.SIGMA..sub.s
q.sub.skz.sup.t of the activation function input n.sub.kz.sup.t
existing in another layer.
[0096] Mathematical Formulas 19 and 20 reflect a feedback loop of
the indoor temperature T.sub.z.sup.t. Mathematical Formula 19
represents the relationship between the feedback input variable
X.sub.iz.sup.t (i .di-elect cons. X.sub.F.sub.z) of the trained
artificial neural network model and the output variable
T.sub.z.sup.t. Time-varying electricity prices C.sub.E are applied
when calculating management costs, and, in an exemplary embodiment
of the present invention, when electricity prices vary by the hour,
the unit sampling time .DELTA.t may be set to 1 hour. As in
Mathematical Formula 20, X.sub.iz.sup.t (i .di-elect cons.
X.sub.F.sub.z) may be set for the temperature
T.sub.z.sup.(N.sup.H.sup.+(t-i.DELTA.t)) predicted a day ago.
Furthermore, Mathematical Formula 21 represents that the
controllable variable and feedback input variable obtained from
Mathematical Formula 10 have a value between -1 and 1. It means
that the variable P.sup.t for the input power is larger than or
equal to 0 and has a value lower than the rated power P.sub.rated.
This way, the maximum and minimum values of the normalized variable
y.sub.z.sup.t can be found from Mathematical Formula 22. Here, the
normalized variable y.sub.z.sup.t may be linked to Mathematical
Formula 18 via Mathematical Formulas 5 to 9, and be
reverse-transformed to the indoor temperature by using Mathematical
Formula 11.
[0097] To apply the trained artificial neural network to an
optimization problem, Mathematical Formulas 23 and 24 involve
constraints on input neurons for time-delayed input power to the
HVAC system. Moreover, Mathematical Formula 25 may reflect shutting
down the HVAC system during after-office hours t.gtoreq.t.sub.E
when there are almost no people in the building and running the
HVAC system for pre-cooling early in the morning before people
arrive their offices in the building. Accordingly, in the objective
function, the HVAC system input power for predetermined hours when
there are no people in the building may be set to zero according to
the settings.
[0098] The optimization problems in Mathematical Formulas 5 to 25
may be actually applied to a variety of building models without any
major modifications. But it should be noted that, for example, five
variables IW.sub.jiz, HW.sub.kjz, LW.sub.iz, b.sub.j(k)z, and
o.sub.z for the first artificial neural network may vary with the
thermodynamic design of the building and the load characteristics
of the HVAC system. Since mathematical Formulas 5 to 25 contain a
linear equation and binary variables, optimization problems
according to the algorithm and/or method according to an aspect of
the present invention may be solved using mixed-integer linear
programing (MILP). This way, an optimal demand response schedule
may be produced.
[0099] Referring again to FIG. 2, in a method for creating a demand
response determination model for an HVAC system according to an
exemplary embodiment of the present invention, optimal solutions to
objective functions for a plurality of electricity price profiles
and thermal state profiles may be determined (step 230). Here, an
electricity price prediction profile may contain information on
time-varying electricity prices, and a thermal state prediction
profile may contain information on the time-varying thermal state.
The intervals of varying electricity prices and/or thermal states
may differ according to implementations--for example, information
specifying that electricity prices vary by the hour. The intervals
of electricity prices and the intervals of thermal states may be
the same or different. Meanwhile, in the step (step 230) of
determining optimal solutions to objective functions for a power
supply schedule, as shown in FIG. 2, optimal solutions to objective
functions for a power supply schedule may be determined by using
mixed-integer linear programing (MILP).
Optimal Demand Response Using Supervised-Learning-Aided
Meta-Prediction
[0100] Referring again to FIG. 2, optimal solutions to objective
functions for a plurality of electricity price profiles and thermal
state profiles may be determined (step 230). A second artificial
neural network may be trained based on a plurality of second
training data sets each including the electricity price profiles,
thermal state profiles, and determined optimal solutions, whereby a
demand response determination model for taking the electricity
price profiles and the thermal state profiles as input and
producing a power supply schedule for the HVAC system as output may
be created (step 240).
[0101] Concretely speaking, given an electricity price
C.sub.E.sup.t and an environmental variable E.sup.t (e.g., thermal
condition), the optimal solutions to Mathematical Formulas 5 to 25
may be found immediately through the SLAMP method (see Algorithm 1
in FIGS. 10 to 12 and FIG. 13). This significantly reduces the time
taken to produce an optimal demand response schedule, and allows a
distribution facility manager to set optimal electricity prices at
a number of buses in a distribution network. This is because the
profile of the overall load demand on the HVAC system across a
distribution network may be predicted, and the SLAMP method, as
mentioned previously, may be implemented by artificial neural
network emulation using building management data.
[0102] Notably, as can be seen from Part 1 of Algorithm 1 in FIG.
13, the optimization problems in Mathematical Formulas 5 to 25 may
be solved offline through iteration. As a result, optimal solutions
P.sup.t and T.sub.z.sup.t may be found, and the corresponding
operating cost E.sub.c=.SIGMA..sub.t C.sub.E.sup.tP.sup.t and the
temperature penalty term T.sub.V=.SIGMA..sub.z.SIGMA..sub.t
C.sub.V.sup.t(.DELTA.T.sub.z.sup.tH+.DELTA.T.sub.z.sup.tL)
associated with the difference with the boundary temperature may be
calculated by using the past data in the current input m=[t,
C.sub.E.sup.t, E.sup.t]. As shown in FIG. 10, an input m from a
BEMS database 1010, for example, may be extended into data M=[t,
C.sub.E.sup.t, E.sup.t, T.sub.z,opt.sup.t, P.sub.opt.sup.t] for
training the second artificial neural network (e.g., deep neural
network) model by an SLAMP algorithm 1020, and then re-processed
into input data I and output data O. To improve the performance of
the deep neural network model, the input data I may be extended by
including time-delayed data in training data M. Then, as shown in
FIG. 11, the input data I and output data O that can be obtained
from the data in the database 1110 may be randomly mixed so that
training data D.sub.r and test data C.sub.E.sup.tD.sub.e for the
deep neural network model 1120 include various profiles of
electricity prices C.sub.E.sup.t and environmental variables
E.sup.t, and the corresponding optimal schedule for the indoor
temperature T.sub.z.sup.t and the input power P.sup.t.
[0103] Referring again to FIG. 13, in Part 2, the deep neural
network is trained and tested by using the training data D.sub.r
and the test data C.sub.E.sup.tD.sub.e, respectively. The training
and the testing are performed on network parameters W(c), and the
parameters include time delay .tau. in input data I, the number of
hidden layers G, and the activation functions F and hidden neurons
U of each layer. After the optimal input power P.sup.t is
meta-predicted, the corresponding indoor temperature T.sub.z.sup.t
may be derived through a zone temperature determination model which
trains the first artificial neural network model (1240 in FIG. 12,
see FIGS. 4 to 7).
[0104] In an aspect of the present invention, the performance of
the deep neural network may be evaluated by using the input power
P.sup.t, indoor temperature T.sub.z.sup.t, operating cost E.sup.c,
and weighted sum of penalty terms Tv (e.sub.c, as in Mathematical
Formula 1) on all daily profiles in the input data D. A set W* for
deriving the maximum value e* of a normalized mean square error
NMSE may be selected from among a number parameter sets W(c) to
implement a deep neural network model N*. Lastly, an optimal demand
response schedule for the input power P.sup.t and indoor
temperature T.sub.z.sup.t may be produced based on the predicted
values of the electricity price C.sub.E.sup.t and environmental
variable E.sup.t for the next scheduling period (see FIG. 12). As
shown in FIG. 12, when a price profile 1210 and an environmental
variable 1220 are inputted, the optimal power supply and the
optimal temperature for each zone may be derived based on a deep
neural network 1230 and artificial neural networks 1240.
[0105] As explained above, according to an aspect of the present
invention, a novel, machine learning-based algorithm and/or method
for optimal demand response for an HVAC system in a multi-zone
building is disclosed. A first artificial neural network model in
the algorithm and/or method may be trained based on a supervised
learning algorithm to reflect a complex thermodynamic design of the
building, and may be emulated using a piecewise linear equation.
This way, an optimization problem may be formulated, and therefore
an optimal solution may be found using mixed-integer linear
programming. Here, the optimal solution may be found based on past
data on electricity prices and the thermal state of the building.
Optimal solutions found on a plurality of electricity prices and
the thermal state of the building are used to train a second
artificial neural network (e.g., deep neural network) model in the
SLAMP method, and this model allows for producing an optimal input
power schedule for the HVAC system. The SLAMP method is effective
for optimal demand response scheduling, given its actual
applicability, operating cost, and calculation time. Moreover, the
SLAMP method reflects time-varying electricity prices and
residents' thermal preferences, thereby ensuring the load shifting
function of the HVAS system.
[0106] The above-described method for creating a demand response
determination model for an HVAC system and method for implementing
demand response according to an exemplary embodiment of the present
invention may be implemented by a computing device. The computing
device may include a processor, a memory, and a transceiver. The
memory may store commands for implementing the above methods, and
the commands, when executed by the processor, may perform the above
methods.
[0107] The disclosed technology may have the following effects.
However, since it does not represent that a specific embodiment
should include all the following effects or should include only the
following effects, it should not be understood that the scope of
the disclosed technology is limited thereby.
[0108] The above-described method for creating a demand response
determination model for an HVAC system and method for implementing
demand response according to an exemplary embodiment of the present
invention can easily and quickly produce an optimal schedule of
input power to the HVAC system by training an artificial neural
network based on data built up through machine learning under
normal building management conditions, emulating the trained
artificial neural network into a mathematical formula using a
piecewise linear equation, and applying this mathematical formula
for a price-based demand response scheduling optimization
problem.
[0109] That is, demand response determination according to an
exemplary embodiment of the present invention may be instantly
applied to various types of HVAC systems and commercial buildings
by using a machine learning technique, not a physics-based modeling
method.
[0110] Moreover, an artificial neural network with a feedback loop,
pre- and post-processors of data, and hidden layers may be
emulated, whereby an optimization problem may be formulated as a
linear equation, and a globally optimal solution may be found using
mixed-integer linear programming.
[0111] Furthermore, a method of supervised-learning-aided
meta-prediction according to an exemplary embodiment of the present
invention allows for instantly producing an optimal schedule of
input power to an HVAC system dependent on the thermal state of the
building for 24 hours and varying electricity prices. This may
significantly reduce calculation time and help a distribution
system manager by controlling the overall load demand on the HVAC
system within a distribution network through electricity price
signals.
[0112] The method according to an embodiment of the present
invention can be implemented as computer-readable instructions on a
computer-readable recording medium. The computer-readable recording
medium comprises all kinds of recording media storing data which
can be interpreted by a computer system. For example, the
computer-readable recording medium may include a Read Only Memory
(ROM), a Random Access Memory (RAM), a magnetic tape, a magnetic
disk, a flash memory, an optical data storage device, and the like.
In addition, the computer-readable recording medium may be
distributed in computer systems connected to a computer network,
and may be stored and executed as a code readable in a distribution
manner.
[0113] While the present invention has been described with
reference to the accompanying drawings and exemplary embodiments,
it is to be understood that the invention is not limited by the
accompanying drawings and embodiments. It will be understood by
those skilled in the art that various changes in form and details
may be made therein without departing from the spirit and scope of
the invention as defined by the appended claims.
[0114] In particular, the described features may be implemented
within digital electronic circuitry, or computer hardware,
firmware, or combinations thereof. The features may be implemented
in a computer program product embodied in a storage device in a
machine-readable storage device, for example, for execution by a
programmable processor. Also, the features may be performed by a
programmable processor executing a program of instructions for
performing functions of the described embodiments, by operating on
input data and generating an output. The described features may be
implemented in at least one computer programs that can be executed
on a programmable system including at least one programmable
processor, at least one input device, and at least one output
device which are combined to receive data and directives from a
data storage system and to transmit data and directives to the data
storage system. A computer program includes a set of directives
that can be used directly or indirectly within a computer to
perform a particular operation on a certain result. A computer
program may be written in any form of programming language
including compiled or interpreted languages, and may be used in any
form included as modules, elements, subroutines, or other units
suitable for use in other computer environments or independently
operable programs.
[0115] Suitable processors for execution of the program of
directives include, for example, both general-purpose and
special-purpose microprocessors, and a single processor or one of
multiple processors of other type of computer. In addition, storage
devices suitable for implementing the computer program directives
and data implementing the described features include, for example,
semiconductor memory devices such as EPROM, EEPROM, and flash
memory devices, magnetic devices such as internal hard disks and
removable disks, magneto-optical disks, and all forms of
nonvolatile memories including CD-ROM and DVD-ROM disks. The
processor and memory may be integrated within Application-Specific
Integrated Circuits (ASICs) or added by ASICs.
[0116] While the present invention has been described on the basis
of a series of functional blocks, it is not limited by the
embodiments described above and the accompanying drawings, and it
will be apparent to those skilled in the art that various
substitutions, modifications and variations can be made without
departing from the scope of the present invention.
[0117] The combination of the above-described embodiments is not
limited to the above-described embodiments, and various forms of
combination in addition to the above-described embodiments may be
provided according to implementation and/or necessity.
[0118] In the above-described embodiments, the methods are
described on the basis of a flowchart as a series of operations or
blocks, but the present invention is not limited to the order of
the operations, and some operations may occur in different orders
or at the same time unlike those described above. It will also be
understood by those skilled in the art that the operations shown in
the flowchart are not exclusive, and other operations may be
included, or one or more operations in the flowchart may be omitted
without affecting the scope of the present invention.
[0119] The above-described embodiments include examples of various
aspects. While it is not possible to describe every possible
combination for expressing various aspects, one of ordinary skill
in the art will recognize that other combinations are possible.
Accordingly, it is intended that the present invention include all
alternatives, modifications and variations that fall within the
scope of the following claims.
* * * * *