U.S. patent application number 17/426809 was filed with the patent office on 2022-05-12 for libraries, systems, and methods for minimizing air pollution in enclosed structures.
This patent application is currently assigned to URECSYS- URBAN ECOLOGY SYSTEMS- INDOOR AIR QUALITY MANAGEMENT LTD.. The applicant listed for this patent is URECSYS-URBAN ECOLOGY SYSTEMS-INDOOR AIR QUALITY MANAGEMENT LTD.. Invention is credited to Shimon AMIT, Nir BASSA, Kobi RICHTER.
Application Number | 20220146128 17/426809 |
Document ID | / |
Family ID | |
Filed Date | 2022-05-12 |
United States Patent
Application |
20220146128 |
Kind Code |
A1 |
BASSA; Nir ; et al. |
May 12, 2022 |
LIBRARIES, SYSTEMS, AND METHODS FOR MINIMIZING AIR POLLUTION IN
ENCLOSED STRUCTURES
Abstract
The disclosure relates to libraries used in conjunction with
integrated ventilation and temperature controls for enclosed
structures. Specifically, the disclosure is directed to libraries,
systems and methods for minimizing pollution while simultaneously
conserving energy and maintaining required levels of fresh air
inside a multi-storied structure and its internal spaces, in an
optimal manner, utilizing dynamic, user-defined threshold values
and implementing strategies based on user defined goals.
Inventors: |
BASSA; Nir; (Jerusalem,
IL) ; RICHTER; Kobi; (Arsuf, IL) ; AMIT;
Shimon; (Jerusalem, IL) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
URECSYS-URBAN ECOLOGY SYSTEMS-INDOOR AIR QUALITY MANAGEMENT
LTD. |
Jerusalem |
|
IL |
|
|
Assignee: |
URECSYS- URBAN ECOLOGY SYSTEMS-
INDOOR AIR QUALITY MANAGEMENT LTD.
Jerusalem
IL
|
Appl. No.: |
17/426809 |
Filed: |
January 29, 2020 |
PCT Filed: |
January 29, 2020 |
PCT NO: |
PCT/IL2020/050113 |
371 Date: |
July 29, 2021 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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62798027 |
Jan 29, 2019 |
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International
Class: |
F24F 11/00 20060101
F24F011/00; F24F 11/64 20060101 F24F011/64; F24F 11/46 20060101
F24F011/46; G05B 13/02 20060101 G05B013/02 |
Claims
1. A processor accessible library comprising control information
for a multi-storied structure's heating, ventilation and air
conditioning (HVAC) process, wherein said library is configured to
implement methods to identify optimized period for ventilation
and/or heating and air conditioning, employing dynamic ventilation
criteria, and wherein the library further contains external and
internal HVAC parameters.
2. The library of claim 1, wherein the ventilation parameters
comprise: a. a first ventilation-associated parameter (VAP1),
related to the inside of the multi-storied structure; b. a second
ventilation-associated parameter (VAP2), related to the outside of
the multi-storied structure; and c. a third ventilation-associated
parameter (VAP3), related to temporal ventilation history, wherein
the library further comprises a plurality of master process objects
linked to the first, second, and third ventilation-associated
parameters.
3. The library of claim 2, further comprised of: a. a fourth
ventilation-associated parameter (VAP.sub.4), related to a hub
encompassing the enclosure; and b. a fifth ventilation-associated
parameter (VAP.sub.5), designating the location of the enclosure
within the hub, wherein the library further comprises a plurality
of master process objects linked to the fourth, and fifth
ventilation-associated parameters.
4. The library of claim 3, further comprised of: a. a sixth heating
and air-conditioning associated parameter (HACAP.sub.6), related to
an internal temperature of the enclosure; and b. a seventh heating
and air-conditioning associated parameter (HACAP.sub.7), related to
an external temperature of the enclosure, wherein the library
further comprises a plurality of master process objects linked to
the sixth, and seventh ventilation-associated parameters.
5. The library of claim 4, wherein the library is configured such
that said control information is modifiable by a user.
6. The library of claim 4, wherein a set of operations configured
to achieve a predetermined optimization objective from the
plurality of master process' optimization sub-goals in the library
is configured to be selectable.
7. The library of claim 6, wherein the first ventilation-associated
parameter (VAP.sub.1), comprise selectably determined pollutants'
concentration within the multi-storied structure; the second
ventilation-associated parameter (VAP.sub.2), comprise selectably
determined pollutants' concentration immediately outside the
multi-storied structure.
8. The library of claim 7, wherein a rule-based algorithm is
configured to select the set of master process' objects configured
to minimize pollution within the multi-storied enclosure and
minimize the energy requirements of the heating and
air-conditioning process.
9. The library of claim 8, wherein the optimization objectives
comprise: reduction of concentration of indoor and/or outdoor
sourced pollutants, maximizing incoming air flow, maintaining
internal temperature range, minimizing a breach period, minimizing
energy requirement by the HVAC system, or a combination of
optimization objectives comprising the foregoing.
10. The library of claim 9, wherein the library is dynamically
linked to remote databases.
11. The library of claim 10, wherein the library further comprises
parameters associated with physical properties of the multi-storied
structure, physical properties of the HVAC system, topographical
and/or geographical characteristics of the immediate surroundings
of the multi-storied structure, occupancy in the multi-storied
structure, meteorological data, or a combination of parameters
comprising the foregoing.
12. The library of claim 9, further comprising an eighth heating
and air-conditioning associated parameter (HACAP.sub.8), the eighth
heating and air-conditioning associated parameter (HACAP.sub.8)
comprising wet bulb temperature.
13. The library of claim 1, further comprising a parameter
associated with minimally required external air supply.
14. A computerized method for optimizing heating, ventilation and
air conditioning (HVAC) process in a multi-storied structure
implementable in a system comprising the multi-storied structure, a
heating, ventilation and air conditioning (HVAC) system, a
processing module in communication with a non-volatile memory
having thereon a processor-readable media and a library comprising:
a first ventilation-associated parameter (VAP.sub.1), related to
the inside of the multi-storied structure, a second
ventilation-associated parameter (VAP.sub.2), related to the
outside of the multi-storied structure, a third
ventilation-associated parameter (VAP.sub.3), related to temporal
ventilation history, air-conditioning associated parameter
(HACAP.sub.6), related to an internal temperature of the
multi-storied structure and an air-conditioning associated
parameter (HACAP.sub.7), related to an external temperature of the
multi-storied structure, wherein the library further comprises a
plurality of master process objects comprised of a plurality of
sub-goals, with a dynamic threshold value, the dynamic threshold
value linked to the first, second, and third ventilation-associated
parameters as well as to the heating and air-conditioning
associated parameter related to the internal and external
temperature of the multi-storied structure; the method comprising:
a. responsive to a ventilation prompt, selecting a set of
operations configured to achieve a predetermined optimization
objective from the plurality of master process' optimization
objectives in the library; b. associating the selected set of
operations to create a set of process commands within the
ventilation request and forming a ventilation command, wherein the
set of master process objects in the library are linked to the
ventilation command without copying the set of master process
objects into the ventilation command; and c. executing the
ventilation command.
15. The method of claim 14, wherein selecting a set of master
process objects from the plurality of master process objects in the
library is implemented using rule-based algorithm.
16. The method of claim 15, wherein the first
ventilation-associated parameter (VAP.sub.1), comprise selectably
determined pollutants' concentration within the multi-storied
structure; and the second ventilation-associated parameter
(VAP.sub.2), comprise selectably determined pollutants'
concentration immediately outside the multi-storied structure.
17. The method of claim 16, wherein the system further comprises a
multi-directional air-inlet module, adapted to provide selectable
inflow of air from a discrete direction.
18. The method of claim 14, wherein the optimization objective is
comprised of at least one of a plurality sub-goals of: reduction of
concentration of indoor and/or outdoor sourced pollutants,
maximizing incoming air flow, maintaining internal temperature
range, minimizing a breach period, minimizing energy requirement by
the HVAC system, or a combination of sub-goals comprising the
foregoing.
19. The method of claim 14, wherein the library further comprises
parameters associated with physical properties of the multi-storied
structure, physical properties of the HVAC system, topographical
and/or geographical characteristics of the immediate surroundings
of the multi-storied structure, occupancy in the multi-storied
structure, meteorological data, or a combination of parameters
comprising the foregoing.
20. The method of claim 15, wherein the rule-based algorithm is
configured to select the set of master process objects configured
to minimize pollution within the enclosure and minimize the energy
requirements of the heating and air-conditioning process.
21. The method of claim 17, wherein the library further comprises:
a. a fourth ventilation-associated parameter (VAP.sub.4), related
to a hub encompassing the enclosure; and b. a fifth
ventilation-associated parameter (VAP.sub.5), designating the
location of the enclosure within the hub, wherein the library
further comprises a plurality of master process objects linked to
the fourth, and fifth ventilation-associated parameters.
22. The method of claim 17, further comprising a step of
determining a wet bulb temperature and limiting air flow so as to
prevent condensation of moisture in the HVAC system.
23. The method of claim 17, wherein the ventilation direction is
configured to draw air from the air inlet direction associated with
the lowest determined pollutants' concentration immediately outside
the multi-storied structure.
24. The method of claim 14, wherein the library further comprises a
parameter associated with minimally required external air
supply.
25. The method of claim 14, wherein the step of executing the
ventilation command further comprises maintaining a predetermined
air pressure differential between portions of the enclosed
structure.
26. The method of claim 25, wherein maintaining predetermined air
pressure differential between portions of the enclosed structure
comprises controlling exhaust ventilation air flow and the fresh
air airflow.
27. The method of claim 14, wherein the dynamic threshold is varied
as a function of an expected energy requirement by the fresh air
system.
28. The method of claim 27, further comprising calculating the
expected energy requirement based on forecasted weather
parameters.
29. The method of claim 14, wherein the step of executing the
ventilation command further comprises ventilating unoccupied
portions of the enclosed structures, or the whole unoccupied
enclosed structure.
30. The method of claim 14, further comprising: activating,
deactivating and tuning components of the HVAC system to improve
energy efficiency.
31. The method of claim 30, wherein the HVAC system's components
are at least one of: chillers, heat-pumps, fan coils, heating
coils, heat exchangers, cooling towers, water pumps, motors, fans,
and compressors.
32. The method of claim 14, wherein the step of executing the
ventilation command further comprises controlling at least one of:
baffles, and dumpers each affecting outdoor air flow distribution
between different sub-zones in the structure.
33. The method of claim 30, further comprising: a. detecting a
faulty component of the HVAC system, or an improper distribution of
fresh air flow in the enclosed structure or in a portion thereof;
and b. upon detecting a faulty component, issuing an alert.
34. The method of claim 30, wherein the energy efficiency improved
is at least one of: Watts, coefficient of performance (COP), and
energy efficiency ratio (EER).
35. A processor-readable media in communication with and a library
comprising: a first ventilation-associated parameter (VAP.sub.1),
related to the inside of the multi-storied structure, a second
ventilation-associated parameter (VAP.sub.2), related to the
outside of the multi-storied structure, a third
ventilation-associated parameter (VAP.sub.3), related to temporal
ventilation history, air-conditioning associated parameter
(HACAP.sub.6), related to an internal temperature of the
multi-storied structure and an air-conditioning associated
parameter (HACAP.sub.7), related to an external temperature of the
multi-storied structure, wherein the library further comprises a
plurality of master process objects with dynamic threshold, linked
to the first, second, and third ventilation-associated parameters
as well as to the heating and air-conditioning associated parameter
related to the internal and external temperature of the
multi-storied structure, the processor-readable media having a set
of executable instructions, which, when executed, are configured to
cause a processor to: a. receive a ventilation request prompt from
a heating, ventilation and air conditioning (HVAC) system; b.
responsive to the ventilation request, select a set of operations
configured to achieve a predetermined optimization objective from
the plurality of master process objects in the library; c.
associate the selected set of operations with the ventilation
request; d. create a set of process commands within the ventilation
request; e. form a ventilation command, wherein the set of master
process objects in the library are linked to the ventilation
command without copying the set of master process objects into the
ventilation command; and f. execute the set of ventilation
associated master process objects in the ventilation command.
36. The processor-readable media of claim 35, wherein a set of
master process objects is configured to be selectable from the
plurality of master process objects in the library using a
rule-based algorithm.
37. The processor-readable media of claim 36, wherein the
optimization objective is comprised of at least one of a plurality
sub-goals of: reduction of concentration of indoor and/or outdoor
sourced pollutants, maximizing incoming air flow, maintaining
internal temperature range, minimizing a breach period, minimizing
energy requirement by the HVAC system, or a combination of
sub-goals comprising the foregoing.
38. The processor-readable media of claim 37, wherein the library
further comprises: a. a fourth ventilation-associated parameter
(VAP.sub.4), related to a hub encompassing the enclosure, and b. a
fifth ventilation-associated parameter (VAP.sub.5), designating the
location of the enclosure within the hub, wherein the library
further comprises a plurality of master process objects linked to
the fourth, and fifth ventilation-associated parameters.
39. The processor-readable media of claim 35, wherein, when
executed, the processor is further configured to modify control
information using a user input.
40. The processor readable media of claim 39, wherein the user
input comprise: feedback input from the HVAC system and/or sensors
measuring its performance, input from sensors monitoring levels of
contaminants at specified locations, or their combination
41. The processor-readable media of claim 35, wherein, when
executed, the processor readable media is further configured to
cause the processor to determine a wet bulb temperature and limit
air flow so as to prevent condensation of moisture in the HVAC
system.
42. A method for adaptive optimization of heating, ventilation and
air conditioning (HVAC) process in a multi-storied structure
implementable in a system comprising the multi-storied structure, a
heating, ventilation and air conditioning (HVAC) system, a
processing module in communication with a non-volatile memory
having thereon a processor-readable media and a library comprising:
a first ventilation-associated parameter (VAP.sub.1), related to
the inside of the multi-storied structure, a second
ventilation-associated parameter (VAP.sub.2), related to the
outside of the multi-storied structure, a third
ventilation-associated parameter (VAP.sub.3), related to temporal
ventilation history, an air-conditioning associated parameter
(HACAP.sub.8), related to an internal temperature of the
multi-storied structure and an air-conditioning associated
parameter (HACAP.sub.7), related to an external temperature of the
multi-storied structure, wherein the library further comprises a
plurality of master process objects comprised of a plurality of
sub-goals, with a dynamic threshold value, the dynamic threshold
value linked to the first, second, and third ventilation-associated
parameters as well as to the heating and air-conditioning
associated parameter related to the internal and external
temperature of the multi-storied structure; the method comprising:
selecting a historical dataset comprising a first set of forecast
pollutants' values received from one or more predictive forecast
statistical models and a first set of actual pollutants' values
received from one or more measurements of the pollutants;
generating one or more variants of machine learning models to model
performance of the one or more predictive forecast models by
training the one or more variants of the machine learning models on
the historical dataset; receiving a current dataset comprising a
second set of forecast pollutants' values derived from the one or
more predictive forecast models and a second set of actual
pollutants' values derived from the one or more measurements of the
pollutants; correlating the current dataset with the historical
dataset to adaptively obtain a filtered historical dataset;
selecting the one or more variants of the machine learning models
trained on the historical dataset and evaluating them on the
filtered historical dataset to assign weights to each of the one or
more variants of the machine learning models and their outputs; and
deriving a statistical model in the form of an optimal combination
function to determine at least one combined forecast pollutants'
value by combining weights assigned to each of the one or more
variants of the machine learning models trained based on the
evaluating of the one or more variants of the machine learning
models on the filtered historical dataset and the outputs of the
each of the one or more variants of machine learning models trained
on the historical dataset, wherein the selecting, the generating,
the receiving, the correlating, the evaluating and the deriving are
performed by the processor using computer-readable instructions
stored in the memory.
43. The method of claim 42, wherein the one or more predictive
forecast models include a supervisory control and data acquisition
(SCADA) model, a physical model including numerical pollutants'
reaction kinetics prediction model, a statistical model, a machine
learning model, an alternate forecast model, or combinations
thereof.
44. The method of claim 42, wherein the one or more variants of the
machine learning models include Artificial Neural Networks (ANNs),
basis function models, kernel methods, support vector machines,
decision trees, variation methods, distribution sampling methods,
ensemble methods, graphical models, search methods, or combinations
thereof.
Description
COPYRIGHT NOTICE
[0001] A portion of the disclosure herein below contains material
that is subject to copyright protection. The copyright owner has no
objection to the reproduction by anyone of the patent document or
the patent disclosure as it appears in the Patent and Trademark
Office patent files or records, but otherwise reserves all
copyright rights whatsoever.
BACKGROUND
[0002] The disclosure is directed to libraries used in conjunction
with integrated ventilation and temperature controls for enclosed
structures. Specifically, the disclosure is directed to libraries,
systems and methods for minimizing pollution inside an enclosed
structure in an optimal manner, combined with minimizing energy
requirement and maintaining required levels of fresh air, utilizing
dynamic and adaptive, user-defined ventilation criteria.
[0003] Climate change, environmental standards and diminishing land
resources compelled modern architecture standards to design and
build ever higher structures. These structures retain their value
based on superior structural integrity and, as energy prices
continue to rise, are more energy efficient. Due to the
construction of the building envelope, these buildings may or may
not shield the occupant from negative infiltration of outdoor
pollutants, for example, pollen, dust, humidity and the like.
[0004] However, occupants, operators and owners of such structures
also want to be comfortable and free from the risk of exposure to
any indoor pollutants that may cause, for example, health problems.
Buildings having superior structural integrity, require frequent
ventilation of outside air to maintain, for example levels of fresh
O.sub.2, and reduce levels of CO.sub.2 within the structures.
Ventilation inside buildings (opening of windows and door, active
ventilation, etc.), occurs chiefly during the working hours, times
when the urban air is most polluted. Thus, air pollution from the
outside is introduced into the buildings.
[0005] Moreover, in the effort to operate the buildings at
efficient heating and cooling parameters, these buildings and
enclosed structures are being constructed as ever-increasing
insulated systems, with fixed regulation-based parameters on timing
and various pollutants' concentrations within the structures.
[0006] Hence, there is a need for more effective and efficient
means for determining the optimal conditions for ventilating these
enclosed structures.
SUMMARY
[0007] Disclosed, in various embodiments, are libraries, systems,
methods and computer readable media for minimizing pollution inside
an enclosed structure, combined with minimizing energy requirement
and maintaining required levels of fresh air in an optimal manner,
utilizing dynamic and adaptive, user-defined ventilation criteria.
More specifically, provided herein are libraries and methods of
providing instructions to an integral HVAC system in a
multi-storied, enclosed structure for ventilating the structure in
a manner that will maintain preselected parameters, whether user
defined or regulation based.
[0008] The present invention provides means for integrating and
optimizing different and sometimes conflicting requirements in real
time: Maintenance of fresh air supply to the building, reduction of
indoor air pollution concentrations and energy conservation. The
system can calculate a quantified weighted combination of different
conflicting requirements. In some embodiments provided herein, the
system can calculate a single value which takes into account all
the considerations and requirements. Furthermore, in some
embodiments of the present invention, the system and method utilize
threshold values. Instead of utilizing static/fixed predetermined
thresholds, the present system and method utilize dynamic
thresholds which take into account the dynamic changes of air
pollution levels in real time and combine them with fresh air and
energy requirements in order to calculate a value or values that
control the ventilation system. These calculated optimal values
determine the increasing/decreasing of fresh air ventilation into
the multi-storied structure. Moreover, in some embodiments provided
herein the system can estimate and calculate the dynamic threshold
values without using indoor air pollution measurements, but only
using historical data of outdoor air pollution measurements,
ventilation history, models of gas dynamics, e.g., diffusion and
rate of decomposition, heuristics and/or machine learning methods,
and/or statistical methods.
[0009] In certain exemplary implementation, provided herein is a
processor-accessible library comprising control information for a
multi-storied structure's heating, ventilation and air conditioning
(HVAC) process, wherein the library contains data and executable
commands configured, when executed, to identify optimized period
for ventilation and/or heating and air conditioning, and wherein
the data incorporate external and internal HVAC parameters.
[0010] In another embodiment, provided herein is a computerized
method for optimizing heating, ventilation and air conditioning
(HVAC) process in a multi-storied structure implementable in a
system comprising the multi-storied structure, a heating,
ventilation and air conditioning (HVAC) system, a processing module
in communication with a non-volatile memory having thereon a
processor-readable media and a library comprising: a first
ventilation-associated parameter (VAP.sub.1), related to the inside
of the multi-storied structure, a second ventilation-associated
parameter (VAP.sub.2), related to the outside of the multi-storied
structure, a third ventilation-associated parameter (VAP.sub.3),
related to temporal ventilation history, air-conditioning
associated parameter (HACAP.sub.6), related to an internal
temperature of the multi-storied structure and an air-conditioning
associated parameter (HACAP.sub.7), related to an external
temperature of the multi-storied structure, wherein the library
further comprises a plurality of master process objects, linked to
the first, second, and third ventilation-associated parameters as
well as to the heating and air-conditioning associated parameter
related to the internal and external temperature of the
multi-storied structure; the method comprising: responsive to a
ventilation prompt, selecting a set of operations configured to
achieve a predetermined master process' optimization objective from
the plurality of objectives in the library; associating the
selected set of operations to create a set of process commands
within the ventilation request and forming a ventilation command,
wherein the set of master process objects in the library are linked
to the ventilation command without copying the set of master
process objects into the ventilation command; and executing the
ventilation command.
[0011] In yet another embodiment, provided herein is a
processor-readable media in communication with and a library
comprising: a first ventilation-associated parameter (VAP.sub.1),
related to the inside of the multi-storied structure, a second
ventilation-associated parameter (VAP.sub.2), related to the
outside of the multi-storied structure, a third
ventilation-associated parameter (VAP.sub.3), related to temporal
ventilation history, air-conditioning associated parameter
(HACAP.sub.6), related to an internal temperature of the
multi-storied structure and an air-conditioning associated
parameter (HACAP.sub.7), related to an external temperature of the
multi-storied structure, wherein the library further comprises data
and information associated with a single objective associated with
a plurality of master process sub-goals, linked to the first,
second, and third ventilation-associated parameters as well as to
the heating and air-conditioning associated parameter related to
the internal and external temperature of the multi-storied
structure, the processor-readable media having a set of executable
instructions, which, when executed, are configured to cause a
processor to: receive a ventilation request prompt from a heating,
ventilation and air conditioning (HVAC) system; responsive to the
ventilation request, select a set of operations configured to
achieve a predetermined master process' optimization objective from
the plurality of master process' optimization objectives; associate
the selected set of operations with the ventilation request; create
a set of process commands within the ventilation request; form a
ventilation command, wherein the set of master process objects in
the library are linked to the ventilation command without copying
the master process objects into the ventilation command; and
execute the set of ventilation associated process objects in the
ventilation command.
[0012] In another embodiment, provided herein is a method to
control the state of an HVAC system in order to actuate a
predefined optimization strategy designed to pursue a predefined
optimization objective while subject to predefined prerequisites,
utilizing the content of linked data bases featuring various
parameters, among which at least one is continuously updated
through an input channel.
[0013] The prerequisites can be defined according to the custom
requirements of the user. These may be, for example, that the HVAC
system will comply with certain regulation or standard of
ventilation; that the average outdoor airflow within some custom
period of time will be no less than some custom value; that the
concentration of CO2 will not exceed some defined value, that
certain air pressure difference will be maintained between
different zones of the structure and between the indoor and outdoor
air and so forth. At any case some prerequisite is defined related
to a minimal mandatory supply of outdoor air to the multi-storied
enclosed structure.
[0014] The data bases can be configured to contain one
ventilation-associated parameter (VAP3) representing the temporal
history of states of the HVAC system, as well as one
ventilation-associated parameter (VAP2) related to the temporal
history of concentration of one contaminant of interest or a
combination of several contaminants, measured or estimated, in the
outdoor air which was supplied to the building by the HVAC system.
Furthermore, the library comprises a complete set of parameters
required to carry the computation defined by the optimization
strategy and to verify compliance with each of the defined
prerequisites. These may comprise, for example, various parameters
of the building itself and of the characteristic activity in
it.
[0015] The optimization objective can be a weighted combination of
multiple sub-goals, for example, minimizing the contamination of
the indoor air by outdoor sources and minimizing the energy
requirement by the HVAC system.
[0016] The optimization strategy used in the methods provided can
be configured to calculate a dynamic threshold value (DTV), which
is based on a weighted combination of values of VAP2 during
previous ventilation events indicated by VAP3. That value is
further operated on based on an estimate of the expected
instantaneous energy requirement by the HVAC system in case of
ventilation of the multi-storied enclosed structure, with a
monotonic decrease of the DTV with that energy requirement. The
magnitude of the decrease is determined by the respective weights
of the sub-goals within the optimization objective.
[0017] In addition, in the methods provided, a comparison is made
in certain operable examples, between the current value of VAP2,
and the DTV. Ventilation will occur when VAP2 is, for example,
smaller than or equals to DTV. Otherwise, ventilation will stop
providing that this stoppage is not expected to cause violation of
certain defined prerequisites.
[0018] The directives implemented by the optimization strategy, and
executed by the processing module, can be actuated by a control
unit which is physically connected to the HVAC system and with
which the processing unit has a local or remote communication
channel.
[0019] Data created during the processing and actuation of the
optimization objective may continuously be fed back into the data
bases, in order to serve in the following processing or in the
monitoring of the system.
[0020] In certain embodiments, the dynamic threshold can be further
operated on according to the prerequisites (e.g. increase of the
threshold as current conditions in the multi-storied enclosed
structure are approaching conditions where ventilation is mandatory
according to the prerequisites).
[0021] Furthermore, in certain embodiments the weights of the
sub-goals within the optimization objective are adjusted
dynamically according to user-defined criteria (for example, lower
weight to indoor air quality and higher weight to energy saving
when the occupancy is low and the opposite when it is high).
[0022] In certain exemplary implementation, the dynamic threshold
can further be operated on according to a prediction of expected
values of, for example, VAP2 in the near future (for example,
within the next minute, hour, day, etc.).
[0023] In certain exemplary implementations, the dynamic threshold
can further be operated on according to expected energy requirement
by the fresh air system in case of ventilation in each instant in
the near future, considering the forecasted weather parameters such
as temperature and relative humidity, and a model of the effect of
these parameters on energy requirement in the structure.
[0024] In another embodiment, another ventilation associated
parameter (VAP1) can be defined in the library, and be associated
with a measurement or estimation of the concentration of one or
more substances (e.g., NOx) in the indoor air. The DTV can then be
calculated and determined based on the current value of VAP1 and
other prediction tools.
[0025] In yet another embodiment, the dynamic threshold can further
be calculated based on chemical, physical or computational model(s)
of the flow dynamics and disintegration kinetics of gas and other
airborne materials inside the building, considering the influence
of at least one of the HVAC system activity, the influence of
biological activity, and the influence of any other kind of
activity inside the building.
[0026] In certain exemplary implementation, the optimization
objective further comprises a sub-goal of minimization of erosion
and/or contamination of the indoor air as a result of indoor
processes or indoor gas components, such as accumulation of CO2,
dwindling of O2, accumulation of indoor pollutants such as volatile
organic compounds (VOC), appearance of offensive odors or some
combination comprising the foregoing.
[0027] In such case, the dynamic threshold can further be
calculated, for example, based on an estimation of the indoor air
contamination and/or erosion due to indoor processes, based on the
recent values of the parameter VAP3 and on some parameters of the
multi storied enclosed structure, and on the human activity within,
as appear in the data bases.
[0028] In another embodiment, the optimization objective can
further comprise more than one sub-goal of minimizing average
concentration of contaminant of outdoor source. Accordingly, the
DTV is then calculated based on combination of recent values of
VAP2 for all the contaminants of interest. In addition, the DTV is
then compared to an index calculated based on a weighted
combination of the current values of VAP2 for all the contaminants
of interest. The weights in both combinations can be determined
according to the corresponding weights in the optimization
objective.
[0029] In yet another embodiment, additional parameters can be
defined in the data library, associated with data such as at least
one of pollution levels at locations that are remote to the
enclosed structure, occupancy of the building, meteorological,
traffic, etc. and possibly updated through additional input
channels. These parameters can be used by the optimization strategy
uploaded to the processing module of the system.
[0030] In certain exemplary implementation where the decision space
is binary (i.e. where the outdoor airflow can only be turned on or
off), the outdoor airflow at the `on` configuration can further be
adapted dynamically according to variable conditions, for example
changes in the outdoor humidity and temperature and the indoor set
temperature. The flow can then be set to the maximum possible
levels at the current conditions, where the system is still safe
from excessive accumulation of liquid water in it.
[0031] In another embodiment, the HVAC system may modulate the
outdoor air flow among multiple values. Accordingly, the
prerequisites can further comprise the maximum outdoor airflow
allowed as a function of outdoor temperature and humidity.
[0032] In yet another embodiment, the optimization strategy logic
is not based on a comparison between a threshold value and VAP2 but
rather on a predictive search strategy. Thus the dynamic
ventilation criteria are not limited to the DTV.
[0033] In certain exemplary implementation, the system will
activate ventilation into unoccupied zones, also during times when
the entire structure is unoccupied. This, in cases when the
optimization strategy finds that such ventilation may lead to net
reduction of energy requirement or to reduction of indoor air
pollution levels during occupancy times.
[0034] In certain exemplary implementation, the system will
activate, deactivate and tune various components of thermal
treatment in the HVAC system, so as to improve energy efficiency of
the HVAC system. Such components can be, for example, chillers,
heat-pumps, fancoils, heating coils, heat exchangers, cooling
towers, water pumps, engines, fans, compressors etc. For example,
at times when it is detected that certain components are operating
under partial load, redundant components may be turned off (for
example, when low cooling capacity is required from a battery of
several water chillers, some of these chillers may be turned off,
until the required cooling capacity becomes high again).
[0035] As another example, the system may automatically tune the
temperature set point of the fresh air system when operating during
times when the structure is not occupied, in a manner that may lead
to better outcome with respect to the optimization objective. For
example, (during seasons when the indoor environment is kept warmer
than the outdoor) heating of cold fresh air may be reduced and, in
some cases even altogether stopped during night times, while still
maintaining the required indoor temperature during occupancy
times.
[0036] In certain exemplary implementation, the system will control
baffles and dumpers affecting outdoor air flow distribution between
different sub-zones in the structure, leading to better outcome
with respect to the optimization objective. As an example, outdoor
airflow to a zone may be tuned in response to the occupancy of the
zone, reducing the energy requirement by the system when occupancy
reduces and restoring it as occupancy grows again. As another
example, outdoor airflow into zones where the occupancy is found to
be low may be increased when it is predicted that the outdoor air
pollution is about to increase and before the pollution levels rise
above a dynamic threshold. This allows longer stoppage or reduction
of outdoor air supply to the entire structure when the outdoor
pollution subsequently rises.
[0037] In certain exemplary implementation, the system may control
exhaust ventilation air flow while controlling the fresh air
airflow, in order to comply with a prerequisite of a required air
pressure differences between certain zones in the structure (e.g.
toilets, kitchens etc.), the rest of the structure and the outdoor
environment.
[0038] In certain exemplary implementation, the system may detect a
faulty component of the HVAC system or an improper distribution of
fresh air flow in the structure or in a sub space of it. Is such
case, the system may issue an alert.
[0039] In certain exemplary implementation, the system further
comprises a graphic user interface (GUI) configured to allow the
user to selectably adjust the logic determining the weighting of
the sub-goals within the selected optimization objective.
[0040] In certain exemplary implementation, the systems provided
herein, using the libraries provided to implement the methods
disclosed further comprises at least one of a multi-directional,
and a direction-adjustable air-inlet module, adapted to provide
selectable inflow of air from a discrete direction
[0041] Further and in certain exemplary implementation, provided
herein is a method for adaptive optimization of heating,
ventilation and air conditioning (HVAC) process in a multi-storied
structure implementable in a system comprising the multi-storied
structure, a heating, ventilation and air conditioning (HVAC)
system, a processing module in communication with a non-volatile
memory having thereon a processor-readable media and a library
comprising: a first ventilation-associated parameter (VAP.sub.1),
related to the inside of the multi-storied structure, a second
ventilation-associated parameter (VAP.sub.2), related to the
outside of the multi-storied structure, a third
ventilation-associated parameter (VAP.sub.3), related to temporal
ventilation history, an air-conditioning associated parameter
(HACAP.sub.6), related to an internal temperature of the
multi-storied structure and an air-conditioning associated
parameter (HACAP.sub.7), related to an external temperature of the
multi-storied structure, wherein the library further comprises a
plurality of master process objects comprised of a plurality of
sub-goals, with a dynamic threshold value, the dynamic threshold
value linked to the first, second, and third ventilation-associated
parameters as well as to the heating and air-conditioning
associated parameter related to the internal and external
temperature of the multi-storied structure; the method comprising:
selecting a historical dataset comprising a first set of forecast
pollutants' values received from one or more predictive forecast
statistical models and a first set of actual pollutants' values
received from one or more measurements of the pollutants;
generating one or more variants of machine learning models to model
performance of the one or more predictive forecast models by
training the one or more variants of the machine learning models on
the historical dataset; receiving a current dataset comprising a
second set of forecast pollutants' values derived from the one or
more predictive forecast models and a second set of actual
pollutants' values derived from the one or more measurements of the
pollutants; correlating the current dataset with the historical
dataset to adaptively obtain a filtered historical dataset;
selecting the one or more variants of the machine learning models
trained on the historical dataset and evaluating them on the
filtered historical dataset to assign weights to each of the one or
more variants of the machine learning models and their outputs; and
deriving a statistical model in the form of an optimal combination
function to determine at least one combined forecast pollutants'
value by combining weights assigned to each of the one or more
variants of the machine learning models trained based on the
evaluating of the one or more variants of the machine learning
models on the filtered historical dataset and the outputs of the
each of the one or more variants of machine learning models trained
on the historical dataset, wherein the selecting, the generating,
the receiving, the correlating, the evaluating and the deriving are
performed by the processor using computer-readable instructions
stored in the memory.
[0042] These and other features of the libraries, systems, methods
and computer readable media for minimizing pollution inside an
enclosed structure in an optimal manner, utilizing dynamic,
user-defined threshold values, will become apparent from the
following detailed description when read in conjunction with the
figures and examples, which are exemplary, not limiting.
BRIEF DESCRIPTION OF THE FIGURES
[0043] For a better understanding of the libraries, systems,
methods and computer readable media for minimizing pollution inside
an enclosed structure in an optimal manner, utilizing dynamic,
user-defined threshold values, reference is made to the
accompanying examples and figures, in which:
[0044] FIG. 1, Illustrates a generalized information flow between
the units of the system;
[0045] FIG. 2, is a schematic illustrating the decision flow for
selecting the optimal strategy to achieve a certain objective based
on available resources;
[0046] FIG. 3, is a schematic illustrating the command flow for
actuating ventilation based computed DTV;
[0047] FIG. 4, illustrates the processor's logic in selecting
optimization strategy;
[0048] FIG. 5, is a schematic illustrating the logic in accounting
for condensation in the ventilation system; and FIGS. 6-10
illustrates schematics of various embodiments of primary and
secondary external and internal vents' configurations.
DETAILED DESCRIPTION
[0049] Provided herein are embodiments of libraries, systems,
methods and computer readable media for minimizing pollution inside
an enclosed structure in an optimal manner, combined with
minimizing energy requirement by the HVAC system, maintaining
outdoor air supply and additional requirements, utilizing dynamic
and adaptive, user-defined ventilation criteria. More particularly,
provided herein are embodiments of methods of providing
instructions to an integral HVAC system in a multi-storied,
enclosed structure for ventilating the structure in a manner that
will maintain preselected parameters, whether user defined or
regulation based.
Definitions
[0050] Optimization objective: The definition of the optimal
outcome that the system is expected to pursue. Defined by the user
and per embodiment in a given instant it is unique. May be
comprised of several goals and a prioritization logic which
combines them into one objective. The optimization objective can be
configured to weigh various parameters, for example: energy, heat,
CO2 levels, regulation and enclosed structure standards, air
quality, and air pollution.
[0051] Optimization strategy: A method employed by the system in
order to pursue the optimization objective. An instance of the
system may include more than a single optimization strategy, in
which case it will also have the ability to select what strategy is
most suitable to be used in a given instant.
[0052] Prerequisites: A collection of conditions that the system
must make sure are being met at all time, with superior priority.
They are typically defined to make sure that the structure is safe
and comfortable for the inhabitants, before striving to improve any
other aspect of the HVAC operation.
[0053] Linked data bases: The collection of data utilized by the
optimization strategy in order to interpret the optimization
objective and pursue it. The linked databases contain both
continuously-updated parameters (e.g. the temporal concentration of
contaminants in indoor/outdoor air) as well as static parameters
(e.g. physical measures of the structure) and definitions (e.g. the
required average outdoor airflow per occupant).
[0054] Available outdoor air: The outdoor air which at a given
instant can be supplied by the HVAC system into the enclosed
structure. Includes any parcel of air outside the enclosed
structure whose composition has measurable causal effect on the
composition of the indoor air, in case of ventilation.
[0055] The system is configured to attempt reaching optimal
outcomes and to provide that the prerequisite demands are met at
all time. In different embodiments, the system provided may be
initialized with different optimization objectives and different
prerequisites, according to the specific needs of the system or the
user, or according to the specific preferences of the user. In
operating, the system utilizes linked data bases, which may contain
data, definitions, and any other kind of information that may be
provided in order to assist the system to successfully interpret
the prerequisites, to comply with them and to lead a course of
operation which achieves an optimal result with respect to the
optimization objective.
[0056] It is noted, that while various systems are known to compare
external pollution levels to measured internal levels, or to a
fixed threshold, the system provided herein employ ventilation
criteria which are dynamic and adaptive, and which are continuously
updated in real time in relation to different available parameters,
in a manner which reflects the current (or immediately determined)
state of the enclosed structure as well as the preferences of the
user, at the current instant and possibly also during a forecasted
future.
[0057] Accordingly, it is possible that in a given day (or time) a
certain external air pollution level will lead to opening the
system for fresh air ventilation, while in another day (or time),
the same pollution level would lead to shutting off of the outdoor
fresh air ventilation. Furthermore, it is possible that in a given
day (or time), a certain combination of measured external and
internal air pollution levels will lead to opening the system for
fresh air ventilation, while in another day (or time), the same
combination will lead to shutting the fresh air ventilation. Thus,
on a continuous basis, the system establishes new criteria that
will define the opening/closing or increasing/decreasing fresh air
ventilation into the enclosed structure.
[0058] The system can be configured to control the flow of outdoor
air supply into the multi-storied enclosed structure. This control
can be implemented for example, by controlling a relay (referring
to an electrically controlled device having at least two states
which correspond to the open circuiting and the conducting states
of a conduction path in an electric circuit), in order to switch an
outdoor air supply fan on or off. Another option, can be for
example, an outdoor air supply fan, which can be driven by a
variable speed drive (VSD), controlled by the system disclosed
herein in order to modulate the outdoor air flow. Furthermore, the
system can be configured to control at least one outside air dumper
(referring to plates or slats disposed in an air shaft and the like
with variable opening configuration), in order to modulate the
outdoor air flow or the ratio between the flows of outdoor air and
indoor recirculating air supplied to the enclosed structure. As
used herein, the dampers can be any computer controlled machinery
which may modulate or regulate the effective cross section of a
ventilation duct, chimney, VAV box, air handler, or other
air-handling equipment resulting in modification, modulation or
regulation of the total air flow though that ductchimney, VAV box,
air handler, or other air-handling equipment.
[0059] More particularly, in embodiments incorporating ventilation
criteria relying on comparison of external pollution levels to
pollution level thresholds, these are not fixed thresholds, but
rather dynamic and adaptive ones, continuously adjusted by the
optimization strategy in real time, according to the ever-changing
conditions inside the structure and outside of it during the
current instant and possible also during a forecasted future.
[0060] Various systems are known to measure external pollution
levels and to compare it to either measured internal levels or to
fixed thresholds. These systems may yield ventilation criterion
based on simply whether the external pollution level is higher or
lower than the measured internal pollution level or than the fixed
threshold to which it is being compared. In contrast, the system
provided therein may be configured to take into account various
other considerations: [0061] Throughput (flux) requirements and
maintenance of indoor air quality, according to accepted standards
of ventilation and the user's preferences, as defined by the
prerequisites. [0062] A dynamically-weighted combination of
different sub-goals that the system is expected to pursue, as
defined by the optimization objective. These may be various
indicators of indoor air quality and air pollution, heat
maintenance and/or energy preservation. [0063] The dynamic
optimization strategy may take into consideration various
parameters which allow it to achieve better outcomes with respect
to the optimization objective. For example: the cumulative
ventilation effect during previous ventilation events; the lag time
between previous ventilation events and the current instant; the
difference between the current outside pollutants' levels and
internal pollutants' levels relative to historic values of that
difference; ventilation throughput and ventilation volume relative
to the structure's volume; the measured or estimated indoor air
pollution; occupancy of the structure; CO.sub.2 levels inside the
structure; various models allowing estimation of indoor air quality
and indoor air pollution, as a function of the ventilation state
and other measured conditions inside and outside the structure;
various models allowing prediction of outdoor air pollution.
[0064] In certain embodiments, the system will estimate current
pollution levels in the enclosed structure. The estimation can be
based on pollution monitoring devices associated with the structure
(referring in certain exemplary implementation to the concentration
of one or more predetermined gases or other air-borne materials,
e.g. NO.sub.2, in the indoor air). It can also be based on a
calculation which considers pollution of the available outdoor air
during previous ventilation events, the lag time since previous
ventilation event, the cumulative impact of previous ventilation
events persisting to the current instant, and characteristics of
the ventilation system and of the structure. In addition, the
system can also use a dynamic air pollution modeling, which takes
into consideration the measured/estimated difference between the
internal and external pollution levels as well as the
disintegration and decomposition kinetics of indoor pollutants,
factors affecting the disintegration (e.g., temperature, relative
humidity, recirculating indoor air flow etc.), half-life time of
various pollutants, reactions of various pollutants, statistical
characteristics of pollution dynamics in the structure, and the
like parameters that affect indoor pollutant levels that are
connected with the pollutants themselves.
[0065] The impact rate, in other words, the rate at which the
ventilation of external fresh air will affect the internal
pollutant levels, will depend, not only on the throughput (flux) of
outdoor air, but also on characteristics of the ventilation system,
as well as on the difference between outside pollutants' levels and
internal pollutants' levels. That difference will also affect the
impact rate due to passive exchange of air with the structure's
exterior (i.e. by means other than active ventilation flow through
the inlets of the ventilation system, such as diffusion), whether
during ventilation or when ventilation stops. It stands to reason,
that the larger the difference between outside pollutants' levels
and internal pollutants' levels, the faster will the impact rate
be.
[0066] In certain embodiments, forecasting of future pollution
levels and trends will be implemented in the systems and libraries
disclosed. At each point in time, the system will perform
forecasting of future internal and external pollution levels,
whether it will increase or decrease and to what level. Based on
the forecasting, the system can be configured to alter the decision
of whether at that point in time, ventilation should be initiated
or increased, or whether to shut/decrease the ventilation
system.
[0067] In certain exemplary implementation, the system may
implement modeling of energy requirement as a function of various
indoor and outdoor variables and various parameters of the HVAC
system. These may include measured or forecasted outdoor
temperature and relative humidity, solar radiation, indoor
occupancy, indoor heat sources, thermal capacity and thermal,
mechanical and electric efficiency of the different components of
the HVAC system, etc. The modeling may employ thermodynamic,
psychrometric, electrical, physical and other calculations in order
to accurately estimate the instantaneous energy requirement by the
HVAC system under any given combination of indoor and outdoor
variables. In certain exemplary implementation, this modeling may
facilitate input from measurement of the actual energy requirement
(at the entire structure, of the HVAC system as a whole or of
discrete components of it) at each instant and may further use
machine learning modules.
[0068] In certain exemplary implementation, the systems provided
herein may further comprise a user interface which may contain
commands for controlling various aspects of the functionality of
the system and/or monitoring data and plots about the state of the
HVAC system and the multi-storied structure. These include, for
example, dynamic control of some parameters of it, through a
graphical user interface (GUI), accessed over the internet from any
network access terminal. For example, incorporating strategy such
as those disclosed herein, the weighting parameter A quantifies the
desired prioritization of minimizing contaminant concentration and
minimizing energy consumption. In cases where the user wishes to
have a dynamic control on a tuning parameter such as A, a user
interface will be provided. Such user interface may be virtual
(e.g. web portal interface or application) or within a control
panel as a part of the processing module and/or the HVAC control
module.
[0069] Additional types of dynamic control that may be given to the
user via such user interface can be, for example: any other tuning
parameter incorporated by an active optimization strategy;
definition of the optimization objective, such as adjustment of the
hierarchy between the different sub-goals, switching on and off of
different sub-goals from a predefined list of possible sub-goals;
selection of relevant prerequisites from a predefined list of
possible prerequisites; input concerning expected occupancy in the
multi-storied structure (e.g. the user may inform the system that
an exceptionally high or low occupancy is expected in the structure
in certain future time, allowing the system to make a better
optimization), and/or their combination.
[0070] The systems provided may also comprise a display and the GUI
may also include graphical representation of various kinds of data,
for example: the overall operational state of the system; the
recent activity logbook of the system, e.g. a plot of the recent
HVAC systems' states; measured or calculated recent data about
indoor and outdoor concentrations of contaminants, supply of
outdoor air to the structure and other kinds of data of interest;
aggregative statistics and plots summarizing the system's
performance (e.g. with respect to each of the defined goals), or a
combination thereof.
[0071] Accordingly and in certain exemplary implementation,
provided herein is a processor-accessible library comprising
control information for a multi-storied structure's heating,
ventilation and air conditioning (HVAC) process, wherein said
library implements a method identifying optimized period for
ventilation and/or heating and air conditioning. It is noted, that
the libraries, methods and systems provided herein can be
configured to pursue the predefined optimization objective in any
enclosed volume within the multi-storied structure. For example,
rooms, offices, apartments, open floor plans and the like or their
combination and not necessarily to the whole multi-storied
structure.
[0072] The ventilation parameters used in the libraries, systems,
methods and computer readable media for minimizing pollution inside
an enclosed structure in an optimal manner, utilizing dynamic and
adaptive, user defined (i.e., selectable) ventilation criteria
provided herein, can comprise: a first ventilation-associated
parameter (VAP.sub.1), related to the inside of the multi-storied
structure; a second ventilation-associated parameter (VAP.sub.2),
related to the outside of the multi-storied structure; and a third
ventilation-associated parameter (VAP.sub.3), related to temporal
ventilation history, wherein the library further comprises a
plurality of master process objects linked to the first, second,
and third ventilation-associated parameters.
[0073] Likewise, the library can further comprise a fourth
ventilation-associated parameter (VAP.sub.4), related to a hub
encompassing the enclosure; and a fifth ventilation-associated
parameter (VAP.sub.5), designating the location of the enclosure
within the hub, wherein the library further comprises a plurality
of master process objects linked to the fourth, and fifth
ventilation-associated parameters; and also, sixth heating and
air-conditioning associated parameter (HACAP.sub.6), related to an
internal temperature of the enclosure; and a seventh heating and
air-conditioning associated parameter (HACAP.sub.7), related to an
external temperature of the enclosure, wherein the library further
comprises a plurality of master process objects linked to the
sixth, and seventh ventilation-associated parameters. Furthermore,
the library is configured such that the information used to control
the system is selectably modifiable by a user. Furthermore, the
library can comprise an eighth heating and air-conditioning
associated parameter (HACAP.sub.8), the eighth heating and
air-conditioning associated parameter (HACAP.sub.8) comprising dew
point used in calculation of energy requirement by the fresh air
system and in circumstances where the venting power is configured
to be tuned among multiple values. In these cases, the system is
configured to determine the highest airflow without injecting
liquid water into the multi-storied enclosed structure instead of
cooled humid air.
[0074] In certain exemplary implementation, the system can be
initialized with a user-defined (in other words, selectable)
prerequisites, in the form of a set of rules which the system must
make sure to meet at all time.
[0075] Examples of Possible Prerequisites: [0076] Maintain
compatibility with regulation or standard of ventilation, for
example the ASHRAE 62 standard. [0077] Maintain the CO2
concentration inside the enclosed structure bellow some predefined
threshold. [0078] Supply to the enclosed structure at least volume
V of outdoor fresh air in each time window of duration T, where V
and T are predefined values. [0079] Never stop the outdoor air
ventilation to the enclosed structure for duration longer than some
predefined value. [0080] Maintain certain relation between the
indoor and the outdoor air pressures and between the air pressure
in different sub zones of the indoor environment.
[0081] In certain exemplary implementation, the system can be
initialized with a user-defined (in other words, selectable)
optimization objective, comprised of a set of sub-goals and a
prioritization logic. The prioritization logic defines the way the
sub-goals are combined together, for example in any case where
optimizing with respect to one sub-goal may result in sub-optimized
outcome with respect to another.
[0082] Examples of Possible Sub-Goals can be: [0083] Reduction of
indoor concentration of one or more contaminants of outdoor source
(e.g. NO, NOx, Benzene, PM2.5 etc.) during occupancy times. [0084]
Minimizing at least one indicator of corruption and contamination
of the indoor air as a result of indoor sources or indoor processes
(such as accumulation of CO.sub.2, diminishing of O.sub.2,
accumulation of contaminants such as volatile organic compounds,
appearance of offensive odors etc.) during occupancy times. [0085]
Reduction of energy requirements of the heating, ventilation,
air-conditioning and refrigeration processes. [0086] Minimization
of times where some criterion demanding minimal outdoor air supply
is violated. [0087] Any demand which can be given as a prerequisite
can alternatively be given in a less prescriptive manner as a
sub-goal in the optimization objective. In such case, complying
with it will be considered with the rest of the defined
sub-goals.
[0088] Accordingly and in certain exemplary implementation, the
first ventilation-associated parameter (VAP.sub.1), of the library
can comprise selectably determined, predicted and/or forecasted
pollutants' concentration within the multi-storied structure; the
second ventilation-associated parameter (VAP.sub.2) of the library,
can comprise selectably determined, predicted and/or forecasted
pollutants' concentration in the air immediately outside the
multi-storied structure (the available outdoor air, as defined
herein). In another embodiment, the first ventilation associated
parameter (VAP1) is a data set containing at least one of temporal
and spatial quantification of the pollution level of the air inside
the enclosed structure. Similarly, the second ventilation
associated parameter (VAP2) can be comprised of a data set
containing at least one of the temporal and spatial states of
outdoor pollution into the enclosed structure. Further, the third
ventilation associated parameter (VAP3) can be comprised of a data
set containing at least one of temporal and spatial quantification
of the vent states of the enclosed structure.
[0089] Along with the set of goals, the optimization objective may
comprise a prioritization logic by which they are combined
together, for example: [0090] Strict Hierarchy: Optimization is
first made with respect to a first sub-goal and only then, to the
extent that this is possible without compromising the outcome with
respect to that first sub-goal, further optimization is done with
respect to the sub-goal next in priority, and so forth. [0091]
Separation in time: Optimization is done with respect to only a
single sub-goal in a given time, the selection of that single
sub-goal changing between different times, according to some
condition (which can be for example the assumed or measured
occupancy of the multi-storied enclosed structure). [0092] Weighted
combination: A weight is assigned to each of the sub-goals and
optimization with respect to that sub-goal is prioritized according
to that weight. [0093] Health impact in humans: When combining a
sub-goal which is derived from an attempt to minimize exposure to
health hazards, the combination can be done by using toxicity
function. For example, minimization of concentration of some
contaminant in the indoor air will get lower weight the less toxic
the concentration of it is. [0094] Prioritization logic combining
more than one of the above is also possible, for example as a
rule-based algorithm. For example, a weighted combination where the
weights are tuned dynamically in time according to the occupancy of
the multi-storied enclosed structure and also according to the
toxicity of the contaminants of interest.
[0095] The libraries used in the systems, can be dynamically linked
to remote databases, and further comprise: parameters associated
with physical properties of the multi-storied structure; physical
properties of the enclosed structure and of the HVAC system;
topographical and/or geographical characteristics of the immediate
surroundings of the multi-storied structure; temporal data about
occupancy in the multi-storied structure; temporospatial
meteorological data.
[0096] Temporospatial (in other words time and location related)
data about concentration of contaminants in an indoor or outdoor
air, which may be referenced at some location inside the discrete
structure, at the vicinity of it or at locations further away;
temporospatial data about the state of the HVAC system; various
quantities which may serve as indicators for the current or the
future levels of contaminants of interest in the vicinity of the
multi-storied enclosed structure; transformed (e.g., log, ln, 1/x,
e{circumflex over ( )}x, etc.) data of data items; dimensionless
representations of data items; or a combination of parameters
comprising the foregoing.
[0097] The physical structure data can be, for example, ceiling
height and floor area; division to floors and separated spaces;
specification of volumes in which the air is not circulated
directly and efficiently by the ventilation system (e.g. rooms
without openings of the ventilation system, volumes above ceilings
and below floors etc.); specification of interfaces and openings
(e.g. openable windows and doors) allowing exchange of gases with
the surrounding of the discrete structure not through the
ventilation system; the use of the discrete structure (e.g. an
office, a gym, a factory, a private residence etc), or a
combination of the foregoing.
[0098] Likewise, the physical properties of the HVAC system can be,
for example: The different states of operation of it which are
accessible to the system; the heating and cooling capacity of the
system under any given combination of conditions; mechanical
characteristics such as electrical power of engines, coefficient of
performance (COP) or energy efficiency ratio (EER) of heating and
cooling equipment and components (such as chillers, heat pumps, fan
coilsetc) etc.; parameters allowing calculation of the energy
consumption by it, as a function of combination of conditions
(which may be, for example: the outdoor temperature, the outdoor
relative humidity, the target indoor temperature, the flows of
outdoor air, the flow of indoor air circulation, indoor occupancy,
independent indoor heat sources etc., or a combination thereof); or
a combination thereof.
[0099] Likewise, the topographical and geographical information
about the surroundings of the discrete structure can be, for
example: coordinates of the discrete structure (longitude,
latitude, altitude); a 3D model of the area or an estimated 3D
model of the area based on a 2D map; or a combination thereof. In
addition, the temporospatial meteorological data can comprise, for
example, logbooks of meteorological parameters, or logbooks of
descriptive statistics values (e.g. periodical averages) of
meteorological parameters, for example in the vicinity of the
structure. Examples of the meteorological parameters are:
temperature, relative humidity, wind speed and direction,
precipitations, sky cover, clouds, atmospheric pressure, flux of
solar radiation etc.
[0100] Also, the concentration data about contaminants in the
indoor or outdoor air may include, for example: a logbook of
concentration values, measured or calculated; logbooks of
quantities which are calculated based on concentration levels of
one or more contaminants (e.g. weighted average of the
concentration of several contaminants, combined toxicity index of
several contaminants etc.); logbooks of descriptive statistics
values, providing a compact representation of logs of instantaneous
levels of contaminants.
[0101] In certain exemplary implementation where the system
incorporates an input channel providing outdoor measurement of NOx
once every ten seconds, and where it has been found that: the
amplitude of the high-frequency fluctuations of the concentration
during the ten minutes prior to the current instant can be used to
predict the trend of the concentration in a later instant (e.g.
whether it is expected to increase or decrease); and additionally,
the low frequency trend, quantified by averages of 15 minutes,
during the past week, can be used to predict the expected values of
concentration over the next few hours. For that embodiment in that
specific structure, the logbook will only hold high frequency data
for 15 minutes. The rest of the logbook will contain only average
value for every 15 minutes. A periodic internal process will
compute the averages and erase old high frequency data.
[0102] Data about the state of the HVAC system can be, for example:
logbook of the states of the HVAC system (comprised e.g. of the
flows of outdoor air supply and indoor air circulation, target
indoor temperature etc.); logbook of the power consumption by the
HVAC system, measured or calculated; or a combination thereof. In
addition, data about the occupancy of the structure can be, for
example: definition of the expected occupancy of the multi-storied
enclosed structure at any given time; and/or logbook of the
measured or estimated actual occupancy of the multi-storied
enclosed structure.
[0103] Furthermore, the logbooks of various quantities which may
serve as indicators for the current or the future levels of
contaminants of interest at the vicinity of the multi-storied
enclosed structure, can be, for example: data about pollution
sources, such as: information about traffic patterns and density;
information about the activity of other sources of air pollution
such as factories, power plants, commercial perimeters etc.; or a
combination thereof.
[0104] Dimensionless representation of data items, can be, for
example, quantities obtained by division of a physical value by
some weighting scale having the same dimensions. For example,
operation such as
f .function. ( x ) = x - min .function. ( L , x ) max .function. (
H , x ) ##EQU00001##
where L and H are some characteristic high and low threshold
values, respectively, and the representation is a number between 0
(obtained where x.ltoreq.L) and 1 (obtained when x.gtoreq.H).
Likewise, non-linear representations can be used, for example:
f .function. ( x ) = 1 + 1 2 .times. tanh .function. ( x - b a )
##EQU00002##
in which case f varies smoothly between 0 and 1, with its curvature
and center (i.e. the value of x for which f(x)=1/2) defined by a
and b, respectively. Such dimensionless representations can be
used, for example, in calculating a quantified combination of two
different types of data.
[0105] The libraries and databases provided herein may further
comprise: parameters related to the toxicity of each contaminant of
interest at different concentrations and for exposures of different
durations (for example, lethal dose 50%, concentration of no
observed adverse effect, etc.) and possibly the combined effect and
toxicity of multiple contaminants; parameters related to one or
more models describing the indoor and outdoor dynamics of gases and
other air-borne materials in the enclosed structure (for example
diffusion coefficients, rates of various chemical and physical
processes, coefficients of statistical regression model predicting
indoor concentration of contaminant from known outdoor
concentrations of that contaminant, etc.); and parameters required
for the assurance of compatibility with the prerequisites (for
example, for complying with the ASHRAE 62 standard, the library may
include the definition of occupancy category for spaces in the
enclosed structure, according to the standard, and the mandatory
ventilation requirements defined by the standard for each
category). These parameters may or may not be saved in the linked
databases.
[0106] In certain exemplary implementation, the library may further
include implementation of calculations used to assure compatibility
with the prerequisites. For example, in certain operable example,
where the outdoor airflow may be switched by the system between 0
and R (volume/time) and the prerequisites include the demand that
the cumulative outdoor fresh air vented into the enclosed structure
in any time period of duration T, will be at least of volume V,
where T and V are some predefined values, the following calculation
may be implemented in the library, determining until what time the
system is allowed to keep the ventilation continuously stopped,
starting from the current instant t, where the time is discretized
with step dt:
TABLE-US-00001 tV = t+T ti = t while ti>t-T Vi = the total
outdoor air supplied to the structure between times ti and t if Vi
< V and ti+T-(V-Vi)*R <tV tV = ti+T-(V-Vi)*R end ti = ti - dt
end
[0107] In some embodiments, the library will comprise
implementation of one or more optimization strategies which are
based on a dynamic threshold value (DTV). In these strategies, the
system compares between a dynamic threshold value and the current
value of VAP2, and sets the ventilation decision according to the
results of the comparison. For example, in embodiments where the
decision space is binary (i.e. where the outdoor airflow can only
be turned on or off), the resulting ventilation decision will be to
turn the ventilation on in case that the value of VAP2 is smaller
than or equals to the dynamic threshold value. Otherwise, the
decision will be to stop the ventilation. This, providing that the
directive of choice is not expected to cause violation of any of
the prerequisites. A flowchart illustrating these strategies is
presented in FIG. 3.
[0108] In another embodiment where the decision space is non-binary
(i.e. where the outdoor airflow can be switched between multiple
values), an additional calculation may be implemented and used in
the case that the current value of VAP2 is found to be smaller than
or equals to the dynamical threshold value. This calculation
determines the ventilation outdoor airflow associated with the
difference between the current value of VAP2 and the dynamic
threshold value. For example, in certain exemplary implementation
where the ventilation outdoor airflow can be tuned continuously
between
f(x)=min(M,max(0,ax))
zero and some maximum value M, this calculation may be where a is
some predefined coefficient, x is the difference between the
current value of VAP2 and the dynamic threshold value and the
result f is the required ventilation outdoor airflow.
[0109] Additionally and in certain exemplary implementation, the
dynamic threshold value (DTV) is computed based on various
considerations related to the defined optimization objective and
possibly also to the defined prerequisites. These may be, for
example: the current indoor pollution level; a weighted combination
of outdoor pollution levels during previous ventilation events; the
outdoor airflow during previous ventilation events, relative to the
volume of the enclosed structure; a forecast of outdoor pollution
levels; historic trends of outdoor pollution levels; an estimate of
the instantaneous energy requirement by the HVAC system which is
expected to result from any ventilation decision actuated by the
system; the current and expected occupancy and occupancy trends in
the enclosed structure; how close are the conditions in the
enclosed structure to violating the prerequisites; any other
available data related to or associated with the optimization
objective or the prerequisites; or combination thereof.
[0110] It is noted that while the same optimization strategy can be
illustrated in more than one equivalent way, every possible
optimization strategy which satisfies the following condition can
be considered as equivalent to the provided dynamic threshold value
optimization strategy: Let d be the ventilation power decided by
the system in a given situation. Where d is a function of some
quantity, c, related to the composition of the outdoor air, and
possibly of additional parameters as well. There exists at least
one such quantity c such that d is monotonically increasing with
respect to it. I.e., for every two values c1, c2 where c1 is larger
than or equals to c2, the result d(c1, . . . ) is larger than or
equals to d(c2, . . . ), providing that all other parameters
affecting d are kept fixed.
Example I: A Dynamic Threshold Value Strategy
[0111] Given circumstances where the optimization objective
requires simultaneously minimizing indoor air pollution (indicated
in that embodiment by NOx concentration) and minimizing energy
requirement by the HVAC system; where the prerequisites demand that
in every time window T within occupancy times, the HVAC system will
supply total fresh air in volume of at least V1 cubic meters per
square meter of floor area of the enclosed structure per second,
and also at least V2 cubic meters per occupant in the enclosed
structure per second; where occupancy of the structure is defined
to be P during defined occupancy times and zero otherwise; where an
air pollution monitoring station provides continuous real time data
about concentration of NOx in the available outdoor air, as well as
the real-time temperature outside the structure; where the indoor
temperature is assumed to be fixed during all occupancy times;
where the system is capable of switching the outdoor airflow on or
off, with the outdoor airflow being F when ventilation is on; where
the library comprises an executable model which yields estimation
of the energy requirement by the HVAC system as a function of the
temperature outside the structure; and where the library further
comprises the calculation yielding, per given relevant history of
ventilation up to the current instant, the longest period of time
it is allowed to continuously stop outdoor air ventilation to the
building, starting from the current instant, without violating the
prerequisites. In such an example of technical circumstances, the
optimization strategy may be, for example, a dynamic threshold
value strategy, where VAP2 comprise the measured outdoor NOx
concentration and the dynamic threshold value is calculated as
follows. A first value v.sub.1 is calculated as a weighted
combination of the measured previous values of VAP2, if the current
instant is within the defined occupancy times of the structure. If
the current instant is not within the defined occupancy times of
the structure, v.sub.1 is set to zero. A second value v.sub.2 is
calculated as the expected energy requirement by the HVAC system in
case of ventilation at the current instant. A third value v.sub.3
is calculated as the longest period of time it is allowed to
continuously stop the ventilation, starting from the current
instant. The dynamic threshold value is then calculated as
DTV=v.sub.1+(B.sub.2xv2)+(B.sub.3xv3)-B.sub.0, where B.sub.2,
B.sub.3, B.sub.0 are predefined coefficients. The current value of
VAP2 and the dynamic threshold value are then re-evaluated
periodically, and compared. The fresh air ventilation to the
building is then set to be on whenever the current value of VAP2 is
found to be lower than or equals to the dynamic threshold value,
and otherwise set to be off whenever the current value of VAP2 is
found to be higher than the dynamic threshold value.
Example II: Statistical Predictive Strategy
[0112] A statistical predictive strategy, using one of the
predictive models described herein to calculate and associate an
expected future of conditions in the enclosed structure (comprised
e.g. indoor levels of the contaminants of interest and energy
consumption) with every considered future course of action.
Actuating the course of action for which the overall expected
future is optimal according to the optimization objective.
Example III: Meta-Strategy
[0113] A meta-strategy, designed to examine different possible
strategies and choose to operate according to a subset of them
found to be the most suitable, at each given instance. Examples:
Simulate operation according to the different strategies over the
past data. Assess the outcomes obtained according to each and
currently choose to operate according to the best performing
strategy; and/or a meta-strategy which prioritize the strategies.
The top-priority strategy is used unless some resource (e.g.
library element, hardware device, etc.) required by it is
unavailable, in which case the meta-strategy turns to the next in
priority strategy and so on.
[0114] Further included in the library can be a collection of
predictive models, yielding expectations for future behavior of
data. The prediction for a given data may be based on past values
of the same data and/or on past values of other available data
(e.g. levels of the contaminants, occupancy, activity of pollution
sources, meteorological measures etc.), and/or on reduced
statistical representation of available historic data.
[0115] For example: statistical predictor for the expected value or
other statistic (e.g. the standard deviation) of the concentration
of a given contaminant in a specific location, at a defined future
instant or over a defined future interval of time, such as
calculating the mean (or, alternatively, the median) historic
change of the concentration for the current instant over some
defined cross section (e.g. same time of the day, same day of the
week and same season of the year). Use this mean (or median) as the
expectation for the concentration change at the current moment.
Other examples are machine learning model using a deep neural
network in order to make one of the above predictions; and/or
prediction model incorporating physical or chemical models, such as
those described herein, predicting the future trend of the
concentration levels of one or more contaminants based on their
past and current levels, past, current and planned states of the
HVAC system and other inputs required by those models.
[0116] The library may contain elements which are stored in a
memory at the local computer, alongside elements which are stored
remotely. The physical distribution of the library is abstracted
from other component of the system by the option to mark each
element of the library as unavailable. Thus, in case that a device
hosting one or more elements of the library becomes unavailable
(e.g. due to network failure or malfunction of the device itself),
all the elements hosted by this device (and, possibly, also other
elements which have mandatory dependency on them) will be marked as
unavailable by the processing unit. The processing module will then
refrain from attempting to access unavailable elements, until they
become available again.
[0117] The directives made by the optimization strategy, processed
over a processing unit, can be initiated and actuated by a control
unit in electric communication with the HVAC system and to which
the processing unit have a local or remote communication
channel.
[0118] As indicated, the libraries provided herein are used to
implement the methods provided herein, which are implementable
using the systems disclosed. Accordingly and in an exemplary
implementation, provided herein is a computerized method for
optimizing heating, ventilation and air conditioning (HVAC) process
in a multi-storied structure implementable in a system comprising
the multi-storied structure, a heating, ventilation and air
conditioning (HVAC) system, a processing module in communication
with a non-volatile memory having thereon a processor-readable
media and a library comprising: a first ventilation-associated
parameter (VAP.sub.1), related to the inside of the multi-storied
structure, a second ventilation-associated parameter (VAP.sub.2),
related to the outside of the multi-storied structure, a third
ventilation-associated parameter (VAP.sub.3), related to temporal
ventilation history, air-conditioning associated parameter
(HACAP.sub.6), related to an internal temperature of the
multi-storied structure and an air-conditioning associated
parameter (HACAP.sub.7), related to an external temperature of the
multi-storied structure, wherein the library further comprises a
plurality of master process objects, linked to the first, second,
and third ventilation-associated parameters as well as to the
heating and air-conditioning associated parameters related to the
internal and external temperature of the multi-storied structure;
the method comprising: responsive to a ventilation prompt,
selecting a set of operations configured to achieve a predetermined
optimization objective; associating the selected set of operations
to create a set of process commands within the ventilation request
and forming a ventilation command, wherein the set of master
process objects in the library are linked to the ventilation
command without copying the master process objects into the
ventilation command; and executing the ventilation command.
[0119] The method provided, to control the state of an HVAC system
in order to actuate a defined optimization strategy, can be
configured to pursue a defined optimization objective (and its
associated sub-goals), while subject to defined prerequisites,
utilizing the contents of linked databases featuring various
parameters, among which; one or more is continuously updated
through an input channel that is a part of a plurality of input
channels (see e.g., FIG. 1).
[0120] The operation of any optimization strategy relies in certain
exemplary implementations on utilizing data which can be stored as
a part of the linked data bases. Some items of the data bases may
be updated through input channels, while others may be constant,
initialized only once as the system is initially deployed, or
updated only manually by the provider.
[0121] Possible data stored in the data bases can be, for example:
[0122] Parameters related to the operation of the HVAC system, such
as those defining indoor recirculating and outdoor airflows,
heating and cooling rates, etc. at different subspaces of the
multi-storied enclosed structure and under various conditions.
[0123] Parameters allowing estimation of the energy requirement by
the HVAC system under various conditions, such as mechanical,
electric and thermal parameters of the various components of the
HVAC system. [0124] Ceiling height and/or area at the structure,
division of it to floors and other subspaces. [0125] The typical
activity which takes place in the multi-storied enclosed structure
and in each of the subspaces (office, storage, gym, kitchen, etc.).
[0126] Specification of interfaces and openings allowing passive
exchange of air and other gases with the surrounding of the
multi-storied enclosed structure. [0127] Structural data related to
the efficiency of ventilation in subspaces of the multi-storied
enclosed structure. In particular, specification of volumes in
which the air is not circulated directly and efficiently by the
ventilation system (e.g. rooms which are not directly fed by the
ventilation system, volumes above ceilings and below floors, major
office cabinets etc.). [0128] Topographical and geographical
information about the multi-storied enclosed structure and the
surroundings of it. May include the coordinates of the
multi-storied enclosed structure and a 2D or 3D model of the
surroundings. [0129] Parameters related to health hazards of
airborne materials per concentration and other conditions (e.g.
saturation levels of the toxicity, joint effects of groups of
materials etc.) [0130] Parameters related to processes which may
take place in the multi-storied enclosed structure and affect the
concentration of materials of interest: [0131] Parameters related
to chemical and physical processes such as flow, diffusion,
dispersion, decomposition, molecular interactions and reactions,
etc. Possibly involving more than a single species of molecules and
possibly affected by temperature, humidity and other meteorological
data. [0132] Parameters related to the effect of the HVAC system in
each of its possible states on such processes. For example, of the
thermal processing (heating/cooling), of indoor recirculating
airflow and of ventilation airflow. [0133] Parameters related to
the effect of the activity in the multi-storied enclosed structure
on the concentration of materials of interest. [0134] Temporal data
about concentration of materials (gases or other airborne
materials) in an indoor or outdoor air, which may be referenced at
locations inside the multi-storied enclosed structure, at the
vicinity of it or at locations further away. These may include:
[0135] Temporal concentration of material of interest, measured or
estimated. [0136] Descriptive statistics or other reduced
mathematical representation of temporal concentration data. [0137]
An index calculated based on concentration of one or more materials
and on other data and parameters defined in the library. [0138]
Temporal data about the activity of the HVAC system, such as the
states of it and the energy required by it. [0139] Assumed,
estimated or measured temporal data about the occupancy of the
multi-storied enclosed structure and about human or other
activities which occur in it. May include data about holidays and
weekends and the expected occupancy during them. [0140] Temporal
meteorological data such as temperature, relative humidity, wind
speed and direction, precipitations, sky cover, clouds, atmospheric
pressure, ux of solar radiation etc. [0141] Temporal data about
various other quantities which may be in use by the optimization
strategy. For example, this may include quantities which may serve
as indicators for the concentration of a material of interest, such
as traffic, industrial activity, etc.
[0142] It is noted, that the system for implementing the methods
provided herein, can be initialized with a set of demands that must
be met by the system at all times (in other words, prerequisites).
For example, in cases of malfunction, these demands are required to
be met in any case that the system is running and has control over
the HVAC system. These determine the boundaries of the decision
space inside which the system operates. For example, in cases of
malfunction, these demands are required to be met in any case that
the system is running and has control over the HVAC system.
Different embodiments of the system may be initialized with
different sets of prerequisites. These prerequisites can be, for
example, definition of minimal mandatory outdoor air supply, and/or
definition of the allowed states of the HVAC system, and/or
definition of the fallback policy to be exercised in case of
partial system malfunction. The fallback policy contains simple
instructions to be executed by the HVAC control unit, defined
hereinafter, whenever it detects that the input stream from the
processing unit has stopped (e.g. due to communication problem or
due to malfunction in the processing unit itself). The HVAC control
unit should be able to interpret these instructions as long as it
is functional, regardless of the state of other components of the
system and the communication with them. Therefore, the fallback
policy does not require mandatory access to resources and data
items which are stored or hosted elsewhere than on a local
computer.
[0143] As indicated, the prerequisites can be defined according to
the custom requirements of the user. In certain embodiments, some
prerequisite can be related to a minimal mandatory supply of
outdoor air to the multi-storied enclosed structure. The
prerequisites of the outdoor air supply can be, for example, demand
to maintain compatibility with a known standard of ventilation such
as the ASHRAE 62 standard; definition that the average outdoor
airflow within some custom period of time will be no less than some
predetermined threshold; definition of a maximal allowed indoor
concentration of CO2, either a constant threshold of indoor value
or a threshold of the difference between indoor and outdoor
concentrations of CO2; definition of a longest allowed period of
time during which outdoor air supply for the multi-storied enclosed
structure is continuously stopped; definition of a maximal
percentage of time during which the outdoor air supply for the
multi-storied enclosed structure is stopped; demand that the indoor
temperature will be in a vicinity of a specified size from the
chosen set temperature in any given time; or a combination
thereof.
[0144] Similarly, the prerequisites may be related to definition of
the allowed states of the HVAC system, which may comprise: possible
values of outdoor air flow; possible values of indoor circulating
flow; possible values of heating or cooling power, or their
combination. Furthermore, values of heating or cooling power may
take into account various considerations such as: the maximal
possible air flow determined by the mechanical and thermal
limitations of the machinery used by the HVAC system; this maximal
flow typically depends on the outdoor temperature and dew point,
the target indoor temperature set for the HVAC system and the
mechanical and thermodynamic properties of the HVAC system. In
embodiments where prerequisite that the indoor temperature will be
in a vicinity of a specified size from the chosen set temperature
in any given time is indeed defined, another prerequisite is
required, concerning the maximal outdoor airflow which still allows
the HVAC system to comply with the former. This limitation
typically depends on the cooling or heating capabilities of the
HVAC system, on the outdoor temperature and relative humidity, the
target indoor temperature and the mechanical and thermodynamic
properties of the HVAC system.
[0145] The term "system" shall also be taken to include any
collection of systems or sub-systems that individually or jointly
execute a set, or multiple sets, of instructions to perform one or
more functions. Also, the term "system" refers to a logical
assembly arrangement of multiple devices, and is not restricted to
an arrangement wherein all of the component devices are in the same
housing.
[0146] In certain exemplary implementation, the systems provided
herein can comprise: a multi-storied structure with an HVAC system;
a control module in communication with the HVAC system configured
to control HVAC inside the multi-storied structure; a processing
module in communication with the HVAC control module, the
processing module comprising a processor, the processing module in
communication with a plurality of input channels, each input
channel in communication with a data source; a memory in
communication with the processing module, the memory having stored
thereon a library comprising a set of executable instructions
configured, when executed, to cause the processing module to
initiate and actuate instructions configured to maintain
preselected parameters within the multi-storied enclosed structure,
for example, minimizing air pollution within the multi-storied
structure, while simultaneously minimizing energy requirement, in
an optimal manner.
[0147] In certain exemplary implementation, the input channels are
in continuous communication with the control module and/or the
processor. The term "input channel" is from the view point of the
controller and refers to the communication direction between
various sources of data (for example sensors, prediction algorithms
and the like) and the control module. The input channels can be,
for example: [0148] a. Feedback input from the HVAC system and/or
sensors measuring its performance and/or state. This may be, for
example, reports about measures of its actual state, such as the
set and actual outdoor airflow, set and actual indoor temperature,
its power consumption etc.; [0149] b. Input from sensors monitoring
levels of contaminants at specified locations, which may be either
inside the multi-storied enclosed structure and/or at the vicinity
of it and/or at remote locations; [0150] c. Input from sensors
monitoring different measures of the weather; [0151] d. Input from
systems used to estimate or measure the occupancy of the
multi-storied enclosed structure, such as attendance systems,
connected turnstiles, CCTVs, CO2 sensors and the like; [0152] e.
Manual or automated data load (e.g. download and upload or
automated pull/push notifications) from public databases such as
from that of the EPA, NOA, NASA and the like; [0153] f. Inputs from
the end user's interface of remote management of the device; [0154]
g. Inputs from a remote server including software updates and
updates of data items; [0155] h. Manual upload of any of the
possible items of the library, or combination thereof.
[0156] Also, as used herein, the term "processor" is defined as
including, but not necessarily being limited to, an instruction
execution system such as a computer/processor based system, an
Application Specific Integrated Circuit (ASIC), a computing device,
or a hardware and/or software system that can fetch or obtain the
logic from a non-transitory storage medium or a non-transitory
computer-readable storage medium and execute the instructions
contained therein. "Processor" can also include any controller,
state-machine, microprocessor, cloud-based utility, service or
feature, or any other analogue, digital and/or mechanical
implementation thereof.
[0157] In certain exemplary implementation, the processor is part
of a central processing module, which can be integral to or
separate from the control module. The processing module can be
configured to, for example: [0158] a. Operate according to one of
the strategies or meta-strategies described hereinabove; and/or
[0159] b. Execute the logic determined by the strategy of choice,
using the data available to the library. At each given instance,
yield an instruction for a set state sent to the HVAC system
according to the strategy. Validate that each instruction complies
with the prerequisites defined; and/or [0160] c. Contain
self-control subunit which adjusts and updates the strategy used to
the currently available and relevant data items and computation
resources.
[0161] Examples of the need for such an adjustment can be: in case
of hardware malfunction, which significantly reduces the
computation power available for the processing module, the control
sub-unit may choose to switch strategy from a heavy (e.g.,
multivariable optimization) strategy to a lighter one (e.g., less
variables for optimization), assuring the continuous operation of
the system even at the cost of compromising its performance; and/or
upon failure which makes a certain data item unavailable or out of
date, the system could make sure not to adhere to a strategy which
is dependent on access to that data item; and/or in case of a
communication failure which makes all the elements of the library
which are remote from the local processing unit unavailable, the
system could be configured, using the appropriate sub-unit, to
switch to a local fallback strategy.
[0162] Thus, in certain exemplary implementation having a local
minimal processing module and a major remote processing module (a
master-slave mode), the remote module will direct its instructions
to the local module. The latter in turn will accept the
instructions from the former and forward them as they are to the
HVAC control module, unless some malfunction is detected (e.g. an
error message is accepted from the remote module, or no message is
accepted from it, reaching a defined timeout). In such case, the
local module will override the remote module and operate according
to the best strategy accessible.
[0163] Additionally, or alternatively, a local and a remote
processing module may operate in a shared mode, assigning each of
them with the part of the computation load expected to match its
computing resources. Thus, aggregative calculations over large data
sets may be set to be performed by the remote module while smaller
operations and the main workflow may be done by the local
processing module
[0164] In certain embodiments, the HVAC control module could be
configured to: [0165] a. Accept the instructions from the
processing module and attempt to actuate them with the HVAC system.
[0166] b. The HVAC control unit will detect any failure (e.g. in
the communication with the processing module or severe failure of
the processing module itself), at which point, the HVAC control
module can be configured to actuate the fallback policy as
described hereinabove. [0167] c. The result of a command received
and handled by the HVAC control module can be reported back to the
processing module, which may forward it to be stored in the
library.
[0168] In certain exemplary implementation, the systems provided
herein, using the libraries provided to implement the methods
disclosed further comprise a multi-directional, or
direction-adjustable air-inlet module (see e.g., FIGS. 6-10),
adapted to provide selectable inflow of air from a discrete
direction, whether pre-processed or direct fresh air. Consequently,
the ventilation direction is configured to draw air from the air
inlet direction associated with the lowest determined pollutants'
concentration immediately outside the multi-storied structure (i.e.
the available outdoor air as defined herein). It is noted, that the
air inlet can be located at various heights along the multi-storied
enclosed structure, and air inflow can be configured to be taken
from any height so as to minimize internal air pollution.
[0169] In certain exemplary implementation, the systems provided
herein, using the libraries provided to implement the methods
disclosed, further comprise a bypass outlet and a three-way valve
and a secondary fan, adapted to provide a selectable bypass flow of
air from the inlet directly to the bypass outlet. This allows
maintaining a residual air flow through the inlet also in times
when outdoor air supply is stopped, thus preventing persistence of
high contaminant levels inside the ducts to the time when the air
supply to the structure is restored. The secondary fan may be
lighter than the main fan, designed only to circulate air through
the volume of the ducts between the inlet and outlet and not
through the entire volume of the structure, thus consuming less
power than the main fan. In examples, the bypass outlet and
three-way valve and the secondary fan are operable to be used in
conjunction with measured temporal data of pollution of the
available outdoor air. The optimization strategy my then be
configured to consider the time it takes a parcel of air to travel
from the inlet to the three-way valve.
[0170] In certain exemplary implementation, the systems provided
herein, using the libraries provided to implement the methods
disclosed, further comprise ventilation outlets into unoccupied
spaces (for example unoccupied rooms and hallways and volumes above
ceiling and below floors) as well as vents, ducts or openings
allowing free exchange of air between these spaces and the occupied
spaces. This may extend the duration that the outdoor air supply to
the structure can be stopped, which in turn may improve the ability
of the optimization strategy to reduce indoor air pollution due to
outdoor sources. In certain examples, ventilation of these
unoccupied spaces, as well as the air flow between these spaces and
occupied spaces, are operably configured to be active and
selectable. Such implementations may comprise a baffle which the
system may command in order to selectably ventilate unoccupied
spaces. These embodiments may also comprise a light fan, ducts and
vents or openings, adapted to selectably establish circulating
airflow between occupied and unoccupied spaces. The optimization
strategy may then be configured to utilize ventilation of the
unoccupied spaces and circulation air flow between occupied and
unoccupied spaces.
[0171] As indicated, the methods disclosed, implementable using the
systems provided using the libraries are computerized methods
utilizing processor-readable media such as various computer
programs. The computer programs (software and/or firmware), can
comprise program code means for carrying out the steps of the
methods described herein, as well as a computer program product
comprising program code means stored on a medium that can be read
by a computer, such as a floppy disk, a hard disk, CD-ROM, DVD, USB
memory stick, or a storage medium that can be accessed via a data
network, such as the Internet or Intranet, when the computer
program product is loaded in the main memory of a computer and is
carried out by the computer. Thus, the terms "non-transitory
storage medium" and "non-transitory computer-readable storage
medium" are defined as including, but not necessarily being limited
to, any media that can contain, store, or maintain programs,
information, and data. Non-transitory storage medium and
non-transitory computer-readable storage medium may include any one
of many physical media such as, for example, electronic, magnetic,
optical, electromagnetic, or semiconductor media. More specific
examples of suitable non-transitory storage medium and
non-transitory computer-readable storage medium include, but are
not limited to, a magnetic computer diskette such as floppy
diskettes or hard drives, magnetic tape, a random access memory
(RAM), a read-only memory (ROM), an erasable programmable read-only
memory (EPROM), a flash drive, a compact disc (CD), or a digital
video disk (DVD).
[0172] Accordingly and in certain exemplary implementation,
provided herein is a processor-readable media in communication with
and a library comprising: a first ventilation-associated parameter
(VAP.sub.1), related to the inside of the multi-storied structure,
a second ventilation-associated parameter (VAP.sub.2), related to
the outside of the multi-storied structure, a third
ventilation-associated parameter (VAP.sub.3), related to temporal
ventilation history, air-conditioning associated parameter
(HACAP.sub.6), related to an internal temperature of the
multi-storied structure and an air-conditioning associated
parameter (HACAP.sub.7), related to an external temperature of the
multi-storied structure, wherein the library further comprises a
plurality of master process objects, linked to the first, second,
and third ventilation-associated parameters as well as to the
heating and air-conditioning associated parameter related to the
internal and external temperature of the multi-storied structure,
the processor-readable media having a set of executable
instructions, which, when executed, are configured to cause a
processor to: receive a ventilation request prompt from a heating,
ventilation and air conditioning (HVAC) system; response to the
ventilation request, select a set of operations configured to
achieve a predetermined optimization objective; associate the
selected set of operations with the ventilation request; create a
set of process commands within the ventilation request; form a
ventilation command, wherein the set of master process objects in
the library are linked to the ventilation command without copying
the master process objects into the ventilation command; and
execute the set of ventilation associated process operations in the
ventilation command.
[0173] Memory device(s) as used in the methods described herein can
be any of various types of non-transient memory devices or storage
devices (in other words, memory devices that do not lose the
information thereon in the absence of power). The term "memory
device" is intended to encompass an installation medium, e.g., a
CD-ROM, floppy disks, or tape device or a non-transient memory such
as a magnetic media, e.g., a hard drive, optical storage, or ROM,
EPROM, FLASH, etc. The memory device may comprise other types of
memory as well, or combinations thereof. In addition, the memory
medium may be located in a first computer in which the programs are
executed, and/or may be located in a second different computer
which connects to the first computer over a network, such as the
Internet. In the latter instance, the second computer may further
provide program instructions to the first computer for execution.
The term "memory device" can also include two or more memory
devices which may reside in different locations, e.g., in different
computers that are connected over a network.
[0174] Further, the processor may be operably coupled to the
various modules and components with appropriate circuitry, may also
be used herein, the term(s) "operably coupled to", "coupled to",
and/or "coupling" includes direct coupling between items and/or
indirect coupling between items via an intervening item (e.g., an
item includes, but is not limited to, a component, an element, a
circuit, an engine, and/or a module) where, for indirect coupling,
the intervening item does not modify the information of a signal
but may adjust its current level, voltage level, and/or power
level. As may further be used herein, inferred coupling (i.e.,
where one element is coupled to another element by inference)
includes direct and indirect coupling between two items in the same
manner as "coupled to". As may even further be used herein, the
term "operable to" or "operably coupled to" indicates that an item
includes one or more of power connections, input(s), output(s),
etc., to perform, when activated, one or more its corresponding
functions and may further include inferred coupling to one or more
other items. As may still further be used herein, the term
"associated with", includes direct and/or indirect coupling of
separate items and/or one item being embedded within another
item.
[0175] The term "module" is used herein to refer to software
computer program code and/or any hardware or circuitry utilized to
provide the functionality attributed to the module. Further, the
term "module" or "component" can also refer to software objects or
routines that execute on the computing system. The different
components, modules, engines, and services described herein may be
implemented as objects or processes that execute on the computing
system (e.g., as separate threads).
[0176] Unless specifically stated otherwise, as apparent from the
following discussions, it is appreciated that throughout the
specification discussions utilizing terms such as "processing,"
"loading," "in communication," "detecting," "calculating,"
"determining", "analyzing," or the like, refer to the action and/or
processes of a computer or computing system, or similar electronic
computing device, that manipulate and/or transform data represented
as physical, such as a transistor architecture into other data
similarly represented as physical and structural layers.
[0177] As may also be used herein, the terms "communication
processing module" (CPM), "module", "processing circuit", and/or
"processing unit" may be a single processing device or a plurality
of processing devices. Such a processing device may be a
microprocessor, micro-controller, digital signal processor,
microcomputer, central processing unit, field programmable gate
array, programmable logic device, state machine, logic circuitry,
analog circuitry, digital circuitry, and/or any device that
manipulates signals (analog and/or digital) based on hard coding of
the circuitry and/or operational instructions (in other words,
firmware). The processor, processing circuit, and/or processing
unit may have an associated memory and/or an integrated memory
element, which may be a single memory device, a plurality of memory
devices, and/or embedded circuitry of the processing module,
module, processing circuit, and/or processing unit. Such a memory
device may be a read-only memory, random access memory, transient
memory, non-transient memory, static memory, dynamic memory, flash
memory, cache memory, and/or any device that stores digital
information.
[0178] Note that if the processor, module, servers, network
switches etc., processing circuit, and/or processing unit includes
more than one processing device, the processing devices may be
centrally located or may be distributed (e.g., cloud computing via
indirect coupling via a local area network and/or a wide area
network). Still further it is noted that, the memory element may
store, and processor, module, processing circuit, and/or processing
unit executes, hard coded and/or operational instructions
corresponding to at least some of the steps and/or functions
illustrated in one or more of FIG. 1 elements. Such a memory device
or memory element can be and is included in certain exemplary
implementation as an article of manufacture.
[0179] With reference now to the figures and in particular with
reference to FIGS. 1-2, exemplary flowcharts of data processing
environments are provided in which illustrative embodiments may be
implemented. It should be appreciated that FIGS. 1-10 are only
exemplary and are not intended to assert or imply any limitation
with regard to the environments in which different embodiments may
be implemented. Many modifications to the depicted environments may
be made.
[0180] Turning now to FIG. 1 and FIG. 2, illustrating the system
components and the interrelationships between these components. As
illustrated, different elements of the system may depend in their
operation on input and feedback from other components. For example,
availability of one element of the system may be mandatory for the
functionality of another element (e.g., master-slave relationship,
thus not selectable). In another embodiment, reducing exposure to a
certain contaminant of outdoor source, incorporating a single input
channel 150.sub.i from outdoor sensor 600.sub.p (not shown) of that
contaminant and a main strategy. The availability of the outdoor
data 151 may be mandatory for the strategy to be carried out, and
that sensor 107 input data 151 is unavailable, processing unit 200
will search library 100 for another strategy 102.sub.k, which
either; does not depend on availability of outdoor sensor 107 data
151 to operate, or can be calculated based on indoor data. In other
cases, availability of one element of the system may not be
mandatory for the functionality of another element, but rather
defined as assisting it or enhancing its performance. Thus, as
described above, assuming additionally that library 100 contains
measured indoor concentrations of a particular contaminant (e.g.,
NOx), received through input channel 150.sub.i from indoor sensor
106 (not shown), and also via calculation, which allows estimating
the indoor concentration of the contaminant based only on the
measured outdoor concentration using outdoor sensor 107 and the
ventilation history (not shown, referring to previous time and
concentrations of the contaminants as measured by indoor sensor
106). In this case, data 151 from indoor sensor 106 may not be
mandatory for the functionality of the strategy and its
unavailability will not result in unavailability of the latter.
[0181] As illustrated in FIGS. 1-4 input channels 150.sub.i feed
changes to items 111.sub.j of database 110. Upon initiation, 40,
processing module 200 loads 101 available optimization strategy 74
and associated models 102.sub.k from library 100 and executes two
kinds of operations: Responds 212 to updates of the data in
database 100 by, for example, recalculating the instruction of the
loaded strategy and actuates that instruction by sending a command
220 to HVAC system through the HVAC control module 400. HVAC
control module 400 responds to command 220 with feedback 401 which
may be forwarded 212 to be saved in database 110. Processing module
200 then responds 22 to changes in the availability status of
different items of library 110 by checking whether the active
strategy is still available 24 (see e.g., FIG. 2), in other words,
if all the other library items 102.sub.k, which are mandatory for
its functionality are available 24. In case that the active
strategy is found to be unavailable 25, processing module 200 loads
the next-in-priority strategy 26 until an available strategy is
found (in terms of available necessary resources as per 22). Such
strategy can always be found, since the fallback strategy and all
the components mandatory to its functionality are a physical part
of processing module 200 itself. HVAC control module 400 actuates
the required changes in the state of the HVAC system and also makes
a final validation of the changes with respect to prerequisites
302, overriding them according to the fallback strategy described
hereinabove, if violations of prerequisites 302 are detected. When
the system is launched 20 processing module 200 is initialized 20
with the optimization objectives based on the preselected
optimization strategy 74 loaded 102.sub.k from library 100 and HVAC
control module 400 is initialized with prerequisites 302.
[0182] As illustrated in FIGS. 1, and 2, processing module 200,
upon launch or after reset 20, will monitor 21 the availability of
all library items 22 to see if there are changes to any parameter
111j and dynamically changed thresholds stored in library 100 or in
database 110. If changes are found, processing module 200 will
communicate 202 with library 100, to choose the highest priority
strategy 26 available and again communicate with library 100 to
recheck 22 the strategy's resources' availability, then 24 if the
strategy is unavailable 25, choose the highest priority strategy 74
available and again communicate with library 100 to recheck 22 the
strategy's resources' availability. If however the strategy's
resources' are available 29, the strategy's instructions are
obtained from library 100 and recalculated 30 with the updated data
from database 100, whereupon if the recalculation is unsuccessful
37, the process resets 20, otherwise 33 instructions are sent 34 to
HVAC control module 400 whereupon, processing module 200 receives
feedback 401 following operation of the HVAC control module 400 and
reinitiates 21 the recheck of the strategy's availability 22.
[0183] Turning now to FIG. 3, is an illustration of the logic of
the "Dynamic Threshold Value" (DTV) binary strategy. As
illustrated, upon receiving command from processing module 200 the
process is initialized 40 and required data is collected 42 from
database 110, the data is retrieved 43 to the processing module
200, where indoor conditions are further computed 44 and queried as
to whether the indoor conditions in the structure allow closing of
the vent 46. If it is found that closing of the vent is forbidden
at the current instant 47, the vent is opened 48. If closing of the
vent is allowed 49, the collected data is used to compute the
dynamic threshold value 50, which is then 51 compared to the
current value of VAP2 52. If the current value of VAP2 is lower
than or equals to the DTV 55, the vent is opened 48, otherwise 53
the vent is closed 54.
[0184] Turning now to FIG. 4, which is a schematic description of
the optimization objective 68 is comprised of a set of optimization
goals 60.sub.n and a prioritization logic 62. FIG. 4 outlines what
are the outcomes that the system should pursue. Prerequisites 70
are a list of demands that must be met at all time by the system.
Optimization strategy 74 defines the set of executable operations
actuated by processor 200 and followed by the system using library
100 and database 110 to pursue optimization strategy 74, while
complying with prerequisites 701, for example maintaining CO.sub.2
levels at a predetermined concentrations.
[0185] Turning now to FIG. 5, which is an illustration of the logic
executed, when actuated by processing unit 200 in case that the
maximal power of outdoor airflow should be adjusted according to
the outdoor temperature (T.sub.out) and relative humidity
(RH.sub.out) and to the indoor set temperature (T.sub.set). As
illustrated, upon initiation 80 processing module 200 reads the
current outdoor temperature and relative humidity 82, recalculate
the maximum vent power (P.sub.max) 84, which would still comply
with the defined prerequisites such as eliminating condensation,
send 85 the new value of P.sub.max to be updated 86 in the HVAC
control module 400 (so it can be used by HVAC control module 400),
then recalculate the instruction of the strategy according to the
new value of P.sub.max and actuate the ventilation 88 with HVAC
control module 400. Additionally, the HVAC control unit 400 checks
whether the current power of the vent, P, is larger than P.sub.max,
88 in which case 89 it immediately reduces P to be equal to
P.sub.max 90. In addition, processing module 200, after reading the
current outdoor temperature and relative humidity 82, and
recalculating the maximum vent power (P.sub.max) 84, sends command
91, to (if necessary), recalculated and actuate optimization
strategy 74 (see e.g., FIG. 4).
[0186] Turning now to FIGS. 6-10, which are schematics
illustrations of various embodiments of primary and secondary
vents' configurations. As illustrated for example in FIG. 6, air
enters through main conduit 610 where sensors can monitor air
quality at the inlet 601, with fan unit 650 having inlet 601 and
outlet 602 configured to move the air through post fan duct 616
towards valve 605 that is based on HVAC control module 400, in
communication with processing module 200, will determine whether to
allow the air to flow into structure inlet duct 615, or have the
air exit without entering the structure through outlet duct 614
having exit 604. In other words, main conduit 610 conveys outdoor
air from the inlet (601,--opening of main conduit 610). The air
quality is measured at the inlet (601). Three-way valve 605 can
then be switched between routing the air into the building 700 or
exhausting the air out 602.
[0187] Alternatively or additionally, as illustrated in FIG. 7,
additional light fan unit 611 allows replacement of the air inside
main conduit 610, also when the fan unit 650 is not activated to
ventilate the multi-storied enclosed structure.
[0188] FIG. 8, illustrates a configuration allowing for selectable
air inlet from 2 directions, with controllable baffles 605, 606 and
secondary, light fan unit 611 with inlet ducts 617, 618 extending
at opposite directions from main fan unit 650. In certain exemplary
implementation, processing module 200 can be configured to, when
executed by the programs disclosed, to cause at least one processor
to initiate airflow to main conduit 610, from directions of inlet
601, 603 that will have the lowest concentration of outdoor air
pollutant, or in another embodiment, airflow to main conduit 610,
from directions of inlet 601, 603 that will provide the most
optimal results for energy conservation in the multi-storied
enclosed structure. That latter main conduit 610 can be the same or
different than the former. In certain exemplary implementation,
secondary fan 611 can be configured, when executed, to maintains
weak airflow through the inlets 601, 603 into the exhaust 602, at
times when inlets 601, 603 are not supplying air to structure 700
(see e.g., FIG. 6)
[0189] Turning now to FIG. 9, illustrating ventilation inlet duct
configuration placed at the space 750 above drop ceiling 705 of
space 700 inside the multi-storied enclosed structure. As
illustrated, internal inlet duct 901, in communication with
external main conduit 610, has two outlets (vents). The first 915
opens to the space 700 below the drop ceiling 705, and the second,
902 opens to space 750 above drop ceiling 705. Also shown is baffle
905. HVAC control module 400 (not shown), using the systems and
processing module 200 provided herein, can be configured to use the
volume 750 above drop ceiling 705 to store fresh air. Valves 710
located in openings 701 in the ceiling 705 and in the internal
inlet duct 901 allow determining whether the air is blown into
volume 750 above drop ceiling 705 or not and whether passive
passage of air between volume 750 and the room (700) through
ceiling opening 701 is allowed or not.
[0190] Similarly, FIG. 10, illustrates another embodiment of the
ventilation inlet duct configuration placed at the space above drop
ceiling 705 of a space inside the multi-storied enclosed structure.
As illustrated, an additional internal light fan unit 950 having
inlets/outlets 951 and 952 opening into the space below drop
ceiling 705, and additional inlets/outlets 953 and 954, opening
into the space 705 above drop ceiling 705. Also shown are baffles
(or valves) 905, 954, and 955. In this configuration, it is
possible to recirculate air inside space 700 below drop ceiling
705, while shutting off three way valve 905, opening baffles 954
and 955, thus driving the impact (in other words, accelerating the
air replacement) within the space below drop ceiling 705. In
certain exemplary implementation, the systems provided herein,
comprise the ventilation inlet duct configuration placed at the
space below drop ceiling of a space inside the multi-storied
enclosed structure as substantially illustrated in FIGS. 6-10.
Likewise, unit 950 located in the room allow active transfer of air
between the room 700 and volume 750 above drop ceiling 705.
[0191] The term "comprising" and its derivatives, as used herein,
are intended to be open ended terms that specify the presence of
the stated features, elements, components, groups, integers, and/or
steps, but do not exclude the presence of other unstated features,
elements, components, groups, integers and/or steps. The foregoing
also applies to words having similar meanings such as the terms,
"including", "having" and their derivatives.
[0192] All ranges disclosed herein are inclusive of the endpoints,
and the endpoints are independently combinable with each other.
"Combination" is inclusive of blends, mixtures, alloys, reaction
products, and the like. The terms "a", "an" and "the" herein do not
denote a limitation of quantity, and are to be construed to cover
both the singular and the plural, unless otherwise indicated herein
or clearly contradicted by context. The suffix "(s)" as used herein
is intended to include both the singular and the plural of the term
that it modifies, thereby including one or more of that term (e.g.,
the stream(s) includes one or more stream). Reference throughout
the specification to "one implementation", "another exemplary
implementation", "certain exemplary implementation", "an operable
example" and so forth, when present, means that a particular
element (e.g., feature, structure, and/or characteristic), or
elements described in connection with certain implementations,
executions, operations, and working examples, is/are included in at
least one such implementation described herein, and may or may not
be present in other implementations, executions, operations, and
working examples. In addition, it is to be understood that the
described elements may be combined in any suitable manner in the
various exemplary implementations.
[0193] Likewise, the term "about" means that amounts, sizes,
formulations, parameters, and other quantities and characteristics
are not and need not be exact, but may be approximate and/or larger
or smaller, as desired, reflecting tolerances, conversion factors,
rounding off, measurement error and the like, and other factors
known to those of skill in the art. In general, an amount, size,
formulation, parameter or other quantity or characteristic is
"about" or "approximate" whether or not expressly stated to be
such.
[0194] Accordingly and in an exemplary implementation, provided
herein is a processor accessible library comprising control
information for a multi-storied structure's heating, ventilation
and air conditioning (HVAC) process, wherein said library is
configured to implement methods to identify optimized period for
ventilation and/or heating and air conditioning, employing dynamic
ventilation criteria, and wherein the library further contains
external and internal HVAC parameters, wherein (i) the ventilation
parameters comprise: a first ventilation-associated parameter
(VAP1), related to the inside of the multi-storied structure; a
second ventilation-associated parameter (VAP2), related to the
outside of the multi-storied structure; and a third
ventilation-associated parameter (VAP3), related to temporal
ventilation history, wherein the library further comprises a
plurality of master process objects linked to the first, second,
and third ventilation-associated parameters, and (ii) further
comprised of: a fourth ventilation-associated parameter
(VAP.sub.4), related to a hub encompassing the enclosure; and a
fifth ventilation-associated parameter (VAP.sub.5), designating the
location of the enclosure within the hub, wherein the library
further comprises a plurality of master process objects linked to
the fourth, and fifth ventilation-associated parameters, and (iii)
further comprised of: a sixth heating and air-conditioning
associated parameter (HACAP.sub.6), related to an internal
temperature of the enclosure; and a seventh heating and
air-conditioning associated parameter (HACAP.sub.7), related to an
external temperature of the enclosure, wherein the library further
comprises a plurality of master process objects linked to the
sixth, and seventh ventilation-associated parameters, wherein (iv)
the library is configured such that said control information is
modifiable by a user, wherein (v) a set of operations configured to
achieve a predetermined optimization objective from the plurality
of master process' optimization sub-goals in the library is
configured to be selectable, wherein (vi) the first
ventilation-associated parameter (VAP.sub.1), comprise selectably
determined pollutants' concentration within the multi-storied
structure; the second ventilation-associated parameter (VAP.sub.2),
comprise selectably determined pollutants' concentration
immediately outside the multi-storied structure, wherein (vii) a
rule-based algorithm is configured to select the set of master
process' objectives configured to minimize pollution within the
multi-storied enclosure and minimize the energy requirements of the
heating and air-conditioning process, (viii) the optimization
objectives comprise: reduction of concentration of indoor and/or
outdoor sourced pollutants, maximizing incoming air flow,
maintaining internal temperature range, minimizing a breach period,
minimizing energy requirement by the HVAC system, or a combination
of optimization objectives comprising the foregoing, wherein (ix)
the library is dynamically linked to at least one remote database,
(x) the library further comprises parameters associated with
physical properties of the multi-storied structure, physical
properties of the HVAC system, topographical and/or geographical
characteristics of the immediate surroundings of the multi-storied
structure, occupancy in the multi-storied structure, meteorological
data, or a combination of parameters comprising the foregoing,
wherein (xi) the library further comprising an eighth heating and
air-conditioning associated parameter (HACAP.sub.8), the eighth
heating and air-conditioning associated parameter (HACAP.sub.8)
comprising wet bulb temperature, and (xii) a parameter associated
with minimally required external air supply.
[0195] In another exemplary implementation, provided herein is a
computerized method for optimizing heating, ventilation and air
conditioning (HVAC) process in a multi-storied structure
implementable in a system comprising the multi-storied structure, a
heating, ventilation and air conditioning (HVAC) system, a
processing module in communication with a non-volatile memory
having thereon a processor-readable media and a library comprising:
a first ventilation-associated parameter (VAP.sub.1), related to
the inside of the multi-storied structure, a second
ventilation-associated parameter (VAP.sub.2), related to the
outside of the multi-storied structure, a third
ventilation-associated parameter (VAP.sub.3), related to temporal
ventilation history, air-conditioning associated parameter
(HACAP.sub.6), related to an internal temperature of the
multi-storied structure and an air-conditioning associated
parameter (HACAP.sub.7), related to an external temperature of the
multi-storied structure, wherein the library further comprises a
plurality of master process objects comprised of a plurality of
sub-goals, with a dynamic threshold value, the dynamic threshold
value linked to the first, second, and third ventilation-associated
parameters as well as to the heating and air-conditioning
associated parameter related to the internal and external
temperature of the multi-storied structure; the method comprising:
responsive to a ventilation prompt, selecting a set of operations
configured to achieve a predetermined optimization objective from
the plurality of master process' optimization objectives in the
library; associating the selected set of operations to create a set
of process commands within the ventilation request and forming a
ventilation command, wherein the set of master process objects in
the library are linked to the ventilation command without copying
the set of master process objects into the ventilation command; and
executing the ventilation command, wherein (xiii) selecting a set
of master process object from the plurality of master process
objects in the library is implemented using rule-based algorithm,
wherein (xiv) the first ventilation-associated parameter
(VAP.sub.1), comprise selectably determined pollutants'
concentration within the multi-storied structure; and the second
ventilation-associated parameter (VAP.sub.2), comprise selectably
determined pollutants' concentration immediately outside the
multi-storied structure, wherein (xv) the system further comprises
a multi-directional air-inlet module, adapted to provide selectable
inflow of air from a discrete direction (azimuth), wherein (xvi)
the optimization objective is comprised of at least one of a
plurality sub-goals of: reduction of concentration of indoor and/or
outdoor sourced pollutants, maximizing incoming air flow,
maintaining internal temperature range, minimizing a breach period,
minimizing energy requirement by the HVAC system, or a combination
of sub-goals comprising the foregoing, (xvii) the library further
comprises parameters associated with physical properties of the
multi-storied structure, physical properties of the HVAC system,
topographical and/or geographical characteristics of the immediate
surroundings of the multi-storied structure, occupancy in the
multi-storied structure, meteorological data, or a combination of
parameters comprising the foregoing, wherein (xviii) the rule-based
algorithm is configured to select the set of master process objects
configured to minimize pollution within the enclosure and minimize
the energy requirements of the heating and air-conditioning
process, (xix) the library further comprises: a fourth
ventilation-associated parameter (VAP.sub.4), related to a hub
encompassing the enclosure; and a fifth ventilation-associated
parameter (VAP.sub.5), designating the location of the enclosure
within the hub, wherein the library further comprises a plurality
of master process objects linked to the fourth, and fifth
ventilation-associated parameters, the method (xx) further
comprising a step of determining a wet bulb temperature and
limiting air flow so as to prevent condensation of moisture in the
HVAC system, wherein (xxi) the ventilation direction is configured
to draw air from the air inlet direction associated with the lowest
determined pollutants' concentration immediately outside the
multi-storied structure, (xxii) the library further comprises a
parameter associated with minimally required external air supply,
wherein (xxiii) the library further comprises a parameter
associated with minimally required external air supply, wherein
(xxiv) the step of executing the ventilation command further
comprises maintaining a predetermined air pressure differential
between portions of the enclosed structure, (xxv) wherein
maintaining predetermined air pressure differential between
portions of the enclosed structure comprises controlling exhaust
ventilation air flow and the fresh air airflow, wherein (xxvi) the
dynamic threshold is varied as a function of an expected energy
requirement by the fresh air system, the method (xxvii) further
comprising calculating the expected energy requirement based on
forecasted weather parameters, wherein (xxviii) the step of
executing the ventilation command further comprises ventilating
unoccupied portions of the enclosed structures, or the whole
unoccupied enclosed structure, the method (xxix) further
comprising: activating, deactivating and tuning components of the
HVAC system to improve energy efficiency, (xxx) the HVAC system's
components are at least one of: chillers, heat-pumps, fan coils,
heating coils, heat exchangers, cooling towers, water pumps,
motors, fans, and compressors, (xxxi) the energy efficiency
improved is at least one of: Watts, coefficient of performance
(COP), and energy efficiency ratio (EER), wherein (xxxii) the step
of executing the ventilation command further comprises controlling
at least one of: baffles, and dumpers each affecting outdoor air
flow distribution between different sub-zones in the structure, and
the method (xxxiii) further comprising: detecting a faulty
component of the HVAC system, or an improper distribution of fresh
air flow in the enclosed structure or in a portion thereof; and
upon detecting a faulty component, issuing an alert.
[0196] In yet another exemplary implementation, provided herein is
a processor-readable media in communication with and a library
comprising: a first ventilation-associated parameter (VAP.sub.1),
related to the inside of the multi-storied structure, a second
ventilation-associated parameter (VAP.sub.2), related to the
outside of the multi-storied structure, a third
ventilation-associated parameter (VAP.sub.3), related to temporal
ventilation history, air-conditioning associated parameter
(HACAP.sub.6), related to an internal temperature of the
multi-storied structure and an air-conditioning associated
parameter (HACAP.sub.7), related to an external temperature of the
multi-storied structure, wherein the library further comprises a
plurality of master process objects with dynamic threshold, linked
to the first, second, and third ventilation-associated parameters
as well as to the heating and air-conditioning associated parameter
related to the internal and external temperature of the
multi-storied structure, the processor-readable media having a set
of executable instructions, which, when executed, are configured to
cause at least one processor to: receive a ventilation request
prompt from a heating, ventilation and air conditioning (HVAC)
system; responsive to the ventilation request, select a set of
operations configured to achieve a predetermined optimization
objective from the plurality of master process objects in the
library; associate the selected set of operations with the
ventilation request; create a set of process commands within the
ventilation request; form a ventilation command, wherein the set of
master process objects in the library are linked to the ventilation
command without copying the set of master process objects into the
ventilation command; and execute the set of ventilation associated
master process objects in the ventilation command, wherein (xxxiv)
a set of master process objects is configured to be selectable from
the plurality of master process objects in the library using a
rule-based algorithm, (xxxv) the optimization objective is
comprised of at least one of a plurality sub-goals of at least one
of: reduction of concentration of indoor, outdoor sourced
pollutants, maximizing incoming air flow, maintaining internal
temperature range, minimizing a breach period, minimizing energy
requirement by the HVAC system, and a combination of sub-goals
comprising the foregoing, (xxxvi) the library further comprises: a
fourth ventilation-associated parameter (VAP.sub.4), related to a
hub encompassing the enclosure; and a fifth ventilation-associated
parameter (VAP.sub.5), designating the location of the enclosure
within the hub, wherein the library further comprises a plurality
of master process objects linked to the fourth, and fifth
ventilation-associated parameters, wherein (xxxvii) when executed,
the at least one processor is further configured to modify control
information using a user input, (xxxviii) the user input comprise:
feedback input from the HVAC system and/or sensors measuring its
performance, input from sensors monitoring levels of contaminants
at specified locations, or their combination, and wherein (xxxix)
when executed, the processor readable media is further configured
to cause the at least one processor to determine a wet bulb
temperature and limit air flow so as to prevent condensation of
moisture in the HVAC system.
[0197] In another exemplary implementation, provided herein is a
method for adaptive optimization of heating, ventilation and air
conditioning (HVAC) process in a multi-storied structure
implementable in a system comprising the multi-storied structure, a
heating, ventilation and air conditioning (HVAC) system, a
processing module in communication with a non-volatile memory
having thereon a processor-readable media and a library comprising:
a first ventilation-associated parameter (VAP.sub.1), related to
the inside of the multi-storied structure, a second
ventilation-associated parameter (VAP.sub.2), related to the
outside of the multi-storied structure, a third
ventilation-associated parameter (VAP.sub.3), related to temporal
ventilation history, an air-conditioning associated parameter
(HACAP.sub.6), related to an internal temperature of the
multi-storied structure and an air-conditioning associated
parameter (HACAP.sub.7), related to an external temperature of the
multi-storied structure, wherein the library further comprises a
plurality of master process objects comprised of a plurality of
sub-goals, with a dynamic threshold value, the dynamic threshold
value linked to the first, second, and third ventilation-associated
parameters as well as to the heating and air-conditioning
associated parameter related to the internal and external
temperature of the multi-storied structure; the method comprising:
selecting a historical dataset comprising a first set of forecast
pollutants' values received from one or more predictive forecast
statistical models and a first set of actual pollutants' values
received from one or more measurements of the pollutants;
generating one or more variants of machine learning models to model
performance of the one or more predictive forecast models by
training the one or more variants of the machine learning models on
the historical dataset; receiving a current dataset comprising a
second set of forecast pollutants' values derived from the one or
more predictive forecast models and a second set of actual
pollutants' values derived from the one or more measurements of the
pollutants; correlating the current dataset with the historical
dataset to adaptively obtain a filtered historical dataset;
selecting the one or more variants of the machine learning models
trained on the historical dataset and evaluating them on the
filtered historical dataset to assign weights to each of the one or
more variants of the machine learning models and their outputs; and
deriving a statistical model in the form of an optimal combination
function to determine at least one combined forecast pollutants'
value by combining weights assigned to each of the one or more
variants of the machine learning models trained based on the
evaluating of the one or more variants of the machine learning
models on the filtered historical dataset and the outputs of the
each of the one or more variants of machine learning models trained
on the historical dataset, wherein the selecting, the generating,
the receiving, the correlating, the evaluating and the deriving are
performed by the processor using computer-readable instructions
stored in the memory, wherein (xl) the one or more predictive
forecast models include a supervisory control and data acquisition
(SCADA) model, a physical model including numerical pollutants'
reaction kinetics prediction model, a statistical model, a machine
learning model, an alternate forecast model, or combinations
thereof, and wherein (xli) the one or more variants of the machine
learning models include Artificial Neural Networks (ANNs), basis
function models, kernel methods, support vector machines, decision
trees, variation methods, distribution sampling methods, ensemble
methods, graphical models, search methods, or combinations
thereof.
[0198] Although the foregoing disclosure has been described in
terms of some embodiments, other embodiments will be apparent to
those of ordinary skill in the art from the disclosure herein.
Moreover, the described embodiments have been presented by way of
example only, and are not intended to limit the scope of the
inventions. Indeed, the novel methods, programs, devices and
systems described herein may be embodied in a variety of other
forms without departing from the spirit thereof. Accordingly, other
combinations, omissions, substitutions and modifications will be
apparent to the skilled artisan in view of the disclosure
herein.
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