CLIAMS:WE CLAIM

1. A real time thermal comfort, air quality control and energy management system, comprising:
a building energy simulation unit that is configured to
(i) process a first set of inputs specific to a location in real time, wherein said first set of inputs comprises a geometry, material description of said location, occupancy data, weather data, and a specification and operating parameters of a heating ventilating air conditioning (HVAC) equipment that is situated in said location, and
(ii) generate in said real time, using said first set of inputs, a first set of simulated parameters comprising a wall temperature, a room temperature, a heating ventilating air conditioning (HVAC) operating parameters, an energy consumption of said location, or combinations thereof;
a reduced order model unit coupled to said building energy simulation unit in said real time, wherein said reduced order model unit is configured to
(i) obtain a second set of input comprising temperature, velocity, moisture, concentration of contaminants at said locations, wall heat transfer coefficients and the intermixing of flow at zone boundaries to said location or combinations thereof, and
(ii) estimate in said real time, one or more corrected values of heat transfer coefficients, temperature of zones in said location, velocity, moisture, concentration of contaminants at said location, the intermixing of flow at said zone boundaries based on said first set of simulated parameters and said second set of inputs to obtain a first set of estimated parameters.
2. The real time thermal comfort, air quality control and energy management system as claimed in claim 1, wherein said building energy simulation unit is further configured to generate in said real time, a second set of simulated parameters based on said first set of estimated parameters.

3. The real time thermal comfort, air quality control and energy management system as claimed in claim 2, wherein said reduced order model unit coupled to said building energy simulation unit is further configured to estimate in said real time, a second set of estimated parameters based on said second set of simulated parameters, wherein said second set of estimated parameters comprises one or more corrected heat transfer coefficients, temperature of zones in said location, the intermixing of flow at said zone boundaries.

4. The real time thermal comfort, air quality control and energy management system as claimed in claim 2, wherein said building energy simulation unit is configured to generate in said real time, a final set of simulated parameters until a difference between a first parameter of said final set of simulated parameters and a first parameter of a penultimate set of simulated parameters reaches a first predetermined threshold.

5. The real time thermal comfort, air quality control and energy management system as claimed in claim 4, wherein said reduced order model unit is configured to generate in said real time, a final set of estimated parameters until a difference between a first parameter of said final set of estimated parameters and a first parameter of a penultimate set of estimated parameters reaches a second predetermined threshold.

6. The real time thermal comfort, air quality control and energy management system as claimed in claim 5, wherein a set of output parameters comprising thermal comfort, air quality and energy are obtained from at least one of said final set of simulated parameters and said final set of estimated parameters.

7. The real time thermal comfort, air quality control and energy management system as claimed in claim 6, further comprising an optimization unit that is configured to receive said set of output parameters comprising said thermal comfort, said air quality and said energy and generate a set of optimized operating parameters comprising an optimized thermal comfort, an optimized air quality and an optimized energy for said first set of inputs.
8. A method for real time thermal comfort, air quality control and energy management, comprising:
processing in real time, using a building energy simulation unit, a first set of inputs specific to a location, wherein said first set of inputs comprises a geometry, material description of said location, occupancy data, weather data, and a specification and operating parameters of a heating ventilating air conditioning (HVAC) equipment that is situated in said location;
generating in said real time, using said first set of inputs, a first set of simulated parameters comprising a wall temperature, a room temperature, a heating ventilating air conditioning (HVAC) operating parameters, an energy consumption of said location, or combinations thereof;
obtaining, using a reduced order model unit coupled to said building energy simulation unit in said real time, a second set of input comprising temperature, velocity, moisture, concentration of contaminants at said locations, wall heat transfer coefficients and the intermixing of flow at zone boundaries to said location or combinations thereof; and
estimating in said real time, one or more corrected values of heat transfer coefficients, temperature of zones in said location, velocity, moisture, concentration of contaminants at said location, the intermixing of flow at said zone boundaries based on said first set of simulated parameters and said second set of inputs to obtain a first set of estimated parameters.

9. The method claimed in claim 8, further comprising generating in said real time, using said building energy simulation unit, a second set of simulated parameters based on said first set of estimated parameters.

10. The method as claimed in claim 9, further comprising estimating in said real time, using said reduced order model unit coupled to said building energy simulation unit, a second set of estimated parameters based on said second set of simulated parameters, wherein said second set of estimated parameters comprises one or more corrected values of heat transfer coefficients, temperature of zones in said location, velocity, moisture, concentration of contaminants at said location, the intermixing of flow at said zone boundaries.

11. The method as claimed in claim 8, further comprising, generating in said real time, using, said building energy simulation unit, a final set of simulated parameters until a difference between a first parameter of said final set of simulated parameters and a first parameter of a penultimate set of simulated parameters reaches a first predetermined threshold.

12. The method as claimed in claim 11, further comprising, estimating in said real time, using said reduced order model unit, a final set of estimated parameters until a difference between a first parameter of said final set of estimated parameters and a first parameter of a penultimate set of estimated parameters reaches a second predetermined threshold.

13. The method as claimed in claim 12, further comprising, obtaining a set of output parameters comprising thermal comfort, air quality and energy from at least one of said final set of simulated parameters and said final set of estimated parameters.

14. The method as claimed in claim 13, further comprising, generating, using an optimization unit, a set of optimized operating parameters comprising an optimized thermal comfort, an optimized air quality and an optimized energy for said first set of inputs based on said set of output parameters. ,TagSPECI:FORM 2

THE PATENTS ACT, 1970
(39 of 1970)
&
THE PATENT RULES, 2003

COMPLETE SPECIFICATION
(See Section 10 and Rule 13)

Title of invention:
SYSTEM AND METHOD FOR REAL TIME CONTROL OF THERMAL COMFORT, AIR QUALITY AND ENERGY MANAGEMENT

Applicant:
Tata Consultancy Services Limited
A company Incorporated in India under the Companies Act, 1956
Having address:
Nirmal Building, 9th floor,
Nariman point, Mumbai 400021,
Maharashtra, India

The following specification particularly describes the invention and the manner in which it is to be performed.

TECHNICAL FIELD
[0001] The embodiments herein generally relate to environmental condition control systems, and, more particularly, system and method for controlling environmental conditions and energy consumption for buildings.

BACKGROUND
[0002] People spend most of their time (e.g., day in and day out) in closed environments or locations for example, house, office, etc. Indoor thermal comfort and air quality play a very important role today because they directly affect the health, sense of well-being and the productivity of occupants. It is very important not only to monitor but control these aspects. Along with these, the energy spent on conditioning the indoor space forms a critical part of total energy spent in such locations. Considering world energy scenario, it is imperative to reduce the energy consumption as far as possible without compromising the comfort and air quality in buildings.

[0003] Monitoring of critical parameters for example, thermal comfort, air quality and energy consumption need a lot of metering and sensing in buildings. Typical implementations involve enormous sensor networks for monitoring of such critical parameters, for example in older and existing buildings, which lead to change in infrastructure modifications, and substantial increase in cost. Monitoring these critical parameters in real-time are also important because the occupants’ sense of well-being is largely connected to the psychological factor of knowing how the surroundings actually are.

[0004] Considering the dynamic changes of occupancy (for example, increase or decrease in occupants), electronic heat loads as well as environmental conditions, buildings not only need real-time monitoring but also a real-time control of thermal comfort, air quality and energy on cooling/heating. Moreover, a building for example, hospitals require special comfort-quality needs on operation theatres; a shopping mall or the like needs to adjust its cooling/heating depending upon the occupancy with respect to location and time. These changes are only possible by controlling heating, ventilating, and air conditioning (“HVAC”) operational parameters in real-time according to the demand on each part of the building. The decision making however relies on tools to predict an outcome of making the changes in HVAC operational parameters.

[0005] Computational modeling has been used to predict the heat loads, effect of weather conditions-occupancy on the thermal comfort, quality and energy in buildings. Building Energy Simulations (“BES”) provide very fast, but relatively an inaccurate prediction of above mentioned parameters, due to underlying assumptions, especially for non-uniform environments. Dynamic dispersion of pollutants, distribution of comfort may not be accurate multi-zone models due to their ‘perfect mixing’ assumption. Computational Fluid Dynamics (“CFD”) on the other hand provides predictions of comfort quality but is very time-consuming.

[0006] In addition, changes in the distribution of cold air from an air handling unit (the AHU) cannot be effectively handled by a BES model alone. These changes include changes in individual supply openings in the AHU supply duct, in the form of dampers to vary mass flow through each opening, direction of flow and positioning of the individual openings. Such variations may alter the thermal comfort, air quality and energy scenario considerably, which can only be captured through a CFD by consuming a large amount of processing time.

SUMMARY
[0007] The following presents a simplified summary of some embodiments of the disclosure in order to provide a basic understanding of the invention. This summary is not an extensive overview of the invention. It is not intended to identify key/critical elements of the invention or to delineate the scope of the invention. Its sole purpose is to present some embodiments of the invention in a simplified form as a prelude to the more detailed description that is presented below.

[0008] In one aspect, a real time thermal comfort, air quality control and energy management system is provided. The system comprises: a building energy simulation unit that is configured to (i) process a first set of inputs specific to a location in real time, wherein the first set of inputs comprises a geometry, material description of the location, occupancy data, weather data, and a specification and operating parameters of a heating ventilating air conditioning (HVAC) equipment that is situated in the location, and (ii) generate in the real time, using the first set of inputs, a first set of simulated parameters comprising a wall temperature, a room temperature, a heating ventilating air conditioning (HVAC) operating parameters, an energy consumption of the location, or combinations thereof; a reduced order model unit coupled to the building energy simulation unit in real time, wherein the reduced order model unit when coupled with the building energy simulation unit in real time, is configured to (i) obtain a second set of input comprising temperature, velocity, moisture, concentration of contaminants at the locations, wall heat transfer coefficients and the intermixing of flow at zone boundaries to the location or combinations thereof, and (ii) estimate in the real time, one or more corrected values of heat transfer coefficients, temperature of zones in the location, velocity, moisture, concentration of contaminants at the location, the intermixing of flow at the zone boundaries based on the first set of simulated parameters and the second set of inputs to obtain a first set of estimated parameters.

[0009] The building energy simulation unit is further configured to generate in real time, a second set of simulated parameters based on the first set of estimated parameters. The reduced order model unit coupled to the building energy simulation unit and is further configured to estimate in the real time, a second set of estimated parameters based on the second set of simulated parameters, wherein the second set of estimated parameters comprises one or more corrected heat transfer coefficients, temperature of zones in the location, velocity, contaminant concentration, moisture, the intermixing of flow at the zone boundaries. The building energy simulation unit is configured to generate in real time, a final set of simulated parameters until a difference between a first parameter of the final set of simulated parameters and a first parameter of the penultimate set of simulated parameters reaches a first predetermined threshold. The reduced order model unit is configured to generate in real time, a final set of estimated parameters until a difference between a first parameter of the final set of estimated parameters and a first parameter of the penultimate set of estimated parameters reaches a second predetermined threshold.

[0010] The system obtains a set of output parameters comprising thermal comfort, air quality and energy from at least one of the final set of simulated parameters and the final set of estimated parameters. The system further comprises an optimization unit that is configured to receive the set of output parameters comprising at least one of the thermal comfort, the air quality and the energy and generate a set of optimized operating parameters comprising at least one of an optimized thermal comfort, an optimized air quality and an optimized energy for the first set of inputs.

[0011] In another aspect, a method is provided. The method comprises processing in real time, using a building energy simulation unit, a first set of inputs specific to a location, wherein the first set of inputs comprises a geometry, material description of the location, occupancy data, weather data, and a specification and operating parameters of a heating ventilating air conditioning (HVAC) equipment that is situated in the location; generating in the real time, using the first set of inputs, a first set of simulated parameters comprising a wall temperature, a room temperature, a heating ventilating air conditioning (HVAC) operating parameters, an energy consumption of the location, or combinations thereof; obtaining, using a reduced order model unit coupled to the building energy simulation unit, a second set of input comprising temperature, velocity, moisture, concentration of contaminants at the locations, wall heat transfer coefficients and the intermixing of flow at zone boundaries to the location or combinations thereof, and estimating in real time, one or more corrected values of heat transfer coefficients, temperature of zones in the location, velocity, moisture, concentration of contaminants at the location, the intermixing of flow at the zone boundaries based on the first set of simulated parameters and the second set of inputs to obtain a first set of estimated parameters.

[0012] The method further comprises generating in real time, using the building energy simulation unit, a second set of simulated parameters based on the first set of estimated parameters, estimating in the real time, using the reduced order model unit coupled to the building energy simulation unit, a second set of estimated parameters based on the second set of simulated parameters, wherein the second set of estimated parameters comprises one or more corrected values of heat transfer coefficients, temperature of zones in the location, velocity, moisture, concentration of contaminants at the location, the intermixing of flow at the zone boundaries.

[0013] The method further comprises generating in real time, using, the building energy simulation unit, a final set of simulated parameters until a difference between a first parameter of the final set of simulated parameters and a first parameter of a penultimate set of simulated parameters reaches a first predetermined threshold.

[0014] The method further comprises estimating in real time, using the reduced order model unit, a final set of estimated parameters until a difference between a first parameter of the final set of estimated parameters and a first parameter of a penultimate set of estimated parameters reaches a second predetermined threshold. The method further comprises obtaining a set of output parameters comprising thermal comfort, air quality and energy from the final set of simulated parameters and the final set of estimated parameters, and generating, using an optimization unit, a set of optimized operating parameters comprising an optimized thermal comfort, an optimized air quality and an optimized energy for the first set of inputs based on the set of output parameters.

[0015] These and other aspects of the embodiments herein will be better appreciated and understood when considered in conjunction with the following description and the accompanying drawings. It should be understood, however, that the following description, while indicating preferred embodiments and numerous specific details thereof, are given by way of illustration. The summary is not intended to identify essential features of the claimed subject matter nor is it intended for use in determining or limiting the scope of the claimed subject matter. Changes and modifications may be made within the scope of the embodiments herein.

BRIEF DESCRIPTION OF THE DRAWINGS
[0016] The embodiments herein will be better understood from the following detailed description with reference to the drawings, in which:

[0017] Figure 1 is a block diagram illustrating a real time thermal and air quality control and energy management system according to an embodiment of the present disclosure;

[0018] Figure 2 is a flow diagram illustrating a method of real time coupling of a BES unit and a ROM unit for obtaining thermal comfort, air quality and energy, according to an embodiment of the present disclosure; and

[0019] Figure 3 is a flow diagram illustrating a method of real time monitoring and control of an indoor environment and energy using the BES unit coupled to the ROM unit, according to embodiment of the present disclosure; and

[0020] Figure 4, is an exemplary view that illustrates multiple AHUs setup supplying air to multiple zones through a common chiller configuration, according to embodiment of the present disclosure.

DETAILED DESCRIPTION OF EMBODIMENTS
[0021] The embodiments herein and the various features and advantageous details thereof are explained more fully with reference to the non-limiting embodiments that are illustrated in the accompanying drawings and detailed in the following description. The examples used herein are intended merely to facilitate an understanding of ways in which the embodiments herein may be practiced and to further enable those of skill in the art to practice the embodiments herein. Accordingly, the examples should not be construed as limiting the scope of the embodiments herein.

[0022] Referring now to the drawings, and more particularly to Figures. 1 through 4, where similar reference characters denote corresponding features consistently throughout the figures, there are shown preferred embodiments and these embodiments are described in the context of the following exemplary system and/or method.

[0023] Figure. 1 is a block diagram of a thermal comfort, air quality control and energy management system 100 according to an embodiment of the present disclosure. The real time thermal comfort, air quality control and energy management system 100 includes a Building Energy Simulation (also referred herein as BES) unit 102, a Computational Fluid Dynamics (also referred herein as CFD) unit 104, a Reduced Order Model (also referred herein as ROM) unit 106, and an optimization unit 108. The Building Energy Simulation unit 102 is configured to process a first set of inputs specific to a location in real time. The first set of inputs comprises geometry such as dimensions, orientation, design of the location, air distribution system, material description of said location such as type of glass used in construction, occupancy data, weather data, and a specification and operating parameters of a heating ventilating air conditioning (HVAC) equipment that is situated in the location. The location comprises an indoor environment such as but not limited to a building, a room, etc., in one example embodiment. The Building Energy Simulation unit generates a first set of simulated parameters comprising a wall temperature, a room temperature, a heating ventilating air conditioning (HVAC) operating parameters, an energy consumption of said location, or combinations thereof based on the first set of inputs in real time. The BES unit 102 calculates the total heat load on the building (or one or more zones in the location) by accounting for, but not limited to, solar, human, electronic heat gains through conduction/convection and radiation in real time. The BES unit 102 further provides the HVAC/AHU sizing and operating parameters (for each zone) such as set-points, supply flow-temperature etc., in real time (if not provided earlier). The computational fluid dynamics unit 104 is configured to receive one or more inputs comprising but not limited to, detailed geometry and layout of each zone of the location (e.g., a building or a room), heat load distribution along with the design-operation details of the HVAC and air handling unit (AHU). The CFD unit 104 outputs a set of parameters comprising, but are not limited to, temperature, velocity, moisture, concentration of contaminants at the locations, wall heat transfer coefficients and the intermixing of flow at zone boundaries of the location or combinations thereof. The CFD unit is run in offline mode for several parametric runs by varying the operating and design parameters of the Heating Ventilating and Air-Conditioning unit, such that the entire range is covered. The corresponding output in terms of set of parameters mentioned above is stored for online use later. The CFD unit 104 outputs the set of parameters in an offline mode. The ROM unit 106 is coupled to the BES unit 102 in real time.

[0024] The ROM unit 106 obtains the set of offline parameters from the CFD unit 104. The ROM unit 106 is a Proper Orthogonal Decomposition based ROM unit, in a preferred embodiment. The ROM unit 106 further receives in real time, the first set of simulated parameters from the BES unit 102. The ROM unit 106 estimates in real time, one or more corrected values of heat transfer coefficients, temperature of zones in said location, velocity, moisture, concentration of contaminants at said location, the intermixing flow at said zone boundaries based on the first set of simulated parameters received from the BES unit 102 and the set of offline parameters obtained from the CFD unit 104 to generate a first set of estimated parameters. The first set of estimated parameters is fed to the BES unit 102 to generate in real time, a second set of simulated parameters. The first set of simulated parameters and the second set of simulated parameters are identical, in one example embodiment. The first set of simulated parameters and the second set of simulated parameters are different from each other, in another example embodiment. The second set of simulated parameters is fed to the ROM Unit 106 to generate a second set of estimated parameters in real time. The first set of estimated parameters and the second set of estimated parameters are identical, in one example embodiment. The first set of estimated parameters and the second set of estimated parameters are different, in another example embodiment. The BES unit 102 is configured to generate in real time, a final set of simulated parameters until a difference between a first parameter of the final set of simulated parameters and a first parameter of a penultimate set of simulated parameters reaches a first predetermined threshold. For example, the first set of simulated parameters comprises B11, B12, B13, B14, …, B1n, etc. Similarly, the second set of simulated parameters comprises B21, B22, B23, B24, ….B2n, etc. Likewise, a third set of simulated parameters comprises B31, B32, B33, B34, …, B3n, etc. When a difference between (i) at least one parameter from one of the above set of simulated parameters (e.g., the second set of parameters) and (ii) at least one corresponding parameter from another one of the above set of simulated parameters (e.g., the third set of parameters) reaches the first predetermined threshold, the BES unit 102 terminates the process. For example, the first predetermined threshold is 5%, where B11, B21, and B31 being energy parameters. When the difference between any one of the parameters from the first set of simulated parameters (B11) and any one of the parameters from the second set of simulated parameters (B21) results in 10%, the BES unit 102 generates the third set of simulated parameters. Likewise, at least one parameter (B31) from the third set of simulated parameters is considered and compared with any of the previous parameters (B11) from the one of above set of simulated parameters. When the difference results in 5%, the BES unit 102 terminates the process.

[0025] The ROM unit 106 is configured to generate in real time, a final set of estimated parameters until a difference between a first parameter of the final set of estimated parameters and a first parameter of the penultimate set of estimated parameters reaches a second predetermined threshold. For example, the first set of estimated parameters comprises R11, R12, R13, R14, …, R1n, etc. Similarly, the second set of estimated parameters comprises R21, R22, R23, R24, ….R2n, etc. Likewise, a third set of estimated parameters comprises R31, R32, R33, R34, …, R3n, etc. When a difference between (i) at least one parameter from one of the above set of estimated parameters (e.g., the second set of parameters) and (ii) at least one parameter from another one of the above set of estimated parameters (e.g., the third set of parameters) reaches the second predetermined threshold, the ROM unit 106 terminates the process. For example, the second predetermined threshold is 4%, where R11, R21, and R31 being air quality parameter. When the difference between any one of the parameters from the first set of estimated parameters (R11) and any one of the parameters from the second set of estimated parameters (R21) results in 12%, the ROM unit 106 generates the third set of estimated parameters. Likewise, at least one parameter (R31) from the third set of estimated parameters is considered and compared with any of the parameters (R11) from the one of above set of estimated parameters. When the difference results in 4%, the ROM unit 106 terminates the process. Using the final set of simulated parameters and the final set of estimated parameters a set of output parameters comprising thermal comfort, air quality and energy is generated in real time. At least one of the BES unit 102 and the ROM unit 106 will continue to output set of simulated parameters and estimated parameters until a difference between a current parameter and any previous parameter (or penultimate parameter) reaches a threshold.

[0026] The optimization unit 108 is configured to receive the set of output parameters comprising thermal comfort, air quality and energy and generate a set of optimized operating parameters comprising an optimized thermal comfort, an optimized air quality and an optimized energy for the first set of inputs based on the set of output parameters.

[0027] The BES unit 102, the ROM unit 106, and the optimization unit 108 are implemented as a logically self-contained part of a software program that when executed perform the above method described herein, in one embodiment. In another embodiment, the BES unit 102, the ROM unit 106, and the optimization unit 108 are implemented as a self-contained hardware component. In yet another embodiment, the above units 102, 106 and 108 may be implemented as a self-contained hardware component, with a logically self-contained part of a software program embedded into each of the hardware component.

[0028] Figure. 2, with reference to Figure. 1, is a flow diagram 200 illustrating a method of real time coupling of the BES unit 102 and the ROM unit 106 for obtaining thermal comfort, air quality and energy, according to an embodiment of the present disclosure. In step 202, one or more inputs comprising: a starting time t, interval tsub and an ending time tend are provided. For example, the one or more inputs are provided by the user. In step 204, additional inputs such as conditions prevailing over next tsub minutes that include the occupancy schedule, weather conditions and other heat loads and constraints such as, but are not limited to, zone set-point temperature are provided to both the BES unit 102 and the ROM unit 106. In Step 206, the BES unit 102 performs simulation on one or more inputs and the additional inputs. In Step 208, energy consumption Eiter is obtained based on the simulation.

[0029] For first iteration, (iter=1), the interior wall temperatures calculated by the BES unit 102, Twall and the HVAC operation parameters such as msup, Tsup (along with humidity and contaminant concentration conditions) are obtained in Step 216. In Step 218, the interior wall temperatures calculated by the BES unit 102, Twall and the HVAC operation parameters such as msup, Tsup (along with humidity and contaminant concentration conditions) are processed by the ROM unit 106 to obtain the corrected heat transfer coefficients, the corrected room/zone temperatures as well as inter-zone mixing of flow which are fed back to the BES unit 102 for a re-simulation.

[0030] The BES unit 102 performs simulation/reruns with altered inputs and in step 210, it is determined whether the change in energy (or an error in energy) after successive iterations reaches a predetermined threshold. The change in energy is determined in accordance with an equation Error = (Eiter – E iter-1) < Threshold). In Step 212, when Error = (Eiter – E iter-1) is less than threshold: Et = Eiter , and final Thermal comfort and air quality parameters are obtained in step 214 and the step 202 is repeated. This procedure ensures accurate prediction of energy. Else, (when the Error is greater than the threshold), the interior wall temperatures calculated by the BES unit 102, Twall and the HVAC operation parameters such as msup, Tsup (along with humidity and contaminant concentration conditions) are obtained in step 216. In step 218, the interior wall temperatures calculated by the BES unit 102, Twall and the HVAC operation parameters such as msup, Tsup (along with humidity and contaminant concentration conditions) are processed by the ROM unit 106 to obtain the corrected heat transfer coefficients, the corrected room/zone temperatures as well as inter-zone mixing in seconds which are fed back to the BES unit 102 for a re-simulation. In other words, unlike conventional systems and methods, the similar time frames of the ROM unit 106 and the BES unit 102, allow "real-time" coupling and optimization of parameters.

[0031] Figure. 3, with reference to Figures. 1 and 2, is a flow diagram 300 illustrating a method for real-time monitoring and control of an indoor environment and energy using the BES unit 102 coupled to the ROM unit 106 according to an embodiment of the present disclosure. In step 302, one or more input conditions comprising occupancy data, weather data, and electronic equipment status are obtained. These input conditions may be obtained for example, either from a user, or detected by one or more sensors. For example, the occupancy of zones can be an input provided by the user based on typical patterns. In another example, the occupancy of zones can be detected by one or more occupancy sensors. In a shopping mall, each retail outlet can have one or more sensors for counting people/users/occupants present in a particular store. The weather data can be obtained from a typical available weather files. Alternately, ambient temperature, humidity etc., can be sensed and fed to the system 100 real-time. Other static inputs for example, design of the zones, materials etc., are already entered in a database (not shown in Figure. 3) during the offline activities. When the changes in occupancy or weather are relatively quick, then time interval of optimizing/coupling tsub can be reduced accordingly. The parameter tsub can range from one or more seconds to one or more hours, depending upon the variation in conditions.

[0032] In step 304, the BES unit 102, the CFD unit 104, the ROM unit 106, and the optimization unit 108 process one or more inputs to obtain optimum operating parameters. One or more auxiliary inputs which may not play a direct role in modeling either the BES unit 102 (and/or the ROM unit 106), but may have an impact on the optimization process when provided by the user. One or more design parameters such as individual supply duct damper positions (for varying mass flow through each opening, mdistribution), direction at which the air is supplied (mdir), part of the duct that is open (location of the supply opening) (mlocate), occupancy variation (location wise) are defined. Likewise, one or more operational parameters such as, but are not limited to, supply mass flow through individual openings (mdistribution), total AHU/HVAC mass flow rates (msup), HVAC supply temperatures (Tsup), human occupancy heat load variation, solar heat load (heat flux variations), Equipment heat load, Enclosure wall temperatures (Twall) may further be defined.

[0033] One or more operational parameters which are to be modified to optimize the indoor environment have a range of operation for respective zones/buildings. In one example, a particular AHU/HVAC may comprise a fixed flow rate msup, which reduces the possible options for determining an optimal mix of operational parameters. In a scenario, where parameters like Tsup, mdistribution for which one or more inputs are to be obtained from the user, the user is prompted to specify the operating range for each of the parameters such as msup, Tsup, mdistribution, mdir etc. In case, where parameters cannot be changed (or modified), then the user can eliminate it from the optimization cycle. Possible combinations of the operating parameters are derived based on these constraints. Another input that may be required from the user is the interval at which a new value of parameters should be considered. For example, when msup range varies from 0 to 10 kg/s, then the user can decide to divide the range between ‘n’ numbers of intervals. If ‘n’ is 5, then possible values of msup are (0,2,4,6,8,10) kg/s. Similar procedure is followed for other operating parameters.

[0034] Further, in step 304, based on the auxiliary inputs the system 100 finds all the possible combinations of the operating parameters selected. The combinations are stored in the database (not shown in Figure. 3). The combinations are fed to the coupled BES-ROM system (e.g., the BES unit 102 and the ROM unit 106) to determine respective thermal comfort, internal air quality and Energy distributions/values which are also stored in the database. The optimization unit 108 computes a set of optimized parameters then finds the best case based on the optimization rules provided earlier for the time tsub. The optimization unit 108 implements one or more rules that include details required for the optimization process for example, optimization function and constraints. Indoor air parameters comprising: Thermal comfort, Air Quality and Energy are optimized. Based on the scenarios, the parameters are prioritized for optimization. For example, in an occupied zone, thermal comfort and air quality are prioritized over energy for optimization. However, in a zone where only electronic equipment is present, energy on cooling may be a critical parameter. The priorities and criticality of the three parameters (Thermal comfort, Air Quality and Energy) also changes as per the type of building. For example, a hospital operation theatre may have very stringent comfort and quality parameters compared to a data center, where energy may be a critical parameter. Depending upon the priority and criticality, the optimization function will change providing different set of operating parameters each time. Therefore, before optimization is run, one or more inputs corresponding to the priority and the criticality of these three parameters may be obtained (for example, from a user) for each zone of the building. Along with the priorities, the threshold range of values for thermal comfort and air quality also need to be known for the optimization unit 108 to perform optimization. In addition, a number of algorithms for example, Genetic Algorithm can be used to find the optimum parameters. The user is prompted to select settings for the algorithm used.

[0035] As an example of optimization process, a single ventilated room with a given solar heat, electronic heat loads and human occupancy schedule is considered. The detailed design, layout, materials of construction, orientation, weather and location of heat sources, etc., are pre-configured. Assume that the room is ventilated by a single AHU with mass flow ranging between 2-10 kg/s and supply temperature ranging between 15-25 deg C, and the air is supplied through a single vent with capacity to vary the angle of supply (either 30 or 45 degree to the horizontal). The optimization unit 108 determines a set of operating and design parameters of HVAC (mentioned above) which ensure the best thermal comfort, air quality and least energy consumption. All possible combinations of the varying parameters are shortlisted (or selected or identified) using an interval input provided by the user or through a genetic algorithm. In one embodiment, consider AHU flow rate varying between 5 to 10 Kg/s. When user inputs "0.5" as interval input, the thermal comfort, air quality and energy will be evaluated at the following flow rates - 5, 5.5,6,6.5,7,7.5 and so on. When the user inputs "1" as interval input, the thermal comfort, air quality and energy will be evaluated at the following flow rates - 5, 5.5, 6, 6.5,7,7.5 and so on. A coupled BES-ROM module (e.g., the BES unit 102 and the ROM unit 106) are executed for every such combination for a given set of conditions over time tsub. Each combination of HVAC parameters produces a set of thermal comfort, air quality and energy parameters. A combination of HVAC parameters best suited for the user provided preference for thermal comfort, air quality and energy is selected for implementation. For example, when combination A (msup of 6 kg/s, Tsup of 22 deg C and supply angle of 45 deg) and combination B (msup of 4 kg/s, Tsup of 20 deg C and supply angle of 30 deg ) produces similar energy consumption and air quality output, but differs in thermal comfort distribution, the one with better comfort will be selected.

[0036] In step 306, the system 100 then implements the recommended operating parameter combination (manually or automatically). The corresponding values of thermal comfort, internal air quality distribution and energy are displayed on display screen.

[0037] Figure. 4, with reference to Figures 1 through 3, is an exemplary view that illustrates multiple AHUs setup 400 supplying air to multiple zones through a common chiller configuration according to an embodiment of the present disclosure. In one embodiment, for a given building, there are multiple independent HVAC systems supplying air to a plurality of zones. In step 402, one or more inputs comprising chiller water flow rate range and chiller water supply temperature range are provided based on the ambient and the capacity of the chiller. In step 404, a plurality of related dependencies are described. The constraint on the supply flow rate of AHUs depends on the make and capacity of the AHU. Similarly, the range of supply temperature through the AHU depends upon the chiller water flow rate and the incoming chiller water temperature.