Smart air conditioning system with micro-zonal occupant-centric control

Abstract

A smart air conditioning system having microcontroller processor based air flow control and in being configured to a method of controlling airflow is able to set and plan setback temperature and setback flow in unoccupied regions of a large room, is provided, while maintaining setpoint temperature in the occupied regions so as to avoid thermal discomfort and increase in energy consumption and so that an energy efficient system is realized. The smart air-conditioning system of the present invention could be thus provided for air conditioning only the occupied regions in a thermal zone using advanced airflow control / strategies thereto with Micro-Zonal Occupant-Centric Control to reduce air conditioning energy consumption without compromising thermal comfort.

Description

[0001] TITLE: Smart air conditioning system with Micro-Zonal Occupant- Centric Control Field of the Invention The present invention relates to smart air conditioning system having microcontroller processor based air flow control and in being configured to a method of controlling airflow is able to set and plan setback temperature and setback flow in unoccupied regions of a large room, while maintaining setpoint temperature in the occupied regions so as to avoid thermal discomfort and increase in energy consumption and so that an energy efficient system is realized. The smart air-conditioning system of the present invention could be thus provided for air conditioning only the occupied regions in a thermal zone using advanced airflow control / strategies thereto with Micro-Zonal Occupant-Centric Control to reduce air conditioning energy consumption without compromising thermal comfort. Background Art Occupant-centric control (OCC) of air conditioning systems is a smart air conditioning system and method by which the controller identifies the occupancy conditions and operates the air conditioner to create comfortable conditions only in the occupied thermal zones (Pang et al., 2020). To employ OCC within large thermal zones, the zone is first divided into virtual micro-zones and airflow through each diffuser is controlled independently and the process is termed Micro-Zonal Occupant-Centric Control. In the absence of physical partitions, cool air in the occupied micro-zones easily escapes to the adjacent unoccupied micro-zones and may even increase the energy consumption and thermal discomfort. Controlling inter-micro-zonal interaction is a challenge. Recent studies show that employing Occupant-centric control (OCC) in virtual micro- zones leads to several challenges such as increase in cooling time due to uncontrolled movement of cool air through the virtual partitions, increase in return air temperature (Jacob et al., 2023a) as only small portions of thermal zones are air- conditioned; concentration of cool air below the supply diffusers (Jacob et al., 2022) due to lack of attraction from adjacent air-jets, merging or air jets from adjacent diffusers and deflection of air jets to unoccupied regions, attachment of cool air to walls or ceilings (Jacob et al., 2023c) and discomfort in occupied micro-zones due to draft and horizontal and vertical thermal gradients (Jacob et al., 2023b, 2024b, 2024a). Few strategies have also been proposed to control airflow to address the aforementioned challenges. Two strategies proposed are (1) using a setback flow (SBF), which is a low velocity flow, in the unoccupied micro-zones (Jacob et al., 2024b). This low velocity flow is stated to get attracted towards high velocity flow in the occupied micro-zone to increase the spread of air jets in the occupied micro-zone. Hence, it helps in reducing attachment of cool air to surfaces and concentration of cool air at specific locations. (2) Another strategy is to maintain a higher setpoint temperature called setback temperature (SBT) in the unoccupied micro-zones. This helps in reducing the temperature difference between occupied and unoccupied regions, which reduces the loss of cool air from occupied to unoccupied regions. This also reduces discomfort due to thermal gradients. The velocity of supply air in SBF and the setpoint temperature in SBT influences air distribution and thermal comfort. There exists an optimal setback velocity in SBF and setpoint temperature in SBT at which thermal comfort may be achieved at minimal energy consumption. The present disclosure gives a method by which optimal SBF and SBT can be planned in unoccupied micro-zones to maintain comfortable conditions in occupied micro-zones at minimum energy. The micro-zones with SBF or SBT can be planned by evaluating thermal comfort. This evaluation can be done considering different scenarios of occupancy prior to actually implementation. This will help in arriving at the range of optimal values for SBF and SBT, from which the optimal values can be selected considering real-time changes in the occupancy conditions. Large office rooms, shopping spaces in commercial buildings, classrooms or auditoriums in academic buildings are unhindered by partitions and are considered as a single thermal zone. These spaces are air conditioned using centralized systems or controlled using a single thermostat. In such spaces locally air conditioning only the occupied regions can help in reducing building energy consumption. But only air conditioning the occupied micro- zones can lead to thermal discomfort due to thermal gradients or draft and may also increase the energy consumption if cool air escapes to the unoccupied regions. Using SBT or SBF in the unoccupied regions adjacent to the occupied region can reduce thermal discomfort. The inputs needed to plan SBF and SBT are the layout of the zone, characteristics of HVAC system and diffusers, planned occupancy conditions and furniture arrangement and weather conditions. A validated CFD solver can be used to evaluate thermal comfort. CFD software packages can be used for this purpose, but they are time consuming and are suitable to be used in design phase. During real- time working, CFD using advanced deep learning models can be used to quickly estimate the indoor conditions in the micro-zones. Recently, deep learning model - Long Short Term memory has been used to estimate CFD parameters without running full scale CFD simulations. Such approaches can be applied to the invention discussed here (Pandey et al., 2024). References: Jacob, J. C., Pandit, D., & Sen, J. (2023a). An explorative study on transient cooling pattern and energy efficiency while using micro-zonal Occupant-centric control. Architectural Engineering and Design Management, 19(4), 340–359. https: / / doi.org / doi: 10.1080 / 17452007.2022.2049439 Jacob, J. C., Pandit, D., & Sen, J. (2023b). Reimagining energy-efficient cooling through comfort-based occupant-centric micro-zonal control. ASHRAE Transactions, 129(2023), 428–436. https: / / www.scopus.com / record / display.uri?eid=2-s2.0- 85191176596&origin=resultslist Jacob, J. C., Pandit, D., & Sen, J. (2023c). Energy-saving potential in Indian open-plan offices using Micro-Zonal Occupant Centric Control (MZOCC). Energy and Buildings, 282, 112799. https: / / doi.org / 10.1016 / j.enbuild.2023.112799 Jacob, J. C., Pandit, D., & Sen, J. (2024a). Developing a validated simulation model of micro- zonal air-conditioning to evaluate thermal comfort parameters. Architectural Engineering and Design Management. https: / / doi.org / 10.1080 / 17452007.2024.2319756 Jacob, J. C., Pandit, D., & Sen, J. (2024b). Investigating enhanced thermal comfort and energy efficiency through strategized airflow in Micro-Zonal Occupant-Centric Control (MZOCC). Energy & Buildings, 318(2024). https: / / doi.org / https: / / doi.org / 10.1016 / j.enbuild.2024.114497. Jacob, J. C., Pandit, D., & Sen, J. (2022). Reducing HVAC Energy Consumption Through Optimal Sub-Zoning Considering Occupant-Centric Control (OCC). International Conference on Efficient Building Design: Material and HVAC Equipment Technologies, 199–208. https: / / www.scopus.com / record / display.uri?eid=2-s2.0- 85169915496&origin=resultslist&sort=plf- f&src=s&st1=Jacob&st2=Jeslu&nlo=1&nlr=20&nls=count- f&sid=c2ab008d43e4810e46a123c99fc75d40&sot=anl&sdt=aut&sl=40&s=AU- ID%2528%2522Jacob%252C+Jeslu+Celine%2522+5753 Pandey, A., Mahajan, J., P., S., Rastogi, A., Roy, A., & Chakrabarti, P. P. (2024). DARSI: A deep auto-regressive time series inference architecture for forecasting of aerodynamic parameters. Journal of Computational Science, 82, 102401. https: / / doi.org / 10.1016 / J.JOCS.2024.102401 Pang, Z., Chen, Y., Zhang, J., O’Neill, Z., Cheng, H., & Dong, B. (2020). Nationwide HVAC energy-saving potential quantification for office buildings with occupant-centric controls in various climates. Applied Energy, 279, 115727. https: / / doi.org / 10.1016 / j.apenergy.2020.115727 References are drawn to prior patents in relation to the same: Previous inventions have discussed the use of setback mode in air conditioners where the air conditioner operates for a setback temperature in the zone. US11994307 (B2) discloses a method to operate lighting and air conditioner in conjunction with each other on detecting occupancy. As the lights are switched on, the light triggers the operation of the HVAC (Heating, ventilation, and air conditioning system). A setback mode is maintained in the HVAC system which is a state where the air conditioner cools the room for a higher setpoint temperature which lies within the comfort limits specified by standards such as ASHRAE 55. The setback mode is activated when no communication is received regarding air conditioning control after a predetermined time. Other inventions such as US9599382 (B2) have developed remote controllers through which setback temperature can be maintained in the zone. US2015330652 (A1) defines setback control as a “method of temporarily changing a setpoint temperature of a room in order to save energy during a time period, such as at night, when no user is in the room or is out of the room for short durations”. The invention also gives a method of dynamically controlling setback mode by determining occupancy status and ambient temperature of the room. Process inventions that simulate airflow using HVAC system components and airflow models to plan optimal operative conditions are also common. JP7337271B2 uses a CFD (computational fluid dynamics) model to plan optimal setpoints considering thermal comfort of occupants. US20200073347A1 discloses a detailed process of using CFD to evaluate indoor conditions and plan airflow in each zone. US9644857B1 develops a system of using CFD simulations to plan setpoint controls by evaluating thermal comfort in a zone. The area to be air-conditioned can be adjusted by the user in the simulation based system. The sensing component of the system can identify the exact location of occupants and direct the controller to cool only the region around the occupant. However, the invention does not discuss how airflow may be controlled to avoid discomfort arising of out localized air-conditioning. Also, in the absence of physical partitions cool air may escape to adjacent unoccupied regions and this will result in increase in energy consumption. So, it is important to plan airflow control in the unoccupied regions in addition to the occupied region to avoid thermal discomfort and increase in energy consumption. Few inventions such as WO2019239812A1 have looked at reducing transmission of diseases by sensing sneezing or coughing and changing the direction of airflow from the diffusers. It is thus a need in the art to further explore smart air conditioning systems that would be able to set and plan airflow control in the unoccupied regions of a large room in addition to the occupied region to avoid thermal discomfort and increase in energy consumption and so that an energy efficiency can be achieved. Objects of the Invention It is thus the basic object of the present invention to provide for smart air conditioning systems having microcontroller based air flow control to set and plan setback temperature (SBT) and setback flow (SBF) in unoccupied regions of a large room, while maintaining setpoint temperature in the occupied regions so as to avoid thermal discomfort and increase in energy consumption and so that energy efficiency can be realized. It is another object of the present invention to provide for said smart air conditioning system which based on said airflow control and upon establishing cooperative connection with air diffusers would be able to efficiently air condition only the occupied regions in a thermal zone using advanced airflow control / strategies thereto adapted to Micro-Zonal Occupant-Centric Control to reduce air conditioning energy consumption without compromising thermal comfort. It is still another object of the present invention to provide for microcontroller configured smart air conditioning system which in having embedded analytics which would be capable of dynamically planning setback temperature and setback flow in unoccupied regions of a large room both in real time and / or when fed with occupancy details. It is another object of the present invention to provide for said microcontroller configured smart air conditioning system to find end use and application in thermal zones such as open-plan offices, academic buildings and the like where occupancy is scattered in different parts of the room. Brief Description of Figures: Figure 1: Occupied and adjacent micro-zones; Figure 2: Processes involved in the invention; Figure 3: Evaluation of thermal comfort in the occupied micro-zone; Figure 4: Process to select the ranges of setback flow and setback temperature using the airflow strategizing unit; Figure 5: Process to select best airflow control strategy; Figure 6: The office chosen to evalaute the proposed process; Figure 7: The best strategies with minimum energy consumption Summary of the invention Thus according to a basic aspect of the present invention there is provided a smart air conditioning system including advanced airflow control with micro-zonal occupant centric control for reducing air conditioning energy consumption maintaining desired occupant centric thermal comfort comprising: an input unit for inputs including room geometry, occupancy status at a given point and weather conditions; a CFD (computational fluid dynamics) operative system including a grid independent micro zonal mesh developer for a thermal zone, boundary condition determinant based on occupancy status, geometry of said thermal zone and weather conditions, means for initial condition setting based on ambient conditions of said thermal zone at a given time and CDF simulator and solver unit estimating conditions within thermal micro zone defining imaginary region catered by an individual diffuser under varying airflow control; said CDF simulator and solver unit operatively connected to an airflow strategizing unit enabling dynamic control of setback flow (SBF) and set back temperature (SBT) in unoccupied micro zones in real time to maintain controlled air conditioning in select unoccupied micro zones and to maintain setpoint temperature and thermal comfort in occupied micro zones at minimum energy consumption thus ensuring energy efficient maintaining desired thermal comfort only around occupied micro zone which is free of any thermal discomfort in the unoccupied micro zones. Preferably said smart air conditioning system is provided wherein said controlled air conditioning in select unoccupied micro zones include layered micro zones adjacent to occupied zone with selective controlled set back flow therein in energy efficient manner including air flow / set back flow controller for controlling airflow based on CFD simulation and solving. More preferably said smart air conditioning system is provided including air flow / set back flow controller providing real time maximum and minimum air flow value based air flow strategic control following dynamic changes in occupancy, said CFD solver unit developing a grid independent mesh based on room geometry inputs, deriving boundary conditions as set point conditions from occupancy status and weather conditions and in having comparator based computational means estimates thermal conditions within the zones for varying airflow control strategies involving said air flow strategizing unit which is integrated with comparator feedback on thermal comfort within micro zones to generate air flow strategy and finalized output of optimal setback flow or setback temperature. According to another aspect of the present invention there is provided said smart air conditioning system wherein said CFD simulation and solver unit is Eulerian method based and discretizes thermal zone / room space by involving finite volume computation that operates by following conservation of mass, momentum and energy as per equations 1-4 applying humidity modelling based on non-reacting species transport as per Equation 5 Equation 5 and involving additional mass diffusion computed as per Equation 6 by taking into account Ficks law of diffusion by considering gravity in y-direction Where, P is the static pressure T is the effective stress tensor s the mass added from the continuous phase to the dispersed phase he source term is the effective conductivity he diffusion flux s the volumetric heat source is the local mass fraction of water vapour (Yi) present in air is the rate of production of water vapour through chemical reaction is the rate of creation of water vapour through addition is the diffusion flux of water vapour is the turbulent Schmidt number assumed as 0.7 wherein the diffusion coefficient is computed based on turbulent analytics and type of fluid flow preferably considering at least two layer turbulence to account for viscous flow near the surfaces; said CFD simulator evaluating and strategizing thermal comfort based on simulations including of transient air temperature, velocity and humidity for every micro zone in thermal zone preferably as grid independent tetrahedral mesh selected for given geometry and tested for temporal independence for timestep based simulations, and with the residual convergence criteria for continuity, velocity, k and epsilon set as 10-3and that of energy and do-intensity set as 10-6, and operating cooperative PMV and PD based thermal comfort determinant based on said simulations by accounting for horizontal thermal gradients and vertical thermal gradients at lines near occupants, as per below Equation 7 where ‘L’ in the PMV Equation is the thermal load, which is the difference in heat produced by the body and heat lost to the environment that depends on the mechanical work done (W), metabolic rate (M), ambient humidity (or partial pressure of water vapour (Pa)), air temperature (ta) and clothing insulation (fcl, tcl, icl) and is estimated by Equation 8. Equation 8 The clothing temperature is found by iteration using Equations 9 to 12 Percentage of people dissatisfied is evaluated using Equation 13 Equation 13 Where, M - metabolic rate (W / m2) W - the effective mechanical power (W / m2) Icl - the clothing insulation, in square metres kelvin per watt (m2⋅ K / W); fcl - the clothing surface area factor; ta - the air temperature, in degrees Celsius (°C); - the mean radiant temperature, in degrees Celsius (°C); Var - the relative air velocity, in metres per second (m / s); pa - the water vapour partial pressure, in pascals (Pa); hc - the convective heat transfer coefficient, in watts per square metre kelvin [W / (m2⋅K)]; tcl - the clothing surface temperature, in degrees Celsius (°C) Percentage of people dissatisfied due to draft is estimated using Equation 14 Equation 14 and wherein said thermal comfort is considered to be satisfactory if PMV lies within ± 0.5, PD<20% with horizontal thermal gradients being <3 °C and vertical thermal gradients being < 3°C for seated and <4°C for standing occupants. 5. The smart air conditioning system as claimed in anyone of claims 1 to 4 wherein IF thermal comfort is attained, the evaluative strategy is stored in the system as a suitable strategy and the energy consumed while employing the strategy is evaluated and stored as a SUITABLE strategy requiring minimum energy consumption for future use by CFD solver module, said minimum energy consumption includes energy required to attain thermal comfort and energy required to maintain thermal comfort computed as per the below equations: Where, - energy consumed (kWh) - power for reaching comfort (PMV=0) (kW) - cooling time (s) - power for maintaining comfort (PMV=0) (kW) - duration for which AC is operated (s) with power consumed at an instance during cool down and maintenance being computed separately as the sum of cooling power and fan power using Equation 16. Equation 16 With power for reaching comfort is computed using Equation 17, where enthalpy values are obtained from the psychometric chart and fan power is calculated using Equation 18 Where, volumetric flow rate (m3 / s) specific weight of air (taken as 2.09N / m3 for 13°C) the enthalpy of unconditioned air (return air + outdoor air for ventilation) the enthalpy of supply air (kJ / kg) total fan pressure (assume 750 Pa) efficiency of the fan (assume 70%) with ventilation rates are computed as per ASHRAE 62.1 (Hedrick et al., 2013) using Equation 19. Equation 19 Where, - volumetric flow required per person (5 for office spaces) - number of people - ventilation to remove toxic gases (0.06 for office spaces) - area of the occupied micro-zones With mass-flow rate of air required to remove heat generated in the space while maintaining cooling is computed as given in Equation 20 following ASHRAE standards. Equation 20 Where, - mass flow rate - heat generated -humid specific heat of moist air = 1.0216 kJ / kg dry air. K -indoor temperature -supply air temperature converted to by dividing with the mass of air, whereby this flow comprises of outdoor air and return air mixed as per ventilation requirements obtained from Equation 19. According to another preferred aspect of the present invention there is provided said smart air conditioning system wherein said system includes air diffusers as strategically positioned ceiling diffusers controllably operable in both unoccupied micro-zones and occupied micro-zones; integrated to microcontroller and sensor configured Micro-Zonal Occupant-Centric Control unit including (i) input module for acquiring dynamic occupancy status of thermal zone in relation to its geometry and surrounding ambience both internal and external to the thermal zone; (ii) CFD (computational fluid dynamics) simulation based comparator and solver module that segregates the thermal zone into grid independent mesh for deriving (a) boundary conditions from initial conditions including occupancy status, room geometry mapped to prevailing weather dependent ambient conditions and applying said set boundary conditions to such dynamically occupied thermal zones to comparatively evaluate, estimate and update set point conditions for thermal comfort in occupied micro-zones, (b) corresponding setback flow (SBF) and setback temperature (SBT) for selectively specific unoccupied micro-zones both validated based on minimum energy consumption and maximum thermal comfort in occupied micro-zones; (iii) airflow strategizing module in communication for activating diffusion of air via said air diffusers based on said CFD acceptable and updated thermal comfort feedback towards occupied micro-zones and simultaneous finalization / updation of setback flow (SBF) and setback temperature (SBT) related air diffusion towards said selectively specific unoccupied micro-zones, adapted for real time variation of airflow control in said select unoccupied micro- zones while maintaining thermal comfort based set point conditions in all occupied micro-zones validated by minimum energy considerations. Preferably said smart air conditioning system is provided that is configured to dynamically set and update setback temperature (SBT) and setback flow (SBF) in unoccupied micro-zones including of a large room in real time and under said pre- decided boundary conditions mapped by initial conditions to maintain select levels of air conditioning in said unoccupied micro-zones near to the occupied micro-zones to prevent thermal discomfort due to draft, thermal gradient and escape of cool air to said unoccupied micro-zones while maintaining setpoint temperature in the occupied regions so as to avoid thermal discomfort and increase in energy consumption and to realize energy efficiency. According to another preferred aspect of the present invention there is provided said smart air conditioning system wherein said airflow strategizing module maintains setback flow (SBF) / setback velocity maintained in (1) one first layer of virtual grid of unoccupied micro-zone that is face sharing with adjacent occupied micro-zones, or (2) one second layer of virtual grid of unoccupied micro-zone that is face sharing with one first layer of the unoccupied micro-zone and is free of any maintenance of setback flow (SBF) / setback velocity in the second layer sharing an edge with adjacent occupied micro-zones, while also maintaining setback temperature (SBT) layer after layer and eventually in all unoccupied micro-zone, whereby said setback velocity and temperature levels are so controlled to attain thermal comfort in occupied micro-zones validated by minimum energy consumption. Preferably said smart air conditioning system is provided wherein said first layer of adjacent micro-zones share micro-zonal virtual boundaries with the occupied micro- zone, said second layer of micro-zones share virtual boundary with the adjacent first layer, wherein the first layer of adjacent micro-zones in the diagonal sides / edge of the occupied micro-zones are free from SBF consideration that influences spread of air jets in the occupied micro-zones whereas SBF considerations are taken into account for rest micro-zone layers that are not diagonally but adjacently disposed to the occupied micro-zones where SBF minimally influences spread of air jets. More preferably said smart air conditioning system is provided wherein air flow control is based on dynamic changes in occupancy and evaluation of thermal comfort which if not attained allows CFD simulation and solver module to update airflow strategizing module with conditioning input for the entire thermal zone to suit minimum energy requirements. According to another preferred aspect of the present smart air conditioning system wherein in said airflow strategizing unit, SBF and SBT values are parallelly optimized to select suitable setback conditions considering thermal comfort is satisfied in the occupied micro-zone given full air flow. According to another aspect of the present invention a method for carrying out advanced airflow control with micro-zonal occupant centric control for reducing air conditioning energy consumption maintaining desired occupant centric thermal comfort involving the smart air conditioning system is provided comprising: providing inputs in said input unit including room geometry, occupancy status at a given point and weather conditions; involving said CFD (computational fluid dynamics) operative system for developing micro zonal grid independent mesh for a thermal zone and determining boundary condition based on occupancy status, room geometry and weather conditions, setting initial condition based on ambient conditions in room at a given time and activating said CDF solver unit for estimating conditions within a micro zone defining imaginary region catered by an individual diffuser under varying airflow control under cooperative operative connect to said airflow strategizing unit enabling determination of dynamic control of setback flow (SBF) and set back temperature (SBT) in unoccupied micro zones in real time to maintain controlled air conditioning in select unoccupied microzones and such as to maintain setpoint temperature and thermal comfort in occupied micro zone at minimum energy consumption thus ensuring energy efficient maintenance of desired thermal comfort only around occupied micro zones free of any thermal discomfort in the unoccupied micro zones. Preferably said method for carrying out advanced airflow control with micro-zonal occupant centric control is provided wherein micro zones developed that are unoccupied include layered micro-zones adjacent to occupied zone with selective controlled set back flow therein in energy efficient manner including air flow / set back flow controller based on CFD simulation. According to another preferred aspect of the method for carrying out advanced airflow control with micro-zonal occupant centric control wherein the SBF and SBT values are parallelly optimized to select suitable ranges of setback conditions where thermal comfort is satisfied maintaining occupied micro zones with full flow and thermal comfort and based thereon selecting suitable strategies in the unoccupied micro zones following iterative loop for SBF and SBT in the airflow strategizing unit. Preferably said method for carrying out advanced airflow control with micro-zonal occupant centric control is provided wherein said set back flow (SBF) or set back temperature (SBT) are maintained in selected unoccupied micro zones in an energy efficient manner and selectively the set back flow is maintained in one layer of micro zones adjacent to the occupied micro zones or two layers of micro zones adjacent to occupied zones while SBT is applied to one layer then next layer and eventually to all unoccupied micro zones wherein the values of set back velocity and temperature used as setback temperature is optimized such as to attain thermal comfort at minimum energy consumption, wherein a first layer of adjacent micro zone is defined as micro zone that share micro-zonal virtual boundaries with the occupied zone the second layer of adjacent micro zone are micro zones that share virtual boundary with first layer of adjacent micro zones and wherein the first layer of adjacent micro zones in the diagonal sides of occupied micro zones is free of SBF to avoid influence of SBF in spread of air jets in occupied micro zones and diagonally opposite micro zones have minimal influence on the spread of air jets and likewise in second layer of adjacency micro zones in the diagonal direction are maintained free of the SBF. More preferably said method for carrying out advanced airflow control with micro- zonal occupant centric control is provided acquiring thermal zone / room geometry, dynamic occupancy, weather conditions at any given time as initial input from sensor based input unit, microcontroller based processing said input by CFD simulation comparator and solver module and (i) generating mesh like virtual grid segregating the thermal zone into occupied and unoccupied thermal zones towards deriving and updating (a) boundary conditions from said initial input mapped to prevailing weather dependent ambient conditions as set point boundary conditions for all such dynamically occupied thermal zones, (b) corresponding setback flow (SBF) and setback temperature (SBT) for select unoccupied micro-zones, (ii) validating said boundary set point conditions and corresponding setback flow (SBF) and setback temperature (SBT) for satisfying maximum thermal comfort in occupied regions based on minimum energy consumption, activating the airflow strategizing module in communication based on said CFD acceptable and updated thermal comfort feedback for occupied micro-zones with simultaneous finalization and updating setback flow (SBF) and setback temperature (SBT) for the unoccupied micro-zones at the airflow strategizing module interface towards attaining real time variation of airflow control in said unoccupied micro- zones while maintaining thermal comfort based set point conditions in occupied micro-zones validated by minimum energy considerations. Preferably said method for carrying out advanced airflow control with micro-zonal occupant centric control is provided wherein said full airflow is maintained in the occupied regions considering duration of occupancy to select appropriate ranges of SBF and SBT based on computing via CFD comparator based solving module selecting for PMV to be in the range of -0.5^PMV<0.5, if consistent computing for PD <20% and if consistent further computing for thermal gradients including horizontal and vertical thermal gradients <3-4 ^C and if consistent, thermal comfort in occupied zones are recorded as achieved for further use and for selection of appropriate ranges of SBF and SBT for working the CFD module for said thermal comfort attainment. More preferably said method for carrying out advanced airflow control with micro- zonal occupant centric control is provided wherein the air flow strategizing module then selects the desired SBF and SBT levels from said ranges of SBF and SBT shared by the CFD module based on deep iterative computational learning considering duration of occupancy where thermal comfort is satisfied at minimum energy considerations as per the following: Initiating setback flow (SBF) in the unoccupied micro-zones from a minimum level value of 50% of the total evaluated volumetric air flow that is increased to the maximum flow level of 100% in each unoccupied layers for simultaneous evaluation of thermal comfort in the occupied micro-zone after 2-3 mins of air conditioning in each set back flow (SBF) level, in case of thermal comfort consistency attained at a select SBF level the same is stored for further use, and in case of thermal comfort inconsistency, the SBF level is then increased by factors to iteratively evaluate different values for SBF preferably at energy efficient 60-70% levels and until SBF reaches 100% of the planned flow for achieving thermal comfort under minimum energy considerations, and in case thermal comfort is not achieved by increasing SBF by factors until 100% said computations are terminated for entire thermal zone to get equally air conditioned. Preferably said method for carrying out advanced airflow control with micro-zonal occupant centric control is provided wherein the air flow strategizing module parallelly selects the desired SBT levels from the said ranges of SBT shared by the CFD module based on deep iterative computational learning considering duration of occupancy where thermal comfort is satisfied at minimum energy considerations and after 2-3 mins of air conditioning at each SBT level as per the following: consideration of maximum temperature difference between setback temperature (SBT) and setpoint temperature where the value of setback temperature is ^6 °C higher than the setpoint temperature, consideration of minimum temperature difference of close to 0 °C between said setback temperature (SBT) and setpoint temperature, modulating related setback (SBF) airflow at preferred energy efficient levels of 60- 70% levels of total evaluated volumetric air flow for attaining minimum setback temperature (SBT) in the first layer of micro-zones adjacent to occupied micro-zones preferably where SBT is not applied, and only initiated at the second layer of micro- zones adjacent to the first layer and then the third layer adjacent to the second layer and continuing the iteration to maintain setback temperature SBT in all unoccupied micro-zones to provide thermal comfort in the occupied micro-zones at minimum energy considerations, and in case thermal comfort is not achieved by increasing SBF by factors until 100% said computations are terminated for entire thermal zone to get equally air conditioned. According to another preferred aspect of the method for carrying out advanced airflow control with micro-zonal occupant centric control wherein for said SBT level processing by strategizing module a value of SBT temperature 4°C higher than the setpoint temperature is considered to be suitable to lie in the range of 2°C and 4°C as values less than 2°C is not considered for processing in being very close to set point temperature, and wherein said setback flow (SBF) velocity includes air velocity levels of 0.1 m / s, 0.25 m / s, 0.35 m / s with setback temperature (SBT) varying in the levels of 25-28 ^C for attaining thermal comfort in the occupied micro-zones. Detailed Description of the Invention As described hereinbefore, the present invention provides for smart air conditioning system having microcontroller processor based air flow control and in being configured to a method of controlling airflow is able to dynamically set and plan setback temperature (SBT) and setback flow (SBF) in unoccupied regions of a large room not only in real time but also under pre-decided conditions, while maintaining setpoint temperature in the occupied regions so as to avoid thermal discomfort and increase in energy consumption and so that an energy efficient system is realized. The smart air-conditioning system of the present invention could be thus provided for air conditioning only the occupied regions in a thermal zone using advanced airflow control / strategies thereto with Micro-Zonal Occupant-Centric Control to reduce air conditioning energy consumption without compromising thermal comfort. The present disclosure having a microcontroller configured to embedded analytics is capable of planning setback temperature (SBT) and setback flow (SBF) in unoccupied regions of a large room, while setpoint temperature is maintained in the occupied regions. The present disclosure provides for a smart air conditioning systems having therein a controller that is configured to reduce air conditioning energy consumption by maintaining thermal comfort only around the occupants. A micro-zone is defined as an imaginary region catered by an individual diffuser. To maintain thermal comfort in occupied micro-zones, in addition to air conditioning the occupied micro-zones, some amount of air conditioning must be maintained in selected unoccupied micro- zones located near the occupied micro-zones. This is important to prevent thermal discomfort such as draft, thermal gradients and escape of cool air to unoccupied regions. Setback flow (SBF) or setback temperature (SBT) must be maintained in selected unoccupied micro-zones. Setback flow can be maintained in (1) one layer of micro-zones adjacent to the occupied micro-zones, or (2) two layers of micro-zones adjacent to occupied micro- zones. SBT can be applied to one layer then next layer and eventually to all unoccupied micro-zones. Further the value of setback velocity and temperature used as setback temperature must be optimized to attain thermal comfort at minimum energy consumption. The first and second layer of adjacent micro-zones are shown in figure 1a and 1b respectively. First layer of adjacent micro-zones are defined as micro-zones that share micro-zonal virtual boundaries with the occupied micro-zone. The second layer of adjacent micro-zones are micro-zones that share virtual boundary with the first layer of adjacent micro-zones. SBF is not considered in the first layer of adjacent micro-zones in the diagonal sides of the occupied micro-zones. This is because SBF tries to influence the spread of air jets in the occupied micro-zones and diagonally opposite micro-zones are observed to have minimal influence on the spread of air jets. Similarly in case of 2ndlayer of adjacency, micro-zones in the diagonal direction are not considered. The present invention is a CFD-based method to determine optimal setback flow (SBF) and setback temperature (SBT) in unoccupied micro-zones to attain thermal comfort in occupied micro-zones at minimum energy consumption. The optimal setback velocity for SBF and optimal setback temperature for SBT must be planned such that thermal comfort can be achieved around the occupants at minimum energy. The present disclosure gives a process to locally air condition occupied regions / micro-zones by selecting the best airflow control strategies using CFD simulations. The process has three units: (1) input unit (2) CFD solver unit and (3) airflow strategizing unit. A schematic of the three units is given in Figure 2. It is assumed that the diffusers arrangement is fixed. This method will be used to plan localized air-conditioning in existing large thermal zones such as offices, shopping malls, academic buildings etc. The method is useful in two stages of service design and operation. In the design stage, the optimal ranges of SBF and SBT can be determined using the flow diagram given in figure 2. In this stage, different scenarios of occupancy conditions and weather conditions are considered and they are assumed to be static for a given time frame. This will give an idea on the maximum and minimum values for velocity of SBF and temperature for SBT. During real-time operations, the maximum and minimum values can be inputted to the same flow diagram given in figure 2 and the best airflow strategy can be selected considering dynamic changes in occupancy. If thermal comfort is not attained using any of the strategies, the entire volume of the zone is to be air conditioned following typical zonal conditioning. The room geometry information is used by the CFD solver unit to develop a grid independent mesh. The boundary conditions are derived from the occupancy status, room geometry and weather conditions. The initial conditions are set considering the ambient conditions in the room at the given time. CFD solver unit estimates the thermal conditions within the zone for varying airflow control strategies, which are decided by the airflow strategizing unit. The feedback on thermal comfort within micro-zones are used by the airflow strategizing unit to improve the airflow strategy and finalize on optimal setback flow or setback temperature. The details of the CFD solver are described here. CFD uses Eulerian method and the room is discretized using finite volume method. The Equations of conservation of mass, momentum and energy, which are given in Equations 1-4, are solved. Humidity is modelled in CFD using non-reacting species transport using Equation 5. Since the flow is turbulent, taking into account Ficks law of diffusion (Sevilgen & Kilic, 2011), the additional mass diffusion is computed using the Equation 6. Gravity is considered along the y-direction Where, P is the static pressure T is the effective stress tensor is the mass added from the continuous phase to the dispersed phase s the source term s the effective conductivity is the diffusion flux s the volumetric heat source is the local mass fraction of water vapour (Yi) present in air is the rate of production of water vapour through chemical reaction s the rate of creation of water vapour through addition is the diffusion flux of water vapour s the turbulent Schmidt number assumed as 0.7 The diffusion coefficient is decided based on turbulent models and type of fluid flow The CFD simulations can be done using ANSYS Fluent or any other commercially available CFD solvers. RNG turbulence model is used with two layer turbulence model to account for viscous flow near the surfaces. A grid independent tetrahedral mesh must be selected for the given geometry and a temporal independence test must be conducted to finalize the timestep for simulations. The residual convergence criteria for continuity, velocity, k and epsilon must be set as 10-3and that of energy and do-intensity must be set as 10-6The results of the CFD simulations gives the transient air temperature, velocity and humidity for every point in the zone. These are taken as input into PMV and PD Equations to evaluate thermal comfort. Horizontal thermal gradients and vertical thermal gradients are also evaluated at lines near occupants. Equation 7 ‘L’ in the PMV Equation is the thermal load, which is the difference in heat produced by the body and heat lost to the environment. This depends on the mechanical work done (W), metabolic rate (M), ambient humidity (or partial pressure of water vapour (Pa)), air temperature (ta) and clothing insulation (fcl, tcl, icl) and is estimated using Equation 8. Equation 8 othing temperature is found by iteration using

[0002] Where, M - metabolic rate (W / m2) 1 / 1 / - the effective mechanical power (W / m2)

[0003] Id - the clothing insulation, in square metres kelvin per watt (m2• K / W); fcl - the clothing surface area factor; ta - the air temperature, in degrees Celsius (°C); - the mean radiant temperature, in degrees Celsius (°C); Var- the relative air velocity, in metres per second (m / s); pa - the water vapour partial pressure, in pascals (Pa); he - the convective heat transfer coefficient, in watts per square metre kelvin [W / (m2-K)]; td - the clothing surface temperature, in degrees Celsius (°C) Percentage of people dissatisfied due to draft is estimated using Equation 14 Equation 14 Thermal comfort is achieved if PMV lies within ± 0.5, PD<20% and horizontal thermal gradients <3 °C and vertical thermal gradients < 3°C for seated and <4°C for standing occupants. This is detailed in figure 3

[0004] IF thermal comfort is attained, the strategy is stored as a suitable strategy and the energy consumed while employing the strategy is evaluated. Finally, all suitable strategies are evaluated to select the strategy with the minimum energy consumption. The energy consumed is evaluated considering the energy required attain thermal comfort and energy required to maintain thermal comfort. The Equations used are given below: 5 Where, - energy consumed (kWh) - power for reaching comfort (PMV=0) (kW) - cooling time (s) - power for maintaining comfort (PMV=0) (kW) - duration for which AC is operated (s) Power consumed at an instance during cool down and maintenance is computed separately as the sum of cooling power and fan power using Equation 16. Equation 16 Power for reaching comfort is computed using Equation 17, where enthalpy values are obtained from the psychometric chart and fan power is calculated using Equation 18 Equation 17 Equation 18 Where, - volumetric flow rate (m3 / s) - specific weight of air (taken as 2.09N / m3 for 13°C) - the enthalpy of unconditioned air (return air + outdoor air for ventilation) (kJ / kg) - the enthalpy of supply air (kJ / kg) -total fan pressure (assume 750 Pa) -efficiency of the fan ( assume 70%) Ventilation rates are computed as per ASHRAE 62.1 (Hedrick et al., 2013) using Equation 19. Equation 19 Where, - volumetric flow required per person (5 for office spaces) number of people ventilation to remove toxic gases (0.06 for office spaces) area of the occupied micro-zones Mass-flow rate of air required to remove heat generated in the space while maintaining cooling is computed as given in Equation 20 following ASHRAE standards. Equation 20 - heat generated -humid specific heat of moist air = 1.0216 kJ / kg dry air. K -indoor temperature -supply air temperature . s converted to by dividing with the mass of air. This flow comprises of outdoor air and return air mixed as per ventilation requirements obtained from Equation 19. In the design stage: Full flow is maintained in the occupied micro-zone. To select appropriate ranges of the airflow strategy (i.e SBT or SBF) in the unoccupied micro-zones, the process defined in figure 2 is used. The invention is the airflow strategizing unit where SBF and SBT values are parallelly optimized to select suitable ranges of setback conditions where thermal comfort is satisfied. It is to be noted that occupied micro-zone has full flow and thermal comfort is evaluated in the occupied micro-zone to select suitable strategies in the unoccupied micro-zones. Setback flow starts from a minimum value of 50% of the planned volumetric flow. This can be increased to the maximum flow of 100%. For each setback flow, thermal comfort is evaluated in the occupied micro-zone after 2-3 mins of air conditioning using the method given in figure 3. The value of setback flow is increased by a factor to iteratively evaluate different values for setback flow. On SBF reaching 100% of the planned flow, setback flow is initiated in the second layer of adjacent micro-zones. The setback velocity in the second layer of adjacent micro-zones must be set to an initial value of 50% of the planned volumetric flow and can be increased to 100% flow. Setback flow in all unoccupied micro-zones will lead to unwanted merging of air jets as understood from literature and is hence avoided. The iterative loop for SBF and SBT in the airflow strategizing unit in the design stage is given in figure 4 It is to be noted that in the design stage, the iterative loop continues even when thermal comfort is achieved in the occupied micro-zone. This is done to understand the best suitable ranges of setback flow. In the application stage, the iterative loop continues only if a strategy fails. This is to quickly arrive at suitable airflow strategies. Similarly, SBT starts from the value with the maximum temperature difference between setback temperature and setpoint temperature. The value of setpoint temperature can be about 6 °C higher than the setpoint temperature as observed in our published studies. The minimum temperature difference can be 0 °C in the design stage. Thermal comfort is evaluated after 2-3 mins of air conditioning. After reaching minimum setback temperature in the first layer of adjacent micro-zones, setback temperature is initiated in the second layer of adjacent micro-zones and then the third and the loop continues to maintain setback temperature in all unoccupied micro-zones as shown in figure 4. The application of this process in the design stage not only gives the range of values for SBT in each layer of adjacent micro-zones, but also gives an idea of the maximum no. of adjacent micro-zones in which SBT must be planned. In the application stage: The process followed in the airflow strategizing unit in the application stage is given in figure 5. The minimum and maximum values for SBT and SBF can be obtained from the previous step or can be assumed based on experience. For assuming the minimum and maximum values for SBT and SBF and level of adjacency to be considered. As per the experimental results observed by analyzing SBF and SBT in few open-plan offices in India, a good starting value for setback flow is 60% of the volumetric flow planned in the occupied micro-zone considering prior literature. This value can increase up to 70% of the planned volumetric flow. These values may vary based on the characteristics of the zone and distance between diffusers. Hence, it is optimal if the ranges of setback conditions are evaluated using the process described earlier. For medium sized open-plan offices in India, the ranges mentioned here can be used. Theoretically the setback flow can go upto 100% of the planned full flow, but any value greater than 70% of the planned flow was found to be not energy efficient. This was because when the difference in momentum between the air jets reduced, low velocity setback flow was less attracted by the high velocity air in the occupied micro-zone. It is therefore better to initiate setback flow in the second layer of adjacent micro-zone. The setback velocity in the second layer can also vary from 60% to 70% of planned volumetric flow in the occupied micro-zone. Hence, the optimal airflow control can be any combination of SBF in the adjacent layers such as 60% in second layer of adjacent micro-zones with 70% flow in first layer of adjacent micro-zones or 70% flow in second layer of adjacent micro-zones with 60% flow in first layer of adjacent micro-zones. If thermal comfort cannot be achieved in any of the evaluated cases, SBF is not a suitable strategy for the given scenario. Similarly, a good starting value that can be assumed for setback temperature is as 4°C higher than the setpoint temperature. This value can lie between 2°C and 4°C. If the value has to be reduced from 2°C, it is better not to use setback temperature as the value becomes very close to setpoint temperature. Following an iterative approach shown in figure 5, the selected value for SBT and SBF are improved by evaluating thermal comfort after 2-3 mins of air conditioning. The strategies that satisfy the comfort criteria of PMV values, percentage of people dissatisfied due to draft, horizontal thermal gradients and vertical thermal gradients are shortlisted as possible strategies. The best strategy is selected by evaluating the energy consumed for each of the shortlisted strategy considering the duration of occupancy. This best strategy can be communicated to the air conditioning system. To use this process in real-time application, machine learning based CFD with less computation time must be used. If no strategy satisfies thermal comfort conditions, the entire thermal zone is to be air conditioned. As discussed, the iterative loop in the real-time application stage terminates on finding an airflow strategy that satisfies thermal comfort as per the least energy consumption. Since the initial values chosen for SBF is the minimum flow value and that for SBT is the one with maximum temperature difference between setpoint and setback temperature, the energy efficiency decreases with each iteration of SBF or SBT. Hence the loop is terminated on finding a strategy that satisfies thermal comfort. The best strategy among SBT and SBF is selected by evaluating the energy consumed and selecting the strategy with least energy consumption. Once thermal comfort is attained in the occupied micro-zone, the airflow in the occupied micro-zone can be reduced to maintenance flow calculated using equation 20. The setback flow is also reduced by the same percentage value. Working example: The process is evaluated in an open office which has an aspect ratio of 0.87, area of 58.6 sqm, occupancy of 23 occupants with square ceiling diffusers and rectangular furniture as shown in Figure 6. An experimentally validated CFD model is used to evaluate thermal comfort and energy consumption. Three variations of SBF and SBT are explored using the proposed process for three diffuser arrangements and three occupancy conditions. Results: Using the process in the design Phase: Thermal comfort compliance on evaluating all possible strategies in the design phase in given in Table 2 below: I 3 D Using the process in the application stage: The percentage of energy saved improves with improvement in airflow control as shown in Figure 7. It seen that for diffuser arrangement 1 (D1) 34% to 50% energy is saved and for diffuser arrangement D380% to 85% of energy is saved. The best strategy for each of the case is the one with maximum energy efficiency and must be directed to the air conditioning unit. On attaining thermal comfort in the occupied micro-zone, airflow in the occupied micro-zone can be reduced to maintenance flow. Thus, the proposed system and the controller with embedded analytics helps in selecting the best airflow control strategy for a given micro-zonal layout and occupancy condition. The uniqueness of the present invention is as follows: The existing systems and methods of localized air conditioning switches on the diffusers in the occupied micro-zone to attain comfort in the occupied regions, but can lead to thermal discomfort and energy wastage. The present invention gives a system and method to operate diffusers in unoccupied micro-zones in addition to operating diffusers in the occupied micro-zones to reduce thermal discomfort and energy consumption. The present system avoids thermal discomfort while using ceiling diffusers to locally air-condition only the occupied region in a large thermal zone. The uniqueness in the system and method includes: a. Using of SBF or SBT in micro-zones adjacent to occupied micro-zones in addition to full flow in occupied micro-zones to attain thermal comfort in occupied micro-zone i. Using setback flow in optimal adjacent micro-zones (i.e, within first layer of adjacent micro-zone or second layer of adjacent micro-zones); ii. Using setback temperature in optimal number of unoccupied micro-zones. This is done by following an iterative method of evaluating thermal comfort in occupied micro-zones by introducing setback flow in first layer of micro-zones, second layer of micro-zones, then the next layer, until maintaining setback temperature in all unoccupied micro-zones. b. System configuration to tune setback flow in unoccupied micro-zones to increase the spread of air jets in occupied micro-zones so as to achieve thermal comfort in occupied micro-zones. c. System configuration to decide which all micro-zones are to have setback flow. Setback flow can be given in micro-zones adjacent to the occupied micro-zones or in two layers of micro-zones adjacent to the occupied micro-zone. d. System configuration to tune setback temperature in unoccupied micro-zones to achieve thermal comfort in the occupied micro-zones e. System configuration to plan the number and location of micro-zones with setback temperature The present system allows to plan the range of SBF and SBT and the micro-zones in which setback conditions are to be maintained. The system of the present invention is so configured that is able to plan airflow control considering changes in real-time occupancy. The system can be used in the application stage either by sensing real time occupancy conditions thereby being able to take inputs after starting the air conditioner or can be fed with analytics to independently assume maximum and minimum values for setback conditions. By such both stage functioning helps in reducing the time taken to decide optimal conditions in the application stage. The disclosed system of the present invention thus can be used in thermal zones such as open-plan offices, academic buildings etc. where occupancy is scattered in different parts of the room. The occupied region can be air-conditioned using full flow, whereas the setback conditions can be maintained in unoccupied regions following the process disclosed here to reduce thermal discomfort on occupants. The system can be used to plan airflow strategies considering planned occupancy and can also be used in real time to plan airflow control considering dynamic changes in occupancy conditions. This helps in reducing HVAC energy by locally air conditioning only the occupied regions.

Claims

Claims:

1. Smart air conditioning system including advanced airflow control with micro-zonal occupant centric control for reducing air conditioning energy consumption maintaining desired occupant centric thermal comfort comprising: an input unit for inputs including room geometry, occupancy status at a given point and weather conditions; a CFD (computational fluid dynamics) operative system including a grid independent micro zonal mesh developer for a thermal zone, boundary condition determinant based on occupancy status, geometry of said thermal zone and weather conditions, means for initial condition setting based on ambient conditions of said thermal zone at a given time and CDF simulator and solver unit estimating conditions within thermal micro zone defining imaginary region catered by an individual diffuser under varying airflow control; said CDF simulator and solver unit operatively connected to an airflow strategizing unit enabling dynamic control of setback flow (SBF) and set back temperature (SBT) in unoccupied micro zones in real time to maintain controlled air conditioning in select unoccupied micro zones and to maintain setpoint temperature and thermal comfort in occupied micro zones at minimum energy consumption thus ensuring energy efficient maintaining desired thermal comfort only around occupied micro zone which is free of any thermal discomfort in the unoccupied micro zones.

2. The smart air conditioning system as claimed in claim 1 wherein said controlled air conditioning in select unoccupied micro zones include layered micro zones adjacent to occupied zone with selective controlled set back flow therein in energy efficient manner including air flow / set back flow controller for controlling airflow based on CFD simulation and solving.

3. The smart air conditioning system as claimed in anyone of claims 1 or 2 including air flow / set back flow controller providing real time maximum and minimum air flow value based air flow strategic control following dynamic changes in occupancy, said CFD solver unit developing a grid independent mesh based on room geometry inputs, deriving boundary conditions as set point conditions from occupancy statusand weather conditions and in having comparator based computational means estimates thermal conditions within the zones for varying airflow control strategies involving said air flow strategizing unit which is integrated with comparator feedback on thermal comfort within micro zones to generate air flow strategy and finalized output of optimal setback flow or setback temperature.

4. The smart air conditioning system as claimed in anyone of claims 1 to 3 wherein said CFD simulation and solver unit is Eulerian method based and discretizes thermal zone / room space by involving finite volume computation that operates by following conservation of mass, momentum and energy as per equations 1-4 n n n napplying humidity modelling based on non-reacting species transport as per Equation 5 Equation 5and involving additional mass diffusion computed as per Equation 6 by taking into account Ficks law of diffusion by considering gravity in y-direction Equation 6Where, P is the static pressure T is the effective stress tensor is the mass added from the continuous phase to the dispersed phase is the source terms the effective conductivity is the diffusion flux is the volumetric heat source is the local mass fraction of water vapour (Yi) present in air is the rate of production of water vapour through chemical reaction is the rate of creation of water vapour through addition is the diffusion flux of water vapour is the turbulent Schmidt number assumed as 0.7 wherein the diffusion coefficient is computed based on turbulent analytics and type of fluid flow preferably considering at least two layer turbulence to account for viscous flow near the surfaces; said CFD simulator evaluating and strategizing thermal comfort based on simulations including of transient air temperature, velocity and humidity for every micro zone in thermal zone preferably as grid independent tetrahedral mesh selected for given geometry and tested for temporal independence for timestep based simulations, and with the residual convergence criteria for continuity, velocity, k and epsilon set as 10-3and that of energy and do-intensity set as 10-6, and operating cooperative PMV and PD based thermal comfort determinant based on said simulations by accounting for horizontal thermal gradients and vertical thermal gradients at lines near occupants, as per belowEquation 7 where ‘L’ in the PMV Equation is the thermal load, which is the difference in heat produced by the body and heat lost to the environment that depends on the mechanical work done (W), metabolic rate (M), ambient humidity (or partial pressure of water vapour (Pa)), air temperature (ta) and clothing insulation (fcl, tcl, icl) and is estimated by Equation 8. Equation 8The clothing temperature is found by iteration using Equations 9 to 12 Equation 9 Equation 10 Equation 11 Equation 12Percentage of people dissatisfied is evaluated using Equation 13Equation 13 Where, M - metabolic rate (W / m2) W - the effective mechanical power (W / m2) Icl - the clothing insulation, in square metres kelvin per watt (m2⋅ K / W); fcl - the clothing surface area factor; ta - the air temperature, in degrees Celsius (°C); - the mean radiant temperature, in degrees Celsius (°C); Var - the relative air velocity, in metres per second (m / s); pa - the water vapour partial pressure, in pascals (Pa); hc - the convective heat transfer coefficient, in watts per square metre kelvin [W / (m2⋅K)]; tcl - the clothing surface temperature, in degrees Celsius (°C) Percentage of people dissatisfied due to draft is estimated using Equation 14Equation 14 and wherein said thermal comfort is considered to be satisfactory if PMV lies within ± 0.5, PD<20% with horizontal thermal gradients being <3 °C and vertical thermal gradients being < 3°C for seated and <4°C for standing occupants.

5. The smart air conditioning system as claimed in anyone of claims 1 to 4 wherein IF thermal comfort is attained, the evaluative strategy is stored in the system as a suitable strategy and the energy consumed while employing the strategy is evaluated and stored as a SUITABLE strategy requiring minimum energy consumption for future use by CFD solver module, said minimum energyconsumption includes energy required to attain thermal comfort and energy required to maintain thermal comfort computed as per the below equations: Where, - energy consumed (kWh) - power for reaching comfort (PMV=0) (kW) - cooling time (s) - power for maintaining comfort (PMV=0) (kW)- duration for which AC is operated (s) with power consumed at an instance during cool down and maintenance being computed separately as the sum of cooling power and fan power using Equation 16.Equation 16 With power for reaching comfort is computed using Equation 17, where enthalpy values are obtained from the psychometric chart and fan power is calculated using Equation 18 Equation 17 Equation 18Where, - volumetric flow rate (m3 / s) - specific weight of air (taken as 2.09N / m3 for 13°C)- the enthalpy of unconditioned air (return air + outdoor air for ventilation) (kJ / kg) - the enthalpy of supply air (kJ / kg) -total fan pressure (assume 750 Pa)-efficiency of the fan (assume 70%) with ventilation rates are computed as per ASHRAE 62.1 (Hedrick et al., 2013) using Equation 19.Equation 19 Where,- volumetric flow required per person (5 for office spaces) - number of people - ventilation to remove toxic gases (0.06 for office spaces)- area of the occupied micro-zones With mass-flow rate of air required to remove heat generated in the space while maintaining cooling is computed as given in Equation 20 following ASHRAE standards. Equation 20Where, - mass flow rate - heat generated -humid specific heat of moist air = 1.0216 kJ / kg dry air. K -indoor temperature-supply air temperature is converted to by dividing with the mass of air, whereby this flow comprises of outdoor air and return air mixed as per ventilation requirements obtained from Equation 19.

6. The smart air conditioning system as claimed in anyone of claims 1 to 5 wherein said system includes air diffusers as strategically positioned ceiling diffusers controllably operable in both unoccupied micro-zones and occupied micro-zones; integrated to microcontroller and sensor configured Micro-Zonal Occupant-Centric Control unit including (i) input module for acquiring dynamic occupancy status of thermal zone in relation to its geometry and surrounding ambience both internal and external to the thermal zone; (ii) CFD (computational fluid dynamics) simulation based comparator and solver module that segregates the thermal zone into grid independent mesh for deriving (a) boundary conditions from initial conditions including occupancy status, room geometry mapped to prevailing weather dependent ambient conditions and applying said set boundary conditions to such dynamically occupied thermal zones tocomparatively evaluate, estimate and update set point conditions for thermal comfort in occupied micro-zones, (b) corresponding setback flow (SBF) and setback temperature (SBT) for selectively specific unoccupied micro-zones both validated based on minimum energy consumption and maximum thermal comfort in occupied micro-zones; (iii) airflow strategizing module in communication for activating diffusion of air via said air diffusers based on said CFD acceptable and updated thermal comfort feedback towards occupied micro-zones and simultaneous finalization / updation of setback flow (SBF) and setback temperature (SBT) related air diffusion towards said selectively specific unoccupied micro-zones, adapted for real time variation of airflow control in said select unoccupied micro- zones while maintaining thermal comfort based set point conditions in all occupied micro-zones validated by minimum energy considerations.

7. The smart air conditioning system as claimed in anyone of claims 1 to 5 that is configured to dynamically set and update setback temperature (SBT) and setback flow (SBF) in unoccupied micro-zones including of a large room in real time and under said pre-decided boundary conditions mapped by initial conditions to maintain select levels of air conditioning in said unoccupied micro-zones near to the occupied micro-zones to prevent thermal discomfort due to draft, thermal gradient and escape of cool air to said unoccupied micro-zones while maintaining setpoint temperature in the occupied regions so as to avoid thermal discomfort and increase in energy consumption and to realize energy efficiency.

8. The smart air conditioning system as claimed in anyone of claims 1 to 7 wherein said airflow strategizing module maintains setback flow (SBF) / setback velocity maintained in (1) one first layer of virtual grid of unoccupied micro-zone that is face sharing with adjacent occupied micro-zones, or (2) one second layer of virtual grid of unoccupied micro-zone that is face sharing with one first layer of the unoccupied micro-zone and is free of any maintenance of setback flow (SBF) / setback velocity in the second layer sharing an edge with adjacent occupied micro-zones, while also maintaining setback temperature (SBT) layer after layer and eventually in all unoccupied micro-zone, whereby said setback velocity and temperature levelsare so controlled to attain thermal comfort in occupied micro-zones validated by minimum energy consumption.

9. The smart air conditioning system as claimed in anyone of claims 1 to 8 wherein said first layer of adjacent micro-zones share micro-zonal virtual boundaries with the occupied micro-zone, said second layer of micro-zones share virtual boundary with the adjacent first layer, wherein the first layer of adjacent micro-zones in the diagonal sides / edge of the occupied micro-zones are free from SBF consideration that influences spread of air jets in the occupied micro-zones whereas SBF considerations are taken into account for rest micro-zone layers that are not diagonally but adjacently disposed to the occupied micro-zones where SBF minimally influences spread of air jets.

10. The smart air conditioning system as claimed in anyone of claims 1 to 9 wherein air flow control is based on dynamic changes in occupancy and evaluation of thermal comfort which if not attained allows CFD simulation and solver module to update airflow strategizing module with conditioning input for the entire thermal zone to suit minimum energy requirements.

11. The smart air conditioning system as claimed in anyone of claims 1 to 10 wherein in said airflow strategizing unit, SBF and SBT values are parallelly optimized to select suitable setback conditions considering thermal comfort is satisfied in the occupied micro-zone given full air flow.

12. A method for carrying out advanced airflow control with micro-zonal occupant centric control for reducing air conditioning energy consumption maintaining desired occupant centric thermal comfort involving the smart air conditioning system as claimed in anyone of claims 1 to 11 comprising: providing inputs in said input unit including room geometry, occupancy status at a given point and weather conditions; involving said CFD (computational fluid dynamics) operative system for developing micro zonal grid independent mesh for a thermal zone and determining boundary condition based on occupancy status, room geometry and weather conditions,setting initial condition based on ambient conditions in room at a given time and activating said CDF solver unit for estimating conditions within a micro zone defining imaginary region catered by an individual diffuser under varying airflow control under cooperative operative connect to said airflow strategizing unit enabling determination of dynamic control of setback flow (SBF) and set back temperature (SBT) in unoccupied micro zones in real time to maintain controlled air conditioning in select unoccupied microzones and such as to maintain setpoint temperature and thermal comfort in occupied micro zone at minimum energy consumption thus ensuring energy efficient maintenance of desired thermal comfort only around occupied micro zones free of any thermal discomfort in the unoccupied micro zones.

13. The method for carrying out advanced airflow control with micro-zonal occupant centric control as claimed in claim 12 wherein micro zones developed that are unoccupied include layered micro-zones adjacent to occupied zone with selective controlled set back flow therein in energy efficient manner including air flow / set back flow controller based on CFD simulation.

14. The method for carrying out advanced airflow control with micro-zonal occupant centric control as claimed in anyone of claims 12 or 13 wherein the SBF and SBT values are parallelly optimised to select suitable ranges of setback conditions where thermal comfort is satisfied maintaining occupied micro zones with full flow and thermal comfort and based thereon selecting suitable strategies in the unoccupied micro zones following iterative loop for SBF and SBT in the airflow strategizing unit.

15. The method for carrying out advanced airflow control with micro-zonal occupant centric control as claimed in anyone of claims 12 to 14 wherein said set back flow (SBF) or set back temperature (SBT) are maintained in selected unoccupied micro zones in an energy efficient manner and selectively the set back flow is maintained in one layer of micro zones adjacent to the occupied micro zones or two layers of micro zones adjacent to occupied zones while SBT is applied to one layer then next layer and eventually to all unoccupied micro zones wherein the values of set back velocity and temperature used as setback temperature is optimized such as to attain thermal comfort at minimum energy consumption,wherein a first layer of adjacent micro zone is defined as micro zone that share micro-zonal virtual boundaries with the occupied zone the second layer of adjacent micro zone are micro zones that share virtual boundary with first layer of adjacent micro zones and wherein the first layer of adjacent micro zones in the diagonal sides of occupied micro zones is free of SBF to avoid influence of SBF in spread of air jets in occupied micro zones and diagonally opposite micro zones have minimal influence on the spread of air jets and likewise in second layer of adjacency micro zones in the diagonal direction are maintained free of the SBF.

16. The method for carrying out advanced airflow control with micro-zonal occupant centric control as claimed in anyone of claims 12 to 15 acquiring thermal zone / room geometry, dynamic occupancy, weather conditions at any given time as initial input from sensor based input unit, microcontroller based processing said input by CFD simulation comparator and solver module and (i) generating mesh like virtual grid segregating the thermal zone into occupied and unoccupied thermal zones towards deriving and updating (a) boundary conditions from said initial input mapped to prevailing weather dependent ambient conditions as set point boundary conditions for all such dynamically occupied thermal zones, (b) corresponding setback flow (SBF) and setback temperature (SBT) for select unoccupied micro-zones, (ii) validating said boundary set point conditions and corresponding setback flow (SBF) and setback temperature (SBT) for satisfying maximum thermal comfort in occupied regions based on minimum energy consumption, activating the airflow strategizing module in communication based on said CFD acceptable and updated thermal comfort feedback for occupied micro-zones with simultaneous finalization and updating setback flow (SBF) and setback temperature (SBT) for the unoccupied micro-zones at the airflow strategizing module interface towards attaining real time variation of airflow control in said unoccupied micro- zones while maintaining thermal comfort based set point conditions in occupied micro-zones validated by minimum energy considerations.

17. The method for carrying out advanced airflow control with micro-zonal occupant centric control as claimed in anyone of claims 12 to 16 wherein said full airflow ismaintained in the occupied regions considering duration of occupancy to select appropriate ranges of SBF and SBT based on computing via CFD comparator based solving module selecting for PMV to be in the range of -0.5^PMV<0.5, if consistent computing for PD <20% and if consistent further computing for thermal gradients including horizontal and vertical thermal gradients <3-4 ^C and if consistent, thermal comfort in occupied zones are recorded as achieved for further use and for selection of appropriate ranges of SBF and SBT for working the CFD module for said thermal comfort attainment.

18. The method for carrying out advanced airflow control with micro-zonal occupant centric control as claimed in anyone of claims 12 to 17 wherein the air flow strategizing module then selects the desired SBF and SBT levels from said ranges of SBF and SBT shared by the CFD module based on deep iterative computational learning considering duration of occupancy where thermal comfort is satisfied at minimum energy considerations as per the following: Initiating setback flow (SBF) in the unoccupied micro-zones from a minimum level value of 50% of the total evaluated volumetric air flow that is increased to the maximum flow level of 100% in each unoccupied layers for simultaneous evaluation of thermal comfort in the occupied micro-zone after 2-3 mins of air conditioning in each set back flow (SBF) level, in case of thermal comfort consistency attained at a select SBF level the same is stored for further use, and in case of thermal comfort inconsistency, the SBF level is then increased by factors to iteratively evaluate different values for SBF preferably at energy efficient 60-70% levels and until SBF reaches 100% of the planned flow for achieving thermal comfort under minimum energy considerations, and in case thermal comfort is not achieved by increasing SBF by factors until 100% said computations are terminated for entire thermal zone to get equally air conditioned.

19. The method for carrying out advanced airflow control with micro-zonal occupant centric control as claimed in anyone of claims 12 to 18 wherein the air flow strategizing module parallelly selects the desired SBT levels from the said ranges of SBT shared by the CFD module based on deep iterative computational learningconsidering duration of occupancy where thermal comfort is satisfied at minimum energy considerations and after 2-3 mins of air conditioning at each SBT level as per the following: consideration of maximum temperature difference between setback temperature (SBT) and setpoint temperature where the value of setback temperature is ^6 °C higher than the setpoint temperature, consideration of minimum temperature difference of close to 0 °C between said setback temperature (SBT) and setpoint temperature, modulating related setback (SBF) airflow at preferred energy efficient levels of 60- 70% levels of total evaluated volumetric air flow for attaining minimum setback temperature (SBT) in the first layer of micro-zones adjacent to occupied micro-zones preferably where SBT is not applied, and only initiated at the second layer of micro- zones adjacent to the first layer and then the third layer adjacent to the second layer and continuing the iteration to maintain setback temperature SBT in all unoccupied micro-zones to provide thermal comfort in the occupied micro-zones at minimum energy considerations, and in case thermal comfort is not achieved by increasing SBF by factors until 100% said computations are terminated for entire thermal zone to get equally air conditioned.

20. The method for carrying out advanced airflow control with micro-zonal occupant centric control as claimed in anyone of claims 12 to 19 wherein for said SBT level processing by strategizing module a value of SBT temperature 4°C higher than the setpoint temperature is considered to be suitable to lie in the range of 2°C and 4°C as values less than 2°C is not considered for processing in being very close to set point temperature, and wherein said setback flow (SBF) velocity includes air velocity levels of 0.1 m / s, 0.25 m / s, 0.35 m / s with setback temperature (SBT) varying in the levels of 25-28 ^C for attaining thermal comfort in the occupied micro-zones.

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