SYSTEM AND METHOD FOR IMPROVING THE ENERGY MANAGEMENT OF HVAC EQUIPMENT

Abstract

Disclosed herein is a system and a method for improving the energy management of HVAC equipment. The system comprising: a plurality of sensors distributed in a building for sensing a set of parameters including environmental information, thermal zone information, energy consumption information, operational parameter information and field information from the building; a network for connecting the plurality of sensors; a server includes a hybrid platform with physics based simulation model and machine learning model for processing and controlling the parameters of the HVAC equipment.

Claims

1. A system for energy management of HVAC equipment comprises of: a. a plurality of distributed sensors for sensing a set of parameters including environmental information, thermal zone information, energy consumption information and operational parameter information from the building; b. a network for connecting the sensors; c. a remote server or an on-premise server for collecting and storing the sensed values of the set of parameters through the network; d. a platform configured for processing the sensed values of the set of parameters and computing control strategies, wherein the platform resides in the remote server and on-premise server and performs the following actions: i. detects irregularities of the HVAC equipment; ii. evaluates at least one control strategy for the HVAC equipment; and iii. selects one or more control strategies for execution simultaneously.

2. The system as claimed in claim 1, wherein the sensors include environmental sensors, energy consumption sensors and operational parameters sensors.

3. The system as claimed in claim 1, wherein the sensors further include one or more field device sensors.

4. The system as claimed in claim 1, wherein the sensors are connected to connecting nodes to collect information from the sensors.

5. The system as claimed in claim 4, wherein the connecting nodes are connected to a base station which communicates with the remote server via the network.

6. The system as claimed in claim 5, wherein the base station includes the platform for collecting, storing, and processing the set of parameters from the sensors and computing an immediate control action.

7. The system as claimed in claim 1, wherein the platform includes a physics based simulation model and a machine learning model for processing the parameters.

8. A method for improving energy management of HVAC equipment, comprising the steps of: a. collecting sensed values of a set of parameters including environmental information, energy consumption information, and operational parameter information from a plurality of distributed sensors; b. connecting the sensors through a network for transmitting the collected sensed values of the set of parameters; c. storing the collected sensed values of the set of parameters from the sensors at a remote server or on-premise server; d. processing the stored sensed values of the set of parameters in a platform and computing control strategies for the HVAC equipment; and e. outputting and executing control parameters for the HVAC equipment.

9. The method as claimed in claim 8, wherein the method is configured for detecting irregularities in the collected parameters of the HVAC equipment.

10. The method as claimed in claim 9, wherein the detected irregularities are displayed in a dashboard display.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The objective of the present disclosure will now be described in detail concerning the accompanying drawings, in which:

[0024] FIG. 1 represents a schematic diagram of the overall HVAC control architecture;

[0025] FIG. 2 represents a few example scenarios for the working model of the platform of remote server or on-premise server or base station/edge device; and

[0026] FIG. 3 represents the network architecture of the HVAC equipment.

REFERENCE NUMERALS

[0027] 100: Overall HVAC control architecture [0028] 110: Sense and Connect [0029] 112: Sensors [0030] 112A: Environmental sensors [0031] 112B: Energy consumption detecting sensors [0032] 112C: Operational parameters sensor [0033] 112D: Field device sensors [0034] 114: Connecting nodes [0035] 116: Base station/Edge device [0036] 118A, 118B, 118C: Router [0037] 119A, 119B: Modem [0038] 120A: Remote server [0039] 120B: On-Premise server [0040] 122: Hybrid platform [0041] 122A: Physics based simulation model [0042] 122A-1: Building model [0043] 122A-2: Machine system/sub-system 1D/2D/3D modelling [0044] 122B: Machine learning model [0045] 124: Database [0046] 126A: Application server [0047] 126B: Web server [0048] 128: Load balancer [0049] 129: Firewall [0050] 130: ACT [0051] 132: Power control devices [0052] 134: Dashboard display [0053] 136: Control Action [0054] 136A: Chiller Plant manager [0055] 136B: DDC Panel [0056] 138: Internet

DETAILED DESCRIPTION

[0057] The present disclosure discloses a system and method for improving energy efficiency of HVAC for a building. The integrated system of HVAC includes, but is not limited to, chiller, air handling unit, cooling towers, and pumps. The system disclosed in this disclosure is implemented using an artificial intelligence platform.

[0058] FIG. 1 depicts the schematic diagram of an overall HVAC control architecture (100) of the present disclosure. The disclosed architecture collaboratively controls both high and low sides that is, of HVAC equipment. The integrated HVAC equipment includes: a plurality of sensors (112), a network and a remote server (120A) or an on-premise server (120B) or an edge device (116) with a platform (122) to improve energy efficiency. The system also, includes a two-layered hierarchical system in which one for sense and connect phase (110) and another for action phase (130). The sense and connect phase (110) comprise the sensors (112) or IOT devices networked in a wireless manner.

[0059] The sensors (112) collect the environment information and controls the energy consuming HVAC equipment. The wireless networked sensors (112) are categorized into four categories as shown in FIG. 1:

[0060] Environmental sensors (112A) that use digital protocol in order to get the information from in-built sensors for (Relative Humidity (RH), Carbon dioxide (CO2), Volatile Organic Compounds (VOC) and other parameters. The environmental parameters also include, but not limited to, the outdoor weather conditions, building thermal comfort and air quality conditions. These parameters act as an essential part for giving the maximum input of the environmental surroundings in and out of the buildings or factories.

1. Energy consumption detecting sensors (112B) to detect the real-time energy consumption of the various HVAC equipment including, but not limited to, Chiller, Air Handling Unit, Pumps, and Cooling Towers. These parameters establish the overall energy consumption of the HVAC equipment.
2. Operational parameters sensors (112C) to monitor the status of the operational parameters of all HVAC equipment including, but not limited to, Chiller, Air Handling Unit, Pumps and Cooling towers (CT). It is a device having hardware and software protocol to monitor and control the equipment (AHU, Chiller, CT and Pumps) parameters.
3. Field Device sensors (112D) to read the Flow-Thermal data at various points in the HVAC equipment starting form AHU supply or return line to water pump lines, decoupler lines, across chiller Evaporator, condenser and not limited to these equipment.

[0061] These networked sensors (112) send the collected information to the remote server (120A) which is represented in the second layer (130) of the FIG. 1. The information processed by the remote server (120A) are described below in detail.

[0062] In one embodiment, the sensors (112) are connected to connecting nodes (114). Number of connecting nodes (114) are connected to a base station (116) which communicates to the remote server (120A) via the network (138).

[0063] In accordance to FIG. 1, the layer 2 is action phase (130) which comprises the remote server (120A), but not limited to, a cloud server or on-premise server (120B) or edge device (116). The action phase (130) is referred as ACT. The information gathered from the wireless networked sensors (112) are sent to the cloud server (120A) through a wireless network (138). This information is evaluated by using a platform (122) that utilizes the physics based simulation model (122A) and machine learning model (122B). The physics based simulation model (122A) and machine learning model (122B) are combined and developed as a hybrid platform (122) which performs calculations to derive various possible control strategies. It evaluates each action with respect to its potential to save energy of overall HVAC equipment which includes, but not limited to, chiller, pumps, cooling towers and AHUs. Also, it detects real-time irregularities in the functioning of HVAC equipment which includes, but not limited to, chiller, pumps, cooling towers and AHUs, to improve mechanical, electrical performance of HVAC equipment at all times, in turn improving energy savings. The evaluated results are sent to the power control devices (132). Now, the power control devices (132) react to the energy management system of buildings. The process is repeated till platform (122) becomes mature enough and thus reduces the dependency on real time sensor data and further improving the ability of the system to bring energy efficiency even when one or more sensors are not operational.

[0064] In accordance with the present disclosure, the collected parameters from the sensors are stored and processed independently as per requirement in the following servers such as remote server (120A), on-premise server (120B) and base station/edge device (116). Based on the requirement, each server computes the different set of control activities to be performed at three different levels (that is, within the base station, inside the premise, at remote level) in order to bring higher granularity and higher coverage. This type of control activities is used to improve the reliability and speed of controls to bring higher energy efficiency improvement and human comfort.

[0065] In another embodiment, the base station/edge device (116) acts as a mini server which performs an immediate control action based on the sensor (112) information.

[0066] In another embodiment, the platform (122) works on certain principles. For example, If the information is adequate, the machine learning model (122B) takes the lead. The physics based simulation model (122A) takes the lead if the information is limited.

[0067] In another embodiment, the hybrid platform (122) also accesses the information, but not limited to, sensors (112) information from past, past HVAC equipment usage information, HVAC's current load information, other zone temperature and various predefined operational and safety boundaries for the equipment for making control strategies of the HVAC equipment.

[0068] In yet another embodiment, the physics based simulation model (122A) includes, but are not limited to, energy model for the building (122A-1), one-dimensional system model of the HVAC equipment and three-dimensional computational fluid dynamics (CFD) analysis (122A-2).

[0069] In yet another embodiment, the sensors (112), connecting nodes (114) and base station (116) (that is within the building) are connected in various network topologies, but not limited to, a mesh topology and a star topology. The communication between various components (112, 114, 116) is made using various network protocols such as, but not limited to, Low-Power Wireless Personal Area Network, Personal Area Network (PAN), Local Area Network (LAN), Wide Area Network (WAN) or wireless network. The communication medium used in this communication is short-range or long-range or medium, but not limited to, radio frequency (RF), Bluetooth, and Zigbee.

[0070] In a further embodiment, the communication between the sensors and connect phase (110), remote server (120A) or on-premise server (120B) or edge device (116) and ACT phase (130) is made using wireless communication protocol or internet (138) and so on, for example.

[0071] In a further embodiment, the information in the cloud server (120A) of action phase (130) is used to predict the energy the consumption of the HVAC equipment. The proposed system is also configured to control (136) the equipment of the overall HVAC equipment based on the evaluation of consumed energy.

[0072] In some embodiments, the power control devices (132) are connected to a dashboard display (134) which displays real-time monitoring and real-time energy data forecasting.

[0073] FIG. 2 illustrates one example to explain the hybrid platform (122) in the remote server (120A) or on-premise server (120B) or edge device/base station (116) which has the ability to evaluate multiple feasible control options for a given practical scenario such as a reduction in building power load. There could be more control options, but for the purposes of demonstration, a few possible actions of the proposed system are described below

a. The first action could be to increase the chilled water temperature. Then, the hybrid platform (122) in the cloud server (120A) predicts the new revised energy consumption for this action and delivers the energy consumption value to the device to operational parameters sensors (112C) to the HVAC equipment.
b. The second action could be to determine the chiller staging levels, in case of multiple chillers, based on the real time health of the chiller and part load efficiency. It reduces the overall load on the chillers by utilising the staging mechanism instead of reducing the load on only one chiller to a certain level. This decision is taken to load multiple chillers by once again estimating the revised energy consumption and sharing the information with operational parameters sensors (112C).
c. The third action could be to decrease the AHU fan speed or reducing the water flow rate. Then, the hybrid platform (122) estimates the new revised energy consumption for the overall HVAC equipment and shares the information with operational parameters sensors (112C).
d. The final control action execution via operational parameters sensors (112C) could be a sequential or parallel execution of all the three actions mentioned above to minimize the overall HVAC system energy consumption.

[0074] FIG. 3 illustrates diagrammatically, the network architecture of the present disclosure. The parameters which are obtained from the sensors (112) are sent as encrypted data packets (encrypted with the advanced encryption standard (AES) 128-bit encrypted packets with session key, for example) to the base station or edge device (116) via the connecting nodes (114). Then, the parameters are directed to the remote server (120A) from the base station (116) through the routers (118A) and modem (119A). The parameters are transmitted securely from sense and connect phase to ACT phase through the SSL. The SSL acts as a firewall (129) to prevent the intrusion and non-genuine activities inside the remote server (120A) or on-premise server (120B) or base station/edge device (116). The information of the parameters is sent to the classic load balancer (128) via the routers (118B) securely. The load balancer (128) balances the load across the remote server (120A) or on-premise server (120B). The router (118B) passes the information to the remote database (124) through the web server (126B) instances. The application server (126A) receives the information from the classic load balancer (128) and sends it to the remote database (124). The hybrid platform (122) processes the information, computes control strategies and output the controls to the HVAC equipment. The base station (116) at ACT phase (130) receives the controlling commands from the platform (122) and send the information to the branch nodes (114). Further, the information passes to the power control devices (132) from the branch nodes (114). The power control devices send the control commands to actuate the control action (136) in the HVAC equipment such as, but not limited to, chiller plant manager (136A) or direct digital control panel that is, DDC panel (136B). Also, the architecture has a dashboard display (134) in order to detect or monitor real-time irregularities of the HVAC equipment. The display (134) collects the information directly from sense and connect phase (110).

[0075] The disclosed architecture herein is oriented towards improving the energy management of HVAC equipment of buildings or commercial premises. It provides hierarchical network layers for managing the energy efficiency of HVAC equipment. It may also reduce the number of devices utilized and hence provide less complex architecture and thereby overcomes the bandwidth/latency related challenges associated with the flow of communication of big data.

[0076] While the foregoing written description of the disclosure enables one of ordinary skill to make and use what is considered presently to be the best mode thereof, those of ordinary skill will understand and appreciate the existence of variations, combinations, and equivalents of the specific embodiment, method, and examples herein. The disclosure should therefore not be limited by the above described embodiment, method, and examples, but by all embodiments and methods within the scope and spirit of the disclosure as claimed.