Refrigerant charge controller for HVAC equipment and controller for VRF equipment

Abstract

1. A refrigerant charge controller for a heating, ventilation, or air conditioning (HVAC) system, the controller comprising: processing circuitry configured to analyze usage data for the HVAC system using a machine learning model to estimate an amount of refrigerant used by the HVAC system; identify a refrigerant shortage based on the amount of refrigerant; and initiate corrective action in response to identifying the refrigerant shortage.

Description

[Technical Field]

[0001] The present disclosure relates generally to the field of operating building facilities, and more particularly to using artificial intelligence to predict the condition of building facilities. [Background technology]

[0002] In order for building services (e.g., heating, ventilation, or air conditioning (HVAC) services) to operate effectively and minimize deterioration of the building services, various operating conditions of the building services need to be monitored and considered. However, in traditional building systems, many operating conditions remain unmonitored, which can lead to rapid deterioration of the building services and increased costs over time.

[0003] While some operating conditions can be monitored by installing additional sensors, installing additional sensors in some locations can be difficult and costly, especially if the sensors must be embedded in existing HVAC equipment to effectively measure the desired conditions. Other operating conditions cannot be feasibly measured, for example, if they do not correspond to measurable quantities or if measurement in some HVAC systems is otherwise impossible. It would be desirable to provide a system capable of monitoring such operating conditions without requiring additional sensors. Summary of the Invention

[0004] One implementation of the present disclosure is a refrigerant charge controller for heating, ventilation, or air conditioning (HVAC) equipment, the controller comprising processing circuitry configured to analyze usage data for the HVAC equipment using a machine learning model to estimate an amount of refrigerant used by the HVAC equipment, identify a refrigerant shortage based on the amount of refrigerant, and initiate corrective action in response to identifying the refrigerant shortage.

[0005] In some embodiments, the HVAC installation includes one or more compressors configured to circulate refrigerant in a refrigerant circuit, and the usage data includes at least one of a suction temperature or a suction pressure of the refrigerant at a suction of the one or more compressors, or a discharge temperature or a discharge pressure of the refrigerant at a discharge of the one or more compressors.

[0006] In some embodiments, the HVAC equipment includes one or more valves configured to control a flow of refrigerant in a refrigerant circuit including one or more fluid conduits, and the usage data includes at least one of a valve position of the one or more valves or a length of the one or more fluid conduits.

[0007] In some embodiments, the HVAC installation comprises a variable refrigerant flow (VRF) installation configured to operate in a heating mode in which the VRF installation provides heating to a building space and in a cooling mode in which the VRF installation provides cooling to the building space, and the usage data is collected while the VRF installation is operating in the heating mode.

[0008] In some embodiments, the processing circuitry is configured to simulate operation of the HVAC equipment under various test conditions to generate a set of simulated operational data, and to train the machine learning model using the set of simulated operational data.

[0009] In some embodiments, identifying a refrigerant shortage includes determining that the amount of refrigerant is below a threshold value, and initiating corrective action includes automatically charging more refrigerant into a refrigerant circuit used by the HVAC equipment.

[0010] In some embodiments, identifying the refrigerant shortage includes detecting a refrigerant leak in a refrigerant circuit used by the HVAC equipment, and initiating corrective action includes initiating maintenance activity to repair the refrigerant leak.

[0011] Another embodiment is a controller for a variable refrigerant flow (VRF) facility, the controller comprising processing circuitry configured to analyze usage data for the VRF facility using a machine learning model to estimate an amount of liquid in the VRF facility, identify a liquid shortage based on the amount of liquid, and initiate corrective action in response to identifying the liquid shortage.

[0012] In some embodiments, the VRF installation includes one or more compressors configured to circulate refrigerant in the refrigerant circuit, and the usage data includes at least one of a suction pressure of the refrigerant at a suction of the one or more compressors, or a discharge temperature or discharge pressure of the refrigerant at a discharge of the one or more compressors.

[0013] In some embodiments, the VRF facility includes one or more compressors configured to circulate a refrigerant in a refrigerant circuit, and the usage data includes at least one of a sub-cooling temperature of the refrigerant vapor, a dry-bulb temperature, and a refrigerant charge.

[0014] In some embodiments, the processing circuitry is configured to simulate operation of the VRF equipment under various test conditions to generate a set of simulated operational data, and to train the machine learning model using the set of simulated operational data.

[0015] In some embodiments, the amount of liquid is an estimated amount of liquid in an accumulator of the VRF facility or one or more indoor VRF units, identifying a liquid shortage includes determining that the amount of liquid exceeds a threshold, and initiating corrective action includes automatically returning a portion of the liquid to a compressor of the VRF facility or an outdoor VRF unit.

[0016] Another embodiment is a controller for a variable refrigerant flow (VRF) facility, the controller comprising one or more processing circuits configured to: analyze a first set of usage data for the VRF facility using a first machine learning model to estimate an amount of refrigerant used by the VRF facility; analyze a second set of usage data for the VRF facility using a second machine learning model to estimate an amount of liquid in the VRF facility; identify a liquid shortage based on the amount of liquid; and initiate corrective action in response to identifying the liquid shortage.

[0017] In some embodiments, the second set of usage data includes an amount of refrigerant used by the VRF estimated by analyzing the first set of usage data.

[0018] In some embodiments, the VRF installation includes one or more compressors configured to circulate refrigerant in the refrigerant circuit, and the first set of usage data includes at least one of a suction temperature or a suction pressure of the refrigerant at a suction of the one or more compressors, or a discharge temperature or a discharge pressure of the refrigerant at a discharge of the one or more compressors.

[0019] In some embodiments, the VRF equipment includes one or more valves configured to control the flow of refrigerant in a refrigerant circuit including one or more fluid conduits, and the first set of usage data includes at least one of a valve position of the one or more valves or a length of the one or more fluid conduits.

[0020] In some embodiments, the VRF equipment is configured to operate in a heating mode in which the VRF equipment provides heating to a building space and in a cooling mode in which the VRF equipment provides cooling to the building space, and the first set of usage data is collected while the VRF equipment is operating in the heating mode.

[0021] In some embodiments, the one or more processing circuits are configured to simulate operation of the HVAC equipment under first varying test conditions to generate a first set of simulated operational data and train a first machine learning model using the first set of simulated operational data, and to simulate operation of the HVAC equipment under second varying test conditions to generate a second set of simulated operational data and train a second machine learning model using the second set of simulated operational data.

[0022] In some embodiments, the amount of liquid is an estimated amount of liquid in an accumulator or one or more indoor VRF units of the VRF system, identifying a liquid shortage includes determining that the amount of liquid exceeds a threshold, and initiating corrective action includes automatically returning a portion of the liquid to a compressor of the VRF system or one or more outdoor VRF units.

[0023] In some embodiments, identifying a liquid shortage includes determining that the amount of liquid exceeds a threshold value in an accumulator of the VRF equipment, and initiating corrective action includes automatically returning a portion of the liquid from the accumulator to a compressor of the VRF equipment.

[0024] Those skilled in the art will appreciate that the summary is merely illustrative and is not intended to be limiting in any way. Other aspects, features, and advantages of the devices and / or processes described herein, as defined solely by the claims, will become apparent in the detailed description set forth herein and taken in conjunction with the accompanying drawings.

[0025] Various objects, aspects, features, and advantages of the present disclosure will become more apparent and will be better understood by reference to the detailed description in conjunction with the accompanying drawings, in which like reference characters identify corresponding elements throughout, and in which like reference numbers generally indicate identical, functionally similar, and / or structurally similar elements. [Brief explanation of the drawings]

[0026] [Figure 1] FIG. 1 is a perspective view of a building including a heating, ventilation, or air conditioning (HVAC) system, according to some embodiments. [Figure 2] FIG. 2 is a block diagram of a waterside system that may be used to service the heating or cooling load of the building of FIG. 1 according to some embodiments. [Figure 3] FIG. 2 is a block diagram of an airside system that may be used to service the heating or cooling loads of the building of FIG. 1 according to some embodiments. [Figure 4] FIG. 2 is a block diagram of a building management system (BMS) that can be used to monitor and control the building of FIG. 1 according to some embodiments. [Figure 5] FIG. 2 is a block diagram of another BMS that can be used to monitor and control the building of FIG. 1 according to some embodiments. [Figure 6A] 1 is a diagram of a variable refrigerant flow (VRF) system having one or more outdoor VRF units and multiple indoor VRF units, according to some embodiments. [Figure 6B] 1 is a diagram of a variable refrigerant flow (VRF) system having one or more outdoor VRF units and multiple indoor VRF units, according to some embodiments. [Figure 7A] 1 is a schematic diagram of a VRF system, according to some embodiments. [Figure 7B] FIG. 1 is a block diagram of a VRF system, according to some embodiments. [Figure 8] FIG. 1 is a block diagram of a controller for predicting refrigerant properties according to some embodiments. [Figure 9A] 1 is an illustration of a recurrent neural network structure, according to some embodiments. [Figure 9B] 1 is an illustration of a long-term short-term memory model structure, according to some embodiments. [Figure 9C]1 is an illustration of a neural network (NN) architecture, according to some embodiments. [Figure 10] FIG. 1 is a flow diagram of a process for monitoring refrigerant properties using an AI model, according to some embodiments. [Figure 11] FIG. 1 is a block diagram of a controller for predicting liquid properties, according to some embodiments. [Figure 12A] 1 is an illustration of a long-term short-term memory model structure, according to some embodiments. [Figure 12B] 1 is an illustration of a neural network (NN) architecture, according to some embodiments. [Figure 13] FIG. 1 is a flow diagram of a process for monitoring liquid properties using an AI model, according to some embodiments. [Figure 14A] 1 is a graph illustrating the change in RMSE based on the number of iterations in an exemplary model training process for an artificial intelligence (AI) model, according to some embodiments. [Figure 14B] 14B is a graph illustrating the change in loss based on the number of iterations associated with the AI ​​model of FIG. 14A, in accordance with some embodiments. [Figure 15] 14B is a graph illustrating a prediction of the liquid level in the accumulator generated by the AI ​​model of FIG. 14A, according to some embodiments. [Figure 16A] 1 is a graph illustrating the change in RMSE based on the number of iterations in an exemplary model training process for an artificial intelligence (AI) model, according to some embodiments. [Figure 16B] 16B is a graph illustrating the change in loss based on the number of iterations associated with the AI ​​model of FIG. 16A, according to some embodiments. [Figure 17] 16B is a graph illustrating a prediction of the liquid level in the accumulator generated by the AI ​​model of FIG. 16A, according to some embodiments. [Figure 18A]1 is a graph illustrating the change in RMSE based on the number of iterations in an exemplary model training process for an artificial intelligence (AI) model, according to some embodiments. [Figure 18B] 18B is a graph illustrating the change in loss based on the number of iterations associated with the AI ​​model of FIG. 18A, according to some embodiments. [Figure 19] 18B is a graph illustrating a prediction of the liquid level of the accumulator generated by the AI ​​model of FIG. 18A, according to some embodiments. [Figure 20A] 1 is a graph illustrating the change in RMSE based on the number of iterations in an exemplary model training process for an artificial intelligence (AI) model, according to some embodiments. [Figure 20B] 20B is a graph illustrating the change in loss based on the number of iterations associated with the AI ​​model of FIG. 20A, in accordance with some embodiments. [Figure 21] 20B is a graph illustrating a prediction of the liquid level in the accumulator generated by the AI ​​model of FIG. 20A, according to some embodiments. DETAILED DESCRIPTION OF THE INVENTION

[0027] overview Reference is generally made to diagrams, systems, and methods for predicting the performance of a building's variable refrigerant flow (VRF) system and utilizing artificial intelligence (AI) in operating the VRF system and VRF system components, according to some embodiments. In particular, the present disclosure uses AI to predict the performance of refrigerants used in VRF systems, as well as to predict various states of the refrigerants in the VRF system.

[0028] However, it should be understood that the systems and methods described herein are not limited to VRF systems. Rather, a VRF system is shown and described for example purposes only as one potential implementation of the present disclosure. The systems and methods described herein may be applied to various systems that require refrigerant to be provided to equipment (e.g., other environmental control systems), as well as other types of systems, including compressors, motors, any type of equipment that uses refrigerant, and / or any type of equipment that requires a liquid. For example, the systems and methods described herein may be applied to various heating, ventilation, or air conditioning (HVAC) systems and devices (e.g., various air conditioners, variable air volume (VAV) systems, residential air conditioning (RAC) systems, etc.), fire suppression systems, etc.

[0029] As referred to herein, AI and AI model can be used to describe a variety of different models that can be used in predicting state and other information associated with devices in a VRF system. In some embodiments, a recurrent neural network (RNN) model is utilized to generate the predictions. RNNs are a class of artificial neural networks in which connections between nodes form a directed graph along a temporal sequence. More specifically, a long short-term memory (LSTM) model can be utilized in generating the predictions. LSTMs are a specific type of artificial RNN architecture primarily used in deep learning. LSTMs can classify and process entire sequences of time-series data and make predictions based on the time-series data. Advantageously, LSTMs can account for delays of unknown duration between significant events in a time series. In some embodiments, other types of AI models, such as convolutional neural networks (CNNs), are utilized in generating the predictions. Thus, it should be understood that various types of AI models can be utilized in generating the predictions.

[0030] As defined herein, a refrigerant characteristic, which is used interchangeably herein with the term "refrigerant property," can refer to a particular property of a refrigerant. In other words, a refrigerant characteristic can be a variable state or condition of a refrigerant. Refrigerant characteristics (i.e., variable states or conditions of a refrigerant) of a VRF system can include, for example, refrigerant levels in one or more compressors, refrigerant levels in an oil separator, refrigerant viscosity, etc.

[0031] Specifically, with regard to VRF systems, components of the VRF system occasionally experience wear and tear (e.g., leaks, etc.) that can reduce overall efficiency and cause failures within the VRF system. When wear and tear occurs, some VRF systems include an automatic refrigerant charge mode. The automatic refrigerant charge mode can automatically determine the optimal amount of refrigerant to charge, thereby preventing energy loss due to refrigerant shortages and / or insufficient refrigerant. However, a conventional automatic refrigerant charge mode can only operate when the VRF system is in cooling mode, which can cause the VRF system to transfer heat from the medium being heated (e.g., a building, a home, etc.). For example, when an indoor VRF system operates the automatic refrigerant charge mode, the building may be required to be in cooling mode, resulting in heat loss to the building.

[0032] As described in more detail below, issues associated with conventional VRF systems (e.g., automatic refrigerant charge mode requirements, etc.) can be addressed through the use of AI. AI can be used to predict refrigerant characteristics (e.g., refrigerant levels, etc.) in various building devices during any building mode (e.g., heating mode, cooling mode, off mode, etc.). Based on the prediction, the AI ​​can perform an automatic refrigerant charge mode function to determine the optimal amount of refrigerant to charge, a refrigerant leak detection function to determine the operating status of devices in the VRF system, and / or control devices (e.g., valves, switches, etc.) to charge the optimal amount of refrigerant and / or reduce energy losses due to insufficient refrigerant.

[0033] Also, as defined herein, a liquid property, which is used interchangeably herein with the term "liquid property," can refer to a particular property of a liquid. In other words, a liquid property can be a variable state or condition of a liquid. Liquid properties (i.e., variable states or conditions of a liquid) of a VRF system can include, for example, a refrigerant level in an accumulator of a VRF system, an oil level in an accumulator of a VRF system, an oil-refrigerant mixture level in an accumulator of a VRF system, a refrigerant level in one or more compressors of a VRF system, a refrigerant level in one or more compressors of a VRF system, etc.

[0034] Specifically, with regard to VRF systems, traditional VRF systems are preferred due to their efficiency, convenient control, and low maintenance costs. During operation, the reliability of a VRF system can be reflected by the liquid level of the gas fraction of the VRF system. Therefore, when designing control logic, high and low liquid level changes can be monitored to avoid abnormal conditions in the VRF system (e.g., maintaining a liquid control state). Under some operating conditions, some VRF devices (e.g., compressors, outdoor VRF units, etc.) cannot receive enough liquid (e.g., oil, refrigerant, etc.) from other VRF devices (e.g., accumulators, indoor VRF units, etc.). For example, in some situations, refrigerant accumulates in the indoor VRF units (e.g., accumulators, etc.), causing the refrigerant level in the outdoor VRF units (e.g., compressors, etc.) to decrease, putting the outdoor VRF units at a higher risk of failure. Thus, the control logic can be designed to control components of the VRF system to return liquid (e.g., oil, refrigerant, etc.) to specific devices in the VRF system (e.g., the VRF unit compressor, the outdoor VRF unit compressor, etc.) to maintain a liquid control state. While conventional control logic can determine how to maintain a liquid control state (e.g., return oil, refrigerant, etc. to the outdoor VRF unit, the indoor VRF unit, the compressor, etc.), conventional VRF systems can only do so indirectly based on VRF system parameters (e.g., exhaust superheat, etc.). Therefore, if gas fraction (e.g., liquid level) is applied as an auxiliary parameter, the control logic can better determine when to return to liquid control. In some embodiments, an AI model can be utilized to predict liquid levels in various building devices. Based on the prediction, the AI ​​can determine the optimal time to maintain a liquid control state (e.g., return liquid, etc.) when the VRF system is in cooling / heating operation. Advantageously, the prediction performed by the AI ​​can be performed without the use of sensors (e.g., accumulator sensors).Not having to utilize sensors to detect liquid properties (eg, refrigerant level, oil level, oil-refrigerant mixture level, etc.) can improve the reliability and operating range of the VRF system.

[0035] Construction of HVAC systems and building management systems 1-5, several building management systems (BMS) and HVAC systems are shown in which the systems and methods of the present disclosure may be implemented, according to several embodiments. In a brief overview, FIG. 1 shows a building 10 with an HVAC system 100. FIG. 2 is a block diagram of a waterside system 200 that may be used to provide services to the building 10. FIG. 3 is a block diagram of an airside system 300 that may be used to provide services to the building 10. FIG. 4 is a block diagram of a BMS that may be used to monitor and control the building 10. FIG. 5 is a block diagram of another BMS that may be used to monitor and control the building 10.

[0036] Buildings and HVAC Systems 1 , a perspective view of a building 10 is shown. The building 10 is served by a BMS. A BMS is generally a system of devices configured to control, monitor, and manage equipment in or around a building or building area. A BMS may include, for example, an HVAC system, a security system, a lighting system, a fire alarm system, any other system capable of managing building functions or devices, or any combination thereof.

[0037] The BMS serving building 10 includes an HVAC system 100. HVAC system 100 may include multiple HVAC devices (e.g., heaters, chillers, air handling units, pumps, fans, thermal energy storage devices, etc.) configured to provide heating, cooling, ventilation, or other services to building 10. For example, HVAC system 100 is shown to include a waterside system 120 and an airside system 130. Waterside system 120 may provide heated or chilled fluid to an air handling unit of airside system 130. Airside system 130 may use the heated or chilled fluid to heat or cool the airflow provided to building 10. Exemplary waterside and airside systems that may be used in HVAC system 100 are described in more detail with reference to FIGS. 2-3.

[0038] HVAC system 100 is shown to include a chiller 102, a boiler 104, and a rooftop air handling unit (AHU) 106. Waterside system 120 may use boiler 104 and chiller 102 to heat or cool a working fluid (e.g., water, glycol, etc.) and circulate the working fluid to AHU 106. In various embodiments, the HVAC devices of waterside system 120 may be located within or around building 10 (as shown in FIG. 1 ) or at an off-site location, such as a central plant (e.g., a chiller plant, steam plant, heat plant, etc.). The working fluid may be heated in boiler 104 or cooled in chiller 102, depending on whether heating or cooling is required in building 10. Boiler 104 may add heat to the circulating fluid, for example, by burning a combustible material (e.g., natural gas) or by using an electric heating element. The chiller 102 may absorb heat from the circulating fluid by placing the circulating fluid in a heat exchange relationship with another fluid (e.g., a refrigerant) in a heat exchanger (e.g., an evaporator). The working fluid from the chiller 102 and / or boiler 104 may be transported to the AHU 106 via piping 108.

[0039] The AHU 106 may place the working fluid in a heat exchange relationship with an airflow passing through the AHU 106 (e.g., via one or more stages of cooling and / or heating coils). The airflow may be, for example, outside air, return air from within the building 10, or a combination of both. The AHU 106 may transfer heat between the airflow and the working fluid to provide heating or cooling to the airflow. For example, the AHU 106 may include one or more fans or blowers configured to pass the airflow over or through a heat exchanger containing the working fluid. The working fluid may then return to the chiller 102 or boiler 104 via piping 110.

[0040] The airside system 130 may deliver the airflow supplied by the AHUs 106 (i.e., supply airflow) to the building 10 via the air supply ducts 112 and may provide return air from the building 10 to the AHUs 106 via the air return ducts 114. In some embodiments, the airside system 130 includes multiple variable air volume (VAV) units 116. For example, the airside system 130 is shown including a separate VAV unit 116 for each floor or zone of the building 10. The VAV units 116 may include dampers or other flow control elements operable to control the amount of supply airflow provided to individual zones of the building 10. In other embodiments, the airside system 130 delivers the supply airflow to one or more zones of the building 10 (e.g., via the supply ducts 112) without the use of intermediate VAV units 116 or other flow control elements. The AHUs 106 may include various sensors (e.g., temperature sensors, pressure sensors, etc.) configured to measure attributes of the supply airflow. The AHU 106 may receive input from sensors located within the AHU 106 and / or within the building zone and may adjust the flow rate, temperature, or other attributes of the supply air flow through the AHU 106 to achieve setpoint conditions for the building zone.

[0041] Waterside System 2 , a block diagram of a waterside system 200 is shown, according to some embodiments. In various embodiments, waterside system 200 may supplement or replace waterside system 120 in HVAC system 100, or may be implemented separately from HVAC system 100. When implemented in HVAC system 100, waterside system 200 may include a subset of the HVAC devices in HVAC system 100 (e.g., boiler 104, chiller 102, pumps, valves, etc.) and may operate to supply heated or chilled fluid to AHUs 106. The HVAC devices of waterside system 200 may be located within building 10 (e.g., as components of waterside system 120) or at an off-site location such as a central plant.

[0042] In FIG. 2 , waterside system 200 is depicted as a central plant having multiple subplants 202-212. Subplants 202-212 are depicted as including heater subplant 202, heat recovery chiller subplant 204, chiller subplant 206, cooling tower subplant 208, high-temperature thermal energy storage (TES) subplant 210, and low-temperature thermal energy storage (TES) subplant 212. Subplants 202-212 consume resources (e.g., water, natural gas, electricity, etc.) from a utility to provide thermal energy loads (e.g., hot water, chilled water, heating, cooling, etc.) for a building or campus. For example, heater subplant 202 may be configured to heat water in hot water loop 214, which circulates hot water between heater subplant 202 and building 10. Chiller subplant 206 may be configured to cool water in chilled water loop 216, which circulates chilled water between chiller subplant 202 and building 10. Heat recovery chiller subplant 204 may be configured to transfer heat from chilled water loop 216 to hot water loop 214 to provide additional heating for the hot water and additional cooling for the chilled water. Condenser water loop 218 may absorb heat from the chilled water in chiller subplant 206 and either reject the absorbed heat in cooling tower subplant 208 or transfer the absorbed heat to hot water loop 214. High-temperature TES subplant 210 and low-temperature TES subplant 212 may store high-temperature and low-temperature thermal energy, respectively, for subsequent use.

[0043] Hot water loop 214 and chilled water loop 216 may deliver heated and / or chilled water to air handlers (e.g., AHU 106) located on the roof of building 10 or to individual floors or zones (e.g., VAV units 116) of building 10. The air handlers push air through heat exchangers (e.g., heating or cooling coils) through which water flows to provide heating or cooling to the air. The heated or cooled air may be delivered to individual zones of building 10 to serve the thermal energy load of building 10. The water then returns to subplants 202-212 for further heating or cooling.

[0044] Although subplants 202-212 are shown and described as providing heated and cooled water for circulation to a building, it is understood that any other type of working fluid (e.g., glycol, CO2, etc.) can be used instead of or in addition to water to provide the thermal energy load. In other embodiments, subplants 202-212 may provide heating and / or cooling directly to a building or campus without the need for an intermediate heat transfer fluid. These and other variations on waterside system 200 are within the teachings of the present disclosure.

[0045] Each of the subplants 202-212 may include various equipment configured to facilitate the function of the subplant. For example, the heater subplant 202 is shown to include a number of heating elements 220 (e.g., boilers, electric heaters, etc.) configured to add heat to hot water in the hot water loop 214. The heater subplant 202 is also shown to include several pumps 222 and 224 configured to circulate hot water in the hot water loop 214 and control the flow rate of the hot water through the individual heating elements 220. The chiller subplant 206 is shown to include a number of chillers 232 configured to remove heat from chilled water in the chilled water loop 216. The chiller subplant 206 is also shown to include several pumps 234 and 236 configured to circulate chilled water in the chilled water loop 216 and control the flow rate of the chilled water through the individual chillers 232.

[0046] Heat recovery chiller subplant 204 is shown to include a plurality of heat recovery heat exchangers 226 (e.g., refrigerant circuits) configured to transfer heat from chilled water loop 216 to hot water loop 214. Heat recovery chiller subplant 204 is also shown to include several pumps 228 and 230 configured to circulate hot and / or chilled water through heat recovery heat exchangers 226 and control the flow rate of water through each of heat recovery heat exchangers 226. Cooling tower subplant 208 is shown to include a plurality of cooling towers 238 configured to remove heat from condenser water in condenser water loop 218. Cooling tower subplant 208 is also shown to include several pumps 240 configured to circulate condenser water in condenser water loop 218 and control the flow rate of condenser water through each of cooling towers 238.

[0047] Hot TES subplant 210 is shown to include a hot TES tank 242 configured to store hot water for later use. Hot TES subplant 210 may also include one or more pumps or one or more valves configured to control the flow of hot water to or from hot TES tank 242. Cold TES subplant 212 is shown to include a cold TES tank 244 configured to store cold water for later use. Cold TES subplant 212 may also include one or more pumps or one or more valves configured to control the flow of cold water to or from cold TES tank 244.

[0048] In some embodiments, one or more pumps in waterside system 200 (e.g., pumps 222, 224, 228, 230, 234, 236, and / or 240) or pipelines in waterside system 200 include isolation valves associated therewith. The isolation valves may be integrated with the pumps or located upstream or downstream of the pumps to control fluid flow within waterside system 200. In various embodiments, waterside system 200 may include more, fewer, or different types of devices and / or subplants based on the particular configuration of waterside system 200 and the type of load served by waterside system 200.

[0049] Airside System 3, a block diagram of an airside system 300 is shown, according to some embodiments. In various embodiments, the airside system 300 may supplement or replace the airside system 130 in the HVAC system 100, or may be implemented separately from the HVAC system 100. When implemented within the HVAC system 100, the airside system 300 may include a subset of the HVAC devices in the HVAC system 100 (e.g., the AHU 106, the VAV unit 116, the ducts 112-114, fans, dampers, etc.) and may be located within or around the building 10. The airside system 300 may operate to heat or cool the airflow provided to the building 10 using the heated or chilled fluid provided by the waterside system 200.

[0050] In FIG. 3 , air-side system 300 is shown to include an economizer-type air handling unit (AHU) 302. The economizer-type AHU varies the amount of outside air and return air used by the air handling unit for heating or cooling. For example, AHU 302 may receive return air 304 from building zone 306 via return air duct 308 and deliver supply air 310 to building zone 306 via supply air duct 312. In some embodiments, AHU 302 is located on the roof of building 10 (e.g., AHU 106 as shown in FIG. 1 ) or is otherwise a rooftop unit positioned to receive both return air 304 and outside air 314. AHU 302 may be configured to operate exhaust damper 316, mixing damper 318, and outside air damper 320 to control the amount of outside air 314 and return air 304 that combine to form supply air 310. Any return air 304 that does not pass through the mixing damper 318 may be exhausted from the AHU 302 through the exhaust damper 316 as exhaust air 322 .

[0051] Each of the dampers 316-320 may be operated by an actuator. For example, the exhaust damper 316 may be operated by an actuator 324, the mixing damper 318 may be operated by an actuator 326, and the outside air damper 320 may be operated by an actuator 328. The actuators 324-328 may communicate with the AHU controller 330 via a communication link 332. The actuators 324-328 may receive control signals from the AHU controller 330 and may provide feedback signals to the AHU controller 330. The feedback signals may include, for example, an indication of current actuator or damper position, the amount of torque or force exerted by the actuator, diagnostic information (e.g., results of diagnostic tests performed by the actuators 324-328), status information, commissioning information, configuration settings, calibration data, and / or any other type of information or data that may be collected, stored, or used by the actuators 324-328. The AHU controller 330 may be an economizer controller configured to control the actuators 324-328 using one or more control algorithms (e.g., a state-based algorithm, an extremum-seeking control (ESC) algorithm, a proportional-integral (PI) control algorithm, a proportional-integral-derivative (PID) control algorithm, a model predictive control (MPC) algorithm, a feedback control algorithm, etc.).

[0052] 3 , AHU 302 is shown to include a cooling coil 334, a heating coil 336, and a fan 338 positioned within supply air duct 312. Fan 338 can be configured to force supply air 310 through cooling coil 334 and / or heating coil 336 and provide supply air 310 to building zone 306. AHU controller 330 can communicate with fan 338 via communication link 340 to control the flow rate of supply air 310. In some embodiments, AHU controller 330 controls the amount of heating or cooling applied to supply air 310 by adjusting the speed of fan 338.

[0053] Cooling coil 334 may receive chilled fluid from waterside system 200 (e.g., from chilled water loop 216) via piping 342 and may return chilled fluid to waterside system 200 via piping 344. Valves 346 may be positioned along piping 342 or piping 344 to control the flow rate of chilled fluid through cooling coil 334. In some embodiments, cooling coil 334 includes multiple stages of cooling coils that can be independently activated and deactivated (e.g., by AHU controller 330, BMS controller 366, etc.) to regulate the amount of cooling applied to supply air 310.

[0054] Heating coil 336 may receive heated fluid from waterside system 200 (e.g., from hot water loop 214) via piping 348 and may return heated fluid to waterside system 200 via piping 350. Valves 352 may be positioned along piping 348 or piping 350 to control the flow rate of heated fluid through heating coil 336. In some embodiments, heating coil 336 includes multiple stages of heating coils that can be independently activated and deactivated (e.g., by AHU controller 330, BMS controller 366, etc.) to adjust the amount of heating applied to supply air 310.

[0055] Each of valves 346 and 352 may be controlled by an actuator. For example, valve 346 may be controlled by actuator 354, and valve 352 may be controlled by actuator 356. Actuators 354-356 may communicate with AHU controller 330 via communication links 358-360. Actuators 354-356 may receive control signals from AHU controller 330 and provide feedback signals to AHU controller 330. In some embodiments, AHU controller 330 receives supply air temperature measurements from temperature sensor 362 positioned in supply air duct 312 (e.g., downstream of cooling coil 334 and / or heating coil 336). AHU controller 330 may also receive building zone 306 temperature measurements from temperature sensor 364 located in building zone 306.

[0056] In some embodiments, the AHU controller 330 operates the valves 346 and 352 via actuators 354-356 to adjust the amount of heating or cooling provided to the supply air 310 (e.g., to achieve a setpoint temperature for the supply air 310 or to maintain the temperature of the supply air 310 within a setpoint temperature range). The positions of the valves 346 and 352 affect the amount of heating or cooling provided to the supply air 310 by the cooling coil 334 or heating coil 336 and may correlate to the amount of energy consumed to achieve the desired supply air temperature. The AHU controller 330 may control the temperature of the supply air 310 and / or building zone 306 by activating or deactivating the coils 334-336, adjusting the speed of the fan 338, or a combination of both.

[0057] 3 , airside system 300 is shown to include a building management system (BMS) controller 366 and client devices 368. BMS controller 366 may include one or more computer systems (e.g., servers, supervisory controllers, subsystem controllers, etc.) that function as system-level controllers, application or data servers, head nodes, or master controllers for airside system 300, waterside system 200, HVAC system 100, and / or other controllable systems servicing building 10. BMS controller 366 may communicate with multiple downstream building systems or subsystems (e.g., HVAC system 100, security system, lighting system, waterside system 200, etc.) via communication links 370 according to similar or different protocols (e.g., LON, BACnet, etc.). In various embodiments, AHU controller 330 and BMS controller 366 may be separate (as shown in FIG. 3 ) or integrated. In an integrated implementation, AHU controller 330 may be a software module configured for execution by a processor of BMS controller 366.

[0058] In some embodiments, the AHU controller 330 receives information (e.g., commands, set points, operating boundaries, etc.) from the BMS controller 366 and provides information (e.g., temperature measurements, valve or actuator positions, operational status, diagnostics, etc.) to the BMS controller 366. For example, the AHU controller 330 may provide the BMS controller 366 with temperature measurements from the temperature sensors 362-364, on / off status of equipment, operational capabilities of equipment, and / or any other information that can be used by the BMS controller 366 to monitor or control variable states or conditions within the building zone 306.

[0059] Client device 368 may include one or more human-machine interfaces, or client interfaces (e.g., graphical user interfaces, report interfaces, text-based computer interfaces, client-facing web services, web servers serving pages to web clients, etc.) for controlling, viewing, or otherwise interacting with HVAC system 100, its subsystems, and / or devices. Client device 368 may be a computer workstation, a client terminal, a remote or local interface, or any other type of user interface device. Client device 368 may be a fixed terminal or a mobile device. For example, client device 368 may be a desktop computer, a computer server with a user interface, a laptop computer, a tablet, a smartphone, a PDA, or any other type of mobile or non-mobile device. Client device 368 may communicate with BMS controller 366 and / or AHU controller 330 via communication link 372.

[0060] Building Management System Referring now to FIG. 4 , a block diagram of a building management system (BMS) 400 is shown, according to some embodiments. The BMS 400 may be implemented in the building 10 to automatically monitor and control various building functions. The BMS 400 is shown to include a BMS controller 366 and multiple building subsystems 428. The building subsystems 428 are shown to include a building electrical subsystem 434, an information and communication technology (ICT) subsystem 436, a security subsystem 438, an HVAC subsystem 440, a lighting subsystem 442, an elevator / escalator subsystem 432, and a fire safety subsystem 430. In various embodiments, the building subsystems 428 may include fewer, additional, or alternative subsystems. For example, the building subsystems 428 may also, or alternatively, include a refrigeration subsystem, an advertising or signage subsystem, a cooking subsystem, a vending subsystem, a printer or copy service subsystem, or any other type of building subsystem that uses controllable equipment and / or sensors to monitor or control the building 10. In some embodiments, the building subsystems 428 include the waterside system 200 and / or the airside system 300, as illustrated in Figures 2-3.

[0061] Each of the building subsystems 428 may include any number of devices, controllers, and connections to complete its individual functions and control activities. The HVAC subsystem 440 may include many of the same components as the HVAC system 100, as described in FIGS. 1-3 . For example, the HVAC subsystem 440 may include a chiller, a boiler, any number of air handling units, economizers, field controllers, supervisory controllers, actuators, temperature sensors, and other devices for controlling the temperature, humidity, airflow, or other variable conditions within the building 10. The lighting subsystem 442 may include any number of lighting fixtures, ballasts, lighting sensors, dimmers, or other devices configured to controllably adjust the amount of light provided to a building space. The security subsystem 438 may include occupancy sensors, video surveillance cameras, digital video recorders, video processing servers, intrusion detection devices, access control devices and servers, or other security-related devices.

[0062] 4 , BMS controller 366 is shown to include a communications interface 407 and a BMS interface 409. Interface 407 may facilitate communication between BMS controller 366 and external applications (e.g., monitoring and reporting application 422, enterprise control application 426, remote systems and applications 444, applications resident on client devices 448, etc.) to enable user control, monitoring, and adjustments over BMS controller 366 and / or building subsystems 428. Interface 407 may also facilitate communication between BMS controller 366 and client devices 448. BMS interface 409 may facilitate communication between BMS controller 366 and building subsystems 428 (e.g., HVAC, lighting security, elevators, power distribution, business, etc.).

[0063] Interfaces 407, 409 can be or include wired or wireless communication interfaces (e.g., jacks, antennas, transmitters, receivers, transceivers, wired terminals, etc.) for communicating data with building subsystems 428 or other external systems or devices. In various embodiments, communication over interfaces 407, 409 can be direct (e.g., local wired or wireless communication) or over a communications network 446 (e.g., a WAN, the Internet, a cellular network, etc.). For example, interfaces 407, 409 can include an Ethernet card and port for transmitting and receiving data over an Ethernet-based communications link or network. In another example, interfaces 407, 409 can include a Wi-Fi transceiver for communicating over a wireless communications network. In another example, one or both of interfaces 407, 409 can include a cellular or mobile phone communications transceiver. In one embodiment, communications interface 407 is a powerline communications interface and BMS interface 409 is an Ethernet interface. In other embodiments, the communication interface 407 and the BMS interface 409 are both Ethernet interfaces or are the same Ethernet interface.

[0064] 4, the BMS controller 366 is shown to include a processing circuit 404 that includes a processor 406 and a memory 408. The processing circuit 404 may be communicatively coupled to a BMS interface 409 and / or a communication interface 407 such that the processing circuit 404 and its various components can send and receive data via the interfaces 407, 409. The processor 406 may be implemented as a general-purpose processor, an application-specific integrated circuit (ASIC), one or more field-programmable gate arrays (FPGAs), a group of processing components, or other suitable electronic processing components.

[0065] Memory 408 (e.g., memory, memory unit, storage device, etc.) may include one or more devices (e.g., RAM, ROM, flash memory, hard disk storage, etc.) for storing data and / or computer code for completing or facilitating the various processes, layers, and modules described herein. Memory 408 may be or include volatile or non-volatile memory. Memory 408 may include database components, object code components, script components, or any other type of information structure for supporting the various activities and information structures described herein. According to some embodiments, memory 408 is communicatively coupled to processor 406 via processing circuitry 404 and includes computer code for executing one or more processes described herein (e.g., by processing circuitry 404 and / or processor 406).

[0066] In some embodiments, BMS controller 366 is implemented within a single computer (e.g., within one server, within one enclosure, etc.). In various other embodiments, BMS controller 366 may be distributed across multiple servers or computers (e.g., may be in distributed locations). Additionally, while FIG. 4 shows applications 422 and 426 residing external to BMS controller 366, in some embodiments, applications 422 and 426 may be hosted within BMS controller 366 (e.g., within memory 408).

[0067] 4, memory 408 is shown to include an enterprise integration layer 410, an automatic measurement and verification (AM&V) layer 412, a demand response (DR) layer 414, a fault detection and diagnosis (FDD) layer 416, an integrated control layer 418, and a subsequent building subsystem integration layer 420. Layers 410-420 may be configured to receive inputs from building subsystems 428 and other data sources, determine optimal control actions for building subsystems 428 based on the inputs, generate control signals based on the optimal control actions, and provide the generated control signals to building subsystems 428. The following paragraphs describe some of the general functions performed by each of layers 410-420 within BMS 400.

[0068] Enterprise integration layer 410 can be configured to provide information and services to client or local applications to support various enterprise-level applications. For example, enterprise control application 426 can be configured to provide subsystem-spanning control to a graphical user interface (GUI) or any number of enterprise-level business applications (e.g., accounting system, user identification system, etc.). Enterprise control application 426 can also, or alternatively, be configured to provide a configuration GUI for configuring BMS controller 366. In still yet other embodiments, enterprise control application 426 can interface with layers 410-420 to optimize building performance (e.g., efficiency, energy use, comfort, or safety) based on inputs received at interface 407 and / or BMS interface 409.

[0069] The building subsystem integration layer 420 may be configured to manage communications between the BMS controller 366 and the building subsystems 428. For example, the building subsystem integration layer 420 may receive sensor data and input signals from the building subsystems 428 and provide output data and control signals to the building subsystems 428. The building subsystem integration layer 420 may also be configured to manage communications between the building subsystems 428. The building subsystem integration layer 420 may translate communications (e.g., sensor data, input signals, output signals, etc.) across multiple multi-vendor / multi-protocol systems.

[0070] The demand response layer 414 can be configured to optimize resource usage (e.g., electricity usage, natural gas usage, water usage, etc.) and / or the monetary cost of such resource usage in response to meeting demands of the building 10. The optimization can be based on time-of-use prices, curtailment signals, energy availability, or other data received from utility providers, distributed energy generation systems 424, energy storage devices 427 (e.g., high-temperature TES 242, low-temperature TES 244, etc.), or other sources. The demand response layer 414 can receive inputs from other layers of the BMS controller 366 (e.g., building subsystem integration layer 420, integrated control layer 418, etc.). Inputs received from other layers can include environmental or sensor inputs such as temperature, carbon dioxide levels, relative humidity levels, air quality sensor outputs, occupancy sensor outputs, room schedules, etc. The inputs can also include inputs such as power usage (e.g., expressed in kWh), heat load measurements, pricing information, forecasted pricing, smoothed pricing, curtailment signals from utilities, etc.

[0071] According to some embodiments, demand response layer 414 includes control logic for responding to the data and signals it receives. These responses may include communicating with control algorithms in integrated control layer 418, changing control strategies, changing set points, or activating / deactivating building facilities or subsystems in a controlled manner. Demand response layer 414 may also include control logic configured to determine when to utilize stored energy. For example, demand response layer 414 may determine to begin using energy from energy storage device 427 just before the start of peak usage hours.

[0072] In some embodiments, the demand response layer 414 includes control modules configured to actively initiate control actions (e.g., automatically change set points) that minimize energy costs based on one or more inputs (e.g., prices, curtailment signals, demand levels, etc.) that represent or are based on demand. In some embodiments, the demand response layer 414 uses equipment models to determine an optimal set of control actions. The equipment models may include, for example, thermodynamic models that describe the inputs, outputs, and / or functions performed by various sets of building equipment. The equipment models may represent collections of building equipment (e.g., subplants, chiller arrays, etc.) or individual devices (e.g., individual chillers, heaters, pumps, etc.).

[0073] The demand response layer 414 may further include or utilize one or more demand response policy definitions (e.g., databases, XML files, etc.). The policy definitions can be edited or adjusted by a user (e.g., via a graphical user interface) so that control actions initiated in response to a demand input can be tailored to the user's application, desired comfort level, specific building equipment, or based on other concerns. For example, a demand response policy definition can specify which equipment can be turned on or off in response to a particular demand input, how long a system or equipment should be turned off, what setpoints can be changed, what the allowable setpoint adjustment range is, how long to hold a high demand setpoint before returning to a normally scheduled setpoint, how close to a capacity limit to approach, which equipment mode to utilize, energy transfer rates (e.g., maximum rates, alarm rates, other rate boundary information, etc.) to and from energy storage devices (e.g., thermal storage tanks, battery banks, etc.), and when to dispatch on-site generation of energy (e.g., via fuel cells, motor-generator sets, etc.).

[0074] The integrated control layer 418 can be configured to use data inputs or outputs from the building subsystem integration layer 420 and / or the subsequent demand response layer 414 to make control decisions. The subsystem integration provided by the building subsystem integration layer 420 allows the integrated control layer 418 to coordinate the control activities of the building subsystems 428 so that they operate as a single, integrated super-system. In some embodiments, the integrated control layer 418 includes control logic that uses inputs and outputs from multiple building subsystems to provide greater comfort and energy savings compared to the comfort and energy savings that the separate subsystems could provide alone. For example, the integrated control layer 418 can be configured to use inputs from a first subsystem to make energy-saving control decisions for a second subsystem. The results of these decisions can be sent back to the building subsystem integration layer 420.

[0075] Integrated control layer 418 is shown logically below demand response layer 414. Integrated control layer 418 can be configured to enhance the effectiveness of demand response layer 414 by allowing building subsystems 428 and their respective control loops to be controlled in coordination with demand response layer 414. This configuration can advantageously reduce disruptive demand response behavior compared to traditional systems. For example, integrated control layer 418 can be configured to ensure that demand response, i.e., drive-up adjustments to the setpoint of chilled water temperature (or another component that directly or indirectly affects temperature), do not result in an increase in fan energy (or other energy used to cool the space) that results in a greater total building energy usage than is saved by the chiller.

[0076] The integrated control layer 418 can be configured to provide feedback to the demand response layer 414 so that the demand response layer 414 ensures that constraints (e.g., temperature, lighting levels, etc.) are maintained appropriately even when requested load shedding is in progress. Constraints may also include set points or sensed boundaries related to safety, equipment operating limits and performance, comfort, fire codes, electrical codes, energy codes, etc. The integrated control layer 418 is also logically below the fault detection and diagnostics layer 416 and the automatic measurement and verification layer 412. The integrated control layer 418 can be configured to provide calculated inputs (e.g., aggregates) to these higher levels based on outputs from two or more building subsystems.

[0077] The automatic measurement and verification (AM&V) layer 412 can be configured to verify that control strategies commanded by the integrated control layer 418 or the demand response layer 414 are operating properly (e.g., using data aggregated by the AM&V layer 412, the integrated control layer 418, the building subsystem integration layer 420, the FDD layer 416, or otherwise). The calculations performed by the AM&V layer 412 can be based on building system energy models and / or equipment models for individual BMS devices or subsystems. For example, the AM&V layer 412 can compare model predicted outputs to actual outputs from the building subsystems 428 to determine the accuracy of the models.

[0078] The fault detection and diagnosis (FDD) layer 416 can be configured to provide continuous fault detection for the building subsystems 428, building subsystem devices (i.e., building equipment), and control algorithms used by the demand response layer 414 and the integrated control layer 418. The FDD layer 416 may receive data input from the integrated control layer 418, directly from one or more building subsystems or devices, or from another data source. The FDD layer 416 may automatically diagnose and respond to detected failures. Responses to detected or diagnosed faults may include providing a user with a warning message, a maintenance scheduling system, or a control algorithm configured to attempt to repair or avoid the fault.

[0079] The FDD layer 416 can be configured to output a specific identification of the faulty component or cause of the fault (e.g., a loose damper interlock) using detailed subsystem inputs available at the building subsystem integration layer 420. In other exemplary embodiments, the FDD layer 416 is configured to provide a "fault" event to the integrated control layer 418, which executes control strategies and policies in response to the received fault event. According to some embodiments, the FDD layer 416 (or policies executed by an integrated control engine or business rules engine) may shut down systems or direct control actions around the faulty device or system to reduce energy waste, extend equipment life, or ensure appropriate control response.

[0080] The FDD layer 416 can be configured to store or access a variety of different system data stores (or data points for raw data). The FDD layer 416 may use some content of the data stores to identify faults at the equipment level (e.g., a specific chiller, a specific AHU, a specific terminal unit, etc.) and other content to identify faults at the component or subsystem level. For example, the building subsystem 428 may generate temporal (i.e., time series) data indicative of the performance of the BMS 400 and its various components. The data generated by the building subsystem 428 may include measurements or calculations that exhibit statistical characteristics and provide information about how the corresponding system or process (e.g., a temperature control process, a flow control process, etc.) is performing in terms of error from its setpoint. These processes are examined by the FDD layer 416, and when the system's performance begins to degrade, it can be published to alert a user to repair the fault before it becomes more severe.

[0081] 5, a block diagram of another building management system (BMS) 500 is shown, according to some embodiments. The BMS 500 can be used to monitor and control the HVAC system 100, the waterside system 200, the airside system 300, the building subsystems 428, and other types of BMS devices (e.g., lighting equipment, security equipment, etc.) and / or HVAC equipment devices.

[0082] The BMS 500 provides a system architecture that facilitates automatic equipment discovery and equipment model distribution. Equipment discovery can occur at multiple levels in the BMS 500, across multiple different communication buses (e.g., system bus 554, zone buses 556-560 and 564, sensor / actuator bus 566, etc.), and across multiple different communication protocols. In some embodiments, equipment discovery is achieved using an active node table that provides status information for devices connected to each communication bus. For example, each communication bus can be monitored for new devices by monitoring the corresponding active node table for new nodes. When a new device is detected, the BMS 500 can begin interacting with the new device (e.g., using data from the device to send control signals) without user interaction.

[0083] Some devices in the BMS 500 present themselves to the network using equipment models. The equipment models define equipment object attributes, view definitions, schedules, trends, and associated BACnet value objects (e.g., analog values, binary values, multi-state values, etc.) used for integration with other systems. Some devices in the BMS 500 store their own equipment models. Other devices in the BMS 500 have equipment models stored externally (e.g., in other devices). For example, the zone coordinator 508 may store an equipment model for the bypass damper 528. In some embodiments, the zone coordinator 508 automatically creates an equipment model for the bypass damper 528 or other devices on the zone bus 558. Other zone coordinators may also create equipment models for devices connected to the zone bus. Equipment models for devices can be automatically created based on the type of data points exposed by the device on the zone bus, the device type, and / or other device attributes. Some examples of automatic equipment discovery and equipment model distribution are discussed in more detail below.

[0084] 5 , the BMS 500 is shown to include a system manager 502, several zone coordinators 506, 508, 510, and 518, and several zone controllers 524, 530, 532, 536, 548, and 550. The system manager 502 can monitor data points within the BMS 500 and report the monitored variables to various monitoring and / or control applications. The system manager 502 can communicate with client devices 504 (e.g., user devices, desktop computers, laptop computers, mobile devices, etc.) via data communication links 574 (e.g., BACnet IP, Ethernet, wired or wireless communication, etc.). The system manager 502 can provide a user interface to the client devices 504 via the data communication links 574. The user interface can allow a user to monitor and / or control the BMS 500 via the client devices 504.

[0085] In some embodiments, the system manager 502 is connected to the zone coordinators 506-510 and 518 via a system bus 554. The system manager 502 can be configured to communicate with the zone coordinators 506-510 and 518 via the system bus 554 using a Master-Slave Token Passing (MSTP) protocol or any other communication protocol. The system bus 554 can also connect the system manager 502 to other devices, such as a constant volume (CV) rooftop unit (RTU) 512, an input / output module (IOM) 514, a thermostat controller 516 (e.g., a TEC5000 series thermostat controller), and a network automation engine (NAE) or third-party controller 520. The RTU 512 can be configured to communicate directly with the system manager 502 and can be directly connected to the system bus 554. Other RTUs can communicate with the system manager 502 through intermediate devices. For example, wired input 562 may connect third-party RTU 542 to thermostat controller 516 which connects to system bus 554 .

[0086] The system manager 502 can provide a user interface for any device that includes an equipment model. Devices such as the zone coordinators 506-510 and 518 and the thermostat controller 516 can provide their equipment models to the system manager 502 via the system bus 554. In some embodiments, the system manager 502 automatically creates equipment models for connected devices that do not include an equipment model (e.g., the IOM 514, the third-party controller 520, etc.). For example, the system manager 502 can create an equipment model for any device that responds to a device tree request. The equipment models created by the system manager 502 can be stored within the system manager 502. The system manager 502 can then use the equipment models created by the system manager 502 to provide user interfaces for devices that do not include their own equipment models. In some embodiments, the system manager 502 stores view definitions for each type of equipment connected via the system bus 554 and uses the stored view definitions to generate user interfaces for the equipment.

[0087] Each zone coordinator 506-510 and 518 can connect to one or more of the zone controllers 524, 530-532, 536, and 548-550 via zone buses 556, 558, 560, and 564. The zone coordinators 506-510 and 518 can communicate with the zone controllers 524, 530-532, 536, and 548-550 via the zone buses 556-560 and 564 using the MSTP protocol or any other communication protocol. The zone buses 556-560 and 564 can also connect the zone coordinators 506-510 and 518 to other types of devices, such as variable air volume (VAV) RTUs 522 and 540, switching bypass (COBP) RTUs 526 and 552, bypass dampers 528 and 546, and peak controllers 534 and 544.

[0088] Zone coordinators 506-510 and 518 can be configured to monitor and issue commands to various zoning systems. In some embodiments, each zone coordinator 506-510 and 518 monitors and commands a separate zoning system and is connected to the zoning system via a separate zone bus. For example, zone coordinator 506 can be connected to VAV RTU 522 and zone controller 524 via zone bus 556. Zone coordinator 508 can be connected to COBP RTU 526, bypass damper 528, COBP zone controller 530, and VAV zone controller 532 via zone bus 558. Zone coordinator 510 can be connected to PEAK controller 534 and VAV zone controller 536 via zone bus 560. The zone coordinator 518 may be connected to a PEAK controller 544 , a bypass damper 546 , a COBP zone controller 548 , and a VAV zone controller 550 via a zone bus 564 .

[0089] A single model of zone coordinators 506-510 and 518 can be configured to handle multiple different types of zoning systems (e.g., VAV zoning systems, COBP zoning systems, etc.). Each zoning system can include an RTU, one or more zone controllers, and / or a bypass damper. For example, zone coordinators 506 and 510 are shown as Verasys VAV Engines (VVEs) connected to VAV RTUs 522 and 540, respectively. Zone coordinator 506 is directly connected to VAV RTU 522 via zone bus 556, while zone coordinator 510 is connected to third-party VAV RTU 540 via wired input 568 provided to PEAK controller 534. Zone coordinators 508 and 518 are shown as Verasys COBP Engines (VCEs) connected to COBP RTUs 526 and 552, respectively. The zone coordinator 508 is directly connected to the COBP RTU 526 via a zone bus 558 , while the zone coordinator 518 is connected to the third-party COBP RTU 552 via a wired input 570 provided to the PEAK controller 544 .

[0090] Zone controllers 524, 530-532, 536, and 548-550 can communicate with individual BMS devices (e.g., sensors, actuators, etc.) via a sensor / actuator (SA) bus. For example, VAV zone controller 536 is shown connected to networked sensors 538 via SA bus 566. Zone controller 536 can communicate with networked sensors 538 using MSTP protocol or any other communication protocol. While only one SA bus 566 is shown in FIG. 5 , it should be understood that each zone controller 524, 530-532, 536, and 548-550 can be connected to a different SA bus. Each SA bus can connect the zone controller to various sensors (e.g., temperature sensors, humidity sensors, pressure sensors, light sensors, occupancy sensors, etc.), actuators (e.g., damper actuators, valve actuators, etc.), and / or other types of controllable equipment (e.g., chillers, heaters, fans, pumps, etc.).

[0091] Each zone controller 524, 530-532, 536, and 548-550 can be configured to monitor and control a different building zone. Zone controllers 524, 530-532, 536, and 548-550 can monitor and control various building zones using inputs and outputs provided via their SA bus. For example, zone controller 536 can use temperature input (e.g., the measured temperature of the building zone) received from networked sensor 538 via SA bus 566 as feedback in its temperature control algorithms. Zone controllers 524, 530-532, 536, and 548-550 may use various types of control algorithms (e.g., state-based algorithms, extremum-seeking control (ESC) algorithms, proportional-integral (PI) control algorithms, proportional-integral-derivative (PID) control algorithms, model predictive control (MPC) algorithms, feedback control algorithms, etc.) to control variable states or conditions (e.g., temperature, humidity, airflow, lighting, etc.) within or around building 10.

[0092] Variable Refrigerant Flow System 6A-6B, a variable refrigerant flow (VRF) system 600 is shown according to some embodiments. The VRF system 600 is shown to include multiple outdoor VRF units 602 and multiple indoor VRF units 604. The outdoor VRF units 602 may be located outside a building and may operate to heat or cool a refrigerant. The outdoor VRF units 602 may consume electricity to convert the refrigerant between liquid, vapor, and / or superheated vapor phases. The indoor VRF units 604 may be distributed throughout various building zones within the building and may receive heated or cooled refrigerant from the outdoor VRF units 602. Each indoor VRF unit 604 may provide temperature control for the particular building zone in which the indoor VRF unit is located.

[0093] A key advantage of a VRF system is that some indoor VRF units 604 can operate in cooling mode and other indoor VRF units 604 can operate in heating mode. For example, each of the outdoor VRF units 602 and indoor VRF units 604 can operate in heating mode, cooling mode, or off mode. Each building zone can be independently controlled and can have a different temperature setpoint. In some embodiments, each building has up to three outdoor VRF units 602 located on the outside of the building (e.g., on the roof) and up to 128 indoor VRF units 604 distributed throughout the building (e.g., in various building zones).

[0094] Many different configurations exist for the VRF system 600. In some embodiments, the VRF system 600 is a two-pipe system in which each outdoor VRF unit 602 connects to a single refrigerant return line and a single refrigerant outlet line. In a two-pipe system, only one of the heated or cooled refrigerants can be provided through a single refrigerant outlet line, so all of the outdoor VRF units 602 operate in the same mode. In other embodiments, the VRF system 600 is a three-pipe system in which each outdoor VRF unit 602 connects to a refrigerant return line, a hot refrigerant outlet line, and a cold refrigerant outlet line. In a three-pipe system, both heating and cooling can be provided simultaneously through the dual refrigerant outlet lines.

[0095] VRF system 600 may represent an example of a VRF system that may utilize AI to predict refrigerant properties (e.g., refrigerant level, etc.), liquid properties (e.g., refrigerant level, oil level, oil-refrigerant level, etc.), etc. to ensure that components (e.g., outdoor VRF unit 602, indoor VRF unit 604, etc.) are operating properly. Specifically, VRF system 600 may utilize the systems and methods described throughout Figures 8-10 to ensure that all components have an adequate supply of refrigerant (e.g., refrigerant charge, etc.), an adequate supply of oil, etc.

[0096] Referring now to FIG. 7A , an illustration of a VRF system 700 is shown, according to some embodiments. In some embodiments, the VRF system 700 is similar to and / or the same as the VRF system 600 described with reference to FIGS. 6A and 6B . More specifically, the VRF system 700 can illustrate how oil is utilized in a VRF system. Note that the VRF system 700 is shown merely as an example of how a VRF system may operate. The components, relationships, and / or other features of the VRF system 700 can be customized and configured based on a particular implementation. For example, the VRF system 700 can include more or fewer compressors 701 than shown in FIG. 7A .

[0097] VRF system 700 is shown to include compressor 701, heat exchanger 702, double-tube heat exchanger 703, oil separator 704, and accumulator 705. To operate properly, compressor 701 may require oil to ensure its components are properly lubricated. Without oil, the components may deteriorate quickly, and compressor 701 may not be able to provide adequate cooling / heating to the zones. Heat exchangers 702 and 703 can transfer heat between fluids (e.g., oil and refrigerant). Oil separator 704 can separate oil from the refrigerant and / or other fluids within VRF system 700. Specifically, during operation, compressor 701 may leak and / or otherwise allow some oil to mix with the refrigerant output by compressor 701. If the oil is not back-extracted from the oil / refrigerant mixture, the oil may be provided to components beyond VRF system 700 (e.g., an indoor AHU) which may result in rapid loss of oil within VRF system 700. Thus, oil separator 704 can distill the oil from the fluid mixture and provide the oil to accumulator 705 for temporary storage. Accumulator 705 can store the oil and can be accessed as needed to recover oil for other components of VRF system 700.

[0098] VRF system 700 is also shown to include a strainer 706, a distributor 707, a reversing valve 708, a capillary tube 709, and a microcomputer-controlled expansion valve 710. The strainer 706 can remove impurities (e.g., dirt, debris, etc.) from the oil and / or oil / refrigerant mixture that may inadvertently become incorporated with the oil and / or oil / refrigerant mixture during operation of VRF system 700. The impurities can result in malfunctions of building services and can affect the properties of the oil within VRF system 700 (e.g., by increasing or decreasing the viscosity of the oil). The distributor 707 can assist in distributing fluid throughout heat exchanger 702. The reversing valve 708 can change the direction of refrigerant flow within VRF system 700 to switch VRF system 700 between heating and cooling modes. The capillary tube 709 can assist in lowering the temperature of the refrigerant within VRF system 700 by affecting the pressure of the refrigerant. A microcomputer controlled expansion valve 710 can regulate the amount of refrigerant entering the components of the VRF system 700 .

[0099] VRF system 700 is also shown to include a check valve 711, a solenoid valve 712, a check joint 713, a stop valve 714 for the liquid line, a stop valve 715 for the gas (cryogenic) line, a stop valve 716 for the gas (hot / cold) line, a refrigerant pressure sensor 717, another refrigerant pressure sensor 718, and a high-pressure switch 719. Check valve 711 can help ensure that fluid is flowing in the correct direction within VRF system 700 by restricting fluid from flowing in a direction opposite to the desired flow direction. Solenoid valve 712 can regulate the flow of fluid within VRF system 700. Check joint 713 can help regulate stress on the components of VRF system 700. Stop valves 714, 715, and 716 can restrict fluid flow in the liquid line, gas (cryogenic) line, and gas (hot / cold) line, respectively, as shown in the illustration of FIG. 7A . With regard to refrigerant pressure sensors 717 and 718, refrigerant pressure sensor 717 can be a high pressure sensor, while refrigerant pressure sensor 718 can be a low pressure sensor in VRF system 700. When refrigerant returns to compressor 701, high pressure switch 719 can stop refrigerant from entering compressor 701 if the refrigerant pressure is too high or too low to prevent damage to compressor 701.

[0100] VRF system 700 is also shown to include various thermistors. In VRF system 700, the resistance across the thermistors can be primarily based on the temperature of the connected components. In the illustration of FIG. 7A, VRF system 700 is shown to include thermistors 720-725. Thermistor 720 is associated with the upper side of first compressor 701. Thermistor 721 is associated with the upper side of second compressor 701. Thermistor 722 is associated with the gas side of heat exchanger 702. Thermistor 723 is associated with the liquid side of heat exchanger 702. Thermistor 724 is associated with the subcooler bypass side. Thermistor 725 is associated with refrigerant autocharge.

[0101] Each pipe in VRF system 700 is also labeled with its corresponding outer diameter OD and thickness T, which are shown below in Table A. It should be noted that the material used in VRF system 700 throughout all piping is C1220T-O. [Table 1]

[0102] Referring now to FIG. 7B, an illustration of a VRF system 750 is shown, according to some embodiments. In some embodiments, the VRF system 750 is similar to and / or the same as the VRF system 700 described with reference to FIG. 7 and / or the VRF system 600 described with reference to FIGS. 6A and 6B. Specifically, the VRF system 750 illustrates the flow of oil through a VRF system. As with the VRF system 700, the VRF system 750 is provided for example purposes only. The components, structure, and / or other characteristics of the VRF system 750 can be customized and configured depending on the implementation.

[0103] VRF system 750 is shown to include suction piping 752. Suction piping 752 can provide refrigerant (e.g., refrigerant vapor) to compressor 754 for use by one or more devices / systems (e.g., indoor units). In some embodiments, some oil can be included in the fluid provided to compressor 754 by suction piping 752. Based on the received refrigerant, compressor 754 can operate to compress the refrigerant into a higher pressure gas and output the higher pressure gas via discharge piping 756. Because compressor 754 can require oil to function properly, the compression process performed by compressor 754 can result in some oil being mixed into the output high-pressure gas, thereby resulting in an oil / refrigerant mixture.

[0104] VRF system 750 is also shown to include an oil separator 758. Based on the received oil / refrigerant mixture, oil separator 758 can operate to separate the oil from the refrigerant. After separation, the refrigerant can be provided to several devices / systems (e.g., indoor units) via refrigerant piping 762. The separated oil can be provided to accumulator 760 via oil piping 764. Accumulator 760 can serve as a storage container for the oil separated by oil separator 758. Accumulator 760 can have several maximum capacities that define the maximum amount of oil that can be stored in accumulator 760.

[0105] The oil stored by the accumulator 760 can be returned to the compressors 754 via an oil return line 766. Given that the accumulator 760 has some non-zero amount of stored oil, the accumulator 760 can provide oil to any of the compressors 754 if that particular compressor 754 needs more oil. In some embodiments, the VRF system 750 includes a valve 768 that regulates the flow of oil to the compressors 754. In this case, the valve 768 can prevent excess oil from being provided to the compressor 754 and / or otherwise regulate the oil provided to the compressor 754.

[0106] In some embodiments, the refrigerant level within VRF system 750 is estimated / predicted by an AI model, which may take inputs such as the operating speed of compressor 754, the suction temperature and pressure of suction line 752, the discharge temperature and pressure of discharge line 756, valve positions, pipe lengths (e.g., conduit lengths, etc.), etc. These inputs may be measured by sensors (e.g., temperature sensors, pressure sensors, etc.) throughout VRF system 750 and / or may be provided directly by components of VRF system 750 (e.g., compressor 754 may directly output its operating speed).

[0107] Based on the inputs, the AI ​​model may predict characteristics of the refrigerant in VRF system 750 (e.g., refrigerant level, etc.). In some embodiments, the AI ​​model uses the predicted characteristics to predict additional characteristics of VRF system 750 (e.g., optimal amount of refrigerant to charge based on an automatic refrigerant charge mode, operating status of devices in VRF system 750 based on a refrigerant leak detection mode, etc.). If the refrigerant and / or characteristics of VRF system 750 do not meet predefined thresholds (or some other constraints), one or more corrective actions may be initiated to address the deficiency in the refrigerant characteristics and / or VRF system 750 to meet the predefined thresholds. For example, if the refrigerant level in VRF system 750 is too low, a corrective action (e.g., refrigerant charge, etc.) may be initiated to operate a particular compressor 754, a particular valve (e.g., valves 711-716), etc. so that VRF system 750 may receive more refrigerant at a particular time, resulting in a relatively low impact on the efficiency of VRF system 750. Similarly, if the refrigerant level in VRF system 750 is too low, another corrective action (e.g., leak maintenance, etc.) may be initiated to operate components of VRF system 750 such that VRF system 750 may be repaired to correct the leak and have a relatively low impact on the efficiency of VRF system 750. The AI ​​models and corrective actions that may be initiated are described in more detail below with reference to Figures 8-10.

[0108] In other embodiments, the liquid level in each of the compressor 754, the accumulator 760, and / or other components of the VRF system 750 is estimated / predicted by an AI model. In this case, the AI ​​model may take inputs such as the operating speed of the compressor 754, the discharge temperature and pressure of the discharge line 756, the suction pressure of the suction line 752, the temperature of the refrigerant vapor and / or the temperature of other gases in the VRF system 750 (e.g., the sub-cooling temperature of the VRF system 750, etc.), the ambient temperature near the VRF system 750 (e.g., the dry-bulb temperature of the VRF system 750, etc.), the refrigerant charge (e.g., an estimated refrigerant charge, etc.). The inputs may be measured by sensors (e.g., temperature sensors, pressure sensors, etc.) throughout the VRF system 750, provided directly by the components of the VRF system 750 (e.g., the compressor 754 may directly output their operating speed), and / or estimated / predicted by the AI ​​model.

[0109] Based on the inputs, the AI ​​model may predict the characteristics of the liquid in the VRF system 750 (e.g., the refrigerant level in the accumulator 760, the oil level in the accumulator 760, the oil-refrigerant mixture level in the accumulator 760, etc.). If the characteristics of the liquid do not meet a predefined threshold (or some other constraint), one or more corrective actions may be initiated to address the deficiency in the liquid characteristics so that the predefined threshold is met. For example, if the liquid level in the accumulator 760 is too high, a corrective action may be initiated to operate the accumulator 760, a particular valve 768, etc., so that the accumulator 760 may return liquid (e.g., oil to the compressor 754) at a specific time, resulting in a relatively low impact on the efficiency of the VRF system 750. The AI ​​model and corrective actions that may be initiated are described in more detail below with reference to FIGS. 11-13.

[0110] Systems and methods for refrigerant estimation 8-10 , systems and methods for estimating and predicting refrigerant properties in a VRF system are shown and described, according to some embodiments. It should be understood that the following description is described with reference to a VRF system for illustrative purposes only and should not be considered limiting. The systems and methods described throughout Figures 8-10 can likewise be applied to various systems that utilize refrigerants (e.g., other building systems, fire systems, etc.) and are not meant to be limited to VRF systems.

[0111] The systems and methods described below may utilize artificial intelligence (AI) models to predict how refrigerant properties will change over time based on various inputs. The AI ​​models may include any suitable type of AI model. For example, the AI ​​models may be or include long short-term memory (LSTM) models, other types of recurrent neural networks (RNNs), convolutional neural networks (CNNs), etc. The type of AI model utilized may be selected based on, for example, the accuracy of a given AI model, the specific inputs / outputs to be considered, user preference, etc. In some embodiments, RNN models, such as LSTM models, are preferred due to the time-series nature of refrigerants. Note that machine learning models may be referred to herein synonymously as AI models.

[0112] An AI model can be trained to predict specific refrigerant characteristics based on a set of training data. The training data can be provided by a variety of sources. For example, a user can provide a set of inputs including various variables that can help the AI ​​model determine how refrigerant is utilized in a VRF system, and a corresponding set of outputs based on the actual measured operating conditions of the system or a similar system. In this case, the inputs can include, for example, compressor operating speed, suction temperature and pressure, discharge temperature and pressure, valve position, pipe length (e.g., conduit length, etc.). As defined herein, suction pressure can refer to the suction pressure generated by the compressor during operation, discharge temperature can refer to the temperature measurement of superheated refrigerant vapor in a VRF system, and discharge pressure can refer to the pressure generated at the output side of the compressor. Outputs can include, for example, refrigerant level, etc. The AI ​​model can then be trained using the inputs and corresponding outputs to predict the value of the output based on the inputs. Of course, the inputs and outputs are given for purposes of example and are not meant to be limiting with respect to possible inputs to the AI ​​model.

[0113] In some embodiments, a simulation model is utilized to generate training data used to train the AI ​​model. The training data generated using the simulation model may be used separately or in addition to training data collected from other sources (e.g., from measured conditions of the actual system). The simulation model may be constructed to simulate changes in refrigerant properties over time based on various conditions. In other words, the simulation model may be constructed to digitally mimic the behavior of refrigerant properties in a VRF system. The state of the simulation model (e.g., refrigerant levels, etc.) may be manipulated to generate training data representing a wide variety of conditions. The simulation model may be run multiple times to generate training data representing the evolution of the system over time under a variety of different loads, using different building devices, under different weather / environmental conditions, at different times, etc. For a VRF system, the simulation model may be, for example, a closed-loop functional mockup unit (FMU) model, which may typically be used to operate a VRF system if an AI model is not used. Advantageously, by utilizing a simulation model, a large amount of training data can be generated in a shorter period of time compared to operating an actual system over time to generate the training data. Furthermore, the simulation model can be run to generate training data representing fringe scenarios (e.g., heating mode, cooling mode, off mode, dangerously high loads, unsafe operating conditions, device failures, etc.) without imposing conditions on the actual system that could be dangerous and disruptive to occupant comfort in an actual building.

[0114] Once trained based on a set of training data, the AI ​​model can predict refrigerant characteristics based on the input. For example, the AI ​​model may predict refrigerant levels. Based on the predicted refrigerant characteristics, it can be determined whether the predicted refrigerant characteristics comply with predefined thresholds (and / or other constraints) on the values ​​of the characteristics. The predefined thresholds can include any limits on values, such as, for example, a range of acceptable values ​​for the characteristic that the characteristic threshold must be above or below. If the value of the refrigerant characteristic meets the predefined threshold, the VRF system can continue normal operation. However, if the value of the refrigerant characteristic does not meet the predefined threshold, a corrective action can be generated and initiated. Corrective action, as defined herein, can refer to any action taken to address one or more refrigerant characteristics that do not meet some predefined constraint / threshold. For example, a corrective action may be or may include automatically providing and / or receiving refrigerant (e.g., filling a VRF system, etc.), automatically providing and / or receiving maintenance to a device in a VRF system (e.g., repairing a leak, rupture, break, etc.), disabling a particular building device (e.g., a device in a VRF system, etc.), generating and transmitting a notification to a user device, scheduling a technician to perform maintenance on the VRF system and / or replace the refrigerant, generating control signals and operating building equipment (e.g., a VRF device) based on the control signal, logging the threshold violation(s) in a database, etc. The corrective action to initiate can be determined based on various factors, such as which threshold was violated, the amount by which the threshold was violated (e.g., the difference between the actual value of the refrigerant characteristic and the threshold), user preference, and / or any other applicable considerations. A violation of a threshold refrigerant characteristic may also indicate a deficiency of refrigerant, in that the refrigerant lacks some desired characteristic (e.g., a desired level, etc.). As described herein, a threshold violation can refer to a value (e.g., a predicted value) exceeding the threshold in the case of a maximum threshold, or falling below the threshold in the case of a minimum threshold.

[0115] Referring now to FIG. 8 , a block diagram of a refrigerant management controller 800 for predicting refrigerant characteristics is shown, according to some embodiments. In particular, the refrigerant management controller 800 may predict refrigerant characteristics in a VRF system. However, the refrigerant management controller 800 may be applied to various other systems / devices that require refrigerant for proper operation (e.g., other HVAC systems, car systems, fire safety systems, etc.). In some embodiments, the refrigerant management controller 800 and / or its components are incorporated into the BMS controller 366 described with reference to FIGS. 3-4 and / or another controller. In some embodiments, the refrigerant management controller 800 is used to operate some and / or all of the VRF systems described throughout FIGS. 7A-7B.

[0116] The refrigerant management controller 800 is shown to include a communication interface 808 and processing circuitry 802. The communication interface 808 may include a wired or wireless interface (e.g., jack, antenna, transmitter, receiver, transceiver, wired terminal, etc.) for communicating data with various systems, devices, or networks. For example, the communication interface 808 may include an Ethernet card and port for transmitting and receiving data over an Ethernet-based communication network and / or a Wi-Fi transceiver for communicating over a wireless communication network. The communication interface 808 may be configured to communicate over a local area network or a wide area network (e.g., the Internet, a building WAN, etc.) and may use various communication protocols (e.g., BACnet, IP, LON, etc.).

[0117] The communication interface 808 may be a network interface configured to facilitate electronic data communication between the refrigerant management controller 800 and various external systems or devices (e.g., equipment 822, sensors 820, user devices 824, etc.). For example, the refrigerant management controller 800 may receive equipment feedback from the equipment 822 via the communication interface 808.

[0118] The processing circuit 802 is shown to include a processor 804 and a memory 806. The processor 804 may be a general-purpose or special-purpose processor, an application-specific integrated circuit (ASIC), one or more field-programmable gate arrays (FPGAs), a group of processing components, or other suitable electronic processing components. The processor 804 may be configured to execute computer code or instructions stored in the memory 806 or received from another computer-readable medium (e.g., a CD-ROM, network storage, a remote server, etc.).

[0119] Memory 806 may include one or more devices (e.g., memory units, memory devices, storage devices, etc.) for storing data and / or computer code for completing and / or facilitating the various processes described in this disclosure. Memory 806 may include random access memory (RAM), read-only memory (ROM), hard drive storage, temporary storage, non-volatile memory, flash memory, optical memory, or any other suitable memory for storing software objects and / or computer instructions. Memory 806 may include database components, object code components, script components, or any other type of information structure for supporting the various activities and information structures described in this disclosure. Memory 806 may be communicatively coupled to processor 804 via processing circuitry 802 and may include computer code for executing (e.g., by processor 804) one or more processes described herein. In some embodiments, one or more components of memory 806 are part of a single component. However, each component of memory 806 is shown separately for ease of explanation.

[0120] Memory 806 is shown to include a training data collector 810. The training data collector 810 can collect training data used to train the artificial intelligence model from one or more training data sources 818. Specifically, the training data collector 810 can obtain training data associated with characteristics of refrigerants in a VRF system. In some embodiments, the training data collector 810 transmits queries to the training data sources 818 to obtain the training data. In some embodiments, the training data collector 810 can passively receive training data from the training data sources 818 without having to actively request the training data.

[0121] Training data source 818 can include any data source capable of storing and / or providing training data to training data collector 810. For example, training data source 818 can be or include a user device (e.g., a laptop, desktop computer, mobile device, tablet, etc.) capable of providing a stored training data set (e.g., usage data, etc.) to training data collector 810. As another example, training data source 818 can be or include a database (e.g., a cloud database) that stores data associated with utilizing a standard VRF plant model with an additional output of refrigerant level. In such an example, the VRF plant model can be a standard model used to operate a VRF system. In this manner, the training data can include measurements from a VRF system in actual operation along with measurements of refrigerant properties.

[0122] In some embodiments, the training data collector 810 utilizes a simulation model to generate some or all of the training data used by the model generator 812 to generate the AI ​​model. The simulation model can model how an actual system may operate under various conditions (e.g., weather conditions, heating / cooling loads, device limitations, etc.) and how refrigerant in the system may be consumed and / or otherwise change over time. In this manner, the training data collector 810 may not need to retrieve training data from the training data source 818 and instead can generate the training data within the refrigerant management controller 800. In some embodiments, the simulation model is hosted by a third-party controller / device / system (e.g., a cloud computing system) that can provide the refrigerant management controller 800 with training data generated as a result of running the simulation model. In either case, the simulation model can be used / executed to generate various training data representing various operating conditions of the refrigerant-utilizing system in a shorter period of time than waiting for the actual system to generate training data through operation. Additionally, the simulation model can be run simply to generate training data that illustrates fringe scenarios under which it may be dangerous for a real system (e.g., a VRF system in heating mode, cooling mode, off mode, etc.) to operate.

[0123] Based on the acquired training data, the training data collector 810 can combine the collected training data into a training data set and provide the training data set to the model generator 812. Based on the training data set, the model generator 812 can generate an AI model that models refrigerant characteristics over time. Specifically, the model generator 812 can train the AI ​​model to predict refrigerant characteristics based on specified inputs. For example, the model generator 812 may train the AI ​​model to predict values ​​of refrigerant in one or more compressors, refrigerant in an accumulator, refrigerant level in a VRF system when the VRF system is in a heating mode, a cooling mode, and / or an off mode, ambient temperature, discharge temperature, suction pressure, discharge pressure, and gas temperature.

[0124] The AI ​​model generated by the model generator 812 can have any of a variety of AI model structures. In some embodiments, the AI ​​model is an RNN model, such as an LSTM model. In this case, for the RNN to operate properly on the VRF system, the original FMU plant model can be utilized to generate enough simulation data for the RNN model to analyze and train on. As training time increases, the final RNN model may function much closer to the original plant model. In particular, some advantages of using an RNN model are that it may be faster and more stable than an FMU plant model. Furthermore, the trained RNN model may reduce the impact of refrigerant characteristics, improving the efficiency of the VRF system. Regarding the LSTM model in particular, an LSTM model is an artificial RNN used in deep learning. An LSTM model can classify and process entire sequences of time series data and make predictions even when there is an unknown lag between significant events in the time series. The LSTM model generated by the model generator 812 can have various structures depending on the implementation. For example, the LSTM model generated by the model generator 812 may include one sequence input layer, one dropout layer, two fully connected layers, and two LSTM layers.

[0125] In some embodiments, the AI ​​model is a CNN model. In this case, the CNN model may include, for example, an input layer, multiple hidden layers (e.g., a normalized linear unit layer, a pooling layer, a fully connected layer, a normalization layer, etc.), an output layer, etc. In some embodiments, the AI ​​model follows some other artificial intelligence model architecture. Exemplary architectures of AI models are described in more detail below with reference to Figures 9A-9C.

[0126] The model generator 812 may generate the AI ​​model using a variety of training techniques. For example, the model generator 812 may use a stochastic gradient descent with momentum approach, an adaptive moment estimation approach, a root-mean-square propagation approach, etc. For the root-mean-square propagation approach, the model generator 812 may use the root-mean-square error (RMSE) to measure how accurate the model predictions are relative to the training data provided by the training data collector 810. Specifically, the model generator 812 may monitor the RMSE over time based on the following formula:

number

[0127] The model generator 812 can provide the generated AI model to a prediction generator 814. The prediction generator 814 can use the AI ​​model to generate predictions of refrigerant properties over time. To generate the predictions, the prediction generator 814 can operate to obtain values ​​for inputs required by the AI ​​model from various sources. For example, the prediction generator 814 can obtain equipment feedback from equipment 822, measured variables from sensors 820, and / or any other suitable source of input values.

[0128] Equipment 822 can be or include any device capable of providing input data required by the AI ​​model. For example, in a VRF system, equipment 822 can include compressors, heat exchangers, accumulators, valves, etc. that can provide usage data (e.g., compressor speed, suction temperature, suction pressure, discharge temperature, discharge pressure, valve position, fluid conduit length, etc.) to refrigerant management controller 800. More specifically, if the AI ​​model requires compressor speed as input, equipment 822 can include one or more compressors that can provide operating speed as equipment feedback to prediction generator 814.

[0129] Sensors 820 may be or include various sensors capable of measuring values ​​of inputs (i.e., variables) required by the AI ​​model. For example, sensors 820 may include pressure sensors that measure suction and / or discharge pressure. As another example, sensors 820 may include temperature sensors that measure discharge, ambient, and / or gas temperatures. As yet another example, sensors 820 may include position sensors that measure valve positions, pipe (e.g., conduit, etc.) lengths, and / or orientations of other components of the VRF system.

[0130] Based on the AI ​​model and the obtained input values, the prediction generator 814 can generate a refrigerant property prediction by passing the obtained input values ​​to the AI ​​model. As a result of passing the obtained input values ​​to the AI ​​model, the AI ​​model can output values ​​for one or more refrigerant properties (e.g., refrigerant level, etc.). In this manner, the properties of the refrigerant in the VRF system can be estimated without the need for additional sensors to measure the refrigerant properties.

[0131] In some embodiments, the prediction generator 814 generates predictions for multiple stages within the VRF system. For example, the prediction generator 814 may generate predictions of refrigerant levels in a compressor, accumulator, or other device of the VRF system when the VRF system is in a particular mode (e.g., heating mode, cooling mode, off mode, etc.). In some embodiments, the prediction generator 814 also predicts the optimal amount of refrigerant to be provided (e.g., charged) and / or predicts the operating state of devices in the VRF system. In such examples, predicting refrigerant levels, the optimal amount of refrigerant to be provided, and / or the operating state of devices in the VRF system may benefit specific corrective actions (e.g., the amount of refrigerant to add / remove from the VRF system, devices in the VRF system to be repaired, etc.). By generating predictions for refrigerant at multiple stages within the VRF system, refrigerant shortages can be predicted and tracked over time throughout the entire VRF system, rather than at a single point in the VRF system.

[0132] The prediction generator 814 can provide predictions of the refrigerant characteristics to a corrective action generator 816. The corrective action generator 816 can analyze the predicted refrigerant characteristics to determine whether and what corrective action should be initiated. As defined above, a corrective action can refer to any action taken to address a refrigerant characteristic that does not meet some predefined threshold. Corrective actions can include, for example, providing and / or receiving refrigerant (e.g., filling a VRF system, etc.), providing and / or receiving maintenance to devices in the VRF system (e.g., repairing leaks, ruptures, breaks, etc.), providing and / or receiving refrigerant at components of the VRF system (e.g., returning refrigerant to an outdoor VRF unit, etc.), distributing a notification / alert to a user device 824 to indicate to a user that a particular refrigerant characteristic violates a predefined threshold, operating equipment 822, disabling equipment 822, automatically scheduling maintenance activities to be performed on equipment 822, logging the threshold violation to a database, etc. The predefined threshold(s) can be user-defined, provided by a manufacturer, estimated based on the operating conditions of the equipment in the VRF system, etc. For example, a user or manufacturer may define a minimum amount of refrigerant at which the VRF system should operate, where low refrigerant levels may result in more rapid compressor degradation. As should be appreciated, thresholds defined for refrigerant characteristics can be obtained from a variety of sources (e.g., manufacturer, user, forecast-based, etc.) and can include a variety of limit types (e.g., ranges, thresholds, exact values ​​that should represent the characteristics, etc.).

[0133] As a more specific example, consider a scenario in which the AI ​​model predicts values ​​of refrigerant properties including the refrigerant level in the compressor, the refrigerant level in the accumulator, and / or the refrigerant level in the VRF system. In an embodiment, corrective action generator 816 may determine whether the refrigerant levels in the compressor, the accumulator, and / or the VRF system are above a first minimum threshold, a second minimum threshold, and / or a third minimum threshold, respectively. If the refrigerant level in the compressor is below the first minimum threshold, corrective action generator 816 may determine that the VRF system should withdraw refrigerant from an external source and / or repair the compressor. If the refrigerant level in the accumulator is below the second minimum threshold, corrective action generator 816 may determine that the VRF system should withdraw refrigerant from an external source and / or repair the accumulator. Similarly, if the refrigerant level in the VRF system (e.g., a device, piping, pipe, or conduit, etc.) falls below a third minimum threshold, corrective action generator 816 may determine that the VRF system should recover refrigerant from an external source and / or repair a device in the VRF system. In some embodiments, refrigerant is added (e.g., recovered, provided, etc.) automatically by a component of the VRF system. In other embodiments, refrigerant is added to the VRF system manually by a user.

[0134] In some embodiments, the corrective action generator 816 compares the output of the AI ​​model over time to determine whether a particular refrigerant characteristic is approaching a threshold violation, thereby including a deficiency. In this case, the corrective action generator 816 may compare the value of the refrigerant characteristic output by the AI ​​model with a previous output value of the refrigerant characteristic. If a particular refrigerant characteristic is trending toward violating a threshold, the corrective action generator 816 may preemptively initiate corrective action before a violation occurs. For example, if the refrigerant level is decreasing over time and, based on the current trend, will fall below a minimum refrigerant level threshold within a future period, the corrective action generator 816 may initiate a corrective action (e.g., adding refrigerant, repairing a device, etc.) before the refrigerant level falls below the minimum threshold. Advantageously, preemptive initiation of corrective action can ensure that the amount of time equipment (e.g., a compressor) operates under conditions associated with a refrigerant characteristic threshold violation is reduced. Reducing this time can, in turn, reduce equipment degradation, reduce energy consumption, and provide other benefits.

[0135] In some embodiments, corrective action generator 816 predicts the time to initiate specific corrective actions to reduce the impact on equipment 822 and / or other devices / systems. For example, corrective action generator 816 may predict the time to begin adding refrigerant to reduce the negative impact on the heating / cooling load required by a building. In some embodiments, corrective action generator 816 also predicts the time to temporarily disable equipment 822 so that additional refrigerant can be safely added to the system. Corrective action generator 816 may also predict the time to repair devices in the VRF system to reduce the negative impact on the equipment in the VRF system.

[0136] The corrective action generator 816 can utilize various techniques to predict the time to initiate a particular corrective action. For example, the corrective action generator 816 may track a particular variable over time and identify a lower range of values ​​that may result in a small amount of disruption to the system. As a particular example, the corrective action generator 816 may identify a range of values ​​associated with low heating / cooling needs such that the impact on environmental conditions within the building is reduced. Based on the identified range of values, the corrective action generator 816 may track the actual heating / cooling needs over time and, in response to identifying a period during which the actual heating / cooling needs fall within the identified range, initiate a corrective action during that period. In this way, the corrective action generator 816 effectively predicts a period during which initiating a corrective action will result in a low overall impact.

[0137] In some embodiments, corrective action generator 816 can operate as a standard equipment controller when corrective action is not required (e.g., when the refrigerant level is at an appropriate value). In other words, corrective action generator 816 can generate control signals for equipment 822 to operate the equipment 822 to affect some variable state or condition (e.g., temperature, humidity, etc.) within the building. In some embodiments, corrective action generator 816 is configured to set boundary conditions for equipment 822 based on the prediction provided by prediction generator 814. For example, corrective action generator 816 may set a maximum speed for a compressor based on the prediction of the refrigerant level. In that example, if the refrigerant level is within an appropriate range and is not approaching a range violation, corrective action generator 816 may generate a control signal to operate the compressor at a higher speed because the refrigerant level is appropriate.

[0138] Any violation of a threshold for a refrigerant property can be addressed by initiating some corrective action. This can ensure that the amount of time during which equipment 822 operates under conditions associated with some refrigerant property violation is reduced (e.g., minimized). Overall, in a VRF system, using predictions based on AI models to initiate corrective actions can have benefits such as saving on hardware costs, reducing the impact of refrigerant levels, and improving the efficiency of the VRF system.

[0139] 9A, an illustration of a recurrent neural network (RNN) structure 900 is shown, according to some embodiments. Specifically, the RNN structure 900 may illustrate the structure of an RNN model (e.g., an LSTM model) that may be generated and utilized as an AI model as described above with reference to FIG.

[0140] An RNN is a class of artificial neural networks in which connections between nodes form a directed graph along a time sequence. For a VRF system, an RNN model represented by RNN structure 900 can be generated using simulation data collected based on an FMU plant model. As the training time increases, the RNN model can function more and more closely to the original plant model for a VRF system.

[0141] RNN structure 900 illustrates a condensed network structure and how the condensed network structure can be "unfolded" to illustrate how the RNN structure 900 operates over a temporal sequence. Specifically, the condensed ("folded") and unfolded structures are equivalent, with the unfolded structure illustrating the use of an RNN model over a temporal sequence in more detail.

[0142] The RNN structure 900 is shown to include inputs represented as x, which may be a vector containing inputs required by the RNN model. For a VRF system, the input vector x may include, for example, compressor speed, suction temperature and pressure, discharge temperature and pressure, valve position, pipe length, etc. A weight vector U may be applied to x, and the result may be provided to a hidden layer vector h. Similarly, a weight vector V may be applied to the hidden layer vector of the previous time step. Based on the weighted inputs and the weighted values ​​of the previous hidden layer vector, a function may be applied to determine a corresponding output, which may result in output o, after the weight vector W is applied. This process may be repeated for each time step in the time sequence. In other words, a new input vector x t can be obtained for time step t, and x t , previous state h t-1 , and the corresponding weight vectors U, V, and W, the output vector o t can be determined for time step t.

[0143] As a result of incorporating RNN structure 900 into the RNN model generated and used by refrigerant management controller 800, the predictions of the RNN model can be modified over time as a result of previous time steps. As refrigerant properties change over time as a result of changing conditions (e.g., changing environmental conditions, operating conditions, etc.), it can be useful to utilize an RNN model in particular because of its unique ability to account for changes over a time sequence, as opposed to being limited by the original training process, as is the case with some other neural network architectures.

[0144] Referring now to FIG. 9B , an illustration of an LSTM model structure 925 is shown, according to some embodiments. The LSTM model structure 925 can illustrate how information is preserved between time steps in an RNN. An LSTM model is a specific artificial RNN architecture that can be used in the field of deep learning. An LSTM model can classify and process entire sequences of time series data and make predictions. Advantageously, an LSTM model can generate predictions even when there is a lag of unknown duration between significant events in the time series data. An LSTM model can include various layers, such as, for example, a sequence input layer, one or more dropout layers, one or more fully connected layers, one or more LSTM layers, and an output layer.

[0145] As shown in FIG. 9B , the LSTM model structure 925 includes functions f, g, i, and o that are used to generate output to the blocks shown in FIG. 9B . The LSTM model structure 925 is further shown to include a forget gate, an update gate, and an output gate. The forget gate can be configured to prevent irrelevant data from being considered and stored for future time steps in the time sequence. The update gate can apply some operation to combine input information and account for changes in the data. Finally, the output gate can determine which information is passed as output to the next time step. The LSTM model structure 925 can include multiple blocks that pass information associated with a particular time step in the time sequence to the next time step. Advantageously, this structure allows information to be retained and not lost between time steps, thereby increasing the accuracy of predictions for time series data.

[0146] Referring now to FIG. 9C , an illustration of a neural network (NN) architecture 950 is shown, according to some embodiments. NN architecture 950 may describe a general architecture that may be utilized by the AI ​​model described above with reference to FIG. 8 for a VRF system (e.g., VRF system 600). Specifically, NN architecture 950 may illustrate how a neural network may generate a set of outputs based on a set of inputs associated with a VRF system. However, it should be noted that NN architecture 950 is provided merely as an example of a neural network architecture that may be utilized and is not intended to be limiting to the neural network architecture that may be utilized by the AI ​​model described with reference to FIG. 8 .

[0147] The NN architecture 950 is shown to include input nodes in an input layer corresponding to a set of inputs. The NN architecture 950 is shown to receive compressor speed, suction temperature, suction pressure, discharge temperature, discharge pressure, valve position, and pipe length as inputs. Each input may be associated with a specific input node in the input layer of the NN architecture 950. In other words, several nodes in the input layer may correspond to several actual inputs in a one-to-one relationship. It should be understood that the inputs shown in FIG. 9C are provided for example purposes only. The NN architecture 950 may be modified to account for various different inputs depending on the implementation. For example, if compressor speed is not considered as an input, the input layer may include only five input nodes.

[0148] NN architecture 950 is also shown to include a hidden layer that includes hidden nodes. In NN architecture 950, the hidden layer is shown to include a single layer that includes a number of hidden nodes corresponding to the number of input nodes in the input layer. However, it should be noted that, according to various embodiments, the hidden layer can include one or more layers that include a varying number of hidden nodes that may or may not correspond to the number of input nodes. For example, in a convolutional neural network architecture, NN architecture 950 can include multiple hidden layers (e.g., multiple convolutional layers) with a varying number of hidden nodes. Furthermore, the nodes in each layer need not necessarily connect to all nodes in adjacent layers, as shown in FIG. 9C .

[0149] In the NN architecture 950, a weight W may be applied with respect to a connection between two nodes. In some embodiments, each connection between nodes includes a specific value for the particular connection. For example, the weight between input node 1 in the input layer and hidden node 1 in the hidden layer may be different from the weight between input node 1 and hidden node 2 in the hidden layer. In some embodiments, various connections between nodes may be associated with the same weight. For example, in an LSTM-specific architecture, the weight associated with a connection between an input node and a hidden node may be the same.

[0150] Based on each weighted value input to a particular node, a function can be applied to determine the node's composite value. For example, for hidden node 1 of NN architecture 950, a function can be applied to the weighted input values ​​input to the node to determine hidden node 1's composite value. The composite value of each node in a particular layer may determine the output of the particular layer. The output of a particular layer can correspond to the input to the subsequent layer, along with the weights between the particular layer and the subsequent layer. This process can be repeated for each layer until the output layer is reached.

[0151] NN architecture 950 is also shown to include an output layer including output nodes. Some output nodes in the output layer may correspond one-to-one to desired outputs of the NN model. For VRF systems in particular, the outputs may include the refrigerant level of the VRF system, the refrigerant level of the compressor, and / or the refrigerant level of the accumulator. Thus, the output nodes may correspond to the refrigerant levels. For NN architecture 950, the composite value of output node 1 may correspond to the refrigerant level. In this manner, by simply providing input values ​​to NN architecture 950, a predicted value of the output can be generated.

[0152] Referring now to FIG. 10 , a flow diagram of a process 1000 for monitoring a refrigerant characteristic using an AI model is shown, according to some embodiments. Process 1000 can utilize an AI model to predict the value of a refrigerant characteristic and can initiate corrective action if the value does not meet a predefined threshold. While process 1000 is described primarily with reference to building systems (e.g., VRF systems), process 1000 can be applied to various systems that include components / devices that require refrigerant for proper operation. For example, process 1000 can be applied to VRF systems, other HVAC systems, fire safety systems, etc. In some embodiments, some and / or all steps of process 1000 are performed by refrigerant management controller 800, described with reference to FIG. 8 .

[0153] In step 1002, training data describing conditions affecting refrigerant used by building equipment of a building is obtained, according to an exemplary embodiment. The building equipment can affect variable states or conditions of the building and can include various equipment that utilizes refrigerant for proper operation. For example, the building equipment can include VRF system components (e.g., compressors, accumulators, heat exchangers, etc.), AHUs, other subplants, etc. The training data can be obtained from various sources and can be in any suitable form (e.g., usage data, etc.). For example, the training data can include usage data obtained from VRF system components (e.g., compressors, accumulators, etc.), building components (e.g., sensors, etc.). The usage data can include, for example, compressor speed, suction temperature, suction pressure, discharge temperature, discharge pressure, valve position, fluid conduit length, ambient temperature, etc. In some embodiments, the training data includes data obtained via direct input from a user, from training data provided by a building equipment manufacturer, by accessing a database (e.g., a cloud database) that stores historical information associated with the operation of the equipment. In some embodiments, training data is acquired when the building service is in a particular mode (e.g., heating mode, cooling mode, off mode, etc.).

[0154] In some embodiments, step 1002 includes generating training data using a simulation model. If a simulation model is used, the simulation model can generate some and / or all of the training data obtained in step 1002. The simulation model can be configured to consider various aspects of the system, including the building equipment, such as, for example, the amount of refrigerant used by the building equipment's devices during operation, how external weather conditions and / or other ambient conditions affect the system, various heating / cooling loads on the building, etc. During training data generation, variables associated with the simulation model can be manipulated to generate training data representing various scenarios. Using a simulation model in step 1002 can result in more training data being available in a shorter time period compared to collecting data based on actual operation of the building equipment. Furthermore, using a simulation model in step 1002 can be useful for obtaining training data that describes fringe cases that may not typically be included in training data collected based on actual device operation (e.g., when the VRF system is in heating mode, cooling mode, off mode, is subject to dangerously high loads, dangerous operating conditions, etc.). In some embodiments, step 1002 is performed by the training data collector 810 .

[0155] In step 1004, an artificial (AI) model is generated based on training data that models the characteristics of the refrigerant, according to an example embodiment. The AI ​​model can be various AI models, such as, for example, an RNN model (e.g., an LSTM model), a CNN model, etc. The AI ​​model can be generated to correlate conditions affecting the refrigerant and the refrigerant characteristics themselves. Specifically, the AI ​​model can be trained to correlate specific inputs (e.g., compressor speed, suction temperature, suction pressure, discharge temperature, discharge pressure, valve position, pipe length, etc.) with specific outputs (e.g., refrigerant level, etc.). In some embodiments, step 1004 can include training weights associated with connections between nodes of the AI ​​model to consider the relationship between conditions and refrigerant characteristics. In some embodiments, step 1004 is performed by model generator 812.

[0156] In step 1006, the AI ​​model is used to generate a prediction of the refrigerant properties over time based on a set of model inputs, according to an example embodiment. As described above in step 1004, the AI ​​model can be trained to associate particular inputs with particular outputs. Thus, once trained, the AI ​​model can utilize values ​​of the inputs to predict corresponding values ​​of the outputs (i.e., refrigerant properties). In some embodiments, step 1006 is performed by prediction generator 814.

[0157] In step 1008, according to an exemplary embodiment, it is determined whether the prediction violates any constraints. In some embodiments, the constraints are predefined constraints that define acceptable values ​​for the refrigerant characteristics. For example, the constraints may include thresholds that the refrigerant characteristics should exceed / below, acceptable ranges of values ​​that the refrigerant characteristics should fall within, etc. Specifically, the constraints may be thresholds that should not be violated. As a particular example, a constraint on the refrigerant level may be defined as a minimum value that the refrigerant level should exceed. In that example, a violation may be identified if the predicted refrigerant level is below the minimum value. If the predicted refrigerant characteristics do not violate any constraints and are therefore not missing (step 1008, “No”), process 1000 may repeat starting with step 1006. In this case, a new set of predictions may be generated for subsequent time steps so that the refrigerant characteristics can be monitored / tracked over time. However, if a constraint violation is identified (step 1008, “Yes”), process 1000 may proceed to step 1010. In some embodiments, a single constraint violation results in process 1000 proceeding to step 1010. In some embodiments, multiple constraint violations (e.g., two constraint violations, three constraint violations, etc.) may be required for process 1000 to proceed to step 1010. In some embodiments, step 1008 includes at least partially considering the severity of the particular constraint violation in determining whether to proceed to step 1010. For example, a refrigerant level below a minimum threshold by a predetermined amount (e.g., 0.5 pounds, 1 pound, etc.) may require that several other constraints also be violated for process 1000 to proceed to step 1010, while a refrigerant level below the minimum by another amount (e.g., 2.5 pounds, 5 pounds, etc.) may independently be sufficient for process 1000 to proceed to step 1010. In some embodiments, step 1008 may include predicting whether a refrigerant characteristic will violate a constraint within a future time period, and if so, proceeding process 1000 to step 1010 to preemptively address the predicted violation. In some embodiments, step 1008 is performed by the corrective action generator 816 .

[0158] In step 1010, a corrective action is determined based on which refrigerant characteristic(s) violated the constraint, according to an example embodiment. In other words, a corrective action may be determined to address the particular refrigerant characteristic(s) that are violating one or more constraints / thresholds. For example, if the refrigerant level violates a constraint (e.g., a minimum allowable value), the determined corrective action may be to withdraw (e.g., charge, etc.) more refrigerant (e.g., automatically from the VRF system, building management system, etc.). As another example, if the refrigerant level violates a constraint (e.g., a minimum allowable value), the determined corrective action may be to provide maintenance activity to devices in the VRF system (e.g., repair a leak, rupture, etc. in a compressor, accumulator, valve, pipe, etc.). In some embodiments, if the refrigerant level violates a constraint, the determined corrective action may be to return refrigerant to a component of the VRF system (e.g., return refrigerant from the indoor VRF unit to the outdoor VRF unit, etc.). In other embodiments, if the refrigerant level violates a constraint, the determined corrective action is to transmit a notification to a user device to notify the user that building services may be required to adjust the refrigerant level. In some embodiments, step 1010 includes determining a specific time and / or time period for the corrective action to occur. To determine the specific time and / or time period, step 1010 may include monitoring system conditions (e.g., device operating conditions, ambient conditions, etc.) to determine a time that will have the least impact on costs, heating / cooling efficiency, etc. In some embodiments, if the corrective action is transmitting a notification or if the constraint violation is severe, the determined time and / or time period may be the earliest possible time (e.g., immediately). In some embodiments, step 1010 is performed by corrective action generator 816.

[0159] In step 1012, according to an example embodiment, a corrective action is initiated. Initiating the corrective action determined in step 1010 may address one or more constraint / threshold violations identified in step 1008. In this manner, the overall time that one or more constraint / threshold violations are active may be reduced. Reducing the time that a constraint / threshold is violated may have benefits such as reducing deterioration of building equipment, helping to reduce costs (e.g., energy costs), and increasing the overall safety of the system. In some embodiments, if step 1010 includes determining when a corrective action should be performed, step 1012 may include initiating the corrective action at the determined time. In some embodiments, step 1012 is performed by corrective action generator 816.

[0160] Systems and methods for VRF fluid estimation 11-13, systems and methods for estimating and predicting liquid properties in a VRF system, according to some embodiments, are shown and described. It should be understood that the following description is described with reference to a VRF system for illustrative purposes only and should not be considered limiting. The systems and methods described throughout Figures 11-13 can likewise be applied to various systems utilizing liquids (e.g., refrigerants, oils, oil-refrigerant mixtures, etc.) and are not meant to be limited to VRF systems.

[0161] The systems and methods described below can utilize AI models to predict how liquid properties will change over time based on various inputs. As discussed above with respect to FIGS. 8-10 , the AI ​​models can include any suitable type of AI model. For example, the AI ​​models can be or include long short-term memory (LSTM) models, other types of recurrent neural networks (RNNs), convolutional neural networks (CNNs), etc. In some embodiments, RNN models, such as LSTM models, are preferred due to the time-series nature of liquid properties. The AI ​​models can be trained to predict specific refrigerant properties based on sets of training data, which can be provided by various sources. In some embodiments, a simulation model is utilized to generate the training data used to train the AI ​​model. The training data generated using the simulation model can be used separately or in addition to training data collected from other sources (e.g., from measured conditions of an actual system). Once trained based on a set of training data, the AI ​​model can predict liquid properties based on the inputs. For example, the AI ​​model can predict the liquid level in an accumulator. As discussed above, based on the predicted liquid properties, it can be determined whether the predicted liquid properties comply with predefined thresholds and / or whether corrective action is generated / initiated. Note that machine learning models may be referred to herein as synonymous with AI models.

[0162] Referring now to FIG. 11 , a block diagram of a VRF liquid controller 1100 for predicting liquid properties is shown, according to some embodiments. In particular, the VRF liquid controller 1100 may predict liquid properties in an accumulator of a VRF system. However, the VRF liquid controller 1100 may be applied to various other systems / devices (e.g., other HVAC systems, car systems, fire safety systems, etc.) that require liquid (e.g., oil, refrigerant, oil-refrigerant mixtures, etc.) for proper operation. In some embodiments, the VRF liquid controller 1100 and / or components therein are incorporated into the BMS controller 366 described with reference to FIGS. 3-4 and / or another controller. In some embodiments, the VRF liquid controller 1100 is used to operate some and / or all of the VRF systems described throughout FIGS. 7A-7B.

[0163] The VRF liquid controller 1100 is shown to include a communication interface 1108 and a processing circuit 1102. The communication interface 1108 may include a wired or wireless interface (e.g., a jack, an antenna, a transmitter, a receiver, a transceiver, a wired terminal, etc.) for communicating data with various systems, devices, or networks. For example, the communication interface 1108 may include an Ethernet card and port for sending and receiving data over an Ethernet-based communication network and / or a Wi-Fi transceiver for communicating over a wireless communication network. The communication interface 1108 may be configured to communicate over a local area network or a wide area network (e.g., the Internet, a building WAN, etc.) and may use various communication protocols (e.g., BACnet, IP, LON, etc.).

[0164] Communications interface 1108 may be a network interface configured to facilitate electronic data communication between VRF liquid controller 1100 and various external systems or devices (e.g., equipment 1122, sensors 1120, user devices 1124, etc.). For example, VRF liquid controller 1100 may receive equipment feedback from equipment 1122 via communications interface 1108.

[0165] The processing circuit 1102 is shown to include a processor 1104 and a memory 1106. The processor 1104 may be a general-purpose or special-purpose processor, an application-specific integrated circuit (ASIC), one or more field-programmable gate arrays (FPGAs), a group of processing components, or other suitable electronic processing components. The processor 1104 may be configured to execute computer code or instructions stored in the memory 1106 or received from another computer-readable medium (e.g., CD-ROM, network storage, a remote server, etc.).

[0166] Memory 1106 may include one or more devices (e.g., memory units, memory devices, storage devices, etc.) for storing data and / or computer code for completing and / or facilitating the various processes described in this disclosure. Memory 1106 may include random access memory (RAM), read-only memory (ROM), hard drive storage, temporary storage, non-volatile memory, flash memory, optical memory, or any other suitable memory for storing software objects and / or computer instructions. Memory 1106 may include database components, object code components, script components, or any other type of information structure for supporting the various activities and information structures described in this disclosure. Memory 1106 may be communicatively coupled to processor 1104 via processing circuitry 1102 and may include computer code for executing (e.g., by processor 1104) one or more processes described herein. In some embodiments, one or more components of memory 1106 are part of a single component. However, each component of memory 1106 is shown separately for ease of explanation.

[0167] The memory 1106 is shown to include a training data collector 1110. The training data collector 1110 may collect training data used to train the artificial intelligence model from one or more training data sources 1118. Specifically, the training data collector 1110 may obtain training data associated with fluid characteristics of the VRF system (e.g., accumulator liquid level, accumulator oil level, accumulator refrigerant level, etc.).

[0168] The training data collected by training data collector 1110 may include any relevant data that can be used to train the AI ​​model to learn associations between particular inputs and liquid properties of the VRF system. The training data may include values ​​for inputs including, for example, compressor speed, suction pressure, discharge temperature, discharge pressure, the temperature of the refrigerant vapor and / or other gases in the VRF system (e.g., the subcooling temperature of the VRF system, etc.), the ambient temperature near the VRF system (e.g., the dry bulb temperature of the VRF system, etc.), the refrigerant charge (e.g., the measured refrigerant charge, the predicted refrigerant charge of FIGS. 8-10, etc.), etc.

[0169] To gather training data, training data collector 1110 may transmit a query to training data source 1118 to obtain the training data. In some embodiments, training data collector 1110 passively receives training data from training data source 1118 without having to actively request the training data. Training data source 1118 may include any data source capable of storing and / or providing training data to training data collector 1110. For example, training data source 1118 may be or include a user device (e.g., a laptop, desktop computer, mobile device, tablet, etc.) capable of providing a stored training data set to training data collector 1110. In some embodiments, training data source 1118 includes a component of FIGS. 8-10 (e.g., refrigerant management controller 800, etc.) capable of providing a training data set (e.g., usage data, etc.). As another example, training data source 1118 may be or include a database (e.g., a cloud database) that stores data collected during operation of an actual VRF system. In this way, AI models can be trained based on data collected directly from actual VRF devices in operation.

[0170] In some embodiments, training data collector 1110 utilizes one or more simulation models to generate some or all of the training data used by model generator 1112 to generate the AI ​​model. The simulation model (also referred to herein as a “simulation” or “simulation framework”) can simulate how an actual VRF system might operate under various conditions and / or limitations (e.g., weather conditions, heating / cooling loads, inherent device limitations, etc.). The simulation model can consider the relationships between components of the VRF system and how the components might react to changing conditions. For example, the simulation model can model the liquid level in an accumulator of the VRF system and / or how the VRF system functions as a result.

[0171] By utilizing a simulation model, the training data collector 1110 may not need to retrieve training data from the training data source 1618. Instead, the training data collector 1110 may generate the training data within the VRF liquid controller 1100. In some embodiments, the simulation model is hosted by a third-party controller / device / system (e.g., a cloud computing system) that provides the VRF liquid controller 1100 with training data generated as a result of running the simulation model. In either case, the simulation model can be used / executed to generate various training data representing various operating conditions that can be used to train the AI ​​model. Advantageously, the simulation model can generate large amounts of data in a shorter amount of time compared to collecting training data from an actual operating VRF system. Furthermore, the simulation model can be run to generate training data that illustrates fringe scenarios (e.g., intense heating / cooling loads, etc.) under which an actual system may be dangerous to operate, simply to generate the training data.

[0172] Based on the acquired training data, the training data collector 1110 can combine the collected training data into a training data set and provide the training data set to the model generator 1112. Based on the training data set, the model generator 1112 can generate an AI model that models liquid properties over time. Specifically, the model generator 1112 can train the AI ​​model to predict liquid properties based on specified inputs. For example, the model generator 1112 may train the AI ​​model to predict values ​​of liquid in an accumulator, oil level in the accumulator, refrigerant level in the accumulator, oil-refrigerant mixture level in the accumulator, ambient temperature, discharge temperature, suction pressure, discharge pressure, and / or gas temperature.

[0173] The AI ​​model generated by the model generator 1112 can be of any of a variety of AI model architectures. For example, the AI ​​model can be an RNN, such as an LSTM network.

[0174] Inputs to the AI ​​model generated by model generator 1112 can include various inputs associated with the operation of the VRF system. For example, inputs to the AI ​​model can include compressor speed, suction pressure, discharge temperature, discharge pressure, the temperature of the refrigerant vapor and / or other gases in the VRF system (e.g., the subcooling temperature of the VRF system, etc.), the ambient temperature near the VRF system (e.g., the dry-bulb temperature of the VRF system, etc.), the refrigerant charge (e.g., the measured refrigerant charge, the predicted refrigerant charge of FIGS. 8-10, etc.), etc. Exemplary examples of AI models that can be generated by model generator 1112 are described in more detail below with reference to FIGS. 12A and 12B.

[0175] The model generator 1112 may utilize various training techniques to generate the AI ​​model. For example, the model generator 1112 may utilize a stochastic gradient descent with momentum approach, an adaptive moment estimation approach, a root-mean-square propagation approach, etc. For the root-mean-square propagation approach, the model generator 1112 may utilize the root-mean-square error (RMSE) to measure how accurate the model predictions are relative to the training data provided by the training data collector 1110. To monitor the RMSE over time, the model generator 1112 may utilize the following formula:

number

[0176] The model generator 1112 can provide the generated AI model to a prediction generator 1114. The prediction generator 1114 can use the AI ​​model to generate predictions of liquid properties over time. To generate the predictions, the prediction generator 1114 can operate to obtain values ​​for inputs required by the AI ​​model from various sources. For example, the prediction generator 1114 can obtain equipment feedback from equipment 1122, measured variables from sensors 1120, and / or any other suitable source of input values.

[0177] The equipment 1122 can be or include any device capable of providing values ​​for inputs required by the AI ​​model. For example, in a VRF system, the equipment 1122 can include compressors, heat exchangers, accumulators, etc. that can provide usage data (e.g., suction temperature, suction pressure, discharge temperature, discharge pressure, ambient temperature, subcooling temperature, dry-bulb temperature, charge volume, etc.) to the VRF liquid controller 1100. More specifically, if the AI ​​model requires compressor speed as an input, the equipment 1122 can include one or more compressors that can provide operating speed as equipment feedback to the prediction generator 1114. In some embodiments, the equipment 1122 includes components of FIGS. 8-11 (e.g., refrigerant management controller 800, etc.). More specifically, if the AI ​​model requires refrigerant charge volume, the equipment 1122 can include refrigerant management controller 800 that provides the refrigerant charge volume (e.g., via a sensor, as a predicted refrigerant level, etc.).

[0178] The sensors 1120 may be or include various sensors capable of measuring values ​​of inputs (i.e., variables) required by the AI ​​model. For example, the sensors 1120 may include pressure sensors that measure suction pressure and / or discharge pressure. As another example, the sensors 1120 may include temperature sensors that measure discharge temperature, ambient temperature (e.g., dry-bulb temperature, etc.), and / or gas temperature (e.g., sub-cooling temperature, etc.).

[0179] Based on the AI ​​model and the obtained input values, the prediction generator 1114 can generate a liquid property prediction by passing the obtained input values ​​to the AI ​​model. As a result of passing the obtained input values ​​to the AI ​​model, the AI ​​model can output values ​​for one or more liquid properties (e.g., liquid level, oil level, refrigerant level, oil-refrigerant mixture level, etc.). In this manner, properties of the liquid in the VRF system can be estimated without the need for additional sensors to measure the liquid properties.

[0180] In some embodiments, the forecast generator 1114 generates forecasts for multiple stages within the VRF system. For example, the forecast generator 1114 may generate a forecast of the liquid level in the accumulator when the VRF system is in a particular mode (e.g., heating mode, cooling mode, off mode, etc.). In such an example, predicting the liquid level may benefit certain corrective actions (e.g., the amount of oil to add / remove from the VRF system, the amount of liquid returning to the compressor, accumulator, etc.). By generating forecasts for the refrigerant at multiple stages within the VRF system, the liquid properties can be predicted and tracked over time throughout the entire VRF system, rather than at a single point in the VRF system.

[0181] The prediction generator 1114 can also provide predictions of the liquid properties to a corrective action generator 1116. The corrective action generator 1116 can analyze the predicted liquid properties to determine whether and what corrective action should be initiated. A corrective action can refer to any action taken to address a liquid property that does not meet some predefined threshold(s). Corrective actions can include, for example, providing and / or receiving liquid to / from components of the VRF system to maintain liquid control conditions (e.g., returning oil to the compressor, returning liquid to the compressor, returning refrigerant from the indoor VRF unit to the outdoor VRF unit, etc.), delivering a notification / alert to a user device 1124 to indicate to a user that a particular liquid property violates a predefined threshold, operating equipment 1122, disabling equipment 1122, automatically scheduling maintenance activities to be performed on equipment 1122, logging the threshold violation(s) to a database, etc. Predefined thresholds can be user-defined, provided by a manufacturer, estimated based on the operating conditions of the equipment in the VRF system, etc. For example, a user or manufacturer may define a maximum amount of liquid at which the VRF system (e.g., accumulator) should operate. In this case, a high liquid level in the accumulator may result in more rapid deterioration of the compressor (e.g., lack of liquid return, lack of oil, etc.). As should be appreciated, thresholds defined for liquid characteristics can be obtained from a variety of sources (e.g., manufacturer, user, based on predictions, etc.) and can include a variety of limit types (e.g., ranges, thresholds, exact values ​​that the characteristic should represent, etc.).

[0182] As a more specific example, consider a scenario in which the AI ​​model predicts the value of a liquid property, including the liquid level in an accumulator. In an example, corrective action generator 1116 may determine whether the liquid level in the accumulator is above a first threshold. If the liquid level in the accumulator is high, corrective action generator 1116 may determine that a VRF system (e.g., compressor, etc.) should be operated to maintain liquid control and provide / receive liquid (e.g., return liquid from the accumulator to the compressor, return refrigerant from the indoor VRF unit to the outdoor VRF unit, return oil from the accumulator of the VRF unit to the compressor of the VRF unit, etc.). In some embodiments, liquid return refers to an operating mode in which a component of the VRF system (e.g., compressor) operates at a high speed to return liquid (e.g., from the accumulator, from the indoor VRF unit, from a component of the VRF system, etc.).

[0183] In some embodiments, the corrective action generator 1116 compares the output of the AI ​​model over time to determine whether a particular liquid property is approaching a threshold violation, thereby including a deficiency. In this case, the corrective action generator 1116 may compare the value of the liquid property output by the AI ​​model with a previous output value of the liquid property. If a particular liquid property is trending toward violating a threshold, the corrective action generator 1116 may preemptively initiate corrective action before a violation occurs. For example, if a liquid level is increasing over time (e.g., the liquid level in an accumulator is increasing) and, based on the current trend, will exceed a maximum liquid threshold level within a future time period, the corrective action generator 1116 may initiate a corrective action (e.g., a liquid return) before the liquid level exceeds the maximum threshold. Advantageously, preemptive initiation of corrective action can ensure that the amount of time equipment (e.g., a compressor) operates under conditions associated with a liquid property threshold violation is reduced. Reducing this time can reduce equipment deterioration, reduce energy consumption, and / or provide other benefits.

[0184] In some embodiments, the corrective action generator 1116 predicts the time to initiate a specific corrective action to reduce the impact on the equipment 1122 and / or other devices / systems. For example, the corrective action generator 1116 may predict the time to initiate a liquid return to reduce the negative impact on the heating / cooling load required by the building. In some embodiments, the corrective action generator 1116 also predicts the time to temporarily disable the equipment 1122 so that liquid can be safely added to the system. In some embodiments, the corrective action generator 1116 can operate as a standard equipment controller when no corrective action is needed (e.g., when the liquid level is at an appropriate value).

[0185] Referring now to FIG. 12A , an illustration of an LSTM model structure 1200 is shown, according to some embodiments. In some embodiments, the LSTM model structure 1200 is similar to and / or the same as the LSTM model structure 925 shown in FIG. 9B . The LSTM model structure 1200 can illustrate how information is preserved between time steps of an RNN and can include various layers, such as a sequence input layer, one or more dropout layers, one or more fully connected layers, one or more LSTM layers, and an output layer. In an exemplary embodiment, the LSTM model structure 1200 includes a sequence input layer and an LSTM layer. As shown in FIG. 12A , the LSTM model structure 1200 includes functions f, g, i, and o that are used to generate output to the blocks shown in FIG. 12A . The LSTM model structure 1200 is shown to include a forget gate, an update gate, and an output gate. The forget gate can be configured to prevent non-relevant data from being considered and stored for future time steps in the time sequence. The update gate can combine the input information and apply some operation to account for changes in the data. Finally, the output gate can determine which information is passed as output to the next time step. In some embodiments, the LSTM model structure 1200 also includes a cell candidate gate. In other embodiments, the LSTM model structure 1200 has learnable weights, such as input weights (W), recurrent weights (R), and biases (b). The learnable weights (e.g., matrices) can be concatenations of the components of W, R, and b, respectively. The LSTM model structure 925 can include multiple blocks that pass information associated with a particular time step in a time sequence to the next time step. Advantageously, this structure allows information to be retained and not lost between time steps, thereby increasing the accuracy of predictions for time series data.

[0186] 12B, an illustration of a neural network (NN) 1250 for predicting a property of a liquid is shown, according to some embodiments. The NN 1250 may illustrate an example structure of an AI model that may be generated by the model generator 1112 as described with reference to FIG. 11. Specifically, the NN 1250 may illustrate how a neural network may generate a set of outputs based on a set of inputs associated with a VRF system. However, it should be noted that the NN 1250 is provided merely as an example of a neural network architecture that may be utilized and is not intended to be limiting to the neural network architecture that may be utilized by the AI ​​model described with reference to FIG. 11.

[0187] NN 1250 is shown to include input nodes in an input layer corresponding to a set of inputs. NN 1250 may receive compressor speed, suction pressure, discharge temperature, sub-cooling temperature (e.g., the temperature of the refrigerant vapor and / or other gases in the VRF system), dry-bulb temperature (e.g., outdoor dry-bulb temperature, ambient temperature near the VRF system), refrigerant charge, etc. Each input may be associated with a specific input node in the input layer in NN 1250. In other words, several nodes in the input layer may correspond to several actual inputs in a one-to-one relationship. It should be understood that the inputs shown in FIG. 12B are provided for example purposes only. NN 1250 may be modified to consider a variety of different inputs depending on the implementation.

[0188] NN 1250 is also shown to include a hidden layer containing hidden nodes. In NN 1250, the hidden layer is shown to include a single layer containing a number of hidden nodes corresponding to the number of input nodes in the input layer. However, according to various embodiments, the hidden layer includes one or more layers containing a varying number of hidden nodes that may or may not correspond to the number of input nodes. Furthermore, nodes in each layer need not necessarily connect to all nodes in an adjacent layer, as shown in FIG. 12B . In NN 1250, a weight W can be applied with respect to a connection between two nodes. In some embodiments, each connection between nodes includes a specific value for the particular connection. In some embodiments, various connections between nodes can be associated with the same weight. For example, in an LSTM-specific architecture, the weight associated with a connection between an input node and a hidden node can be the same. Based on each weighted value input to a particular node, a function can be applied to determine the node's composite value. For example, for hidden node 1 of NN 1250, a function can be applied to the weighted input value input to the node to determine hidden node 1's composite value. The combined value of each node in a particular layer may determine the output of the particular layer. The output of a particular layer may correspond to the input to the subsequent layer, along with the weights between the particular layer and the subsequent layer. This process may be repeated for each layer until the output layer is reached.

[0189] NN 1250 is also shown to include an output layer including output nodes. Some output nodes in the output layer may correspond one-to-one to desired outputs of the NN model. For VRF systems in particular, the outputs may include the liquid level of the VRF system, the liquid level of an accumulator, the oil level of the accumulator, and / or the oil level of a compressor. Thus, an output node may correspond to a liquid level (e.g., in an accumulator). For NN 1250, the resultant value of output node 1 may correspond to a liquid level. In this manner, by simply providing input values ​​to NN 1250, a predicted value of the output can be generated.

[0190] Referring now to FIG. 13 , a flow diagram of a process 1300 for monitoring a liquid property using an AI model is shown, according to some embodiments. Process 1300 can utilize the AI ​​model to predict the value of the liquid property and can initiate corrective action if the value does not meet a predefined threshold. While process 1300 is described primarily with reference to building systems (e.g., VRF systems), process 1300 can be applied to various systems including components / devices that require a liquid (e.g., oil, refrigerant, oil-refrigerant mixtures, etc.) for proper operation. For example, process 1300 can be applied to VRF systems, other HVAC systems, fire safety systems, etc. In some embodiments, some and / or all steps of process 1300 are performed by VRF liquid controller 1100, described with reference to FIG. 11 .

[0191] In step 1302, training data describing conditions affecting the liquid used by building services of a building is obtained, according to an exemplary embodiment. The building services can include various devices that can affect the variable state or conditions of the building and utilize the liquid for proper operation. For example, the building services can include VRF system components (e.g., compressors, accumulators, heat exchangers, etc.), AHUs, other subplants, etc. The training data can be obtained from various sources and can be in any suitable form (e.g., usage data, etc.). For example, the training data can include usage data obtained from VRF system components (e.g., compressors, accumulators, etc.), building components (e.g., sensors, etc.), etc. The usage data can include, for example, compressor speed, suction temperature, suction pressure, discharge temperature, discharge pressure, ambient temperature, subcooling temperature, dry-bulb temperature, wet-bulb temperature, refrigerant charge, oil charge, temperature setpoint, etc. In some embodiments, the training data (e.g., predicted refrigerant charge, etc.) is obtained from the refrigerant management controller 800 of FIG. 8. In other embodiments, the training data includes data obtained through direct input from a user, by accessing a database (e.g., a cloud database) that stores historical information associated with the operation of the equipment, from training data provided by the building equipment manufacturer, etc. In some embodiments, the training data is obtained when the building equipment is in a particular mode (e.g., heating mode, cooling mode, off mode, etc.).

[0192] In some embodiments, step 1302 includes generating training data using a simulation model. If a simulation model is used, the simulation model can generate some and / or all of the training data obtained in step 1302. The simulation model can be configured to consider various aspects of the system, including the building equipment, such as, for example, the amount of liquid used by the building equipment's devices during operation, how external weather conditions and / or other ambient conditions affect the system, various heating / cooling loads on the building, etc. During training data generation, variables associated with the simulation model can be manipulated to generate training data representing various scenarios. Using a simulation model in step 1302 can result in more training data being available in a shorter time period compared to collecting data based on the actual operation of the building equipment. Furthermore, using a simulation model in step 1302 can be useful for obtaining training data that describes fringe cases that may not typically be included in training data collected based on actual device operation (e.g., when the VRF system is in heating mode, cooling mode, off mode, is subject to dangerously high loads, dangerous operating conditions, etc.). In some embodiments, step 1302 is performed by the training data collector 1110 .

[0193] In step 1304, an artificial (AI) model is generated based on training data that models the properties of the liquid, according to an example embodiment. The AI ​​model can be various AI models, such as, for example, an RNN model (e.g., an LSTM model), a CNN model, etc. The AI ​​model can be generated to correlate conditions affecting the liquid and the liquid properties themselves. Specifically, the AI ​​model can be trained to correlate specific inputs (e.g., compressor speed, suction pressure, discharge temperature, discharge pressure, ambient temperature, subcooling temperature, dry-bulb temperature, refrigerant charge, etc.). In some embodiments, step 1304 can include training weights associated with connections between nodes of the AI ​​model to consider relationships between conditions and the properties of the liquid. In some embodiments, step 1304 is performed by the model generator 1112.

[0194] In step 1306, the AI ​​model is used to generate a prediction of the liquid properties over time based on a set of model inputs, according to an example embodiment. As described above in step 1304, the AI ​​model can be trained to associate particular inputs with particular outputs. Thus, once trained, the AI ​​model can utilize the values ​​of the inputs to predict corresponding values ​​of the outputs (i.e., liquid level in the accumulator, oil level in the accumulator, refrigerant level in the accumulator, liquid properties, etc.). In some embodiments, step 1306 is performed by the prediction generator 1114.

[0195] In step 1308, according to an example embodiment, it is determined whether the prediction violates any constraints. In some embodiments, the constraints are predefined constraints that define acceptable values ​​for the liquid property. For example, the constraints may include thresholds that the value of the liquid property should exceed / below, acceptable ranges of values ​​that the value of the liquid property should be within, etc. Specifically, the constraints may be thresholds that should not be violated. As a particular example, a constraint on liquid level may be defined as a maximum value that the liquid level should be below. For example, the constraint may be the liquid level of an accumulator (e.g., oil level, refrigerant level, oil-refrigerant mixture level). In that example, if the predicted liquid level exceeds the maximum value, a violation may be identified. If the predicted liquid property does not violate any constraints and is therefore not missing (step 1308, “No”), process 1300 may repeat starting with step 1306. In this case, a new set of predictions may be generated for subsequent time steps so that the liquid property can be monitored / tracked over time. However, if a constraint violation is identified (step 1308, "Yes"), process 1300 may proceed to step 1310. In some embodiments, a single constraint violation results in process 1300 proceeding to step 1310. In some embodiments, multiple constraint violations (e.g., two constraint violations, three constraint violations, etc.) may be required for process 1300 to proceed to step 1310. In some embodiments, step 1308 includes at least partially considering the severity of the particular constraint violation in determining whether to proceed to step 1320. For example, a liquid level exceeding a maximum threshold by a predetermined amount may require that several other constraints also be violated for process 1300 to proceed to step 1310, while liquid exceeding a maximum by another amount may be independently sufficient for process 1300 to proceed to step 1310. In some embodiments, step 1308 may include predicting whether the liquid properties will violate the constraints within a future time period, and if so, proceeding process 1300 to step 1310 to preemptively address the anticipated violation.In some embodiments, step 1308 is performed by the corrective action generator 1116 .

[0196] In step 1310, a corrective action is determined based on which liquid characteristic(s) violated a constraint, according to an example embodiment. In other words, a corrective action may be determined to address the particular liquid characteristic(s) that are violating one or more constraints / thresholds. For example, if the liquid level violates a constraint (e.g., a maximum allowable value in an accumulator), the determined corrective action may be to return some of the liquid to a component of the VRF system (e.g., from the accumulator to the compressor, from the indoor VRF unit to the outdoor VRF unit, etc.). As another example, if the liquid level violates a constraint (e.g., a maximum allowable value in an accumulator), the determined corrective action may be to return oil and / or refrigerant to a component of the VRF system (e.g., from the accumulator to the compressor, from the indoor VRF unit to the outdoor VRF unit, from the outdoor VRF unit to the indoor VRF unit, etc.) to maintain a liquid control state. In some embodiments, if the liquid level violates a constraint, the determined corrective action is to transmit a notification to a user device to notify the user that building services may be required to adjust the liquid level. In some embodiments, step 1310 includes determining a specific time and / or time period for the corrective action to occur. To determine the specific time and / or time period, step 1310 may include monitoring system conditions (e.g., device operating conditions, ambient conditions, etc.) to determine a time that will have the least impact on costs, heating / cooling efficiency, etc. In some embodiments, if the corrective action is transmitting a notification or if the constraint violation is severe, the determined time and / or time period may be the earliest possible time (e.g., immediately). In some embodiments, step 1310 is performed by corrective action generator 1116.

[0197] In step 1312, according to an example embodiment, a corrective action is initiated. Initiating the corrective action determined in step 1310 may address one or more constraint / threshold violations identified in step 1308. In this manner, the overall time that one or more constraint / threshold violations are active may be reduced. Reducing the time that a constraint / threshold is violated may have benefits such as reducing deterioration of building equipment, helping to reduce costs (e.g., energy costs), and increasing overall system safety. In some embodiments, if step 1310 includes determining when a corrective action should be performed, step 1312 may include initiating the corrective action at the determined time. In some embodiments, step 1312 is performed by corrective action generator 1116.

[0198] Experimental results Referring generally to FIGS. 14-21 , results of exemplary experiments are shown, according to some embodiments. The exemplary experiments of FIGS. 14-24 are provided solely for illustrative purposes and are not intended to limit the present disclosure, but rather to demonstrate the utility of utilizing an AI model in predicting liquid properties. The AI ​​models referenced below throughout FIGS. 14-21 are LSTM models trained for the purpose of predicting liquid properties. According to exemplary embodiments, the exemplary experiments of FIGS. 14-19 demonstrate that the amount of refrigerant (e.g., refrigerant level) utilized in the AI ​​model impacts the accuracy of the prediction of the accumulator liquid level. As discussed in more detail below, the higher the amount of refrigerant in the accumulator (e.g., AI model), the more accurate the AI ​​model prediction of the accumulator liquid level.

[0199] 14A and 14B, a pair of graphs illustrating the results of an AI model training process for an exemplary experiment, according to some embodiments. In an exemplary embodiment, the exemplary experiment includes an accumulator with a low level of refrigerant. FIG. 14A is shown to include a graph 1400 illustrating the change in RMSE based on the number of iterations of the exemplary training process. FIG. 14B is shown to include a graph 1450 illustrating the change in loss based on the number of iterations. The exemplary training process associated with FIGS. 14A and 14B utilized 10 closed-loop test cases from a VRF model-based definition (MBD) liquid level plant model, with approximately 4000 seconds allocated to each test case, as training data. To determine the accuracy of the AI ​​model, a single test data set from the VRF MBD liquid level plant model, with approximately 4000 seconds allocated to each test case, was used for comparison.

[0200] Graph 1400 is shown to include series 1402. Series 1402 may illustrate how the RMSE associated with an AI model changes as a result of additional iterations of the training process. Specifically, series 1402 illustrates a generally decreasing trend as the number of iterations increases. In other words, increasing the number of iterations may improve the accuracy of the AI ​​model. Note that series 1402 represents a smoothed curve of the RMSE data points collected at each iteration.

[0201] Graph 1450 is shown to include series 1452. Series 1452 can illustrate how the loss associated with an AI model changes over time based on several iterations. In this case, the loss describes how inaccurate the AI ​​model's prediction is, with a loss of 0 indicating that the particular prediction is equal to the actual measurement. As is evident from series 1452 and series 1402, the accuracy of the AI ​​model for predicting liquid level increases based on the number of iterations.

[0202] Referring generally to FIG. 15 , a graph 1500 illustrating liquid property predictions of an AI model trained in the exemplary experiment of FIGS. 14A-14B is shown, according to some embodiments. In the exemplary experiment, the AI ​​model incorporated inputs of compressor speed, dry-bulb temperature, wet-bulb temperature, refrigerant charge, oil charge, number of indoor open units, indoor unit wind, and indoor temperature setpoint to generate a predicted output of liquid level in the accumulator. Graph 1500 is shown to include a series 1502 illustrating the predicted liquid level in the accumulator over time. In some embodiments, the objective of operational decisions associated with the accumulator may be to maintain a relatively constant value of the liquid level in the accumulator to ensure stable and reliable operation.

[0203] 16A and 16B, a pair of graphs illustrating the results of an AI model training process for another exemplary experiment, according to some embodiments. In an exemplary embodiment, the exemplary experiment includes an accumulator with a medium level of refrigerant. FIG. 16A is shown to include a graph 1600 illustrating the change in RMSE based on several iterations of the exemplary training process. FIG. 16B is shown to include a graph 1650 illustrating the change in loss based on the number of iterations. The exemplary training process associated with FIGS. 16A and 16B utilized 10 closed-loop test cases from a VRF model-based definition (MBD) liquid level plant model, with approximately 4000 seconds allocated to each test case, as training data. To determine the accuracy of the AI ​​model, a single test data set from the VRF MBD liquid level plant model, with approximately 4000 seconds allocated to each test case, was used for comparison.

[0204] Graph 1600 is shown to include series 1602. Series 1602 may illustrate how the RMSE associated with an AI model changes as a result of additional iterations of the training process. Specifically, series 1602 illustrates a generally decreasing trend as the number of iterations increases. In other words, increasing the number of iterations may improve the accuracy of the AI ​​model. Note that series 1602 represents a smoothed curve of the RMSE data points collected at each iteration.

[0205] Graph 1650 is shown to include series 1652. Series 1652 can illustrate how the loss associated with an AI model changes over time based on several iterations. In this case, the loss describes how inaccurate the AI ​​model's prediction is, with a loss of 0 indicating that a particular prediction is equal to the actual measurement. As is evident from series 1652 and series 1602, the accuracy of the AI ​​model for predicting liquid level increases based on the number of iterations.

[0206] Referring generally to FIG. 17 , a graph 1700 illustrating liquid property predictions of an AI model trained in the exemplary experiment of FIGS. 16A-16B is shown, according to some embodiments. In the exemplary experiment, the AI ​​model incorporated inputs of compressor speed, dry-bulb temperature, wet-bulb temperature, refrigerant charge, oil charge, number of indoor open units, indoor unit wind, and indoor temperature setpoint to generate a predicted output of liquid level in the accumulator. Graph 1700 is shown to include a series 1702 illustrating the predicted liquid level in the accumulator over time. In some embodiments, the objective of operational decisions associated with the accumulator may be to maintain a relatively constant value of the liquid level in the accumulator to ensure stable and reliable operation.

[0207] 18A and 18B, a pair of graphs illustrating the results of an AI model training process for another exemplary experiment, according to some embodiments. In an exemplary embodiment, the exemplary experiment includes an accumulator with a high level of refrigerant. FIG. 18A is shown to include a graph 1800 illustrating the change in RMSE based on the number of iterations of the exemplary training process. FIG. 18B is shown to include a graph 1850 illustrating the change in loss based on the number of iterations. The exemplary training process associated with FIGS. 18A and 18B utilized 10 closed-loop test cases from a VRF model-based definition (MBD) liquid level plant model, with approximately 4000 seconds allocated to each test case, as training data. To determine the accuracy of the AI ​​model, a single test data set from the VRF MBD liquid level plant model, with approximately 4000 seconds allocated to each test case, was used for comparison.

[0208] Graph 1800 is shown to include series 1802. Series 1802 may illustrate how the RMSE associated with an AI model changes as a result of additional iterations of the training process. Specifically, series 1802 illustrates a generally decreasing trend as the number of iterations increases. In other words, increasing the number of iterations may improve the accuracy of the AI ​​model. Note that series 1802 represents a smoothed curve of the RMSE data points collected at each iteration.

[0209] Graph 1850 is shown to include series 1852. Series 1852 can illustrate how a loss associated with an AI model changes over time based on several iterations. In this case, the loss describes how inaccurate the AI ​​model's prediction is, with a loss of 0 indicating that a particular prediction is equal to the actual measurement. As is evident from series 1852 and series 1802, the accuracy of the AI ​​model for predicting liquid level increases based on the number of iterations.

[0210] Referring generally to FIG. 19 , a graph 1900 illustrating liquid property predictions of an AI model trained in the exemplary experiment of FIGS. 18A-18B is shown, according to some embodiments. In the exemplary experiment, the AI ​​model incorporated inputs of compressor speed, dry-bulb temperature, wet-bulb temperature, refrigerant charge, oil charge, number of indoor open units, indoor unit wind, and indoor temperature setpoint to generate a predicted output of liquid level in the accumulator. Graph 1900 is shown to include a series 1902 illustrating the predicted liquid level in the accumulator over time. In some embodiments, the objective of operational decisions associated with the accumulator may be to maintain a relatively constant value of the liquid level in the accumulator to ensure stable and reliable operation.

[0211] 20A and 20B, a pair of graphs illustrating the results of an AI model training process for another exemplary experiment, according to some embodiments. In an exemplary embodiment, the exemplary experiment includes an accumulator with variable levels of refrigerant. FIG. 20A is shown to include graph 2000 illustrating the change in RMSE based on the number of iterations of the exemplary training process. FIG. 20B is shown to include graph 2050 illustrating the change in loss based on the number of iterations. The exemplary training process associated with FIGS. 20A and 20B utilized 10 closed-loop test cases from a VRF model-based definition (MBD) liquid level plant model, with approximately 4000 seconds allocated to each test case, as training data. To determine the accuracy of the AI ​​model, a single test data set from the VRF MBD liquid level plant model, with approximately 4000 seconds allocated to each test case, was used for comparison.

[0212] Graph 2000 is shown to include series 2002. Series 2002 may illustrate how the RMSE associated with an AI model changes as a result of additional iterations of the training process. Specifically, series 2002 illustrates a generally decreasing trend as the number of iterations increases. In other words, increasing the number of iterations may improve the accuracy of the AI ​​model. Note that series 2002 represents a smoothed curve of the RMSE data points collected at each iteration.

[0213] Graph 2050 is shown to include series 2052. Series 2052 can illustrate how the loss associated with an AI model changes over time based on the number of iterations. In this case, the loss describes how inaccurate the AI ​​model's prediction is, with a loss of 0 indicating that a particular prediction is equal to the actual measurement. As is evident from series 2052 and series 2052, the accuracy of the AI ​​model for predicting liquid level increases based on the number of iterations.

[0214] Referring generally to FIG. 21 , a graph 2100 illustrating liquid property predictions of an AI model trained in the exemplary experiment of FIGS. 20A-20B is shown, according to some embodiments. In the exemplary experiment, the AI ​​model incorporated inputs of compressor speed, dry-bulb temperature, wet-bulb temperature, refrigerant charge (e.g., variable), oil charge, number of indoor air-opening units, indoor unit wind, indoor temperature setpoint, outdoor temperature (e.g., ambient), and indoor fan level to generate a predicted output of liquid level in the accumulator. Graph 2100 is shown to include a series 2102 illustrating the predicted liquid level in the accumulator over time. In some embodiments, the objective of operational decisions associated with the accumulator may be to maintain a relatively constant value of the liquid level in the accumulator to ensure stable and reliable operation.

[0215] Configuration of an exemplary embodiment The construction and arrangement of the systems and methods shown in the various exemplary embodiments are merely illustrative. While only a few embodiments have been described in detail in this disclosure, many modifications are possible (e.g., differences in the size, dimensions, structure, shape, and proportions of various elements, parameter values, mounting configurations, material use, color, orientation, etc.). For example, the positions of elements may be reversed or otherwise varied, and the nature or number or location of individual elements may be modified or changed. Accordingly, all such modifications are intended to be included within the scope of this disclosure. The order or sequence of any process or method steps may be changed or reordered according to alternative embodiments. Other substitutions, modifications, changes, and omissions may be made in the design, operating conditions, and arrangement of the exemplary embodiments without departing from the scope of this disclosure.

[0216] The present disclosure contemplates methods, systems, and program products on any machine-readable medium for accomplishing various operations. Embodiments of the present disclosure may be implemented using existing computer processors, by dedicated computer processors for suitable systems incorporated for this or other purposes, or by hardwired systems. Embodiments within the scope of the present disclosure include program products including machine-readable media for carrying or having machine-executable instructions or data structures stored thereon. Such machine-readable media may be any available medium accessible by a general-purpose or special-purpose computer with a processor or other machine. By way of example, such machine-readable media may include RAM, ROM, EPROM, EEPROM, CD-ROM, or other optical disk storage, magnetic disk storage, or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of machine-executable instructions or data structures and that can be accessed by a general-purpose or special-purpose computer with a processor or other machine. Combinations of the above are also included within the scope of machine-readable media. Machine-executable instructions include, for example, instructions and data that cause a general-purpose computer, special-purpose computer, or special-purpose processing machine to perform a certain function or group of functions.

[0217] While the figures show a particular order of method steps, the order of the steps may differ from that depicted. Also, two or more steps may be performed concurrently or with partial concurrence. Such variations depend on the software and hardware systems selected and the designer's choice. All such variations are within the scope of this disclosure. Similarly, software implementations may be achieved by standard programming techniques with rule-based logic and other logic to accomplish the various connecting, processing, comparing, and determining steps.

Claims

1. 1. A refrigerant charge controller for a heating, ventilation, or air conditioning (HVAC) system, comprising: the refrigerant charge controller comprises a processing circuit; The processing circuitry analyzing usage data for the HVAC plant using a machine learning model to estimate an amount of refrigerant in a first one or more devices of the HVAC plant; identifying a refrigerant shortage based on the amount of refrigerant; initiating corrective action in response to identifying said refrigerant shortage; configured to: The refrigerant charge controller, wherein the corrective action includes returning a portion of the refrigerant from the first one or more devices of the HVAC plant to a second one or more devices of the HVAC plant.

2. the HVAC plant comprising one or more compressors configured to circulate the refrigerant in a refrigerant circuit; 2. The refrigerant charge controller of claim 1, wherein the usage data includes at least one of a suction temperature or a suction pressure of the refrigerant at a suction of the one or more compressors, or a discharge temperature or a discharge pressure of the refrigerant at a discharge of the one or more compressors.

3. the HVAC plant comprising one or more valves configured to control the flow of the refrigerant in a refrigerant circuit comprising one or more fluid conduits; The refrigerant charge controller of claim 1 , wherein the usage data includes at least one of a valve position of the one or more valves or a length of the one or more fluid conduits.

4. the HVAC system comprises a variable refrigerant flow (VRF) system; the VRF installation is configured to operate in a heating mode in which the VRF installation provides heating to a building space and in a cooling mode in which the VRF installation provides cooling to the building space; The refrigerant charge controller of claim 1 , wherein the usage data is collected while the VRF equipment is operating in the heating mode.

5. The processing circuitry simulating operation of the HVAC plant under various test conditions to generate a set of simulated operating data; training the machine learning model using the set of simulated motion data; The refrigerant charge controller of claim 1 configured to:

6. identifying the refrigerant shortage includes determining that the amount of refrigerant is below a threshold; The refrigerant charge controller of claim 1 , wherein initiating the corrective action includes automatically charging more refrigerant into a refrigerant circuit used by the HVAC system.

7. identifying the refrigerant shortage includes detecting a refrigerant leak in a refrigerant circuit used by the HVAC equipment; The refrigerant charge controller of claim 1 , wherein initiating the corrective action comprises initiating a maintenance action to repair the refrigerant leak.

8. 1. A controller for a variable refrigerant flow (VRF) installation, comprising: the controller comprises processing circuitry; The processing circuitry analyzing usage data for the VRF equipment using a machine learning model to estimate a volume of liquid in the VRF equipment; identifying a fluid deficiency based on the amount of fluid; initiating corrective action in response to identifying said fluid shortage; configured to: the VRF facility comprises one or more compressors configured to circulate a refrigerant in a refrigerant circuit; The controller, wherein the usage data includes at least one of a sub-cooling temperature of the refrigerant vapor, a dry bulb temperature, and a charge of the refrigerant.

9. A controller as described in claim 8, wherein the usage data further includes a suction pressure of the refrigerant at the time of suction of the one or more compressors, or a discharge temperature or discharge pressure of the refrigerant at the time of discharge of the one or more compressors.

10. The processing circuitry simulating operation of the VRF equipment under various test conditions to generate a set of simulated operational data; training the machine learning model using the set of simulated motion data; The controller of claim 8 configured to:

11. the amount of liquid is an estimate of the amount of liquid in an accumulator or one or more indoor VRF units of the VRF installation; identifying the fluid depletion includes determining that the amount of fluid exceeds a threshold; The controller of claim 8 , wherein initiating the corrective action includes automatically returning a portion of the liquid to a compressor or an outdoor VRF unit of the VRF facility.

12. 1. A controller for a variable refrigerant flow (VRF) installation, comprising: the controller comprises one or more processing circuits; the one or more processing circuits: analyzing a first set of usage data for the VRF equipment using a first machine learning model to estimate an amount of refrigerant used by the VRF equipment; analyzing a second set of usage data for the VRF equipment using a second machine learning model to estimate a volume of liquid in the VRF equipment; identifying a fluid deficiency based on the amount of fluid; initiating corrective action in response to identifying said fluid shortage; configured to: The second set of usage data includes an amount of the refrigerant used by the VRF equipment estimated by analyzing the first set of usage data.

13. the VRF facility comprising one or more compressors configured to circulate the refrigerant in a refrigerant circuit; 13. The controller of claim 12, wherein the first set of usage data includes at least one of a suction temperature or a suction pressure of the refrigerant at a suction of the one or more compressors, or a discharge temperature or a discharge pressure of the refrigerant at a discharge of the one or more compressors.

14. the VRF equipment comprising one or more valves configured to control the flow of the refrigerant in a refrigerant circuit comprising one or more fluid conduits; The controller of claim 12 , wherein the first set of usage data includes at least one of a valve position of the one or more valves or a length of the one or more fluid conduits.

15. the VRF installation is configured to operate in a heating mode in which the VRF installation provides heating to a building space and in a cooling mode in which the VRF installation provides cooling to the building space; The controller of claim 12 , wherein the first set of usage data is collected while the VRF equipment is operating in the heating mode.

16. the one or more processing circuits: simulating operation of the VRF equipment under first various test conditions to generate a first set of simulated operational data, and training the first machine learning model using the first set of simulated operational data; simulating operation of the VRF equipment under second various test conditions to generate a second set of simulated operational data, and training the second machine learning model using the second set of simulated operational data; The controller of claim 12 configured to:

17. the amount of liquid is an estimate of the amount of liquid in an accumulator or one or more indoor VRF units of the VRF installation; identifying the fluid depletion includes determining that the amount of fluid exceeds a threshold; The controller of claim 12 , wherein initiating the corrective action includes automatically returning a portion of the liquid to a compressor or one or more outdoor VRF units of the VRF facility.

18. identifying the liquid shortage includes determining that the amount of liquid exceeds a threshold in an accumulator of the VRF equipment; The controller of claim 12 , wherein initiating the corrective action includes automatically returning a portion of the liquid from the accumulator to a compressor of the VRF plant.

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