Building system with synthetic data compliance control

Abstract

A method for controlling building equipment includes generating building data related to conditions in a building space using a generative artificial intelligence model. The method also includes determining whether the building data corresponds to a condition in the building space that conforms to one or more regulations or authentication criteria. The method includes operating a building device serving the building space using the building data in response to determining that the building data corresponds to a condition in the building space that conforms to the one or more regulatory or authentication criteria.

Description

Background Art

[0001] The present disclosure generally relates to control systems for building equipment. The present disclosure more particularly relates to control systems that use predictive modeling to determine optimal operating strategies and / or maintenance strategies for building equipment. Summary of the invention

[0002] According to some embodiments, one implementation of the present disclosure is a method for controlling building equipment. In some embodiments, the method includes using a generative artificial intelligence model to generate building data related to conditions in a building space. In some embodiments, the method includes determining whether the building data corresponds to a condition in the building space that complies with one or more regulations or certification standards. In some embodiments, the method includes using the building data to operate building equipment serving the building space in response to determining that the building data corresponds to a condition in the building space that complies with one or more regulations or certification standards.

[0003] In some embodiments, using the building data to operate the building equipment includes using the building data to train a predictive model or a reinforcement strategy and using the predictive model to generate operating parameters for the building equipment. In some embodiments, the method includes filtering the building data to remove one or more portions of the building data corresponding to conditions in the building space that do not comply with one or more regulations or certification standards in response to determining that the building data corresponds to conditions in the building space that do not comply with one or more regulations or certification standards. In some embodiments, the method includes initiating a fine-tuning process of the generative artificial intelligence model based on the filtered building data.

[0004] In some embodiments, the method includes generating a compliance report indicating one or more of a plurality of regulations or certification standards with which the building data complies. In some embodiments, the building data includes design parameters for a new building. In some embodiments, the method includes operating a display screen to present the compliance report to a user.

[0005] In some embodiments, the one or more regulations or certification standards include one or more comfort standards, one or more emissions standards, one or more American Society of Heating, Refrigerating and Air-Conditioning Engineers ("ASHRAE") standards, and International WELL Building Institute standards. In some embodiments, the building data includes time series data of operating conditions of building equipment over a period of time. In some embodiments, the generative artificial intelligence model is trained based on a database of real-world building data for multiple different buildings.

[0006] According to some embodiments, another embodiment of the present disclosure is a control system for building equipment. In some embodiments, the control system includes building equipment that serves a building space. In some embodiments, the control system includes a processing circuit system that is configured to use a generative artificial intelligence model to generate building data related to conditions in the building space. In some embodiments, the processing circuit system is configured to determine whether the building data corresponds to a condition in the building space that complies with one or more regulations or certification standards. In some embodiments, the processing circuit system is configured to use the building data to generate a control strategy for building equipment that serves the building space in response to determining that the building data corresponds to a condition in the building space that complies with one or more regulations or certification standards.

[0007] In some embodiments, the processing circuit system is configured to use the building data to operate the building equipment by using the building data to train a predictive model or a reinforcement strategy and using the predictive model to generate operating parameters for the building equipment. In some embodiments, the processing circuit system is further configured to filter the building data to remove one or more portions of the building data corresponding to conditions in the building space that do not comply with one or more regulations or certification standards in response to determining that the building data corresponds to conditions in the building space that do not comply with one or more regulations or certification standards. In some embodiments, the processing circuit system is further configured to initiate a fine-tuning process of the generative artificial intelligence model based on the filtered building data.

[0008] In some embodiments, the processing circuit system is further configured to generate a compliance report indicating one or more of a plurality of regulations or certification standards with which the building data complies. In some embodiments, the building data includes design parameters for a new building. In some embodiments, the processing circuit system is further configured to operate a display screen to present the compliance report to a user.

[0009] In some embodiments, the one or more regulations or certification standards include one or more comfort standards, one or more emissions standards, one or more American Society of Heating, Refrigerating and Air-Conditioning Engineers ("ASHRAE") standards, and International WELL Building Institute standards. In some embodiments, the building data includes time series data of operating conditions of building equipment over a period of time. In some embodiments, the generative artificial intelligence model is trained based on a database of real-world building data for multiple different buildings.

[0010] According to some embodiments, another implementation of the present disclosure is a method for controlling building equipment. In some embodiments, the method includes using a generative artificial intelligence model to generate building data including operating parameters of the building equipment. In some embodiments, the method includes predicting whether the operating parameters will cause conditions in the building space to comply with one or more regulations or certification standards if the operating parameters are used to operate the building equipment serving the building space. In some embodiments, the method includes using the building data to operate the building equipment in response to predicting that the operating parameters will cause conditions in the building space to comply with one or more regulations or certification standards.

[0011] In some embodiments, the building data used to operate the building equipment includes using the building data to train a predictive model or a reinforcement strategy and using the predictive model to generate operating parameters for the building equipment. In some embodiments, the method further includes filtering the building data to remove one or more portions of the building data corresponding to conditions in the building space that do not comply with one or more regulations or certification standards in response to determining that the building data corresponds to conditions in the building space that do not comply with one or more regulations or certification standards. In some embodiments, the method includes initiating a fine-tuning process of the generative artificial intelligence model based on the filtered building data.

[0012] In some embodiments, the method includes generating a compliance report indicating one or more of a plurality of regulations or certification standards with which the building data complies. In some embodiments, the building data includes design parameters for a new building. In some embodiments, the method includes operating a display screen to present the compliance report to a user.

[0013] In some embodiments, the one or more codes or certification standards include one or more comfort standards, one or more emissions standards, one or more American Society of Heating, Refrigerating, and Air-Conditioning Engineers (“ASHRAE”) standards, and International WELL Building Institute standards.

[0014] In some embodiments, the generative artificial intelligence model is trained based on a database of real-world building data for a plurality of different buildings. In some embodiments, the generative artificial intelligence model is a large language model. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Various objects, aspects, features and advantages of the present disclosure will become more apparent and better understood by reference to the detailed description taken in conjunction with the accompanying drawings, in which the same reference numerals identify corresponding elements throughout the drawings. In the drawings, the same reference numerals generally indicate identical, functionally similar and / or structurally similar elements.

[0016] Figure 1is a diagram of a building equipped with an HVAC system according to some embodiments.

[0017] Figure 2 According to some embodiments, Figure 1 Block diagram of a waterside system used in conjunction with a building.

[0018] Figure 3 According to some embodiments, Figure 1 Block diagram of an air side system used in conjunction with a building.

[0019] Figure 4 According to some embodiments, it can be used to monitor and control Figure 1 Block diagram of a building management system (BMS) for a building.

[0020] Figure 5 According to some embodiments, it can be used to monitor and control Figure 1 Block diagram of another BMS for a building.

[0021] Figure 6 is a block diagram of a compliance determination system according to some embodiments.

[0022] Figure 7 According to some embodiments Figure 6 Block diagram of a verification controller of a compliance determination system.

[0023] Figure 8 According to some embodiments Figure 6 Block diagram of the compliance determination system input controller.

[0024] Fig. 9 is a flow chart of a process for controlling building equipment and initiating fine-tuning of a generative artificial intelligence model based on synthetic building data, according to some embodiments.

[0025] Fig.10 is a flow chart of a process for predicting whether a building can obtain a particular certification, according to some embodiments. DETAILED DESCRIPTION

[0026] Referring generally to the accompanying drawings, systems and methods for using building data provided by a generative artificial intelligence model can mitigate the possibility of using unrealistic data to train or inform various building control programs. For example, a generative artificial intelligence (GAI) such as a large language model may occasionally output inaccurate data, which in the case of a building control system may cause the building to operate in violation of one or more criteria (e.g., in an uncomfortable manner). Therefore, the systems and methods described herein predict conditions that will result from using building data output by a generative artificial intelligence model, and filter portions of the building data that will result in uncomfortable conditions in the building. The filtered building data can then be used to fine-tune the generative artificial intelligence so that the generative artificial intelligence does not output building data that will result in uncomfortable conditions.

[0027] Building HVAC systems and building management systems

[0028] Reference now Figures 1 to 5 , according to some embodiments, several building management systems (BMS) and HVAC systems are shown in which the systems and methods of the present disclosure may be implemented. In a brief overview, Figure 1 A building 10 equipped with an HVAC system 100 is shown. Figure 2 is a block diagram of a waterside system 200 that may be used to serve a building 10 . Figure 3 is a block diagram of an airside system 300 that may be used to serve a building 10 . Figure 4 is a block diagram of a BMS that can be used to monitor and control a building 10 . Figure 5 is a block diagram of another BMS that may be used to monitor and control the building 10 .

[0029] Buildings and HVAC Systems

[0030] Specific reference Figure 1 , showing a perspective view of a building 10. 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.

[0031] The BMS serving the building 10 includes an HVAC system 100. The HVAC system 100 may include a plurality of 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 the building 10. By way of example, the HVAC system 100 is shown to include a water side system 120 and an air side system 130. The water side system 120 may provide a heated or cooled fluid to the air handling unit of the air side system 130. The air side system 130 may use the heated or cooled fluid to heat or cool an air flow provided to the building 10. Figures 2 to 3 Exemplary waterside and airside systems that may be used in HVAC system 100 are described in greater detail.

[0032] HVAC system 100 is shown to include chiller 102, boiler 104, and 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 may circulate the working fluid to AHU 106. In various embodiments, HVAC devices of waterside system 120 may be located in or around building 10 (e.g., Figure 1 ), or located at an off-site location such as a central plant (e.g., a chiller plant, a steam plant, a heat generating plant, etc.). Depending on whether heating or cooling is required in the building 10, the working fluid can be heated in the boiler 104 or cooled in the chiller 102. The boiler 104 can add heat to the circulating fluid, for example, by burning a combustible material (e.g., natural gas) or using an electric heating element. The chiller 102 can place the circulating fluid in a heat exchange relationship with another fluid (e.g., a refrigerant) in a heat exchanger (e.g., an evaporator) to absorb heat from the circulating fluid. The working fluid from the chiller 102 and / or the boiler 104 can be delivered to the AHU 106 via a line 108.

[0033] The AHU 106 can place the working fluid in heat exchange relationship with an airflow passing through the AHU 106 (e.g., via one or more stages of cooling coils and / or heating coils). For example, the airflow can be outdoor air, return air from within the building 10, or a combination of both. The AHU 106 can transfer heat between the airflow and the working fluid to provide heating or cooling to the airflow. For example, the AHU 106 can include one or more fans or blowers configured to pass the airflow through or through a heat exchanger containing the working fluid. The working fluid can then be returned to the chiller 102 or boiler 104 via line 110.

[0034] The air side system 130 can deliver the airflow supplied by the AHU 106 (i.e., supply airflow) to the building 10 via the supply duct 112, and can provide return air from the building 10 to the AHU 106 via the return duct 114. In some embodiments, the air side system 130 includes a plurality of variable air volume (VAV) units 116. For example, the air side system 130 is shown as including a separate VAV unit 116 on each floor or zone of the building 10. The VAV unit 116 may include a damper or other flow control element that can operate to control the amount of supply airflow provided to each zone of the building 10. In other embodiments, the air side system 130 (e.g., via the supply duct 112) delivers the supply airflow to one or more zones of the building 10 without using an intermediate VAV unit 116 or other flow control element. The AHU 106 may include various sensors (e.g., temperature sensors, pressure sensors, etc.) configured to measure properties of the supply airflow. The AHU 106 may receive input from sensors located within the AHU 106 and / or within a building zone, and may adjust the flow rate, temperature, or other properties of the supply airflow through the AHU 106 to achieve a set point condition for the building zone.

[0035] Water side system

[0036] Reference now Figure 2 , a block diagram of a waterside system 200 is shown, according to some embodiments. In various embodiments, the waterside system 200 can supplement or replace the waterside system 120 in the HVAC system 100, or can be implemented separately from the HVAC system 100. When implemented in the HVAC system 100, the waterside system 200 can include a subset of the HVAC devices in the HVAC system 100 (e.g., boiler 104, chiller 102, pumps, valves, etc.), and can be operated to supply heated or cooled fluid to the AHU 106. The HVAC devices of the waterside system 200 can be located within the building 10 (e.g., as components of the waterside system 120), or located at an off-site location such as a central facility.

[0037] exist Figure 2, the water side system 200 is shown as a central device having a plurality of sub-devices 202-212. The sub-devices 202-212 are shown to include a heater sub-device 202, a heat recovery chiller sub-device 204, a chiller sub-device 206, a cooling tower sub-device 208, a high temperature thermal energy storage (TES) sub-device 210, and a low temperature thermal energy storage (TES) sub-device 212. The sub-devices 202-212 consume resources (e.g., water, natural gas, electricity, etc.) of a utility to serve the thermal energy loads (e.g., hot water, cold water, heating, cooling, etc.) of a building or campus. For example, the heater sub-device 202 may be configured to heat water in a hot water loop 214 that circulates hot water between the heater sub-device 202 and the building 10. The chiller sub-device 206 may be configured to cool water in a cold water loop 216 that circulates cold water between the chiller sub-device 206 and the building 10. The heat recovery chiller sub-unit 204 can be configured to transfer heat from the chilled water loop 216 to the hot water loop 214 to provide additional heating for the hot water and additional cooling for the chilled water. The condenser water loop 218 can absorb heat from the chilled water in the chiller sub-unit 206 and reject the absorbed heat in the cooling tower sub-unit 208 or transfer the absorbed heat to the hot water loop 214. The high temperature TES sub-unit 210 and the low temperature TES sub-unit 212 can store high temperature thermal energy and low temperature thermal energy, respectively, for subsequent use.

[0038] The hot water loop 214 and the cold water loop 216 can deliver heated and / or cooled water to an air handler (e.g., AHU 106) located on the roof of the building 10 or to various floors or zones (e.g., VAV units 116) of the building 10. The air handler pushes air through a heat exchanger (e.g., a heating coil or a cooling coil) through which water flows to provide heating or cooling to the air. The heated or cooled air can be delivered to various zones of the building 10 to serve the thermal energy load of the building 10. The water then returns to the sub-equipment 202-212 to receive further heating or cooling.

[0039] Although the sub-devices 202-212 are shown and described as heating and cooling water for circulation to the building, it should be understood that any other type of working fluid (e.g., glycol, CO2, etc.) may be used in place of or in addition to water to serve the thermal energy load. In other embodiments, the sub-devices 202-212 may provide heating and / or cooling directly to the building or campus without the need for an intermediate heat transfer fluid. These and other variations of the waterside system 200 are within the teachings of the present disclosure.

[0040] Each of the sub-devices 202-212 can include a variety of devices configured to facilitate the functions of the sub-device. For example, the heater sub-device 202 is shown as including a plurality of heating elements 220 (e.g., boilers, electric heaters, etc.) configured to add heat to the hot water in the hot water loop 214. The heater sub-device 202 is also shown as including a number of pumps 222 and 224 configured to circulate the 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 sub-device 206 is shown as including a plurality of chillers 232 configured to remove heat from the cold water in the cold water loop 216. The chiller sub-device 206 is also shown as including a number of pumps 234 and 236 configured to circulate the cold water in the cold water loop 216 and control the flow rate of the cold water through the individual chillers 232.

[0041] The heat recovery chiller sub-equipment 204 is shown to include a plurality of heat recovery heat exchangers 226 (e.g., refrigeration circuits) configured to transfer heat from the chilled water loop 216 to the hot water loop 214. The heat recovery chiller sub-equipment 204 is also shown to include a plurality of pumps 228 and 230 configured to circulate hot and / or chilled water through the heat recovery heat exchangers 226 and to control the flow rate of water through individual heat recovery heat exchangers 226. The cooling tower sub-equipment 208 is shown to include a plurality of cooling towers 238 configured to remove heat from condenser water in the condenser water loop 218. The cooling tower sub-equipment 208 is also shown to include a plurality of pumps 240 configured to circulate condenser water in the condenser water loop 218 and to control the flow rate of condenser water through individual cooling towers 238.

[0042] The high temperature TES sub-device 210 is shown to include a high temperature TES tank 242 configured to store hot water for subsequent use. The high temperature TES sub-device 210 may also include one or more pumps or valves configured to control the flow rate of hot water entering or leaving the high temperature TES tank 242. The low temperature TES sub-device 212 is shown to include a low temperature TES tank 244 configured to store cold water for subsequent use. The low temperature TES sub-device 212 may also include one or more pumps or valves configured to control the flow rate of cold water entering or leaving the low temperature TES tank 244.

[0043] In some embodiments, one or more of the pumps in the waterside system 200 (e.g., pumps 222, 224, 228, 230, 234, 236, and / or 240) or pipes in the waterside system 200 include isolation valves associated therewith. The isolation valves may be integrated with the pumps or positioned upstream or downstream of the pumps to control fluid flow in the waterside system 200. In various embodiments, the waterside system 200 may include more, fewer, or different types of devices and / or sub-equipment based on the specific configuration of the waterside system 200 and the type of loads served by the waterside system 200.

[0044] Air side system

[0045] Reference now Figure 3 , a block diagram of an air side system 300 is shown, according to some embodiments. In various embodiments, the air side system 300 can supplement or replace the air side system 130 in the HVAC system 100, or can be implemented separately from the HVAC system 100. When implemented in the HVAC system 100, the air side system 300 can include a subset of the HVAC devices in the HVAC system 100 (e.g., AHU 106, VAV unit 116, ducts 112-114, fans, dampers, etc.), and can be located in or around the building 10. The air side system 300 can operate to heat or cool the airflow provided to the building 10 using the heated or cooled fluid provided by the water side system 200.

[0046] exist Figure 3 , the air side system 300 is shown to include an economizer type air handling unit (AHU) 302. An economizer type AHU changes the amount of outside air and return air used by the air handling unit for heating or cooling. For example, the AHU 302 can receive return air 304 from a building zone 306 via a return air duct 308, and can deliver supply air 310 to the building zone 306 via a supply air duct 312. In some embodiments, the AHU 302 is a rooftop unit located on the roof of the building 10 (e.g., a Figure 1 ), or otherwise positioned to receive both return air 304 and outside air 314. The AHU 302 may be configured to operate an exhaust damper 316, a blend damper 318, and an outside air damper 320 to control the amount of outside air 314 and return air 304 that are combined to form the supply air 310. Any return air 304 that does not pass through the blend damper 318 may be exhausted from the AHU 302 through the exhaust damper 316 as exhaust air 322.

[0047] 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 blend 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 a current actuator or damper position, an amount of torque or force applied by the actuator, diagnostic information (e.g., results of a diagnostic test performed by the actuators 324-328), status information, tuning information, configuration settings, calibration data, and / or other types 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.).

[0048] Still refer to Figure 3 , the AHU 302 is shown to include a cooling coil 334, a heating coil 336, and a fan 338 positioned within the supply air duct 312. The fan 338 can be configured to force the supply air 310 through the cooling coil 334 and / or the heating coil 336 and provide the supply air 310 to the building zone 306. The AHU controller 330 can communicate with the fan 338 via a communication link 340 to control the flow rate of the supply air 310. In some embodiments, the AHU controller 330 controls the amount of heating or cooling applied to the supply air 310 by adjusting the speed of the fan 338.

[0049] The cooling coil 334 may receive cooled fluid from the waterside system 200 (e.g., from the chilled water loop 216) via the conduit 342 and may return the cooled fluid to the waterside system 200 via the line 344. A valve 346 may be positioned along the line 342 or the line 344 to control the flow rate of the cooled fluid through the cooling coil 334. In some embodiments, the cooling coil 334 includes multiple stages of cooling coils that may be independently activated and deactivated (e.g., by the AHU controller 330, by the BMS controller 366, etc.) to adjust the amount of cooling applied to the supply air 310.

[0050] The heating coil 336 may receive heated fluid from the waterside system 200 (e.g., from the hot water loop 214) via line 348 and may return the heated fluid to the waterside system 200 via line 350. A valve 352 may be positioned along line 348 or line 350 to control the flow rate of the heated fluid through the heating coil 336. In some embodiments, the heating coil 336 includes multiple stages of heating coils that may be independently activated and deactivated (e.g., by the AHU controller 330, by the BMS controller 366, etc.) to adjust the amount of heating applied to the supply air 310.

[0051] Each of the valves 346 and 352 can be controlled by an actuator. For example, valve 346 can be controlled by actuator 354, and valve 352 can be controlled by actuator 356. The actuators 354-356 can communicate with the AHU controller 330 via communication links 358-360. The actuators 354-356 can receive control signals from the AHU controller 330 and can provide feedback signals to the controller 330. In some embodiments, the AHU controller 330 receives a measurement of the supply air temperature from a temperature sensor 362 positioned in the supply air duct 312 (e.g., downstream of the cooling coil 334 and / or the heating coil 336). The AHU controller 330 can also receive a measurement of the temperature of the building zone 306 from a temperature sensor 364 located in the building zone 306.

[0052] In some embodiments, the AHU controller 330 operates 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 set point temperature of the supply air 310 or to maintain the temperature of the supply air 310 within a set point temperature range). The position of the valves 346 and 352 affects the amount of heating or cooling provided to the supply air 310 by the cooling coil 334 or the heating coil 336 and can be related to the amount of energy consumed to achieve the desired supply air temperature. The AHU 330 can control the temperature of the supply air 310 and / or the building zone 306 by activating or deactivating the coils 334-336, adjusting the speed of the fan 338, or a combination of both.

[0053] Still refer to Figure 3, the airside system 300 is shown to include a building management system (BMS) controller 366 and a client device 368. The BMS controller 366 may include one or more computer systems (e.g., servers, supervisory controllers, subsystem controllers, etc.) that act as a system-level controller, application or data server, head node, or master controller for the airside system 300, the waterside system 200, the HVAC system 100, and / or other controllable systems serving the building 10. The BMS controller 366 may communicate with multiple downstream building systems or subsystems (e.g., the HVAC system 100, security system, lighting system, waterside system 200, etc.) via a communication link 370 according to similar or different protocols (e.g., LON, BACnet, etc.). In various embodiments, the AHU controller 330 and the BMS controller 366 may be separate (e.g., Figure 3 In an integrated embodiment, the AHU controller 330 may be a software module configured for execution by a processor of the BMS controller 366.

[0054] 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, operating 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, equipment on / off status, equipment operating capabilities, and / or any other information that may be used by the BMS controller 366 to monitor or control variable states or conditions within the building zone 306.

[0055] The client device 368 may include one or more human-machine interfaces or client interfaces (e.g., a graphical user interface, a reporting interface, a text-based computer interface, a client-oriented web service, a web server that provides pages to a web client, etc.) for controlling, viewing, or otherwise interacting with the HVAC system 100, its subsystems, and / or devices. The client device 368 may be a computer workstation, a client terminal, a remote or local interface, or any other type of user interface device. The client device 368 may be a fixed terminal or a mobile device. For example, the client device 368 may be a desktop computer, a computer server with a user interface, a laptop computer, a tablet computer, a smart phone, a PDA, or any other type of mobile or non-mobile device. The client device 368 may communicate with the BMS controller 366 and / or the AHU controller 330 via a communication link 372.

[0056] Building Management System

[0057] Reference now Figure 4 , a block diagram of a building management system (BMS) 400 is shown, according to some embodiments. The BMS 400 can be implemented in a building 10 to automatically monitor and control various building functions. The BMS 400 is shown to include a BMS controller 366 and a plurality of 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 additionally 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 devices and / or sensors to monitor or control the building 10. In some embodiments, the building subsystem 428 includes the waterside system 200 and / or the airside system 300, as shown in FIG. Figures 2 to 3 described.

[0058] Each of the building subsystems 428 may include any number of devices, controllers, and connections for accomplishing its respective functions and control activities. The HVAC subsystem 440 may include many of the same components as the HVAC system 100, as described with reference to FIG. Figures 1 to 3 As described above. For example, HVAC subsystem 440 may include chillers, boilers, any number of air handling units, economizers, field controllers, supervisory controllers, actuators, temperature sensors, and other devices for controlling temperature, humidity, airflow, or other variable conditions within building 10. 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. 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.

[0059] Still reference Figure 4, the BMS controller 366 is shown to include a communication interface 407 and a BMS interface 409. The interface 407 can facilitate communication between the BMS controller 366 and external applications (e.g., monitoring and reporting applications 422, enterprise control applications 426, remote systems and applications 444, applications resident on client devices 448, etc.) to allow a user to control, monitor, and adjust the BMS controller 366 and / or subsystems 428. The interface 407 can also facilitate communication between the BMS controller 366 and client devices 448. The BMS interface 409 can facilitate communication between the BMS controller 366 and building subsystems 428 (e.g., HVAC, lighting security, elevators, power distribution, enterprise, etc.).

[0060] Interfaces 407, 409 may be or include wired or wireless communication interfaces (e.g., sockets, antennas, transmitters, receivers, transceivers, wire connectors, etc.) for data communication with building subsystems 428 or other external systems or devices. In various embodiments, communication via interfaces 407, 409 may be direct (e.g., local wired or wireless communication) or via a communication network 446 (e.g., WAN, Internet, cellular network, etc.). For example, interfaces 407, 409 may include an Ethernet card and port for sending and receiving data via an Ethernet-based communication link or network. In another example, interfaces 407, 409 may include a Wi-Fi transceiver for communicating via a wireless communication network. In another example, one or both of interfaces 407, 409 may include a cellular or mobile phone communication transceiver. In one embodiment, communication interface 407 is a power line communication interface, and BMS interface 409 is an Ethernet interface. In other embodiments, both communication interface 407 and BMS interface 409 are Ethernet interfaces or the same Ethernet interface.

[0061] Still reference Figure 4 , the BMS controller 366 is shown to include a processing circuit 404 including a processor 406 and a memory 408. The processing circuit 404 may be communicatively connected to a BMS interface 409 and / or a communication interface 407, such that the processing circuit 404 and its various components may 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.

[0062] 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 device, etc.) for storing data and / or computer code for completing or facilitating the various processes, layers, and modules described in this application. Memory 408 may be or include volatile memory or non-volatile memory. Memory 408 may include a database component, an object code component, a script component, or any other type of information structure for supporting the various activities and information structures described in this disclosure. According to some embodiments, memory 408 is communicatively connected to processor 406 via processing circuit 404 and includes computer code for executing (e.g., by processing circuit 404 and / or processor 406) one or more processes described herein.

[0063] In some embodiments, the BMS controller 366 is implemented within a single computer (e.g., one server, one house, etc.). In various other embodiments, the BMS controller 366 may be distributed across multiple servers or computers (e.g., may exist in distributed locations). Figure 4 The applications 422 and 426 are shown as existing external to the BMS controller 366 , but in some embodiments, the applications 422 and 426 may be hosted within the BMS controller 366 (eg, within the memory 408 ).

[0064] Still reference Figure 4 , the memory 408 is shown to include an enterprise integration layer 410, an automated measurement and verification (AM&V) layer 412, a demand response (DR) layer 414, a fault detection and diagnostics (FDD) layer 416, an integrated control layer 418, and a building subsystem integration layer 420. The layers 410-420 may be configured to receive inputs from the building subsystems 428 and other data sources, determine optimal control actions for the building subsystems 428 based on the inputs, generate control signals based on the optimal control actions, and provide the generated control signals to the building subsystems 428. The following paragraphs describe some of the common functions performed by each of the layers 410-420 in the BMS 400.

[0065] The enterprise integration layer 410 can be configured to provide information and services to clients or local applications to support a variety of enterprise-level applications. For example, the enterprise control application 426 can be configured to provide subsystem spanning control to a graphical user interface (GUI) or to any number of enterprise-level business applications (e.g., billing system, user identification system, etc.). The enterprise control application 426 can additionally or alternatively be configured to provide a configuration GUI for configuring the BMS controller 366. In yet other embodiments, the enterprise control application 426 can work with the layers 410-420 to optimize building performance (e.g., efficiency, energy usage, comfort, or safety) based on input received at the interface 407 and / or the BMS interface 409.

[0066] 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 translates communications (e.g., sensor data, input signals, output signals, etc.) across multiple multi-vendor / multi-protocol systems.

[0067] 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 the needs of the building 10. The optimization can be based on time-of-use prices, curtailment signals, energy availability, or other data received from a utility provider, a distributed energy generation system 424, from an energy storage device 427 (e.g., a high temperature TES 242, a low temperature TES 244, etc.), or from other sources. The demand response layer 414 can receive inputs from other layers of the BMS controller 366 (e.g., a building subsystem integration layer 420, an integrated control layer 418, etc.). The 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 electricity usage (e.g., expressed in kWh), heat load measurements, pricing information, scheduled pricing, smoothed pricing, curtailment signals from utilities, and the like.

[0068] According to some embodiments, the 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 the integrated control layer 418, changing control strategies, changing set points, or activating / deactivating building equipment or subsystems in a controlled manner. The demand response layer 414 may also include control logic configured to determine when to utilize stored energy. For example, the demand response layer 414 may determine to use energy from the energy storage device 427 just before the peak usage period begins.

[0069] In some embodiments, the demand response layer 414 includes control modules configured to proactively initiate control actions (e.g., automatically change set points) that minimize energy costs based on one or more demand-representative or demand-based inputs (e.g., prices, curtailment signals, demand levels, etc.). In some embodiments, the demand response layer 414 uses equipment models to determine optimal sets of control actions. Equipment models may include, for example, thermodynamic models that describe inputs, outputs, and / or functions performed by various sets of building equipment. Equipment models may represent collections of building equipment (e.g., sub-equipment, arrays of chillers, etc.) or individual devices (e.g., individual chillers, heaters, pumps, etc.).

[0070] The demand response layer 414 may further include or utilize one or more demand response policy definitions (e.g., a database, XML file, etc.). The policy definitions may 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 may be customized for the user's application, desired comfort level, specific building equipment, or based on other issues. For example, a demand response policy definition may specify which equipment may be turned on or off in response to a particular demand input, how long a system or equipment should be off, which set points may be changed, what is the allowable set point adjustment range, how long to maintain a high demand set point before returning to a normally scheduled set point, how close to a capacity limit, which equipment modes to utilize, energy transfer rates (e.g., maximum rate, alarm rate, other rate boundary information, etc.), access to and from energy storage devices (e.g., thermal storage tanks, battery banks, etc.), and when to schedule on-site power generation (e.g., via fuel cells, electric generator sets, etc.).

[0071] The integrated control layer 418 may be configured to use data inputs or outputs from the building subsystem integration layer 420 and / or the demand response layer 414 to make control decisions. Due to the subsystem integration provided by the building subsystem integration layer 420, the integrated control layer 418 may integrate the control activities of the subsystems 428 so that the subsystems 428 behave 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 a higher level of comfort and energy savings than a single subsystem could provide alone. For example, the integrated control layer 418 may be configured to use inputs from a first subsystem to make energy saving control decisions for a second subsystem. The results of these decisions may be communicated back to the building subsystem integration layer 420.

[0072] The integrated control layer 418 is shown as being logically located below the demand response layer 414. The integrated control layer 418 can be configured to enhance the effectiveness of the demand response layer 414 by enabling the building subsystems 428 and their respective control loops to be controlled in coordination with the demand response layer 414. This configuration can advantageously reduce disruptive demand response behavior relative to conventional systems. For example, the integrated control layer 418 can be configured to ensure that a demand response-driven upward adjustment to the set point of the chilled water temperature (or another component that directly or indirectly affects the temperature) does not result in an increase in fan energy (or other energy used to cool the space) that would cause the total building energy use to be greater than the energy saved by the chillers.

[0073] The integrated control layer 418 may be configured to provide feedback to the demand response layer 414 so that the demand response layer 414 checks whether constraints (e.g., temperature, lighting levels, etc.) are properly maintained even when required 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 automated measurement and verification layer 412. The integrated control layer 418 may be configured to provide calculated inputs (e.g., aggregates) to these higher levels based on outputs from more than one building subsystem.

[0074] The automated measurement and verification (AM&V) layer 412 can be configured to verify that the 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 other layers). The calculations performed by the AM&V layer 412 can be based on the building system energy model and / or equipment models for individual BMS devices or subsystems. For example, the AM&V layer 412 can compare the model predicted output with the actual output of the building subsystem 428 to determine the accuracy of the model.

[0075] The fault detection and diagnostics (FDD) layer 416 may 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 inputs 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 faults. Responses to detected or diagnosed faults may include providing a warning message to a user, a maintenance scheduling system, or a control algorithm configured to attempt to repair the fault or resolve the fault.

[0076] The FDD layer 416 may be configured to use the detailed subsystem inputs available at the building subsystem integration layer 420 to output a specific identification of a faulty component or a cause of a fault (e.g., a loose damper linkage). 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 strategies executed by an integrated control engine or business rules engine) may shut down a system or direct control activities around a faulty device or system to reduce energy waste, extend equipment life, or ensure an appropriate control response.

[0077] The FDD layer 416 can be configured to store or access a variety of different system data repositories (or data points of live data). The FDD layer 416 can use some of the contents of the data repository to identify faults at the equipment level (e.g., a specific chiller, a specific AHU, a specific terminal unit, etc.) and use other contents to identify faults at the component or subsystem level. For example, the building subsystem 428 can generate temporal (i.e., time series) data indicating the performance of the BMS 400 and its various components. The data generated by the building subsystem 428 may include measured or calculated values ​​that exhibit statistical properties and provide information about how the corresponding system or process (e.g., a temperature control process, a flow control process, etc.) performs based on errors from its set points. These processes can be checked by the FDD layer 416 to expose when the system begins to degrade in performance and alert the user to repair the fault before it becomes more serious.

[0078] Reference now Figure 5 , according to some embodiments, a block diagram of another building management system (BMS) 500 is shown. The BMS 500 can be used to monitor and control devices of the HVAC system 100, the water side system 200, the air side system 300, the building subsystem 428, and other types of BMS devices (e.g., lighting devices, security devices, etc.) and / or HVAC equipment.

[0079] The BMS 500 provides a system architecture that facilitates automatic device discovery and device model distribution. Device discovery can occur 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 at multiple layers of the BMS 500. In some embodiments, device discovery is accomplished using an active node table that provides status information for devices connected to each communication bus. For example, new devices on each communication bus can be monitored by monitoring the corresponding active node table of the new node. When a new device is detected, the BMS 500 can begin interacting with the new device (e.g., sending control signals, using data from the device) without user interaction.

[0080] Some devices in the BMS 500 present themselves to the network using device models. The device model defines device object properties, view definitions, schedules, trends, and associated BACnet value objects (e.g., analog values, binary values, multi-state values, etc.) for integration with other systems. Some devices in the BMS 500 store their own device models. Other devices in the BMS 500 have device models stored externally (e.g., within other devices). For example, the zone coordinator 508 may store a device model for the bypass damper 528. In some embodiments, the zone coordinator 508 automatically creates a device model for the bypass damper 528 or other devices on the zone bus 558. Other zone coordinators may also create device models for devices connected to their zone buses. Device models for devices may 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. Several examples of automatic device discovery and device model distribution are discussed in more detail below.

[0081] Still reference Figure 5 , the BMS 500 is shown to include a system manager 502; a number of zone coordinators 506, 508, 510, and 518; and a number of zone controllers 524, 530, 532, 536, 548, and 550. The system manager 502 can monitor data points in the BMS 500 and report the monitored variables to various monitoring and / or control applications. The system manager 502 can communicate with a client device 504 (e.g., a user device, a desktop computer, a laptop computer, a mobile device, etc.) via a data communication link 574 (e.g., BACnet IP, Ethernet, wired or wireless communication, etc.). The system manager 502 can provide a user interface to the client device 504 via the data communication link 574. The user interface can allow a user to monitor and / or control the BMS 500 via the client device 504.

[0082] 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 with other devices such as constant capacity (CV) rooftop units (RTUs) 512, input / output modules (IOMs) 514, thermostat controllers 516 (e.g., TEC5000 series thermostat controllers), and network automation engines (NAEs) or third-party controllers 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 via intermediate devices. For example, a wired input 562 can connect a third-party RTU 542 to a thermostat controller 516, which is connected to the system bus 554.

[0083] The system manager 502 can provide a user interface for any device that has a device model. Devices such as the partition coordinators 506-510 and 518 and the thermostat controller 516 can provide their device models to the system manager 502 via the system bus 554. In some embodiments, the system manager 502 automatically creates device models for connected devices (e.g., IOM 514, third-party controller 520, etc.) that do not have device models. For example, the system manager 502 can create a device model for any device in response to a device tree request. The device model created by the system manager 502 can be stored within the system manager 502. The system manager 502 can then use the device model created by the system manager 502 to provide a user interface for devices that do not have their own device models. In some embodiments, the system manager 502 stores a view definition for each type of device connected via the system bus 554 and uses the stored view definition to generate a user interface for the device.

[0084] Each zone coordinator 506-510 and 518 can be connected to one or more of the zone controllers 524, 530-532, 536 and 548-550 via the zone bus 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 bus 556-560 and 564 using the MSTP protocol or any other communication protocol. The zone bus 556-560 and 564 can also connect the zone coordinators 506-510 and 518 with other types of devices such as variable air volume (VAV) RTUs 522 and 540, conversion bypass (COBP) RTUs 526 and 552, bypass dampers 528 and 546, and peak controllers 534 and 544.

[0085] The zone coordinators 506-510 and 518 may be configured to monitor and command various zone systems. In some embodiments, each zone coordinator 506-510 and 518 monitors and commands a separate zone system and is connected to the zone systems via a separate zone bus. For example, the zone coordinator 506 may be connected to the VAV RTU 522 and the zone controller 524 via a zone bus 556. The zone coordinator 508 may be connected to the COBP RTU 526, the bypass damper 528, the COBP zone controller 530, and the VAV zone controller 532 via a zone bus 558. The zone coordinator 510 may be connected to the peak controller 534 and the VAV zone controller 536 via a zone bus 560. The zone coordinator 518 may be connected to the peak controller 544, the bypass damper 546, the COBP zone controller 548, and the VAV zone controller 550 via a zone bus 564.

[0086] A single model of the zone coordinators 506-510 and 518 can be configured to handle a variety of different types of zone systems (e.g., VAV zone systems, COBP zone systems, etc.). Each zone system can include an RTU, one or more zone controllers, and / or bypass dampers. For example, the zone coordinators 506 and 510 are shown as Verasys VAV Engines (VVEs) connected to VAV RTUs 522 and 540, respectively. The zone coordinator 506 is directly connected to the VAV RTU 522 via the zone bus 556, while the zone coordinator 510 is connected to the third party VAV RTU 540 via a wired input 568 provided to the peak controller 534. The zone coordinators 508 and 518 are shown as Verasys COBP Engines (VCEs) connected to COBP RTUs 526 and 552, respectively. The partition coordinator 508 is directly connected to the COBP RTU 526 via the partition bus 558 , while the partition coordinator 518 is connected to the third party COBP RTU 552 via a wired input 570 provided to the peak controller 544 .

[0087] The zone controllers 524, 530-532, 536, and 548-550 may communicate with various BMS devices (e.g., sensors, actuators, etc.) via a sensor / actuator (SA) bus. For example, the VAV zone controller 536 is shown connected to the networked sensor 538 via the SA bus 566. The zone controller 536 may communicate with the networked sensor 538 using the MSTP protocol or any other communication protocol. Although Figure 5 Only one SA bus 566 is shown, but 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 with 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 devices (e.g., refrigerators, heaters, fans, pumps, etc.).

[0088] Each partition controller 524, 530-532, 536 and 548-550 can be configured to monitor and control different building partitions. The partition controllers 524, 530-532, 536 and 548-550 can use inputs and outputs provided via their SA bus to monitor and control each building partition. For example, the partition controller 536 can use the temperature input (e.g., the measured temperature of the building partition) received from the networked sensor 538 via the SA bus 566 as feedback in the temperature control algorithm. The partition controllers 524, 530-532, 536 and 548-550 can use various types of control algorithms (e.g., state-based algorithms, extreme value search 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.) in or around the building 10.

[0089] Building Data Compliance

[0090] Data compliance system

[0091] Reference now Figure 6 , according to an exemplary embodiment, a compliance determination system 600 is shown. The compliance determination system 600 can be configured to evaluate whether synthetic data generated by a generative artificial intelligence (GAI) complies with various standards, regulations, thresholds, or other benchmarks. As used herein, the terms "synthetic," "generated," "simulated," "artificial," and similar terms are synonymous and refer to data generated by one or more GAI models. Figure 6As shown in , according to some embodiments, the compliance determination system 600 includes an input controller 602, a verification controller 604, a user interface 606, a building device 608, and one or more artificial intelligence ("AI") models 610. In some embodiments, the input controller 602 is configured to generate synthetic data (e.g., simulated data, artificial data, time series data, etc.) of the building 10 and provide the synthetic data to the verification controller 604. The synthetic data may include a variety of different types of data that may be generated by the AI ​​model 610, including, for example, synthetic building data related to environmental conditions in the building space (e.g., synthetic measurements of environmental conditions in the building space, synthetic sensor data from various sensors of the building 10, etc.), synthetic operating parameters of building equipment serving the building space (e.g., synthetic control signals, synthetic set points, etc.), synthetic operating conditions of one or more pieces of equipment of the building 10 (e.g., equipment of the building subsystem 428). In some embodiments, the synthetic data includes operating conditions of the chiller 102, thermal comfort data of one or more spaces of the building 10, temperature, humidity, and / or air quality of one or more spaces of the building 10.

[0092] In some embodiments, the synthetic data includes operating conditions (e.g., time series data) of equipment of the building subsystem 428, such as temperature, pressure, flow rate of chiller water, refrigerant flow rate, etc. In some embodiments, the synthetic data includes time series data of any conditions or control parameters of chillers, boilers, any number of air handling units, economizers, field controllers, supervisory controllers, actuators, temperature sensors, and other equipment used to control temperature, humidity, airflow, or other variable conditions within the building 10 of the HVAC subsystem 440. The synthetic data may be generated by a generative AI model that is input to the controller 602.

[0093] In some embodiments, input controller 602 may provide input data to verification controller 604. In some embodiments, the input data may be similar to the synthetic data generated by the generative AI model of input controller 602, but may be real-world or simulated time series data of a building (e.g., building 10). In some embodiments, the input data is provided by a customer or building manager to determine whether a building (e.g., a simulated building, a target building, a building to be purchased, a building to be certified, a newly constructed building, etc.) meets one or more certification criteria (e.g., to generate a compliance report).

[0094] In some embodiments, the synthetic data (e.g., synthetic building data) or input data (e.g., simulated building data, real-world building data, etc.) may include time series data of spatial conditions (e.g., temperature, humidity, environmental conditions within the space, etc.). In some embodiments, the synthetic data or input data include operating parameters, such as control signals, set points (e.g., building equipment set points, operating points, settings, etc.), or operating commands for building equipment 608. The operating parameters may be provided as time series values ​​of various operating parameters of building equipment 608. If the synthetic data or input data include operating parameters, the verification controller 604 may be configured to predict the environmental or spatial conditions (e.g., temperature, humidity, infection risk, carbon dioxide, etc.) that result from operating the building equipment 608 according to the operating parameters. Similarly, if the synthetic data or input data include time series data of spatial conditions, the verification controller 608 may be configured to generate time series values ​​of corresponding operating parameters of the building equipment 608 to achieve the time series data of the spatial conditions.

[0095] Still reference Figure 6 , the verification controller 604 can be configured to evaluate whether the synthesized data complies with various regulations, standards, thresholds, or other benchmarks (e.g., ASHRAE standards, comfort standards, certification standards, emissions standards, etc.). In some embodiments, the verification controller 604 directly compares the synthesized data with one or more thresholds specified in the standard, which may be possible if the synthesized data is for the same variables or conditions to which the standard applies. For example, if a given standard involves a minimum acceptable ventilation rate and the synthesized data includes a synthesized value for the ventilation rate, the verification controller 604 can directly compare the synthesized value for the ventilation rate with the minimum ventilation threshold specified by the standard. As another example, if a given standard involves occupant comfort within a building space (e.g., a comfort temperature value or range, a comfort humidity value or range, etc.) and the synthesized data includes a synthesized value for temperature or humidity, the verification controller 604 can directly compare the synthesized temperature or humidity value with the threshold or range specified by the standard.

[0096] In some embodiments, the verification controller 604 is configured to translate or transform the synthesized data into appropriate variables, units, or other values ​​specified by the standard to allow for direct comparison. For example, if a given standard relates to a minimum acceptable ventilation rate, but the synthesized data includes synthesized values ​​of control signals for air handling units, the verification controller 604 can use a model (e.g., a predictive model, an equipment operation model, etc.) to determine the ventilation rate produced by the synthesized values ​​of the control signals, and then compare the determined ventilation rate with the minimum ventilation threshold specified by the standard. As another example, if a given standard relates to occupant comfort within a building space (e.g., a comfortable temperature value or range, a comfortable humidity value or range, etc.) and the synthesized data includes synthesized values ​​of control signals or operating commands for building equipment that operates to affect temperature or humidity, the verification controller 604 can use a predicted temperature or humidity model to predict the temperature or humidity value in the building space that will be produced by operating the building equipment using the synthesized data. The verification controller 604 can then compare the predicted temperature or humidity value with the threshold or range specified by the standard. The verification controller 604 may include any number or types of predictive models (e.g., temperature models, humidity models, air quality models, ventilation models, comfort models, etc.) that can be used to predict values ​​of various environmental conditions of a building space or other variables specified by a standard based on synthetic data.

[0097] In some embodiments, verification controller 604 may be configured to output control signals to building devices 608 to operate building devices 608 to affect environmental or variable conditions of one or more spaces, zones, rooms, etc. of a building (e.g., building 10). In some embodiments, verification controller 604 is configured to control building devices 608 using synthetic data or filtered synthetic data.

[0098] According to some embodiments, the verification controller 604 can be configured to output the filtered synthetic data to the input controller 602 and / or the AI ​​model 610. In some embodiments, the filtered synthetic data is a subset of the synthetic data provided by the input controller 602. For example, the filtered synthetic data can be similar to the synthetic data with one or more portions removed. In some embodiments, the verification controller 604 is configured to provide the filtered synthetic data to the AI ​​model 610 for training the AI ​​model 610 based on the filtered synthetic data, wherein one or more portions of the data that do not meet one or more criteria are removed. In this way, the AI ​​model 610 can be trained to control the building equipment in a manner that meets one or more criteria.

[0099] Validation Controller

[0100] Special References Figure 7 , verification controller 604 includes processing circuit system 702, which includes processor 704 and memory 706. Processing circuit system 702 can be communicatively connected to a communication interface so that processing circuit system 702 and its various components can send and receive data via the communication interface. Processor 704 can be implemented as a general-purpose processor, an application-specific integrated circuit (ASIC), one or more field programmable gate arrays (FPGA), a group of processing components, or other suitable electronic processing components.

[0101] Memory 706 (e.g., memory, memory unit, storage device, etc.) may include one or more devices (e.g., RAM, ROM, flash memory, hard disk storage device, etc.) for storing data and / or computer code for completing or facilitating the various processes, layers, and modules described in this application. Memory 706 may be or include volatile memory or non-volatile memory. Memory 706 may include a database component, an object code component, a script component, or any other type of information structure for supporting the various activities and information structures described in this disclosure. According to some embodiments, memory 706 is communicatively connected to processor 704 via processing circuit system 702 and includes computer code for executing (e.g., by processing circuit system 702 and / or processor 704) one or more processes described herein.

[0102] The memory 706 includes a condition predictor 708, a data compliance manager 720, one or more standards 710, a standards compliance reporter 722, a data filter 724, and a building equipment controller 726. The condition predictor 708 is configured to use the synthesized data to predict a condition, shown as a predicted condition, which is provided to the data compliance manager 720. The data compliance manager 720 can receive the predicted condition and the synthesized data and provide one or more compliance results and an indication of one or more portions of the synthesized data corresponding to the compliance results to the standards compliance reporter 722, the data filter 724, and the building equipment controller 726. The data compliance manager 720 is also configured to receive one or more of the standards 710 and use the standards 710 to determine the compliance results. According to some embodiments, the standards compliance reporter 722 is configured to output a compliance report (e.g., to the user interface 606). According to some embodiments, the data filter 724 is configured to output filtered synthesized data. According to some embodiments, the building equipment controller 726 is configured to output a control signal (e.g., to the building equipment 608). In some embodiments, the building equipment controller 726 includes a predictive model configured to be trained using synthetic data. In some embodiments, the predictive model of the building equipment controller 726 is configured to generate operating parameters of the building equipment 608. In some embodiments, the building equipment controller 726 is configured to provide control signals to the building equipment 608 to operate the building equipment 608 according to the control signals.

[0103] Still reference Figure 7 , the condition predictor 708 can be configured to implement one or more models (e.g., building models) and predict the conditions of one or more spaces by providing synthetic data as input to the one or more models. In some embodiments, the one or more models include a building model, a thermal model, a differential equation, etc., which models or predicts the temperature, humidity, etc. of one or more zones, rooms, or spaces of the building 10. In some embodiments, the one or more models include an energy consumption model configured to predict the power or resource consumption of the building equipment 608. In some embodiments, the one or more models include a resource production model configured to predict clean air delivery, heat load, cooling load, etc. In some embodiments, the one or more models include an emission model configured to predict the number, amount, or rate of carbon emissions as a result of operating the building equipment 608 based on the synthetic data.

[0104] In some embodiments, condition predictor 708 is optional. For example, the synthesized data may include not only control decisions for the equipment, but also thermal comfort data, environmental conditions, etc. In some embodiments, the synthesized data includes one or more operating conditions indicating the quality or condition of the equipment or HVAC chiller operating conditions. In some embodiments, the synthesized data may include temperature, humidity, and air quality of different spaces. In some embodiments, the synthesized data includes environmental conditions in various spaces. In some embodiments, the synthesized data includes temperature set points for various partitions, spaces, or rooms of the building 10.

[0105] Still reference Figure 7 According to some embodiments, the data compliance manager 720 is configured to receive one or more predicted conditions, synthesized data, and one or more of standards 710 from the condition predictor 708 (e.g., model output). In some embodiments, the one or more standards 710 include an American Society of Heating, Refrigerating and Air-Conditioning Engineers (“ASHRAE”) standard 712, one or more comfort standards 714, one or more certification standards 716, and one or more emission standards 718. In some embodiments, the ASHRAE standards 712 include ASHRAE Standard 55 on comfort, ASHRAE standards on ventilation, standards for managing infection risks in buildings, and the like. In some embodiments, the standards 710 include one or more design guidelines that indicate that the building must be able to perform one or more actions (e.g., adequate levels of air conditioning, adequate levels of infection risk reduction, etc.). In some embodiments, the standards 710 may be thresholds or ranges. For example, the comfort standards 714 may include acceptable ranges for indoor temperature, humidity, and air quality. The certification standards 716 may include international WELL building institute standards.

[0106] According to some embodiments, the data compliance manager 720 is configured to use the criteria in order to provide compliance results for the synthesized data (and optionally, predicted conditions output by the condition predictor 708 based on the synthesized data) to the standards compliance reporter 722, the data filter 724, and the building equipment controller 726. In some embodiments, the compliance results indicate which standard 710 the synthesized data complies with or which standard 710 the synthesized data does not comply with. In some embodiments, the data compliance manager 720 is configured to output an indication of which portions or sections of the synthesized data cause the synthesized data to not comply with one or more standards 710. For example, if the synthesized data includes or causes portions that exceed or deviate from one or more comfort standards 714, the data compliance manager 720 may provide an indication of which portions of the synthesized data cause the noncompliance with the standards 710. In some embodiments, the compliance results are provided to the standards compliance reporter 722 and the data filter 724.

[0107] Still reference Figure 7 According to some embodiments, the standards compliance reporter 722 is configured to generate and output a compliance report indicating which standards 710 the synthesized data complies with. For example, according to some embodiments, the compliance report may include a list of all standards 710 that the synthesized data has been tested to determine compliance or non-compliance. The compliance report may be provided as a GUI or UI displayed to a user via the user interface 606, or may be provided as a report file including an indication of which standards 710 the synthesized data complies with.

[0108] Still reference Figure 7 According to some embodiments, the data filter 724 is configured to receive the synthesized data and the compliance results, as well as an indication of which portions of the synthesized data cause the synthesized data to fail to comply with one or more standards 710. In some embodiments, the data filter 724 is configured to filter the synthesized data by removing one or more portions of the synthesized data that cause the synthesized data to fail to comply with one or more standards 710. For example, the data filter 724 may remove portions or time series portions of the synthesized data that cause failure to comply with one or more standards 710, and provide the filtered synthesized data to the AI ​​model 610 and / or the input controller 602. In some embodiments, the filtered synthesized data is a subset of the synthesized data that excludes one or more portions of the synthesized data that cause the synthesized data to fail to comply with the standards 710. In some embodiments, the filtered synthetic data is used to fine-tune or adjust an artificial intelligence or neural network model used to generate the synthetic data so as to train or adjust the artificial intelligence or neural network model to produce synthetic data that complies with one or more of the standards 710.

[0109] Input Controller

[0110] refer to Figure 8 , according to some embodiments, the input controller 602 is shown in more detail. In some embodiments, the input controller 602 includes a processing circuit system 802, which includes a processor 804 and a memory 806. The processing circuit system 802 can be communicatively connected to a communication interface so that the processing circuit system 802 and its various components can send and receive data via the communication interface. The processor 804 can be implemented as a general-purpose processor, an application-specific integrated circuit (ASIC), one or more field programmable gate arrays (FPGA), a group of processing components, or other suitable electronic processing components.

[0111] Memory 806 (e.g., memory, memory unit, storage device, etc.) may include one or more devices (e.g., RAM, ROM, flash memory, hard disk storage device, etc.) for storing data and / or computer code for completing or facilitating the various processes, layers, and modules described in this application. Memory 806 may be or include volatile memory or non-volatile memory. Memory 806 may include a database component, an object code component, a script component, or any other type of information structure for supporting the various activities and information structures described in this disclosure. According to some embodiments, memory 806 is communicatively connected to processor 804 via processing circuit system 802 and includes computer code for executing (e.g., by processing circuit system 802 and / or processor 804) one or more processes described herein.

[0112] like Figure 8 As shown in , according to some embodiments, the memory 806 includes a generative artificial intelligence ("generative AI") 808, building data 810, and an AI fine tuner 812. In some embodiments, the generative AI 808 is configured to generate synthetic data and provide the synthetic data to the verification controller 604. The generative AI 808 can be a large language model network, such as a generative pre-trained transformer ("GPT") network. In some embodiments, the generative AI 808 is a unimodal or multimodal network. In some embodiments, the generative AI 808 may include U.S. Provisional Patent Application No. 63 / 470,074 filed on May 31, 2023, U.S. Provisional Patent Application No. 63 / 470,078 filed on May 31, 2023, U.S. Provisional Patent Application No. 63 / 470,119 filed on May 31, 2023, U.S. Provisional Patent Application No. 63 / 470,122 filed on May 31, 2023, U.S. Provisional Patent Application No. 63 / 470,130 filed on May 31, 2023, No. 63 / 470,118, U.S. Provisional Patent Application No. 63 / 470,120 filed on May 31, 2023, U.S. Provisional Patent Application No. 63 / 470,121 filed on May 31, 2023, and / or one or more of the generative AI models or any other features or functions described in U.S. Provisional Patent Application No. 63 / 470,123 filed on May 31, 2023, the entire disclosures of which are incorporated herein by reference.

[0113] According to some embodiments, the AI ​​tuner 812 may initiate a fine-tuning process of the generative AI 808 based on the filtered synthetic data provided by the data filter 724. In some embodiments, the fine-tuning process initiated by the AI ​​tuner 812 may be initiated to adjust the parameters of the generative AI 808 (e.g., to train or adjust the generative AI 808 based on the filtered synthetic data). The fine-tuning process may include adapting or adjusting the structure of the generative AI 808 based on the filtered synthetic data by adding or removing layers to the existing generative AI 808, so that the generative AI 808 generates synthetic data that meets one or more standards 710 or meets the selected one or more standards 710.

[0114] According to some embodiments, the input controller 602 may include building data 810 and provide the building data 810 as input data to the verification controller 604. In some embodiments, the input data is used by the verification controller 604 similar to the synthetic data, but is not provided to the data filter 724. For example, the building data 810 may be a request from a user to initiate a function of the verification controller 604 to determine whether the building data 810 of a particular building meets one or more standards or certifications. For example, the function of the verification controller 604 may be used to determine whether a particular building has a high likelihood of meeting the standards of the International WELL Building Institute. In some embodiments, the building data 810 is obtained from a database of multiple buildings throughout a geographic area.

[0115] In some embodiments, the building data 810 includes data provided by a user who wishes to know whether the building corresponding to the building data 810 has a high probability of meeting the International WELL Building Institute standard. In some embodiments, the input controller 602 is configured to obtain multiple data sets of the building data 810 and an indication of whether the building corresponding to the building data 810 meets the International WELL Building Institute standard. The input controller 602 can provide the building data 810 and an indication of whether the building corresponding to the building data 810 meets the International WELL Building Institute standard to the AI ​​fine tuner 812 for initiating fine tuning of the generative AI 808 to predict whether a set of building data meets the International WELL Building Institute standard. In this way, the generative AI 808 or a similar AI (e.g., a separate AI implemented on the verification controller 604) can be trained or adjusted to predict whether a particular building has a high probability of meeting the International WELL Building Institute standard. In some embodiments, the input controller 602 and the verification controller 604 are configured to train or fine tune different types of neural networks, AIs, or machine learning to determine or predict whether a particular building has a high probability of meeting the International WELL Building Institute standard.

[0116] Reference now Figure 6 and Figure 8 , the input controller 602 can obtain building data (e.g., multiple sets of building data 810) and International WELL Building Institute standard indications (e.g., whether the building complies with the International WELL Building Institute standard) from one or more server databases 612 (e.g., an Internet database). The input controller 602 can provide queries to various publicly accessible databases on the Internet and use the returned data to fine-tune, train, or update the neural network or AI to predict whether a building corresponding to a new set of building data will likely meet the International WELL Building Institute standard. In some embodiments, the verification controller 604 is also configured to communicate with the server database 612 or with another Internet server to identify when a new standard, certification, etc. (e.g., a new ASHRAE standard, an emissions standard, etc.) is released in order to determine whether the real-world building or synthetic data complies with the new standard, certification, etc. In this way, the functionality of the compliance determination system 600 can be prompted in response to an updated standard, certification, etc. to determine whether a building (e.g., an existing and constructed building, or a building in the design stage) should be changed to comply with the updated standard, certification, etc.

[0117] process

[0118] refer to Fig. 9 According to some embodiments, a flow chart of a process 900 for fine-tuning a generative AI includes steps 902-914. In some embodiments, the process 900 is performed to fine-tune the generative AI so that the generative AI does not generate synthetic data that will result in non-compliant building performance (e.g., too high or too low temperature set points, failure to meet one or more emission standards, etc.). According to some embodiments, the process 900 can be implemented or performed by the compliance determination system 600.

[0119] According to some embodiments, process 900 includes obtaining or generating synthetic data of a building and a building HVAC system from or using a generative AI model (step 902). In some embodiments, the synthetic data is building data generated by simulation, neural network, AI, machine learning, etc., or data that is not collected from sensors of a real-world building over a period of time. The synthetic data may include time series data of any operating conditions, HVAC equipment (e.g., chiller) operating conditions, data indicating equipment quality or condition, temperature, humidity, or air quality of different spaces, environmental conditions, thermal comfort ranges, temperature set points, humidity set points, air quality set points, service recommendations, control decisions, energy or power consumption, resource consumption, resource costs, etc. In some embodiments, step 902 is performed by input controller 602 and / or verification controller 604 of compliance determination system 600. Alternatively, the synthetic data may be historical data or collected data of a real-world building to identify whether the real-world building has a high probability of meeting different standards or obtaining one or more certifications.

[0120] According to some embodiments, process 900 includes determining one or more conditions of a building space or equipment of a building HVAC system based on the synthesized data (step 904). In some embodiments, step 904 includes using a model (e.g., a thermal model of a building representing various thermal or heat transfer characteristics of a building or a space of a building) to predict the condition of the building space or equipment of the building HVAC system. In some embodiments, step 904 is performed by condition predictor 708.

[0121] According to some embodiments, process 900 includes determining whether the synthesized data complies with one or more regulations, standards, or certifications based at least on the synthesized data or one or more conditions of the building space or equipment (step 906). In some embodiments, step 906 is performed by data compliance manager 720 using standard 710. The standards, regulations, certifications, or specifications may include various ASHRAE standards, comfort standards, certification standards, emission standards, etc. In some embodiments, the standards, regulations, certifications, or specifications include thresholds or ranges for various parameters. In some embodiments, the standards, regulations, certifications, or specifications include International WELL Building Institute standards.

[0122] According to some embodiments, process 900 includes generating and providing a compliance report indicating compliance of the synthesized data with respect to one or more regulations, standards, or certifications (step 908). In some embodiments, step 908 includes generating a list of one or more regulations, standards, or certifications, and an indication of which regulations, standards, or certifications the synthesized data meets or does not meet. In some embodiments, the compliance report is generated by standards compliance reporter 722. In some embodiments, the compliance report is provided to a user via user interface 606.

[0123] According to some embodiments, process 900 includes controlling equipment of a building HVAC system based on the synthesized data (step 910). In some embodiments, the synthesized data includes control decisions for building equipment of a real-world building HVAC system. According to some embodiments, if the synthesized data is determined to satisfactorily meet one or more of a regulation, standard, or certification, the building HVAC system may be operated based on the control decisions in the synthesized data. In some embodiments, the building HVAC system is controlled based on the filtered or updated synthesized data (e.g., as described in more detail below with reference to steps 912-914).

[0124] According to some embodiments, process 900 includes filtering the synthesized data to remove one or more portions of the synthesized data that are not compliant with one or more regulations, standards, or certifications (step 912). In some embodiments, step 912 includes identifying which portions of the synthesized data cause the synthesized data to be noncompliant with one or more regulations, standards, or certifications. In some embodiments, step 912 is performed by data compliance manager 720 and data filter 724. In some embodiments, step 912 includes removing portions of the synthesized data that cause the synthesized data to be noncompliant with one or more regulations, standards, or certifications.

[0125] According to some embodiments, process 900 includes performing a fine-tuning process of a generative artificial intelligence model or a different artificial intelligence model based on the filtered synthetic data (step 914). In some embodiments, step 914 is performed by input controller 602, or more specifically by AI fine-tuner 812. For example, the generative artificial intelligence for generating synthetic data can be fine-tuned based on the filtered synthetic data so that the generative AI generates data that complies with one or more standards, regulations, or certifications. In some embodiments, process 900 returns to step 902 in response to executing step 914, and iteratively executes steps 902-906, 912, and 914 until the generative AI model generates synthetic building data that meets one or more regulations, standards, or certifications with high confidence.

[0126] refer to Fig.10 According to some embodiments, a process 1000 for predicting the likelihood of a building obtaining a particular certification includes steps 1002-1012. In some embodiments, the process 1000 is performed by the compliance determination system 600 to identify whether the building has a high likelihood of being certified, and if the building does not have a high likelihood of being certified, what changes can be made to the building to increase the likelihood of the building being certified.

[0127] According to some embodiments, process 1000 includes obtaining multiple data sets of buildings that comply with and do not comply with a particular building certification (step 1002). In some embodiments, step 1002 includes querying multiple online databases of publicly accessible buildings (e.g., building data for buildings with known certifications or where no certification exists). In some embodiments, the building data includes time series data of any operating conditions, HVAC equipment (e.g., chiller) operating conditions, data indicating equipment quality or conditions of different spaces, temperature, humidity or air quality, ambient conditions, thermal comfort ranges, temperature set points, humidity set points, air quality set points, service recommendations, control decisions, energy or power consumption, resource consumption, resource costs, etc. In some embodiments, step 1002 is performed by input controller 602 by querying server database 612. The specific building certification can be an International WELL Building Institute standard.

[0128] According to some embodiments, process 1000 includes training or fine-tuning an artificial intelligence model based on multiple building data sets to predict compliance of the building data set with a specific building certification (step 1004). In some embodiments, step 1004 is performed by input controller 602, or more specifically by AI tuner 812 and generative AI 808. Step 1004 can include fine-tuning the neural network or AI by adjusting the architecture (e.g., adding or removing layers, adjusting weights or coefficients of the neural network or artificial intelligence model, etc.) so that the neural network or AI is configured to predict the likelihood of compliance or certification of the building corresponding to the input data set.

[0129] According to some embodiments, process 1000 includes obtaining a building data set for a target building for which a particular building certification is desired in response to a user request (step 1006). In some embodiments, step 1006 includes obtaining a building data set provided by a user or a building manager who provided the user request. For example, when a building manager wants to seek certification for a particular building (e.g., the International WELL Building Institute standard), the building manager can provide the building data and the request to verification controller 604 or input controller 602, where the building data is provided as input data.

[0130] According to some embodiments, process 1000 includes using an artificial intelligence model and a building dataset to predict whether a particular building complies with a particular building certification (step 1008). In some embodiments, step 1008 is performed by providing the building dataset as an input to the artificial intelligence model trained or fine-tuned in step 1004. In some embodiments, step 1008 is performed by input controller 602 or verification controller 604.

[0131] According to some embodiments, process 1000 includes operating a display screen to notify a user providing a user request whether a particular building complies with a particular building certification or is likely to be certified (step 1012). In some embodiments, step 1012 includes generating and providing a user interface (e.g., a compliance report) indicating whether the particular building has a high likelihood of being certified by providing a particular certification desired by the user providing the user request. In some embodiments, step 1012 includes operating user interface 606 to provide the result of step 1008 to the user.

[0132] According to some embodiments, process 1000 includes operating a display screen to provide one or more suggestions for changes to a particular building that increase the likelihood that the particular building will be certified (step 1014). For example, step 1008 may also include identifying (e.g., by data compliance manager 720) which portions of the building data set cause the particular building to have a reduced likelihood of complying with the particular building certification. In some embodiments, step 1008 includes providing a notification regarding which portions of the building data cause the particular building to have a reduced likelihood of obtaining the particular building certification, and suggestions for improving or reducing the occurrence of such building data. For example, the suggestions may include suggestions to install or adjust the operation of one or more disinfection mechanisms for the particular building so as to increase the risk of infection for individuals within the building.

[0133] Building Rating System

[0134] Reference again Figures 6 to 10 , the compliance determination system 600 (e.g., the input controller 602 and / or the verification controller 604) can be configured to perform any technique or functionality of the systems or devices described in U.S. Application No. 17 / 972,454, filed on October 24, 2022, the entire disclosure of which is incorporated herein by reference. For example, the building data obtained by or provided to the input controller 602 can be the building data of U.S. Application No. 17 / 972,454. Figure 317 / 972,454 is configured to generate a score for one or more buildings based at least in part on the compliance report of the building. In some embodiments, the compliance determination system 600 is configured to generate a user interface similar to the user interface described in U.S. Application No. 17 / 972,454 to show the geographic location of buildings with a high likelihood of obtaining one or more certifications (e.g., WELL Building Certification). The compliance determination system 600 may also or alternatively perform any of the techniques of the systems or devices described in U.S. Application No. 17 / 972,454 to determine one or more facility improvement measures (e.g., step 1014 of process 1000).

[0135] Building design tools

[0136] Reference again Figures 6 to 8 , the compliance determination system 600 can be used to train or fine-tune the generative AI model to output a building design that complies with one or more of the standards 710. For example, the synthetic data output by the generative AI 808 can include building design data, including the number of rooms, partitions, or spaces, the size of the spaces, the size and capacity of chillers, HVAC units, or other building equipment, and the relationship between building equipment, HVAC units, chillers, etc. In some embodiments, the building design data is used by the condition predictor 708 and / or the data compliance manager 720 to identify whether the building design data will produce a building that complies with one or more standards 710. The synthetic data can be filtered or feedback can be provided to the input controller 602 to adjust or initiate the fine-tuning process of the generative AI 808 so that the generative AI 808 outputs building design data that complies with one or more standards 710 (e.g., a subset of the standards 710 selected by the user). In some embodiments, the compliance predictor 708 is a generative AI model (e.g., similar to the generative AI 808) that is configured to use the synthetic data, input data, or building design data to predict conditions used by the data compliance manager 720 to determine compliance results. In some embodiments, condition predictor 708 is an AI trained based on building design data and a database of corresponding conditions associated with the building data.

[0137] Advantageously, the compliance determination system 600 and the various processes described herein provide various improvements to the functionality of building systems. For example, evaluating the synthetic data generated by the generative AI 808 and / or the AI ​​model 610 allows the compliance determination system 600 to determine whether the synthetic data corresponds to a building condition that complies with various standards or certifications, and / or would result in a building condition that complies with various standards or certifications if the synthetic data is used to operate the building equipment 608. Non-compliant synthetic data can be removed from the synthetic data set to generate a filtered synthetic data set that excludes any non-compliant synthetic data. The filtered synthetic data set can then be used to operate the building equipment 608 and / or train or fine-tune one or more predictive models (e.g., AI model 610, state-space model, etc.).

[0138] These features allow the compliance determination system 600 to ensure that the building equipment 608 is not provided with synthetic data (e.g., operating commands, set points, etc.) that will result in non-compliance with standards or certifications, and can ensure that environmental conditions in the building 10 remain compliant with standards or certifications. The compliance determination system 600 can also ensure that non-compliant synthetic data is not used as training data for various models. This functionality ensures that the models are trained only with compliant data and therefore do not learn to replicate or generate model outputs that would result in non-compliance with standards or certifications. These features allow building systems to leverage the benefits of generative AI (e.g., quickly generate large amounts of synthetic data) while ensuring that the synthetic data generated by the generative AI is checked for compliance with any standards or certifications of interest before being used for purposes such as model training, controlling building equipment 608, data analysis, or provided as input to other systems or devices.

[0139] Configuration of the Exemplary Embodiment

[0140] The construction and arrangement of the systems and methods shown in each exemplary embodiment are illustrative only. Although only several embodiments have been described in detail in the present disclosure, multiple modifications are possible (e.g., changes in the size, dimensions, structure, shape and proportion of each element, parameter values, mounting arrangements, material use, color, orientation, etc.). For example, the position of the element may be reversed or otherwise changed, and the nature or number of discrete elements or positions may be changed or varied. Therefore, all such modifications are intended to be included within the scope of the present 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 to the design, operating conditions and arrangements of the exemplary embodiments without departing from the scope of the present disclosure.

[0141] The present disclosure contemplates methods, systems, and program products on any machine-readable medium for completing various operations. The embodiments of the present disclosure may be implemented using an existing computer processor or by a dedicated computer processor in combination with an appropriate system for this purpose or another purpose or by a hard-wired system. Embodiments within the scope of the present disclosure include program products, which include machine-readable media for carrying or having machine-executable instructions or data structures stored thereon. This machine-readable medium may be any available medium that can be accessed by a general or special-purpose computer or other machine with a processor. For example, this machine-readable medium may include RAM, ROM, EPROM, EEPROM, CD-ROM or other optical disk storage device, magnetic disk storage device or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of machine-executable instructions or data structures and can be accessed by a general or special-purpose computer or other machine with a processor. When information is transmitted or provided to a machine via a network or another communication connection (hard-wired, wireless, or a combination of hard-wired or wireless), the machine properly regards the connection as a machine-readable medium. Therefore, any such connection is properly referred to as a machine-readable medium. Combinations of the above are also included within the scope of machine-readable media.Machine-executable instructions include, for example, instructions and data which cause a general purpose computer, special purpose computer, or special purpose processing machines to perform a certain function or group of functions.

[0142] Although the accompanying drawings show a specific order of method steps, the order of steps may be different from that depicted. Also, two or more steps may be performed simultaneously or partially simultaneously. Such variations will depend on the selected software and hardware systems and the designer's choice. All such variations are within the scope of the present disclosure. Similarly, software implementations may be implemented using standard programming techniques with rule-based logic and other logic to implement various connection steps, processing steps, comparison steps, and decision steps.

Claims

1. A method for controlling building equipment, the method comprising: Use generative AI models to generate building data related to conditions in building spaces; determining whether the building data corresponds to a condition in the building space that complies with one or more regulatory or certification standards; as well as The building data is used to operate building equipment serving the building space in response to determining that the building data corresponds to a condition in the building space that complies with the one or more regulatory or certification standards.

2. The method of claim 1 , wherein using the building data to operate the building equipment comprises: Using the building data to train a predictive model or a reinforcement strategy; as well as The predictive model is used to generate operating parameters of the building equipment.

3. The method of claim 1 , further comprising, in response to determining that the building data corresponds to a condition in the building space that does not comply with the one or more regulatory or certification standards: filtering the building data to remove one or more portions of the building data corresponding to conditions in the building space that do not comply with the one or more regulatory or certification standards; and A fine-tuning process of the generative artificial intelligence model is initiated based on the filtered building data.

4. The method according to claim 1, further comprising: generating a compliance report indicating one or more of a plurality of codes or certification standards with which the building data complies, the building data including design parameters for a new building; as well as The display screen is operated to present the compliance report to a user.

5. The method of claim 1 , wherein the one or more codes or certification standards include one or more comfort standards, one or more emissions standards, one or more American Society of Heating, Refrigerating, and Air-Conditioning Engineers ("ASHRAE") standards, and International WELL Building Institute standards.

6. The method of claim 1, wherein the building data comprises time series data of operating conditions of the building equipment over a period of time.

7. The method of claim 1, wherein the generative artificial intelligence model is trained based on a database of real-world building data of a plurality of different buildings.

8. A control system for building equipment, the control system comprising: Building equipment, which serves the building space; as well as A processing circuit system configured to: using a generative artificial intelligence model to generate building data related to conditions in said building space; determining whether the building data corresponds to a condition in the building space that complies with one or more regulatory or certification standards; as well as The building data is used to generate a control strategy for the building equipment servicing the building space in response to determining that the building data corresponds to a condition in the building space that complies with the one or more regulatory or certification standards.

9. A control system according to claim 8, wherein the processing circuitry is configured to use the building data to operate the building equipment by: Using the building data to train a predictive model or a reinforcement strategy; and The predictive model is used to generate operating parameters of the building equipment.

10. The control system of claim 8, wherein the processing circuitry is further configured to, in response to determining that the building data corresponds to a condition in the building space that does not comply with the one or more regulatory or certification standards: filtering the building data to remove one or more portions of the building data corresponding to conditions in the building space that do not comply with the one or more regulatory or certification standards; and A fine-tuning process of the generative artificial intelligence model is initiated based on the filtered building data.

11. The control system of claim 8, wherein the processing circuitry is further configured to: generating a compliance report indicating one or more of a plurality of codes or certification standards with which the building data complies, the building data including design parameters for a new building; and The display screen is operated to present the compliance report to a user.

12. The control system of claim 8, wherein the one or more regulatory or certification standards include one or more comfort standards, one or more emissions standards, one or more American Society of Heating, Refrigerating, and Air-Conditioning Engineers ("ASHRAE") standards, and International WELL Building Institute standards.

13. The control system of claim 8, wherein the building data comprises time series data of operating conditions of the building equipment over a period of time.

14. The control system of claim 8, wherein the generative artificial intelligence model is trained based on a database of real-world building data for a plurality of different buildings.

15. A method for controlling building equipment, the method comprising: using a generative artificial intelligence model to generate building data including operating parameters of building equipment; if the operating parameters are used to operate building equipment serving a building space, predicting whether the operating parameters will cause conditions in the building space to comply with one or more regulatory or certification standards; The building data is used to operate the building equipment in response to predicting that the operating parameters will cause conditions in the building space to comply with the one or more regulatory or certification standards.

16. The method of claim 15, wherein using the building data to operate the building equipment comprises: Using the building data to train a predictive model or a reinforcement strategy; as well as The predictive model is used to generate operating parameters of the building equipment.

17. The method of claim 15, further comprising, in response to determining that the building data corresponds to a condition in the building space that does not comply with the one or more regulatory or certification standards: filtering the building data to remove one or more portions of the building data corresponding to conditions in the building space that do not comply with the one or more regulatory or certification standards; and A fine-tuning process of the generative artificial intelligence model is initiated based on the filtered building data.

18. The method according to claim 15, further comprising: generating a compliance report indicating one or more of a plurality of codes or certification standards with which the building data complies, the building data including design parameters for a new building; as well as The display screen is operated to present the compliance report to a user.

19. The method of claim 15, wherein the one or more codes or certification standards include one or more comfort standards, one or more emissions standards, one or more American Society of Heating, Refrigerating, and Air-Conditioning Engineers ("ASHRAE") standards, and International WELL Building Institute standards.

20. The method of claim 13, wherein the generative artificial intelligence model is trained based on a database of real-world building data for a plurality of different buildings, and wherein the generative artificial intelligence model is a large language model.

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