Patent Publication Number: US-2023152755-A1

Title: Building management systems and methods for tuning fault detection thresholds

Description:
CROSS-REFERENCE TO RELATED PATENT APPLICATIONS 
     The present application is a continuation-in-part of U.S. patent application Ser. No. 17/529,118, filed Nov. 17, 2021, which is incorporated herein by reference in its entirety. 
    
    
     BACKGROUND 
     This application relates generally to a building system of a building. This application relates more particularly to systems for tuning fault detection of the building system. 
     A building management system (BMS) is, in general, a system of devices configured to control, monitor, and manage equipment in or around a building or building area. A BMS can include, for example, a HVAC system, a security system, a lighting system, a fire alerting system, any other system that is capable of managing building functions or devices, or any combination thereof. Specifically, the present disclosure relates to a BMS with an equipment monitoring system to accurately determine whether the BMS is experiencing a fault. 
     SUMMARY 
     One inventive aspect is a non-transitory computer-readable storage medium having instructions stored thereon that, when executed by one or more processors, cause the one or more processors to provide a rule including a threshold, the rule used to determine whether building equipment has a fault. The instructions further cause the one or more processors to receive a state of the building equipment, assess, using a machine learning model, whether the determination of whether the building equipment has a fault is a false positive or a false negative based on the state and the threshold, determine a new threshold based on the assessment of the machine learning model, and replace the threshold with the new threshold to make subsequent determinations of whether the building equipment or other building equipment has a fault. 
     In some embodiments, the new threshold reduces a number of false positives a number of false negatives. 
     In some embodiments, the instructions further cause the one or more processors to accumulate training data for the machine learning model using the state and the threshold as inputs and labels of false positive or false negative as outputs. 
     In some embodiments, the rule includes a condition portion including a comparison of the state to the threshold and an action portion including a determination that the building equipment is faulty when the condition is satisfied or not faulty when the condition is not satisfied. 
     In some embodiments, the machine learning model includes a first machine learning model for predicting a false negative and a second machine learning model for predicting a false positive. 
     In some embodiments, the instructions further cause the one or more processors to perturb the building equipment with multiple values of the threshold to provide additional data for the machine learning model. 
     In some embodiments, the first machine learning model is configured to accurately predict false positives at a first rate, and the instructions further cause the one or more processors to retrain the first machine learning model when the first rate exceeds a first threshold rate. 
     In some embodiments, the second machine learning model is configured accurately predict false negatives at a second rate, and the instructions further cause the one or more processors to retrain the second machine learning model when the second rate exceeds a second threshold rate. 
     In some embodiments, the one or more processors are configured to use a constrained nonlinear optimization to determine the new threshold. 
     Another aspect is a method including providing, by a processing circuit, a rule including a threshold, the rule used to determine whether building equipment has a fault. The method includes receiving, by the processing circuit, a state of the building equipment, assessing, by the processing circuit using a machine learning model, whether the determination of whether the building equipment has a fault is a false positive or a false negative based on the state and the threshold, determining, by the processing circuit, a new threshold based on the assessment of the machine learning model, and replacing, by the processing circuit, the threshold with the new threshold to make subsequent determinations of whether the building equipment or other building equipment has a fault. 
     In some embodiments, the new threshold reduces a number of false positives a number of false negatives. 
     In some embodiments, accumulating, by the processing circuit, training data for the machine learning model using the state and the threshold as inputs and labels of false positive or false negative as outputs. 
     In some embodiments, the rule includes a condition portion including a comparison of the state to the threshold and an action portion including a determination that the building equipment is faulty when the condition is satisfied or not faulty when the condition is not satisfied. 
     In some embodiments, the machine learning model includes a first machine learning model for predicting a false negative and a second machine learning model for predicting a false positive. 
     In some embodiments, perturbing, by the processing circuit, the building equipment with multiple values of the threshold to provide additional data for the machine learning model. 
     In some embodiments, the first machine learning model is configured to accurately predict false positives at a first rate, and the method further includes retraining, by the processing circuit, the first machine learning model when the first rate exceeds a first threshold rate. 
     In some embodiments, the second machine learning model is configured accurately predict false negatives at a second rate, and the method further includes retraining, by the processing circuit, the second machine learning model when the second rate exceeds a second threshold rate. 
     In some embodiments, the method further includes using, by the processing circuit, a constrained nonlinear optimization to determine the new threshold. 
     Another aspect is a building system including one or more storage devices storing instructions thereon and one or more processors which execute the instructions causing the one or more processors to provide a rule used to determine whether a building equipment has a fault, perturb the building equipment having a plurality of states with a plurality of corresponding thresholds for the rule for determining whether a fault exists, determine whether a fault exists based on the perturbed building equipment, and receive feedback of whether the determination of whether a fault exists is a false positive or a false negative, or a true positive. The one or more processors further execute the instructions causing the one or more processors to provide training data to a machine learning model, the training data including the plurality of states and the plurality of corresponding thresholds as inputs and the feedback of false positive or false negative as outputs. The one or more processors further execute the instructions causing the one or more processors to receive a current state of the building equipment and a current threshold of the rule, assess, using the trained machine learning model, whether the determination of whether the building equipment has a fault is a false positive or a false negative based on the current state and the current threshold, determine a new threshold based on the assessment of the trained machine learning model, and replace the current threshold with the new threshold to make subsequent determinations of whether the building equipment or other building equipment has a fault. 
     In some embodiments, the instructions further cause the one or more processors to use constrained nonlinear optimization to find the new threshold that reduces a number of false positives a number of false negatives. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Various objects, aspects, features, and advantages of the disclosure will become more apparent and better understood by referring to the detailed description taken in conjunction with the accompanying drawings, in which like reference characters identify corresponding elements throughout. In the drawings, like reference numbers generally indicate identical, functionally similar, and/or structurally similar elements. 
         FIG.  1    is a drawing of a building equipped with a HVAC system, according to an exemplary embodiment. 
         FIG.  2    is a block diagram of a waterside system that may be used in conjunction with the building of  FIG.  1   , according to an exemplary embodiment. 
         FIG.  3    is a block diagram of an airside system that may be used in conjunction with the building of  FIG.  1   , according to an exemplary embodiment. 
         FIG.  4    is a block diagram of a building automation system (BAS) that may be used to monitor and/or control the building of  FIG.  1   , according to an exemplary embodiment. 
         FIG.  5    is a block diagram of a system including a fault detection system, according to some embodiments. 
         FIG.  6    is a block diagram of a system to find an optimal threshold of a fault detection and diagnosis rule, according to an exemplary embodiment. 
         FIG.  7    is a flow diagram of a process for identifying values for the parameters of the threshold of  FIG.  6   , according to an exemplary embodiment. 
         FIG.  8    is an optimization algorithm that can be performed to identify the optimal threshold for the fault detection and diagnosis rule of  FIGS.  6  and  7   , according to an exemplary embodiment. 
         FIG.  9    is a flow diagram of a process for calculating the optimal threshold, according to an exemplary embodiment. 
     
    
    
     DETAILED DESCRIPTION 
     Overview 
     Referring generally to the FIGURES, systems and methods for autonomous fault detection and diagnosis rule threshold tuning are shown, according to various exemplary embodiments. One method of detecting faults in buildings and their equipment is using rules to detect and diagnose faults. For example, when a building controller detects that a rule is satisfied, the building controller may determine that a subsystem pertaining to or associated with the rule has or may have a fault that should be adjusted and/or fixed. 
     A common technique of detecting faults in building equipment is using rules within building management systems (BMS). There may be one or more rules pertaining any particular building equipment (e.g., air handling unit, lighting equipment, etc.). Ideally the BMS will always correctly determine whether the building equipment is experiencing a fault or not. However, it is possible that the BMS determines that a fault exists even when the building equipment is operating normally (false positive), or that the BMS determines that a fault does not exist even when the building equipment does actually have a fault (false negative). When a fault is detected, the building manager may be notified by an alarm so that the building equipment may be inspected and/or a work order may be generated for a technician to fix the building equipment. This failure in accurate determination can lead to equipment failures, occupant discomfort and/or safety concerns, excessive costs, and waste. 
     Rules often include a condition portion and an action portion. When the condition portion is satisfied, the action portion may be executed. For example, there may be a rule for an air handling unit (AHU) that states, in the condition portion, that the supply air temperature is below a first threshold temperature and the fan speed of the AHU is greater than a second threshold rotations per minute (rpm). The rule may have an action portion that states that the AHU is at fault. In this rule, the condition portion is satisfied if both the supply air temperature is below the first threshold and the fan speed is greater than the second threshold, and the system can receive a determination that the AHU is faulty. 
     Rules are not always set for every condition and for every equipment. Accordingly, the rules may need to be adjust depending on operating conditions, equipment, occupants, etc. Further, the rules may need to be adjust because the equipment may degrade over time. If a threshold is too small, normal variation in operating conditions may result in false alarms, and if the threshold is too great, only a few extremely severe faults may be detected. Furthermore, manually adjusting or tuning thresholds is very difficult because of inaccuracies or overcompensation, which can lead to similar or different problems. Accordingly, there is a need to automatically tune the thresholds in rules. 
     In the present disclosure, systems and methods of automatically tuning the thresholds in rules are described. First, some data needs to be collected concerning the rules and whether the rules resulted in false positives or false negatives. The data can include the state of the building equipment, the threshold of the rule, and a determination of whether the rule resulted in a false positive or a false negative. This data can be provided as training data for a machine learning model or multiple models that can be trained to predict when a false positive or a false negative is going to occur based on the state and threshold. Once the model is trained, an optimization technique can be used to determine the optimal threshold level that minimizes the numbers of false positives and false negatives. The optimal threshold can then replace the existing threshold so that false positives and false negatives are minimized. 
     Building Management System Overview 
     Referring now to  FIGS.  1 - 4   , an exemplary building automation system (BAS) and HVAC system in which the systems and methods of the present invention can be implemented are shown, according to an exemplary embodiment. Referring particularly to  FIG.  1   , a perspective view of a building  10  is shown. Building  10  is served by a BAS. A BAS is, in general, a system of devices configured to control, monitor, and manage equipment in or around a building or building area. A BAS can include, for example, a HVAC system, a security system, a lighting system, a fire alarming system, any other system that is capable of managing building functions or devices, or any combination thereof. 
     The BAS that serves building  10  includes an HVAC system  100 . HVAC system  100  can include a plurality of HVAC devices (e.g., heaters, chillers, air handling units, pumps, fans, thermal energy storage, etc.) configured to provide heating, cooling, ventilation, or other services for building  10 . For example, HVAC system  100  is shown to include a waterside system  120  and an airside system  130 . Waterside system  120  can provide a heated or chilled fluid to an air handling unit of airside system  130 . Airside system  130  can use the heated or chilled fluid to heat or cool an airflow provided to building  10 . An exemplary waterside system and airside system which can be used in HVAC system  100  are described in greater detail with reference to  FIGS.  2 - 3   . 
     HVAC system  100  is shown to include a chiller  102 , a boiler  104 , and a rooftop air handling unit (AHU)  106 . Waterside system  120  can use boiler  104  and chiller  102  to heat or cool a working fluid (e.g., water, glycol, etc.) and can circulate the working fluid to AHU  106 . In various embodiments, the HVAC devices of waterside system  120  can be located in or around building  10  (as shown in  FIG.  1   ) or at an offsite location such as a central plant (e.g., a chiller plant, a steam plant, a heat plant, etc.). The working fluid can be heated in boiler  104  or cooled in chiller  102 , depending on whether heating or cooling is required in building  10 . Boiler  104  can add heat to the circulated fluid, for example, by burning a combustible material (e.g., natural gas) or using an electric heating element. Chiller  102  can place the circulated 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 circulated fluid. The working fluid from chiller  102  and/or boiler  104  can be transported to AHU  106  via piping  108 . 
     AHU  106  can place the working fluid in a heat exchange relationship with an airflow passing through AHU  106  (e.g., via one or more stages of cooling coils and/or heating coils). The airflow can be, for example, outside air, return air from within building  10 , or a combination of both. AHU  106  can transfer heat between the airflow and the working fluid to provide heating or cooling for the airflow. For example, AHU  106  can include one or more fans or blowers configured to pass the airflow over or through a heat exchanger containing the working fluid. The working fluid can then return to chiller  102  or boiler  104  via piping  110 . 
     Airside system  130  can deliver the airflow supplied by AHU  106  (i.e., the supply airflow) to building  10  via air supply ducts  112  and can provide return air from building  10  to AHU  106  via air return ducts  114 . In some embodiments, airside system  130  includes multiple variable air volume (VAV) units  116 . For example, airside system  130  is shown to include a separate VAV unit  116  on each floor or zone of building  10 . VAV units  116  can include dampers or other flow control elements that can be operated to control an amount of the supply airflow provided to individual zones of building  10 . In other embodiments, airside system  130  delivers the supply airflow into one or more zones of building  10  (e.g., via supply ducts  112 ) without using intermediate VAV units  116  or other flow control elements. AHU  106  can include various sensors (e.g., temperature sensors, pressure sensors, etc.) configured to measure attributes of the supply airflow. AHU  106  can receive input from sensors located within AHU  106  and/or within the building zone and can adjust the flow rate, temperature, or other attributes of the supply airflow through AHU  106  to achieve setpoint conditions for the building zone. 
     Referring now to  FIG.  2   , a block diagram of a waterside system  200  is shown, according to an exemplary embodiment. In various embodiments, waterside system  200  can supplement or replace waterside system  120  in HVAC system  100  or can be implemented separate from HVAC system  100 . When implemented in HVAC system  100 , waterside system  200  can include a subset of the HVAC devices in HVAC system  100  (e.g., boiler  104 , chiller  102 , pumps, valves, etc.) and can operate to supply a heated or chilled fluid to AHU  106 . The HVAC devices of waterside system  200  can be located within building  10  (e.g., as components of waterside system  120 ) or at an offsite location such as a central plant. 
     In  FIG.  2   , waterside system  200  is shown as a central plant having a plurality of subplants  202 - 212 . Subplants  202 - 212  are shown to include a heater subplant  202 , a heat recovery chiller subplant  204 , a chiller subplant  206 , a cooling tower subplant  208 , a hot thermal energy storage (TES) subplant  210 , and a cold thermal energy storage (TES) subplant  212 . Subplants  202 - 212  consume resources (e.g., water, natural gas, electricity, etc.) from utilities to serve the thermal energy loads (e.g., hot water, cold water, heating, cooling, etc.) of a building or campus. For example, heater subplant  202  can be configured to heat water in a hot water loop  214  that circulates the hot water between heater subplant  202  and building  10 . Chiller subplant  206  can be configured to chill water in a cold water loop  216  that circulates the cold water between chiller subplant  206  and building  10 . Heat recovery chiller subplant  204  can be configured to transfer heat from cold water loop  216  to hot water loop  214  to provide additional heating for the hot water and additional cooling for the cold water. Condenser water loop  218  can absorb heat from the cold water in chiller subplant  206  and reject the absorbed heat in cooling tower subplant  208  or transfer the absorbed heat to hot water loop  214 . Hot TES subplant  210  and cold TES subplant  212  can store hot and cold thermal energy, respectively, for subsequent use. 
     Hot water loop  214  and cold water loop  216  can deliver the heated and/or chilled water to air handlers located on the rooftop of building  10  (e.g., AHU  106 ) or to individual floors or zones of building  10  (e.g., VAV units  116 ). The air handlers push air past heat exchangers (e.g., heating coils or cooling coils) through which the water flows to provide heating or cooling for the air. The heated or cooled air can be delivered to individual zones of building  10  to serve the thermal energy loads of building  10 . The water then returns to subplants  202 - 212  to receive further heating or cooling. 
     Although subplants  202 - 212  are shown and described as heating and cooling water for circulation to a building, it is understood that any other type of working fluid (e.g., glycol, CO2, etc.) can be used in place of or in addition to water to serve the thermal energy loads. In other embodiments, subplants  202 - 212  can provide heating and/or cooling directly to the building or campus without requiring an intermediate heat transfer fluid. These and other variations to waterside system  200  are within the teachings of the present invention. 
     Each of subplants  202 - 212  can include a variety of equipment configured to facilitate the functions of the subplant. For example, heater subplant  202  is shown to include a plurality of heating elements  220  (e.g., boilers, electric heaters, etc.) configured to add heat to the hot water in hot water loop  214 . Heater subplant  202  is also shown to include several pumps  222  and  224  configured to circulate the hot water in hot water loop  214  and to control the flow rate of the hot water through individual heating elements  220 . Chiller subplant  206  is shown to include a plurality of chillers  232  configured to remove heat from the cold water in cold water loop  216 . Chiller subplant  206  is also shown to include several pumps  234  and  236  configured to circulate the cold water in cold water loop  216  and to control the flow rate of the cold water through individual chillers  232 . 
     Heat recovery chiller subplant  204  is shown to include a plurality of heat recovery heat exchangers  226  (e.g., refrigeration circuits) configured to transfer heat from cold water loop  216  to hot water loop  214 . Heat recovery chiller subplant  204  is also shown to include several pumps  228  and  230  configured to circulate the hot water and/or cold water through heat recovery heat exchangers  226  and to control the flow rate of the water through individual heat recovery heat exchangers  226 . Cooling tower subplant  208  is shown to include a plurality of cooling towers  238  configured to remove heat from the condenser water in condenser water loop  218 . Cooling tower subplant  208  is also shown to include several pumps  240  configured to circulate the condenser water in condenser water loop  218  and to control the flow rate of the condenser water through individual cooling towers  238 . 
     Hot TES subplant  210  is shown to include a hot TES tank  242  configured to store the hot water for later use. Hot TES subplant  210  can also include one or more pumps or valves configured to control the flow rate of the hot water into or out of hot TES tank  242 . Cold TES subplant  212  is shown to include cold TES tanks  244  configured to store the cold water for later use. Cold TES subplant  212  can also include one or more pumps or valves configured to control the flow rate of the cold water into or out of cold TES tanks  244 . 
     In some embodiments, one or more of the pumps in waterside system  200  (e.g., pumps  222 ,  224 ,  228 ,  230 ,  234 ,  236 , and/or  240 ) or pipelines in waterside system  200  include an isolation valve associated therewith. Isolation valves can be integrated with the pumps or positioned upstream or downstream of the pumps to control the fluid flows in waterside system  200 . In various embodiments, waterside system  200  can include more, fewer, or different types of devices and/or subplants based on the particular configuration of waterside system  200  and the types of loads served by waterside system  200 . 
     Referring now to  FIG.  3   , a block diagram of an airside system  300  is shown, according to an exemplary embodiment. In various embodiments, airside system  300  can supplement or replace airside system  130  in HVAC system  100  or can be implemented separate from HVAC system  100 . When implemented in HVAC system  100 , airside system  300  can include a subset of the HVAC devices in HVAC system  100  (e.g., AHU  106 , VAV units  116 , ducts  112 - 114 , fans, dampers, etc.) and can be located in or around building  10 . Airside system  300  can operate to heat or cool an airflow provided to building  10  using a heated or chilled fluid provided by waterside system  200 . 
     In  FIG.  3   , airside system  300  is shown to include an economizer-type air handling unit (AHU)  302 . Economizer-type AHUs vary the amount of outside air and return air used by the air handling unit for heating or cooling. For example, AHU  302  can receive return air  304  from building zone  306  via return air duct  308  and can deliver supply air  310  to building zone  306  via supply air duct  312 . In some embodiments, AHU  302  is a rooftop unit located on the roof of building  10  (e.g., AHU  106  as shown in  FIG.  1   ) or otherwise positioned to receive both return air  304  and outside air  314 . AHU  302  can be configured to operate exhaust air damper  316 , mixing damper  318 , and outside air damper  320  to control an amount of outside air  314  and return air  304  that combine to form supply air  310 . Any return air  304  that does not pass through mixing damper  318  can be exhausted from AHU  302  through exhaust damper  316  as exhaust air  322 . 
     Each of dampers  316 - 320  can be operated by an actuator. For example, exhaust air damper  316  can be operated by actuator  324 , mixing damper  318  can be operated by actuator  326 , and outside air damper  320  can be operated by actuator  328 . Actuators  324 - 328  can communicate with an AHU controller  330  via a communications link  332 . Actuators  324 - 328  can receive control signals from AHU controller  330  and can provide feedback signals to AHU controller  330 . Feedback signals can include, for example, an indication of a current actuator or damper position, an amount of torque or force exerted by the actuator, diagnostic information (e.g., results of diagnostic tests performed by actuators  324 - 328 ), status information, commissioning information, configuration settings, calibration data, and/or other types of information or data that can be collected, stored, or used by actuators  324 - 328 . AHU controller  330  can be an economizer controller configured to use one or more control algorithms (e.g., state-based algorithms, extremum seeking control (ESC) algorithms, proportional-integral (PI) control algorithms, proportional-integral-derivative (PID) control algorithms, model predictive control (MPC) algorithms, feedback control algorithms, etc.) to control actuators  324 - 328 . 
     Still referring to  FIG.  3   , AHU  302  is shown to include a cooling coil  334 , a heating coil  336 , and a fan  338  positioned within supply air duct  312 . Fan  338  can be configured to force supply air  310  through cooling coil  334  and/or heating coil  336  and provide supply air  310  to building zone  306 . AHU controller  330  can communicate with fan  338  via communications link  340  to control a flow rate of supply air  310 . In some embodiments, AHU controller  330  controls an amount of heating or cooling applied to supply air  310  by modulating a speed of fan  338 . 
     Cooling coil  334  can receive a chilled fluid from waterside system  200  (e.g., from cold water loop  216 ) via piping  342  and can return the chilled fluid to waterside system  200  via piping  344 . Valve  346  can be positioned along piping  342  or piping  344  to control a flow rate of the chilled fluid through cooling coil  334 . In some embodiments, cooling coil  334  includes multiple stages of cooling coils that can be independently activated and deactivated (e.g., by AHU controller  330 , by BAS controller  366 , etc.) to modulate an amount of cooling applied to supply air  310 . 
     Heating coil  336  can receive a heated fluid from waterside system  200  (e.g., from hot water loop  214 ) via piping  348  and can return the heated fluid to waterside system  200  via piping  350 . Valve  352  can be positioned along piping  348  or piping  350  to control a flow rate of the heated fluid through heating coil  336 . In some embodiments, heating coil  336  includes multiple stages of heating coils that can be independently activated and deactivated (e.g., by AHU controller  330 , by BAS controller  366 , etc.) to modulate an amount of heating applied to supply air  310 . 
     Each of 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 . Actuators  354 - 356  can communicate with AHU controller  330  via communications links  358 - 360 . Actuators  354 - 356  can receive control signals from AHU controller  330  and can provide feedback signals to controller  330 . In some embodiments, AHU controller  330  receives a measurement of the supply air temperature from a temperature sensor  362  positioned in supply air duct  312  (e.g., downstream of cooling coil  334  and/or heating coil  336 ). AHU controller  330  can also receive a measurement of the temperature of building zone  306  from a temperature sensor  364  located in building zone  306 . 
     In some embodiments, AHU controller  330  operates valves  346  and  352  via actuators  354 - 356  to modulate an amount of heating or cooling provided to supply air  310  (e.g., to achieve a setpoint temperature for supply air  310  or to maintain the temperature of supply air  310  within a setpoint temperature range). The positions of valves  346  and  352  affect the amount of heating or cooling provided to supply air  310  by cooling coil  334  or heating coil  336  and may correlate with the amount of energy consumed to achieve a desired supply air temperature. AHU controller  330  can control the temperature of supply air  310  and/or building zone  306  by activating or deactivating coils  334 - 336 , adjusting a speed of fan  338 , or a combination of both. 
     Still referring to  FIG.  3   , airside system  300  is shown to include a building automation system (BAS) controller  366  and a client device  368 . BAS controller  366  can include one or more computer systems (e.g., servers, supervisory controllers, subsystem controllers, etc.) that serve as system level controllers, application or data servers, head nodes, or master controllers for airside system  300 , waterside system  200 , HVAC system  100 , and/or other controllable systems that serve building  10 . BAS controller  366  can communicate with multiple downstream building systems or subsystems (e.g., HVAC system  100 , a security system, a lighting system, waterside system  200 , etc.) via a communications link  370  according to like or disparate protocols (e.g., LON, BACnet, etc.). In various embodiments, AHU controller  330  and BAS controller  366  can be separate (as shown in  FIG.  3   ) or integrated. In an integrated implementation, AHU controller  330  can be a software module configured for execution by a processor of BAS controller  366 . 
     In some embodiments, AHU controller  330  receives information from BAS controller  366  (e.g., commands, setpoints, operating boundaries, etc.) and provides information to BAS controller  366  (e.g., temperature measurements, valve or actuator positions, operating statuses, diagnostics, etc.). For example, AHU controller  330  can provide BAS controller  366  with temperature measurements from temperature sensors  362 - 364 , equipment on/off states, equipment operating capacities, and/or any other information that can be used by BAS controller  366  to monitor or control a variable state or condition within building zone  306 . 
     Client device  368  can include one or more human-machine interfaces or client interfaces (e.g., graphical user interfaces, reporting interfaces, text-based computer interfaces, client-facing web services, web servers that provide pages to web clients, etc.) for controlling, viewing, or otherwise interacting with HVAC system  100 , its subsystems, and/or devices. Client device  368  can be a computer workstation, a client terminal, a remote or local interface, or any other type of user interface device. Client device  368  can be a stationary terminal or a mobile device. For example, client device  368  can be a desktop computer, a computer server with a user interface, a laptop computer, a tablet, a smartphone, a PDA, or any other type of mobile or non-mobile device. Client device  368  can communicate with BAS controller  366  and/or AHU controller  330  via communications link  372 . 
     Referring now to  FIG.  4   , a block diagram of a building automation system (BAS)  400  is shown, according to an exemplary embodiment. BAS  400  can be implemented in building  10  to automatically monitor and control various building functions. BAS  400  is shown to include BAS controller  366  and a plurality of building subsystems  428 . Building subsystems  428  are shown to include a building electrical subsystem  434 , an information communication technology (ICT) subsystem  436 , a security subsystem  438 , a HVAC subsystem  440 , a lighting subsystem  442 , a lift/escalators subsystem  432 , and a fire safety subsystem  430 . In various embodiments, building subsystems  428  can include fewer, additional, or alternative subsystems. For example, building subsystems  428  can also or alternatively include a refrigeration subsystem, an advertising or signage subsystem, a cooking subsystem, a vending subsystem, a printer or copy service subsystem, or any other type of building subsystem that uses controllable equipment and/or sensors to monitor or control building  10 . In some embodiments, building subsystems  428  include waterside system  200  and/or airside system  300 , as described with reference to  FIGS.  2 - 3   . 
     Each of building subsystems  428  can include any number of devices, controllers, and connections for completing its individual functions and control activities. HVAC subsystem  440  can include many of the same components as HVAC system  100 , as described with reference to  FIGS.  1 - 3   . For example, HVAC subsystem  440  can include a chiller, a boiler, any number of air handling units, economizers, field controllers, supervisory controllers, actuators, temperature sensors, and other devices for controlling the temperature, humidity, airflow, or other variable conditions within building  10 . Lighting subsystem  442  can include any number of light fixtures, ballasts, lighting sensors, dimmers, or other devices configured to controllably adjust the amount of light provided to a building space. Security subsystem  438  can 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. 
     Still referring to  FIG.  4   , BAS controller  366  is shown to include a communications interface  407  and a BAS interface  409 . Interface  407  can facilitate communications between BAS controller  366  and external applications (e.g., monitoring and reporting applications  422 , enterprise control applications  426 , remote systems and applications  444 , applications residing on client devices  448 , etc.) for allowing user control, monitoring, and adjustment to BAS controller  366  and/or subsystems  428 . Interface  407  can also facilitate communications between BAS controller  366  and client devices  448 . BAS interface  409  can facilitate communications between BAS controller  366  and building subsystems  428  (e.g., HVAC, lighting security, lifts, power distribution, business, etc.). 
     Interfaces  407 ,  409  can be or include wired or wireless communications interfaces (e.g., jacks, antennas, transmitters, receivers, transceivers, wire terminals, etc.) for conducting data communications with building subsystems  428  or other external systems or devices. In various embodiments, communications via interfaces  407 ,  409  can be direct (e.g., local wired or wireless communications) or via a communications network  446  (e.g., a WAN, the Internet, a cellular network, etc.). For example, interfaces  407 ,  409  can include an Ethernet card and port for sending and receiving data via an Ethernet-based communications link or network. In another example, interfaces  407 ,  409  can include a Wi-Fi transceiver for communicating via a wireless communications network. In another example, one or both of interfaces  407 ,  409  can include cellular or mobile phone communications transceivers. In one embodiment, communications interface  407  is a power line communications interface and BAS interface  409  is an Ethernet interface. In other embodiments, both communications interface  407  and BAS interface  409  are Ethernet interfaces or are the same Ethernet interface. 
     Still referring to  FIG.  4   , BAS controller  366  is shown to include a processing circuit  404  including a processor  406  and memory  408 . Processing circuit  404  can be communicably connected to BAS interface  409  and/or communications interface  407  such that processing circuit  404  and the various components thereof can send and receive data via interfaces  407 ,  409 . Processor  406  can 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. 
     Memory  408  (e.g., memory, memory unit, storage device, etc.) can include one or more devices (e.g., RAM, ROM, Flash memory, hard disk storage, etc.) for storing data and/or computer code for completing or facilitating the various processes, layers and modules described in the present application. Memory  408  can be or include volatile memory or non-volatile memory. Memory  408  can include database components, object code components, script components, or any other type of information structure for supporting the various activities and information structures described in the present application. According to an exemplary embodiment, memory  408  is communicably 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. 
     In some embodiments, BAS controller  366  is implemented within a single computer (e.g., one server, one housing, etc.). In various other embodiments BAS controller  366  can be distributed across multiple servers or computers (e.g., that can exist in distributed locations). Further, while  FIG.  4    shows applications  422  and  426  as existing outside of BAS controller  366 , in some embodiments, applications  422  and  426  can be hosted within BAS controller  366  (e.g., within memory  408 ). 
     Still referring to  FIG.  4   , memory  408  is shown to include an enterprise integration layer  410 , an automated measurement and validation (AM&amp;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 later  420 . Layers  410 - 420  can be configured to receive inputs from building subsystems  428  and other data sources, determine optimal control actions for building subsystems  428  based on the inputs, generate control signals based on the optimal control actions, and provide the generated control signals to building subsystems  428 . The following paragraphs describe some of the general functions performed by each of layers  410 - 420  in BAS  400 . 
     Enterprise integration layer  410  can be configured to serve clients or local applications with information and services to support a variety of enterprise-level applications. For example, enterprise control applications  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., accounting systems, user identification systems, etc.). Enterprise control applications  426  can also or alternatively be configured to provide configuration GUIs for configuring BAS controller  366 . In yet other embodiments, enterprise control applications  426  can work with layers  410 - 420  to optimize building performance (e.g., efficiency, energy use, comfort, or safety) based on inputs received at interface  407  and/or BAS interface  409 . 
     Building subsystem integration layer  420  can be configured to manage communications between BAS controller  366  and building subsystems  428 . For example, building subsystem integration layer  420  can receive sensor data and input signals from building subsystems  428  and provide output data and control signals to building subsystems  428 . Building subsystem integration layer  420  can also be configured to manage communications between building subsystems  428 . Building subsystem integration layer  420  translate communications (e.g., sensor data, input signals, output signals, etc.) across a plurality of multi-vendor/multi-protocol systems. 
     Demand response layer  414  can be configured to optimize resource usage (e.g., electricity use, natural gas use, water use, etc.) and/or the monetary cost of such resource usage in response to satisfy the demand of building  10 . The optimization can be based on time-of-use prices, curtailment signals, energy availability, or other data received from utility providers, distributed energy generation systems  424 , from energy storage  427  (e.g., hot TES  242 , cold TES  244 , etc.), or from other sources. Demand response layer  414  can receive inputs from other layers of BAS controller  366  (e.g., building subsystem integration layer  420 , 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, and the like. The inputs can also include inputs such as electrical use (e.g., expressed in kWh), thermal load measurements, pricing information, projected pricing, smoothed pricing, curtailment signals from utilities, and the like. 
     According to an exemplary embodiment, demand response layer  414  includes control logic for responding to the data and signals it receives. These responses can include communicating with the control algorithms in integrated control layer  418 , changing control strategies, changing setpoints, or activating/deactivating building equipment or subsystems in a controlled manner. Demand response layer  414  can also include control logic configured to determine when to utilize stored energy. For example, demand response layer  414  can determine to begin using energy from energy storage  427  just prior to the beginning of a peak use hour. 
     In some embodiments, demand response layer  414  includes a control module configured to actively initiate control actions (e.g., automatically changing setpoints) which minimize energy costs based on one or more inputs representative of or based on demand (e.g., price, a curtailment signal, a demand level, etc.). In some embodiments, demand response layer  414  uses equipment models to determine an optimal set of control actions. The equipment models can include, for example, thermodynamic models describing the inputs, outputs, and/or functions performed by various sets of building equipment. Equipment models can represent collections of building equipment (e.g., subplants, chiller arrays, etc.) or individual devices (e.g., individual chillers, heaters, pumps, etc.). 
     Demand response layer  414  can further include or draw upon one or more demand response policy definitions (e.g., databases, XML files, etc.). The policy definitions can be edited or adjusted by a user (e.g., via a graphical user interface) so that the control actions initiated in response to demand inputs can be tailored for the user&#39;s application, desired comfort level, particular building equipment, or based on other concerns. For example, the demand response policy definitions can specify which equipment can be turned on or off in response to particular demand inputs, how long a system or piece of equipment should be turned off, what setpoints can be changed, what the allowable set point adjustment range is, how long to hold a high demand setpoint before returning to a normally scheduled setpoint, how close to approach capacity limits, which equipment modes to utilize, the energy transfer rates (e.g., the maximum rate, an alarm rate, other rate boundary information, etc.) into and out of energy storage devices (e.g., thermal storage tanks, battery banks, etc.), and when to dispatch on-site generation of energy (e.g., via fuel cells, a motor generator set, etc.). 
     Integrated control layer  418  can be configured to use the data input or output of building subsystem integration layer  420  and/or demand response later  414  to make control decisions. Due to the subsystem integration provided by building subsystem integration layer  420 , integrated control layer  418  can integrate control activities of the subsystems  428  such that the subsystems  428  behave as a single integrated supersystem. In an exemplary embodiment, integrated control layer  418  includes control logic that uses inputs and outputs from a plurality of building subsystems to provide greater comfort and energy savings relative to the comfort and energy savings that separate subsystems could provide alone. For example, integrated control layer  418  can be configured to use an input from a first subsystem to make an energy-saving control decision for a second subsystem. Results of these decisions can be communicated back to building subsystem integration layer  420 . 
     Integrated control layer  418  is shown to be logically below demand response layer  414 . Integrated control layer  418  can be configured to enhance the effectiveness of demand response layer  414  by enabling building subsystems  428  and their respective control loops to be controlled in coordination with demand response layer  414 . This configuration can reduce disruptive demand response behavior relative to conventional systems. For example, integrated control layer  418  can be configured to assure that a demand response-driven upward adjustment to the setpoint for chilled water temperature (or another component that directly or indirectly affects temperature) does not result in an increase in fan energy (or other energy used to cool a space) that would result in greater total building energy use than was saved at the chiller. 
     Integrated control layer  418  can be configured to provide feedback to demand response layer  414  so that demand response layer  414  checks that constraints (e.g., temperature, lighting levels, etc.) are properly maintained even while demanded load shedding is in progress. The constraints can also include setpoint or sensed boundaries relating to safety, equipment operating limits and performance, comfort, fire codes, electrical codes, energy codes, and the like. Integrated control layer  418  is also logically below fault detection and diagnostics layer  416  and automated measurement and validation layer  412 . Integrated control layer  418  can be configured to provide calculated inputs (e.g., aggregations) to these higher levels based on outputs from more than one building subsystem. 
     Automated measurement and validation (AM&amp;V) layer  412  can be configured to verify that control strategies commanded by integrated control layer  418  or demand response layer  414  are working properly (e.g., using data aggregated by AM&amp;V layer  412 , integrated control layer  418 , building subsystem integration layer  420 , FDD layer  416 , or otherwise). The calculations made by AM&amp;V layer  412  can be based on building system energy models and/or equipment models for individual BAS devices or subsystems. For example, AM&amp;V layer  412  can compare a model-predicted output with an actual output from building subsystems  428  to determine an accuracy of the model. 
     Fault detection and diagnostics (FDD) layer  416  can be configured to provide on-going fault detection for building subsystems  428 , building subsystem devices (i.e., building equipment), and control algorithms used by demand response layer  414  and integrated control layer  418 . FDD layer  416  can receive data inputs from integrated control layer  418 , directly from one or more building subsystems or devices, or from another data source. FDD layer  416  can automatically diagnose and respond to detected faults. The responses to detected or diagnosed faults can include providing an alarm message to a user, a maintenance scheduling system, or a control algorithm configured to attempt to repair the fault or to work-around the fault. 
     FDD layer  416  can be configured to output a specific identification of the faulty component or cause of the fault (e.g., loose damper linkage) using detailed subsystem inputs available at building subsystem integration layer  420 . In other exemplary embodiments, FDD layer  416  is configured to provide “fault” events to integrated control layer  418  which executes control strategies and policies in response to the received fault events. According to an exemplary embodiment, FDD layer  416  (or a policy executed by an integrated control engine or business rules engine) can shut-down systems or direct control activities around faulty devices or systems to reduce energy waste, extend equipment life, or assure proper control response. 
     FDD layer  416  can be configured to store or access a variety of different system data stores (or data points for live data). FDD layer  416  can use some content of the data stores to identify faults at the equipment level (e.g., specific chiller, specific AHU, specific terminal unit, etc.) and other content to identify faults at component or subsystem levels. For example, building subsystems  428  can generate temporal (i.e., time-series) data indicating the performance of BAS  400  and the various components thereof. The data generated by building subsystems  428  can include measured or calculated values that exhibit statistical characteristics and provide information about how the corresponding system or process (e.g., a temperature control process, a flow control process, etc.) is performing in terms of error from its setpoint. These processes can be examined by FDD layer  416  to expose when the system begins to degrade in performance and alarm a user to repair the fault before it becomes more severe. 
     Autonomous FDD Rule Threshold Tuning 
     Referring now to  FIG.  5   , a block diagram of a system  500  including a false positive and false negative prediction system  502  that is configured to predict false positives and false negatives of faults detected in building equipment by the FDD layer  416  in a building management system (e.g., BMS  400 ) is shown, according to an exemplary embodiment. False positive and false negative prediction system  502  may operate in a cloud environment or locally by a processor at the building management system. False positive and false negative prediction system  502  may implement one or more machine learning models to predict false positives and false negatives of faults that are detected in the building equipment. False positive and false negative prediction system  502  may do so by inputting measurements of various points of the piece of building equipment into the machine learning models and determining whether individual output confidence scores for false positives and false negatives from the models satisfy a predetermined criteria (e.g., exceed a predetermined threshold, is the highest predicted confidence score, etc.). 
     According to various example implementations of the present disclosure, the FDD layer  416  may determine that a building equipment has a fault based on the trigger or satisfaction of a rule. A false positive may indicate that the building equipment actually is not experiencing a fault, and the rule incorrectly determined that a fault has occurred. Furthermore, the FDD layer  416  may determine that a building equipment is not experiencing a fault because a state of the building did not satisfy the rule. A false negative may indicate that the building equipment is actually experiencing a fault, and the rule incorrectly determined that the fault did not occur. Although the false positive prediction model  528  is described in detail herein, similar description may apply for the false negative prediction model  530  except for false negatives instead of false positives. 
     As used herein, “points” or “data points” refer to sensor inputs, control outputs, control values, and/or different characteristics of the inputs and/or outputs. “Points” and/or “data points” may refer to various data objects relating to the inputs and the outputs such as BACnet objects. The objects may represent and/or include a point and/or group of points. The object may include various properties for each of the points. For example, an analog input may be a particular point represented by an object with one or more properties describing the analog input and another property describing the sampling rate of the analog input. For example, in some embodiments, a point is a data representation associated with a component of a BMS, such as a camera, thermostat, controller, VAV box, RTU, valve, damper, chiller, boiler, AHU, supply fan, etc. 
     System  500  may include a user presentation system  538 , a building controller  540 , and building equipment  542 . Building controller  540  may be similar to or the same as BMS controller  366 . False positive and false negative prediction system  502  may be a component of or be within building controller  540 . In some embodiments, false positive and false negative prediction system  502  operates in the cloud as one or more cloud servers. Components  502  and  538 - 542  may communicate over a network (e.g., a synchronous or asynchronous network). 
     False positive and false negative prediction system  502  may include a processing circuit  504 , a processor  506 , and a memory  508 . Processing circuit  504 , processor  506 , and/or memory  508  can be the same as, or similar to, processing circuit  404 , processor  406 , and/or memory  408 , as described with reference to  FIG.  4   . Memory  508  may include a data pre-processor  510 , equipment models  512   a - n , a training manager  514 , a data post-processor  516 , and a measurement database  518 . Memory  508  may include any number of components. 
     Data pre-processor  510  includes instructions performed by one or more servers or processors (e.g., processing circuit  504 ), in some embodiments. In some embodiments, data pre-processor  510  includes a data collector  522 , a vector generator  524 , and an FP/FN identifier  526 . Data collector  522  may be configured to collect data that corresponds to different pieces of building equipment (e.g., building equipment  542 ). Data collector  522  can be configured to retrieve and/or collect building data from a building management system and store the building data in measurement database  518 , in some embodiments. Data collector  522  can be configured to collect data automatically or, in some embodiments, poll sensors associated with building equipment  542  to collect data at predetermined time intervals set by an administrator. In some embodiments, data collector  522  can further be configured to collect data upon detecting that a value changed by an amount exceeding a threshold. In some embodiments, data collector  522  is configured to collect building data upon receiving a request from an administrator. The administrator may make the request from a client device. The administrator can request building data associated with any time period and building device. 
     In some embodiments, the data collector  522  may execute rules related to the building equipment and determine whether any rules have been satisfied. For example, a rule pertaining to the AHU may state that if the air supply temperature is less than a first threshold and if the fan speed is greater than a second threshold, the AHU has a fault. Accordingly, the data collector  522  may collect the measurements (e.g., air supply temperature and fan speed) from the measurement database  518  and determine whether any rule is satisfied such that a fault is determined to exist for the AHU. Then, the data collector  522  may provide the measurements, the thresholds, and the determination of whether there is a fault to the vector generator  524  so that a vector may be generated for the prediction models. 
     Data collector  522  may be configured to tag each data point of the data with timestamps indicating when the data point was generated and/or when data collector  522  collected the data point from the sensors. In some embodiments, data collector  522  can also tag the data with a device identifier tag indicating the building device from which the building data was collected. Thus, data collector  522  may store the timestamped data in measurement database  518  as a timeseries corresponding to how the measured values changed over time. 
     As described herein, timeseries can be a collection of values for a particular point (e.g., a discharge air temperature point of an air handling unit, a discharge air temperature, a supply fan status, a zone air temperature, a humidity, a pressure, etc.) generated at different times (e.g., at periodic intervals). The values may include or be associated with identifiers of the building devices with which the points are associated (e.g., an air handler, a VAV box, a controller, a chiller, a boiler, vents, dampers, etc.). Each timeseries can include a series of values for the same point and a timestamp for each of the data values. For example, a timeseries for a point provided by a temperature sensor (e.g., provided through local gateways) can include a series of temperature values measured by the temperature sensor and the corresponding times at which the temperature values were measured. An example of a timeseries which can be generated by data collector  522  is as follows:
         [&lt;key, timestamp1, value1&gt;, &lt;key, timestamp2, value2&gt;, &lt;key, timestamp3, value3&gt;]
 
where key is an identifier of the source of the raw data samples (e.g., timeseries ID, sensor ID, device ID, etc.), timestampi may identify the time at which the ith sample was collected, and valuei may indicate the value of the ith sample.
       

     Measurement database  518  may be a database configured to store building data associated with a building management system (e.g., BMS  400 ). Measurement database  518  can be a graph database, MySQL, Oracle, Microsoft SQL, PostgreSql, DB2, document store, search engine, device identifier-value store, etc. Measurement database  518  can be configured to hold data including any amount of values and can be made up of any number of components. The data can include various measurements and states (e.g., temperature readings, pressure readings, device state readings, blade speeds, etc.) associated with building equipment (e.g., AHUs, chillers, boilers, VAVs, fans, etc.) of the building management system. In some embodiments, the building data is tagged with timestamps indicating times and dates that the values of the building data were generated by devices (e.g., sensors) of the building management system or retrieved by data collector  522 . It should be understood that, in some embodiments, measurement database  518  or any other type of data or data structure described herein may be or include a data structure configured to store digital twins of the building, building equipment, building spaces, building occupants/people, events, or any other entity of or related to the building. For example, measurement database  518  and/or other elements described herein may be implemented as a knowledge graph having nodes representing entities of the building and edges representing relationships between the entities. Example implementations of such digital twins and knowledge graphs as may be utilized in conjunction with the features of the present disclosure can be found in U.S. patent application Ser. No. 17/529,118, filed Nov. 18, 2021, which is incorporated herein by reference in its entirety. 
     In some embodiments, measurement database  518  may store setpoint values for different points of the building management system. The stored setpoint values may be associated with a schedule indicating the times in which building equipment  542  will operate so points of the building managements system will reach the corresponding stored setpoints. For example, a setpoint schedule may indicate that a kitchen should be 70 degrees at 7 P.M. but 68 degrees at 3 P.M. Accordingly, a controller (e.g., building controller  540 ) may control the building equipment of the building to cause the temperature point to reach the setpoint temperature at the corresponding times. Measurement database  518  may include schedules for setpoints of any point of the building to reach a desired level of comfort for the building&#39;s occupants. 
     Vector generator  524  may be configured to generate a feature vector that is configured to be input into machine learning models of equipment models  512   a - n  from measurement database  518 . For example, the feature vector may include a state of the building equipment and a threshold that is used in the rule. For example, for an AHU of the building, there may be a rule that states if supply air temperature is less than a certain temperature and the fan speed is greater than a certain rpm, there is a fault with the AHU. This may rule may be expressed as follows:
         if (c 1 &lt;ε 1  &amp; c 2 &gt;ε 2 ),   then AHU has fault F 1  
 
where c 1  corresponds to the supply air temperature, ε 1  corresponds to a first threshold, c 2  corresponds to the fan speed, ε 2  corresponds to a second threshold, and F 1  corresponds to the fault ID. For a first machine learning model, the state of the AHU may include the supply air temperature and the first threshold, and for a second machine learning model, the state of the AHU may include the fan speed and the second threshold. These pairs may be provided as feature vectors to the respective machine learning models.
       

     Vector generator  524  may generate such feature vectors upon determining an event has occurred. An event may be or include a detection that a value associated with the piece of building equipment is above a threshold, a determination that a predetermined time interval has passed since vector generator  524  previously executed the machine learning model, receipt of a user input indicating to execute the machine learning model, receipt of a signal from another computing device indicating to execute the machine learning model, etc. Vector generator  524  may monitor various aspects of the building management system to identify such events and determine when the events occur. For example, vector generator  524  may keep track of the times in which vector generator  524  executes the machine learning model. Vector generator  524  may maintain an internal clock and identify when a predetermined (e.g., a pre-programmed) time period has passed since the last time vector generator  524  executed the machine learning model and determine the predetermined time period has passed. Vector generator  524  may identify an event as occurring upon determining the predetermined time period has passed. 
     Upon determining an event has occurred, vector generator  524  may generate a feature vector. Vector generator  524  may generate the feature vector by identifying the piece of building equipment that is associated with the event (e.g., the piece of building equipment that has a stored association with the event) and retrieve data that corresponds to the piece of building equipment. Vector generator  524  may retrieve the data that is associated with attributes or points of the piece of building equipment based on a stored association between the values and the attributes or points. Vector generator  524  may retrieve data that is associated with values from within a pre-configured time frame of the event (e.g., values that are associated with timestamps from a time frame before and/or after the event) and generate a feature vector using the retrieved values. Vector generator  524  may retrieve values that were collected from sensors of the building and/or values of setpoints that are stored in memory (e.g., measurement database  518 ). 
     Upon generating the feature vector, vector generator  524  may identify the machine learning model that is associated with the piece of building equipment that is associated with the event. Vector generator  524  may identify the machine learning model from equipment models  512   a - n  that each includes or is otherwise associated with a different false positive prediction model  528  and/or a false negative prediction model  530 . Each of equipment models  512   a - n  may be a data representation of a different piece of building equipment within the building management system. The false positive prediction models and/or false negative prediction models of each equipment model  512   a - n  may be associated with a device identifier of the respective equipment model  512   a - n . Vector generator  524  may identify, responsive to determining the identified event, false positive prediction model  528  and false negative prediction model  530  are associated with the same or an identical device identifier. Upon identifying false positive prediction model  528 , vector generator  524  may apply the generated feature vector to false positive prediction model  528  and execute false positive prediction model  528 . 
     False positive prediction model  528  may be a machine learning model (e.g., a neural network, a random forest, a support vector machine, etc.) configured to output confidence scores associated with a whether the satisfaction of a fault rule (e.g., determination that the building equipment has a fault) related to a building equipment was a false positive. False positive prediction model  528  may be configured to output confidence scores for fault determinations based on feature vectors that are generated by vector generator  524  based on data that corresponds to a particular piece of building equipment (e.g., the piece of building equipment that the equipment model represents). False positive prediction model  528  may output confidence scores for whether the determination that a fault has occurred in the building equipment is a false positive. FP/FN identifier  526  may identify the confidence scores and/or determine whether the determination of a fault in the piece of building equipment is a false positive in the future based on the confidence scores. 
     FP/FN identifier  526  may be configured to use a predetermined criteria to determine if and/or when a fault is likely to occur in a piece of building equipment. The predetermined criteria may be a threshold and/or one or more rules. For instance, FP/FN identifier  526  may determine a fault determination is a false positive by comparing the confidence score to a predetermined threshold. Responsive to determining the score exceeds the threshold, FP/FN identifier  526  may determine that the fault determination (e.g., the fact that there was a fault in the building equipment) is a false positive. However, responsive to determining the score does not exceed the threshold, the data processing system may determine that the fault determination is not likely a false positive (e.g., the fault is actually a true positive). The data processing system may compare the confidence score to any rule or threshold. 
     Upon determining a confidence score for a rule satisfied the predetermined criteria, FP/FN identifier  526  may identify the rule that was determined to have been satisfied and associated with the confidence score and an identification of the threshold in the rule that was used to determine the fault. In some embodiments, FP/FN identifier  526  may generate an alert indicating the fault did not occur in the building equipment and transmit the alert to a client device (e.g., an administrative device) so an administrator can view the alert and keep the building equipment operation/online. In some embodiments, FP/FN identifier  526  may feed the identification of the false positive back to vector generator  524 , which in turn can use the identification to generate a new feature vector. 
     False negative prediction model  530  may be a machine learning model similar to false positive prediction model  528  that is configured to predict when false negatives occurred. False negative prediction model  530  may be configured to output confidence scores for fault determinations based on feature vectors that are generated by vector generator  524  based on data that corresponds to a particular piece of building equipment (e.g., the piece of building equipment that the equipment model represents). False positive prediction model  528  may output confidence scores for false positives and false negatives indicating likelihoods that the individual false positives and false negatives are the correct prediction. 
     Threshold optimizer  542  may receive the output confidence scores and process the scores to optimize the threshold that was used to determine the faults in the rules that were used in the false positive prediction model  528  and the false negative prediction model  530 . The threshold optimizer  542  may use constrained nonlinear optimization to find the optimal threshold value (based on the accumulated data so far) that minimizes the number of false positives and false negatives. 
     Data post-processor  516  may receive the output confidence scores and the optimal threshold values and process the scores and thresholds to transmit a signal to user presentation system  538  and/or building controller  540  to adjust the threshold of the rules. Data post-processor  516  includes instructions performed by one or more servers or processors (e.g., processing circuit  504 ), in some embodiments. In some embodiments, data post-processor  516  includes a record generator  536 . Record generator  536  may receive the predicted confidence scores and/or optimal threshold value and generate a record (e.g., a file, document, table, listing, message, notification, etc.) including confidence scores and/or the threshold values. Upon generating the record, record generator  536  may transmit the record to user presentation system  538  for display and/or building controller  540  to use to adjust operation or the configuration of building equipment  542  to avoid the false positive and false negative in the rule. 
     False positive and false negative prediction system  502  can provide indications of recommendations of how to adjust the threshold values to user presentation system  538  and/or building controller  540 . In some embodiments, building controller  540  uses the expected recommendations to operate building equipment  542  (e.g., control environmental conditions of a building, cause generators to turn on or off, charge or discharge batteries, etc.). Further, user presentation system  538  can receive the indications and/or recommendations and cause a client device to display indications (e.g., graphical elements, charts, words, numbers, etc.) of the threshold values and/or recommendations. For example, user presentation system  538  may receive the rule and the optimal threshold value so that the user can modify the rule in the FDD layer  416 . 
     In some embodiments, false positive and false negative prediction system  502  trains the prediction models of equipment models  512   a - n  using training manager  514 . Training manager  514  includes instructions performed by one or more servers or processors (e.g., processing circuit  504 ), in some embodiments. In some embodiments, training manager  514  includes a false positive prediction model trainer  532  and/or a false negative prediction model trainer  534 . False positive prediction model trainer  532  may be configured to train false positive prediction model  528  and other false positive prediction models of equipment models  512   a - n  to predict when false positives occur when a fault is detected for pieces of building equipment. False positive prediction model trainer  532  may feed labeled training data including measurements associated with points of a particular piece of building equipment to the false positive prediction model associated with the piece of building equipment. The respective false positive prediction model may output confidence scores for thresholds and false positive prediction model trainer  532  may determine differences between the predicted outputs and the labels and use back-propagation techniques according to a loss function to adjust the false positive prediction model&#39;s weights and parameters proportional to the determined differences. False positive prediction model trainer  532  may repeat these steps for any number of fault prediction machine learning models to train the machine learning models to predict future false positives for individual pieces of building equipment. 
     Similarly, false negative prediction model trainer  534  may be configured to train false negative prediction model  530  and other false negative prediction models of equipment models  512   a - n . False negative prediction model trainer  534  may feed measurement data and/or identifications of false negatives to obtain confidence scores for the threshold in rules used in determining faults in the building equipment. False negative prediction model trainer  534  may identify labels indicating the correct output, determine differences between the correct output and the respective false negative prediction model&#39;s output, and use back-propagation techniques according to a loss function to adjust the false negative prediction model&#39;s weights and parameters according to the determined differences. False negative prediction model trainer  534  may repeat these steps for any number of false negative prediction models to the machine learning models to predict false negatives for individual pieces of building equipment. In some embodiments, false negative prediction model trainer  534  may train a false negative prediction model in real-time. In such embodiments, false negative prediction model trainer  534  may feed measurement data and/or identifications of false negatives into a false negative prediction model to obtain confidence scores for false negatives in a rule. 
     A user may input levels of accuracy (e.g., correct, incorrect, partially correct, etc.) of the recommendations and/or the predicted false positives or false negatives. False positive prediction model trainer  532  or false negative prediction model trainer  534  may identify the input levels of accuracy, determine differences between the predicted confidence scores and the input levels of accuracy, and use back-propagation techniques with the false negative prediction model that predicted the confidence scores for the false positives and false negatives according to a loss function based on the differences. Thus, false negative prediction model trainer  534  may train false negative prediction models in real-time or near real-time, in some embodiments. 
     In some embodiments, training manager  514  may operate in a cloud server and be configured to use training data from multiple building management systems to train false positive prediction models and/or false negative prediction models. Training manager  514  may be configured to train individual machine learning models using training data that is associated with multiple pieces of building equipment (e.g., building equipment of the same type) until the machine learning models are accurate to a threshold, and then deploy the machine learning models to the local building management system to be used to make predictions for individual pieces of building equipment (and be further trained based only on data associated with the piece of building equipment). This may be advantageous in building management systems that do not have enough training data to train machine learning models to make accurate predictions. 
     In such embodiments, training manager  514  may be configured to train the machine learning models using a weighting policy. The weight policy may include weights that can be applied to different training data sets. The weights may correspond to different building management systems and may be determined based on how trustworthy an administrator has determined data from a building management system to be and/or based on whether the data originated at a building management system for which the models are being trained. Training manager  514  may use the weights by weighting the differences in a loss function so that training data that is associated with higher weights cause higher shifts in the weights or parameters of a machine learning model than training data that is associated with lower weights during training. Thus, training manager  514  may control the training to improve the accuracy and speed with which machine learning models are trained to be employed at individual building management systems. 
     Referring now to  FIG.  6   , a system  600  of a rule (or trigger rule)  602  of a building equipment where parameters of the trigger rule  602  are trained is shown, according to an exemplary embodiment. In some embodiments, the system  600  is similar to the system  400  or system  500 . In the example described in  FIG.  6    below, a simple rule for an AHU is described for the sake of clarity and brevity, but embodiments are not limited thereto, and the disclosed technology may be implemented for a variety of other building equipment and/or rules. For example, the building equipment may be related to fire safety  430 , lifts/escalators  432 , electrical  434 , ICT  436 , security  438 , HVAC  440 , lighting  442  or any other building equipment within or around buildings. Further, the rules may be related to any rule that is implemented within the FDD layer  416 . 
     The system  600  can perturb parameters, ε 1  of the trigger rule  602 . For example, the trigger rule  602  may include a rule that if supply air temperature is less than ε 1 ° C., the rule is triggered (or satisfied), and a corresponding action be performed. The corresponding action can be a determination that a fault exists with the AHU. The perturbation of the parameters can be increasing or decreasing the parameters in set amounts from existing values. The perturbation of the parameters can be selecting a space of values for the parameters and/or randomizing the parameters and/or parameter space. 
     With the perturbed values for ε 1 , the BMS controller  366  can simulate the state of the AHU for various temperatures within a range. The simulation can be performed by the false positive and false negative prediction system  502  via the models  512   a - 512   n . The output of the models  512   a - 512   n  can be false positive or false negative. 
     The system  600  can analyze the states produced by the building equipment operation  604  to determine states of the building equipment. For example, the air supply temperature and fan speed can be generated and collected for each state. Furthermore, the state can be any metric that is collected from any portion of the building equipment. For example, the state can include supply air temperature, fan speed, AHU operating mode (heating, cooling with outdoor air, etc.), outside air temperature, zone temperature, zone humidity, etc. Once the state is collected, various rules may be calculated related to the building equipment to determine whether a fault exists. 
     The system  600  can receive feedback  606  from a user on whether the fault that was computed is a false positive, false negative, or true positive. As discussed above, false positive can imply that although a rule was satisfied to determine that a fault existed, there is no fault. False negative can imply that although the rule was not satisfied, and therefore no fault was detected, the building equipment in fact does have a fault. And a true positive can imply that the fault that was detected is true and no adjustment needs to be made to the rule. 
     The system  600  can generate accumulated training data  608 . The accumulated training data can include the values of the parameters ε 1 , the state of the building equipment for each value of the parameters, and the labeled data including whether the fault determination was a false positive, false negative, or true positive. 
     The system  600  can generate neural networks  610  for predicting whether the fault determination was a false positive or a false negative based on the state s and parameters ε. The neural networks  610  can be trained by the system  600  based on the accumulated training data  608 . 
     The system  600  can then determine optimal values for the parameters ε. The system  600  can search a space of potential values for ε that consider false positives and false negatives predicted by the trained neural network models  610 . The optimization can be the relation  800  shown in  FIG.  8   . The optimization  612  performed by the system  600  can be a method of computing the optimal threshold of a trigger condition using the neural network models  610  of rewards and solving a constrained nonlinear optimization model. In some embodiments, the optimal values for the parameters found by the system  600  can be presented to a user for review and/or approval via a user interface, e.g., via the client device  368  or  448 . 
     Referring now to  FIG.  7   , a process  700  for identifying values for the parameters of the trigger rule  602  of  FIG.  6    is shown, according to an exemplary embodiment. The process  700  can be performed by the system  600  and/or any component of the system  600 . Furthermore, the process  700  can be performed by any computing device described herein. 
     In step  702 , the system  600  can perturb a building equipment with various values for thresholds ε. The result of the perturbed parameters can result in various states, s. The states can be states of the building equipment as described herein. The perturbations can result in pairs (s, ε) that can be used to determine feedback for false positive, false negative, or true positive. 
     In step  704 , the system  600  can accumulate the pairs and corresponding feedback to create neural network models, e.g., the neural networks  610  based on the data determined in step  702 . The neural networks  610  can predict false positive and false negative rewards as a function of the state and the threshold, e.g., FP=f(s,ε) and FN=f(s,ε). 
     In step  706 , the system  600  can determine a value for the parameter, ε that minimizes a relation, (α 1 ·FP+α 2 ·FN). The minimization is shown in relation  800  of  FIG.  8   . The values of α 1  and α 2  can weigh the various rewards in the relation that is minimized, e.g., the false positive and false negative reward. 
     In step  708 , the system  600  can periodically repeat the steps  702 - 706 . For example, the system  600  can repeat the steps at a defined time period (e.g., every day, week, month, etc.). In some embodiments, the retraining may occur if the rate of false positives and/or the rate of false negatives exceed an acceptable range set by the user. For example, if the user set the acceptable false positive rate to be 10% or lower, and the neural network model for false positives had a rate of 15%, the model may be retrained. 
     Referring now to  FIG.  9   , a process  900  of determining the optimal threshold for a rule (e.g., building equipment rule, trigger rule, or fault rule) is shown, according to an exemplary embodiment. The process  900  can be performed by the system  400 ,  500 , or  600  and/or any of their components or a combination of the components. The process  900  can be performed by any computing device or processing circuit described herein. Furthermore, the process  900  may be include additional steps or certain steps may be removed depending on embodiments. 
     In step  902 , the system can provide a rule including a threshold. The rule can pertain to any that is used by the FDD layer  416  to determine whether a fault exists within the building equipment or not. For example, if there is a rule that states if AHU mode is mechanical cooling with 100% outdoor air and the magnitude of the difference between outside air temperature and mixed air temperature is greater than a threshold, the AHU is faulty. 
     In step  904 , the system can receive a state of the building equipment. In some embodiments, the state can include any or all values that are used to calculate whether a rule is satisfied or not. In the above example, the AHU running in a mode with mechanical cooling with 100% outdoor air, the outside air temperature, and mixed air temperature can all be part of the state of the AHU. Further, the threshold can be the value that is being optimized so that the system can reduce or minimize the number of false positives and false negatives. 
     In step  906 , the system can assess, using a machine learning model, whether the determination of whether the building equipment has fault is a false positive or a false negative based on the state and the threshold. For example, if the above rule was satisfied, the system may determine that a fault exists in the AHU. However, in reality, the AHU may be operating normally and not have a fault as determined by occupants of the building or the building manager. Accordingly, the model&#39;s administrator may provide feedback to the system indicating that the fault is a false positive. In another example, the rule may not be satisfied and the system may then determine that the AHU does not have a fault. However, based on occupant or building manager feedback, it may be determined that the AHU is actually faulty, and the administrator may provide feedback to the system that there was a false negative. 
     In step  908 , the system may determine a new threshold based on the assessment of the machine learning model. This may be done using constrained nonlinear optimization that solves for the minimal number of false positives and false negatives based on the feedback received in step  906 . The new threshold may be higher or lower than the existing one, depending on the parameters set with the optimization equations. Further, the user may desire to reduce the number of false positives more than the false negatives, in which case the optimization parameters used in the equations may be adjust so that greater weight is given to minimizing the false positives. In other embodiments, the user may be interested in reducing the false negatives more than the false positives, in which case greater weight can be given to reducing the false negatives more than the false positives. 
     In step  910 , the system may replace the threshold with the new threshold to make subsequent determinations of whether the building equipment or other building equipment has a fault. The number of false positives and false negatives may be reduced and/or minimized. 
     Configuration of Exemplary Embodiments 
     The construction and arrangement of the systems and methods as shown in the various exemplary embodiments are illustrative only. Although only a few embodiments have been described in detail in this disclosure, many modifications are possible (e.g., variations in sizes, dimensions, structures, shapes and proportions of the various elements, values of parameters, mounting arrangements, use of materials, colors, orientations, etc.). For example, the position of elements may be reversed or otherwise varied and the nature or number of discrete elements or positions may be altered or varied. Accordingly, 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 varied or re-sequenced according to alternative embodiments. Other substitutions, modifications, changes, and omissions may be made in the design, operating conditions and arrangement of the exemplary embodiments without departing from the scope of the present disclosure. 
     The present disclosure contemplates methods, systems and program products on any machine-readable media for accomplishing various operations. The embodiments of the present disclosure may be implemented using existing computer processors, or by a special purpose computer processor for an appropriate system, incorporated for this or another purpose, or by a hardwired system. Embodiments within the scope of the present disclosure include program products comprising machine-readable media for carrying or having machine-executable instructions or data structures stored thereon. Such machine-readable media can be any available media that can be accessed by a general purpose or special purpose computer or other machine with a processor. By way of example, such machine-readable media can comprise RAM, ROM, EPROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to carry or store desired program code in the form of machine-executable instructions or data structures and which can be accessed by a general purpose or special purpose computer or other machine with a processor. When information is transferred or provided over a network or another communications connection (either hardwired, wireless, or a combination of hardwired or wireless) to a machine, the machine properly views the connection as a machine-readable medium. Thus, any such connection is properly termed 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. 
     Although the figures show a specific order of method steps, the order of the steps may differ from what is depicted. Also two or more steps may be performed concurrently or with partial concurrence. Such variation will depend on the software and hardware systems chosen and on designer choice. All such variations are within the scope of the disclosure. Likewise, software implementations could be accomplished with standard programming techniques with rule based logic and other logic to accomplish the various connection steps, processing steps, comparison steps and decision steps. 
     In various implementations, the steps and operations described herein may be performed on one processor or in a combination of two or more processors. For example, in some implementations, the various operations could be performed in a central server or set of central servers configured to receive data from one or more devices (e.g., edge computing devices/controllers) and perform the operations. In some implementations, the operations may be performed by one or more local controllers or computing devices (e.g., edge devices), such as controllers dedicated to and/or located within a particular building or portion of a building. In some implementations, the operations may be performed by a combination of one or more central or offsite computing devices/servers and one or more local controllers/computing devices. All such implementations are contemplated within the scope of the present disclosure. Further, unless otherwise indicated, when the present disclosure refers to one or more computer-readable storage media and/or one or more controllers, such computer-readable storage media and/or one or more controllers may be implemented as one or more central servers, one or more local controllers or computing devices (e.g., edge devices), any combination thereof, or any other combination of storage media and/or controllers regardless of the location of such devices.