Patent Publication Number: US-2023144177-A1

Title: Building control system with peer analysis based on weighted outlier detection

Description:
BACKGROUND 
     The present disclosure relates generally to control systems for buildings. The present disclosure relates more particularly to determining outlier building control systems based on peer analysis. 
     Peer analysis is a method for evaluating the performance of a system or piece of equipment relative to others with similar characteristics or operating under similar conditions. Peer analysis of the systems and units can involve amassing peer groups with common characteristics and calculating the values for one or more peer metrics for each member of a peer group based on measured and recorded data from the real system or unit. Using outlier detection methods the values of the peer metrics can be analyzed to identify systems or units that deviate markedly from other members in the same peer group. Because peer analysis involves comparing performance across discrete systems or units, sufficient data from each is necessary to generate an accurate analysis. However, if not enough data for a given system is available or the data is uncertain, the resulting analysis can be skewed and generate inaccurate results. 
     SUMMARY 
     One implementation of the present disclosure is a system for assessing relative performance among a plurality of heating, ventilation, or air condition (HVAC) assets, the system comprising a processing circuit configured to identify a peer group comprising two or more of the plurality of HVAC assets having a common characteristic, generate values of a peer metric for the HVAC assets in the peer group based on corresponding operation data associated with the HVAC assets, perform a weighted outlier detection process using the values of the peer metric, and initiate an automated action in response to detecting an outlier HVAC asset. t. 
     In some embodiments, the peer metric includes at least one peer metric selected from a plurality of peer metrics based on the peer group. 
     In some embodiments, the weighted outlier detection process includes assigning weights to the values of the peer metric, the weights a function of an amount of operation data used to generate the values of the peer metric. 
     In some embodiments, the operation data include a measured temperate and a setpoint temperature for the plurality of HVAC assets, and the peer metric is a temperature control error based on the measured temperature and the setpoint temperature for each of the plurality of HVAC assets of the peer group. 
     In some embodiments, the operation data include an actuation signal for a flow control device for the plurality of HVAC assets and the peer metric is a control effort based on the actuation signal for each of the plurality of HVAC assets of the peer group. 
     In some embodiments, the operation data include a compressor suction pressure and a compressor discharge pressure for the plurality of HVAC assets, and the peer metric is a compression ratio based on the compressor suction pressure and a compressor discharge pressure for each of the plurality of HVAC assets of the peer group. 
     In some embodiments, the processing circuit is further configured to generate values of a plurality of peer metric for the HVAC assets in the peer group based on corresponding operation data associated with the HVAC assets, wherein the operation data include a number of off times less than or equal to a first time threshold, a number of cycles, and a number of cycle times less than or equal to a second time threshold for the plurality of HVAC assets, and the plurality of peer metrics includes an off time metric and a cycle time metric based on the number of off times less than or equal to a first time period, number of cycles, and number of cycle times less than or equal to a second time period for each of the plurality of HVAC assets of the peer group. 
     In some embodiments, the plurality of HVAC assets includes a plurality of sensors configured to obtain the operation data associated with the plurality of HVAC assets. 
     In some embodiments, the weighted outlier detection process is a univariate process comparing the peer metric across the plurality of HVAC assets for the peer group and the univariate process includes a weighted Generalized Extreme Studentized Deviate test. 
     In some embodiments, the outlier detection process is a multivariate process comparing a plurality of peer metrics for the plurality of HVAC assets in the peer group and the multivariate process includes a modified Sequential Application of Wilks’s Multivariate Outlier Test. 
     In some embodiments the outlier detection process includes calculating a mean vector for a set comprising the values of the plurality of peer metrics, determining a matrix of the sums of squares and cross-products (“SSCP matrix”) for deviation scores of the set of values using the mean vector, weighting the SSCP matrix, wherein the weighting is a function of an amount of operation data used to generate the values of the plurality of peer metrics, determining a potential outlier HVAC asset from the plurality of HVAC assets as the HVAC asset corresponding to the peer metric value whose removal leads to the greatest reduction in the determinant of the weighted SSCP matrix, calculating a weighted Wilk’s Statistic using the peer metrics associated with the potential outlier HVAC asset and the weighted SSCP matrix, and comparing the weighted Wilk’s Statistic to a critical value to determine the outlier HVAC asset. 
     Another implementation of the present disclosure is a method for assessing relative performance among a plurality heating, ventilation, or air conditioning (HVAC) assets, the method including identifying a peer group comprising two or more of the plurality of HVAC assets having a common characteristic, generating values of a peer metric for the HVAC assets in the peer group based on corresponding operation data associated with the HVAC assets, performing a weighted outlier detection process using the values of the peer metric, and initiating an automated action in response to detecting an outlier HVAC asset. 
     In some embodiments, the automated action includes displaying the results of the outlier detection process to a user. 
     In some embodiments, the method further includes identifying a first time period for wherein the operation data associated with the plurality of HVAC assets belongs to the first time period. 
     In some embodiments, the operation data include a measured temperature and a setpoint temperature for the plurality of HVAC assets and the peer metric is a temperature control error based on the measured temperature and the setpoint temperature for each of the plurality of HVAC assets of the peer group. 
     In some embodiments, the operation data include an actuation signal for a flow control device for the plurality of HVAC assets, and the peer metric is a control effort based on the actuation signal for each of the plurality of HVAC assets of the peer group. 
     In some embodiments, the operation data include a compressor suction pressure and a compressor discharge pressure for the plurality of HVAC assets, and the peer metric is a compression ratio, based on the compressor suction pressure and the compressor discharge pressure for each of the plurality of HVAC assets of the peer group. 
     In some embodiments, the method further includes generating values of a plurality of peer metrics for the HVAC assets in the peer group based on corresponding operation data associated with the HVAC assets; wherein the operation data include a number of off times less than or equal to a first time period, a number of cycles, and a number of cycle times less than or equal to a second time period for the plurality of HVAC asset, and the plurality of peer metrics include an off time metric and a cycle time metric based on the number of off times less than or equal to a first time threshold, number of cycles, and number of cycle times less than or equal to a second time threshold for each of the plurality of HVAC assets of the peer group. 
     In some embodiments, the operation data include an energy consumption and heating or cooling output for the plurality of HVAC assets, and the peer metric is an efficiency based on the energy consumption and the heating or cooling output for each of the plurality of HVAC assets of the peer group. 
     In some embodiments, the weighted outlier detection process is at least one of a univariate process comparing the peer metric across the plurality of HVAC assets of the peer group and a multivariate process comparing a plurality of peer metrics for the plurality of HVAC assets in the peer group of a plurality of types. 
    
    
     
       BRIEF DESCRIPTION OF THE FIGURES 
       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 asset, according to some embodiments. 
         FIG.  2    is a block diagram of a waterside system which can be used to serve the heating or cooling loads of the building of  FIG.  1   , according to some embodiments. 
         FIG.  3    is a block diagram of an airside system which can be used to serve the heating or cooling loads of the building of  FIG.  1   , according to some embodiments. 
         FIG.  4    is a block diagram of a building management system (BMS) which can be used to monitor and control the building of  FIG.  1   , according to some embodiments. 
         FIG.  5    is a block diagram of another BMS which can be used to monitor and control the building of  FIG.  1   , according to some embodiments. 
         FIGS.  6 A- 6 B  are drawings of a variable refrigerant flow (VRF) system having one or more outdoor VRF units and multiple indoor VRF units, according to some embodiments. 
         FIG.  7    is a schematic diagram of a VRF system, according to some embodiments. 
         FIG.  8    is a block diagram of a building system  800  connected to a peer analysis system  802 , according to an exemplary embodiment. 
         FIG.  9    is a block diagram of a peer analysis system  802  connected to the building system  800  of  FIG.  8   , according to some embodiments. 
         FIG.  10    is an example of a peer group generated by the peer group generator of  FIG.  9   , according to some embodiments. 
         FIG.  11 A  is a graph illustrating relating a number of IDUs to a temperature control mean peer metric, according to some embodiments. 
         FIG.  11 B  is a graph illustrating relating a temperature control standard deviation to a temperature control mean, according to some embodiments. 
         FIG.  12    is a pair of graphs illustrating an unmodified Wilk’s multivariate outlier detection process to a weighted multivariate outlier detection process for a number of IDUs, according to some embodiments. 
         FIG.  13    is a flowchart of a peer analysis process, according to some embodiments. 
         FIG.  14    is a flowchart of the outlier detection process implemented in the peer analysis process of  FIG.  13   , according to some embodiments. 
         FIG.  15    is an illustration of a user interface for an univariate outlier detection process, according to some embodiments. 
         FIG.  16    is an illustration of a user interface for a multivariate outlier detection process, according to some embodiments. 
         FIG.  17    is an illustration of a user interface for a peer analysis test run using univariate outlier detection, according to some embodiments. 
         FIG.  18    is an illustration of a user interface including a peer analysis time period element for a selecting the time period for a peer analysis, according to some embodiments. 
         FIG.  19    is an illustration of a user interface for a peer analysis test run using multivariate outlier detection, according to some embodiments. 
     
    
    
     DETAILED DESCRIPTION 
     Referring generally to the FIGURES, systems and methods for performing peer analysis based on weighted outlier detection are shown, according to some embodiments. Peer analysis involves comparing peer metrics of HVAC assets or units amongst peer group members. Peer groups are groupings of HVAC assets or units that share similar characteristics or operate under similar conditions. Peer metrics can be measured or derived, and which peer metrics are calculated and compared may depend on the characteristics of the chosen peer group. The values of the peer metrics can be weighted according to the amount of data used to calculate the peer metric to account for small or uncertain data sets. 
     The overarching presumption behind peer analysis is that HVAC assets that are similar should have similar peer metrics. Peer metrics that deviate from the norm among members of a given peer group can be flagged and the corresponding HVAC assets or units marked as outliers. Outlier analysis on the values of the peer metrics is performed to discover these outliers, and it can include multivariate outlier analysis, univariate outlier analysis, DBCAN, isolation forest, Z-Score, etc. Univariate outlier detection analyzes the distribution of values for a single peer metric in the group. Multivariate outlier detection can detect outliers in an n-dimensional space of n-peer metrics. In some embodiments multivariate detection methods may detect outliers that are not detectable by univariate methods. The values of the peer metrics can be weighted based on the amount of available data used to calculate the metric relative to the total amount of data in the peer group to compensate for data collection methodologies that provide uncertain data. Advantageously, weighting the value of the peer metrics can reduce the effect of observations with uncertain data on outlier detection (i.e., reduce the number of false positives and false negatives). 
     Outlier systems or units may be faulty, operating under a non-ideal control scheme, a result of instrument error, etc. In some embodiments outlier systems or units may be over-performers, and instead indicate under-performance in the rest of the members of the peer group. Outlier detection allows for identifying faults before they become critical and for improving the overall performance of a peer group. 
     If a specific HVAC asset or unit is determined to be an outlier, an automated action can be initiated, and the automated action can be directed to address the outlier. The automated action can be a part of various applications such as control-based applications to improve the performance of the outlier, the peer group, or both. For example, automated actions can include performing a new peer analysis, alerting a user to the outlier, repairing existing building equipment, changing a data value or set point in a system or unit, modifying a control scheme, etc. Based on the initiation of an automated action, the automated action can be performed in order to address the outlier, and/or the non-outlier systems. These and other features of peer analysis based on weighted outlier detection are discussed in greater details below. 
     Building HVAC Assets 
     Referring now to  FIGS.  1 - 5   , several building management systems (BMS) and HVAC assets in which the systems and methods of the present disclosure can be implemented are shown, according to some embodiments. In brief overview,  FIG.  1    shows a building  10  equipped with a HVAC asset  100 .  FIG.  2    is a block diagram of a waterside system  200  which can be used to serve building  10 .  FIG.  3    is a block diagram of an airside system  300  which can be used to serve building  10 .  FIG.  4    is a block diagram of a BMS which can be used to monitor and control building  10 .  FIG.  5    is a block diagram of another BMS which can be used to monitor and control building  10 . 
     Referring particularly to  FIG.  1   , a perspective view of a building  10  is shown. Building  10  is served by a BMS. A 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 asset, 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. 
     The BMS that serves building  10  includes a HVAC asset  100 . HVAC asset  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 asset  100  is shown to include a waterside system  120  and an airside system  130 . Waterside system  120  may provide a heated or chilled fluid to an air handling unit of airside system  130 . Airside system  130  may use the heated or chilled fluid to heat or cool an airflow provided to building  10 . 
     HVAC asset  100  is shown to include a chiller  102 , a boiler  104 , and a rooftop air handling unit (AHU)  106 . Waterside system  120  may use boiler  104  and chiller  102  to heat or cool a working fluid (e.g., water, glycol, etc.) and may 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  may 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  may 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  may 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  may 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 may then return to chiller  102  or boiler  104  via piping  110 . 
     Airside system  130  may deliver the airflow supplied by AHU  106  (i.e., the supply airflow) to building  10  via air supply ducts  112  and may 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  may receive input from sensors located within AHU  106  and/or within the building zone and may adjust the flow rate, temperature, or other attributes of the supply airflow through AHU  106  to achieve setpoint conditions for the building zone. 
     HVAC asset  100  thereby provides heating and cooling to the building  10 . The building  10  also includes other sources of heat transfer that the indoor air temperature in the building  10 . The building mass (e.g., walls, floors, furniture) influences the indoor air temperature in building  10  by storing or transferring heat (e.g., if the indoor air temperature is less than the temperature of the building mass, heat transfers from the building mass to the indoor air). People, electronic devices, other appliances, etc. (“heat load”) also contribute heat to the building  10  through body heat, electrical resistance, etc. Additionally, the outside air temperature impacts the temperature in the building  10  by providing heat to or drawing heat from the building  10 . 
     Waterside System 
     Referring now to  FIG.  2   , a block diagram of a waterside system  200  is shown, according to some embodiments. In various embodiments, waterside system  200  may supplement or replace waterside system  120  in HVAC asset  100  or can be implemented separate from HVAC asset  100 . When implemented in HVAC asset  100 , waterside system  200  can include a subset of the HVAC devices in HVAC asset  100  (e.g., boiler  104 , chiller  102 , pumps, valves, etc.) and may 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 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  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  may 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  may store hot and cold thermal energy, respectively, for subsequent use. 
     Hot water loop  214  and cold water loop  216  may 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 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 thermal energy loads. In other embodiments, subplants  202 - 212  may 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 disclosure. 
     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  may 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  may 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 . 
     Airside System 
     Referring now to  FIG.  3   , a block diagram of an airside system  300  is shown, according to some embodiments. In various embodiments, airside system  300  may supplement or replace airside system  130  in HVAC asset  100  or can be implemented separate from HVAC asset  100 . When implemented in HVAC asset  100 , airside system  300  can include a subset of the HVAC devices in HVAC asset  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  may 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  may receive return air  304  from building zone  306  via return air duct  308  and may 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 air 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  may communicate with an AHU controller  330  via a communications link  332 . Actuators  324 - 328  may receive control signals from AHU controller  330  and may 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  may 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  may receive a chilled fluid from waterside system  200  (e.g., from cold water loop  216 ) via piping  342  and may 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 BMS controller  366 , etc.) to modulate an amount of cooling applied to supply air  310 . 
     Heating coil  336  may receive a heated fluid from waterside system  200 (e.g., from hot water loop  214 ) via piping  348  and may 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 BMS 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  may communicate with AHU controller  330  via communications links  358 - 360 . Actuators  354 - 356  may receive control signals from AHU controller  330  and may provide feedback signals to AHU 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  may 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  may 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 management system (BMS) controller  366  and a client device  368 . BMS 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 asset  100 , and/or other controllable systems that serve building  10 . BMS controller  366  may communicate with multiple downstream building systems or subsystems (e.g., HVAC asset  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 BMS 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 BMS controller  366 . 
     In some embodiments, AHU controller  330  receives information from BMS controller  366  (e.g., commands, setpoints, operating boundaries, etc.) and provides information to BMS controller  366  (e.g., temperature measurements, valve or actuator positions, operating statuses, diagnostics, etc.). For example, AHU controller  330  may provide BMS 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 BMS 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 asset  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  may communicate with BMS controller  366  and/or AHU controller  330  via communications link  370 . 
     Building Management Systems 
     Referring now to  FIG.  4   , a block diagram of a building management system (BMS)  400  is shown, according to some embodiments. BMS  400  can be implemented in building  10  to automatically monitor and control various building functions. BMS  400  is shown to include BMS 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  may also or alternatively include a refrigeration subsystem, an advertising or signage subsystem, a cooking subsystem, a vending subsystem, a printer or copy service subsystem, or any other type of building subsystem that uses controllable equipment and/or sensors to monitor or control building  10 . In some embodiments, building subsystems  428  include waterside system  200  and/or airside system  300 , as described in  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 asset  100 , as described in  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   , BMS controller  366  is shown to include a communications interface  407  and a BMS interface  409 . Interface  407  may facilitate communications between BMS 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 BMS controller  366  and/or building subsystems  428 . Interface  407  may also facilitate communications between BMS controller  366  and client devices  448 . BMS interface  409  may facilitate communications between BMS 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 BMS interface  409  is an Ethernet interface. In other embodiments, both communications interface  407  and BMS interface  409  are Ethernet interfaces or are the same Ethernet interface. 
     Still referring to  FIG.  4   , BMS 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 BMS 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 some embodiments, 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, BMS controller  366  is implemented within a single computer (e.g., one server, one housing, etc.). In various other embodiments BMS 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 BMS controller  366 , in some embodiments, applications  422  and  426  can be hosted within BMS 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 BMS  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  may also or alternatively be configured to provide configuration GUIs for configuring BMS 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 BMS interface  409 . 
     Building subsystem integration layer  420  can be configured to manage communications between BMS controller  366  and building subsystems  428 . For example, building subsystem integration layer  420  may 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  may also be configured to manage communications between building subsystems  428 . Building subsystem integration layer  420  may 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  may receive inputs from other layers of BMS 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 may 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 some embodiments, 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  may also include control logic configured to determine when to utilize stored energy. For example, demand response layer  414  may determine to begin using energy from energy storage  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 may 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  may 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’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 building subsystems  428  such that the building subsystems  428  behave as a single integrated supersystem. In some embodiments, 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 may advantageously 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 may 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 BMS devices or subsystems. For example, AM&amp;V layer  412  may 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  may 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  may automatically diagnose and respond to detected faults. The responses to detected or diagnosed faults can include providing an alert 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 some embodiments, FDD layer  416  (or a policy executed by an integrated control engine or business rules engine) may 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  may 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  may generate temporal (i.e., time-series) data indicating the performance of BMS  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 alert a user to repair the fault before it becomes more severe. 
     Referring now to  FIG.  5   , a block diagram of another building management system (BMS)  500  is shown, according to some embodiments. BMS  500  can be used to monitor and control the devices of HVAC asset  100 , waterside system  200 , airside system  300 , building subsystems  428 , as well as other types of BMS devices (e.g., lighting equipment, security equipment, etc.) and/or HVAC equipment. 
     BMS  500  provides a system architecture that facilitates automatic equipment discovery and equipment model distribution. Equipment discovery can occur on multiple levels of BMS  500  across multiple different communications busses (e.g., a system bus  554 , zone buses  556 - 560  and  564 , sensor/actuator bus  566 , etc.) and across multiple different communications protocols. In some embodiments, equipment discovery is accomplished using active node tables, which provide status information for devices connected to each communications bus. For example, each communications bus can be monitored for new devices by monitoring the corresponding active node table for new nodes. When a new device is detected, BMS  500  can begin interacting with the new device (e.g., sending control signals, using data from the device) without user interaction. 
     Some devices in BMS  500  present themselves to the network using equipment models. An equipment model defines equipment object attributes, view definitions, schedules, trends, and the associated BACnet value objects (e.g., analog value, binary value, multistate value, etc.) that are used for integration with other systems. Some devices in BMS  500  store their own equipment models. Other devices in BMS  500  have equipment models stored externally (e.g., within other devices). For example, a zone coordinator  508  can store the equipment model for a bypass damper  528 . In some embodiments, zone coordinator  508  automatically creates the equipment model for bypass damper  528  or other devices on zone bus  558 . Other zone coordinators can also create equipment models for devices connected to their zone busses. The equipment model for a device can be created automatically based on the types of data points exposed by the device on the zone bus, device type, and/or other device attributes. Several examples of automatic equipment discovery and equipment model distribution are discussed in greater detail below. 
     Still referring to  FIG.  5   , BMS  500  is shown to include a system manager  502 ; several zone coordinators  506 ,  508 ,  510  and  518 ; and several zone controllers  524 ,  530 ,  532 ,  536 ,  548 , and  550 . System manager  502  can monitor data points in BMS  500  and report monitored variables to various monitoring and/or control applications. System manager  502  can communicate with client devices  504  (e.g., user devices, desktop computers, laptop computers, mobile devices, etc.) via a data communications link  574  (e.g., BACnet IP, Ethernet, wired or wireless communications, etc.). System manager  502  can provide a user interface to client devices  504  via data communications link  574 . The user interface may allow users to monitor and/or control BMS  500  via client devices  504 . 
     In some embodiments, system manager  502  is connected with zone coordinators  506 - 510  and  518  via a system bus  554 . System manager  502  can be configured to communicate with zone coordinators  506 - 510  and  518  via system bus  554  using a master-slave token passing (MSTP) protocol or any other communications protocol. System bus  554  can also connect system manager  502  with other devices such as a constant volume (CV) rooftop unit (RTU)  512 , an input/output module (IOM)  514 , a thermostat controller  516  (e.g., a TEC5000 series thermostat controller), and a network automation engine (NAE) or third-party controller  520 . RTU  512  can be configured to communicate directly with system manager  502  and can be connected directly to system bus  554 . Other RTUs can communicate with system manager  502  via an intermediate device. For example, a wired input  562  can connect a third-party RTU  542  to thermostat controller  516 , which connects to system bus  554 . 
     System manager  502  can provide a user interface for any device containing an equipment model. Devices such as zone coordinators  506 - 510  and  518  and thermostat controller  516  can provide their equipment models to system manager  502  via system bus  554 . In some embodiments, system manager  502  automatically creates equipment models for connected devices that do not contain an equipment model (e.g., IOM  514 , third party controller  520 , etc.). For example, system manager  502  can create an equipment model for any device that responds to a device tree request. The equipment models created by system manager  502  can be stored within system manager  502 . System manager  502  can then provide a user interface for devices that do not contain their own equipment models using the equipment models created by system manager  502 . In some embodiments, system manager  502  stores a view definition for each type of equipment connected via system bus  554  and uses the stored view definition to generate a user interface for the equipment. 
     Each zone coordinator  506 - 510  and  518  can be connected with one or more of zone controllers  524 ,  530 - 532 ,  536 , and  548 - 550  via zone buses  556 ,  558 ,  560 , and  564 . Zone coordinators  506 - 510  and  518  can communicate with zone controllers  524 ,  530 - 532 ,  536 , and  548 - 550  via zone busses  556 - 560  and  564  using a MSTP protocol or any other communications protocol. Zone busses  556 - 560  and  564  can also connect zone coordinators  506 - 510  and  518  with other types of devices such as variable air volume (VAV) RTUs  522  and  540 , changeover bypass (COBP) RTUs  526  and  552 , bypass dampers  528  and  546 , and PEAK controllers  534  and  544 . 
     Zone coordinators  506 - 510  and  518  can be configured to monitor and command various zoning systems. In some embodiments, each zone coordinator  506 - 510  and  518  monitors and commands a separate zoning system and is connected to the zoning system via a separate zone bus. For example, zone coordinator  506  can be connected to VAV RTU  522  and zone controller  524  via zone bus  556 . Zone coordinator  508  can be connected to COBP RTU  526 , bypass damper  528 , COBP zone controller  530 , and VAV zone controller  532  via zone bus  558 . Zone coordinator  510  can be connected to PEAK controller  534  and VAV zone controller  536  via zone bus  560 . Zone coordinator  518  can be connected to PEAK controller  544 , bypass damper  546 , COBP zone controller  548 , and VAV zone controller  550  via zone bus  564 . 
     A single model of zone coordinator  506 - 510  and  518  can be configured to handle multiple different types of zoning systems (e.g., a VAV zoning system, a COBP zoning system, etc.). Each zoning system can include a RTU, one or more zone controllers, and/or a bypass damper. For example, zone coordinators  506  and  510  are shown as Verasys VAV engines (VVEs) connected to VAV RTUs  522  and  540 , respectively. Zone coordinator  506  is connected directly to VAV RTU  522  via zone bus  556 , whereas zone coordinator  510  is connected to a third-party VAV RTU  540  via a wired input  568  provided to PEAK controller  534 . Zone coordinators  508  and  518  are shown as Verasys COBP engines (VCEs) connected to COBP RTUs  526  and  552 , respectively. Zone coordinator  508  is connected directly to COBP RTU  526  via zone bus  558 , whereas zone coordinator  518  is connected to a third-party COBP RTU  552  via a wired input  570  provided to PEAK controller  544 . 
     Zone controllers  524 ,  530 - 532 ,  536 , and  548 - 550  can communicate with individual BMS devices (e.g., sensors, actuators, etc.) via sensor/actuator (SA) busses. For example, VAV zone controller  536  is shown connected to networked sensors  538  via SA bus  566 . Zone controller  536  can communicate with networked sensors  538  using a MSTP protocol or any other communications protocol. Although only one SA bus  566  is shown in  FIG.  5   , it should be understood that each zone controller  524 ,  530 - 532 ,  536 , and  548 - 550  can be connected to a different SA bus. Each SA bus can connect a zone controller with various sensors (e.g., temperature sensors, humidity sensors, pressure sensors, light sensors, occupancy sensors, etc.), actuators (e.g., damper actuators, valve actuators, etc.) and/or other types of controllable equipment (e.g., chillers, heaters, fans, pumps, etc.). 
     Each zone controller  524 ,  530 - 532 ,  536 , and  548 - 550  can be configured to monitor and control a different building zone. Zone controllers  524 ,  530 - 532 ,  536 , and  548 - 550  can use the inputs and outputs provided via their SA busses to monitor and control various building zones. For example, a zone controller  536  can use a temperature input received from networked sensors  538  via SA bus  566  (e.g., a measured temperature of a building zone) as feedback in a temperature control algorithm. Zone controllers  524 ,  530 - 532 ,  536 , and  548 - 550  can use various types of control algorithms (e.g., state-based algorithms, extremum seeking control (ESC) algorithms, proportional-integral (PI) control algorithms, proportional-integral-derivative (PID) control algorithms, model predictive control (MPC) algorithms, feedback control algorithms, etc.) to control a variable state or condition (e.g., temperature, humidity, airflow, lighting, etc.) in or around building  10 . 
     Variable Refrigerant Flow Systems 
     Referring now to  FIGS.  6 A- 6 B , a variable refrigerant flow (VRF) system  600  is shown, according to some embodiments. VRF system  600  is shown to include one or more outdoor VRF units  602  and a plurality of indoor VRF units  604 . Outdoor VRF units  602  can be located outside a building and can operate to heat or cool a refrigerant. Outdoor VRF units  602  can consume electricity to convert refrigerant between liquid, gas, and/or super-heated gas phases. Indoor VRF units  604  can be distributed throughout various building zones within a building and can receive the heated or cooled refrigerant from outdoor VRF units  602 . Each indoor VRF unit  604  can provide temperature control for the particular building zone in which the indoor VRF unit  604  is located. Although the term “indoor” is used to denote that the indoor VRF units  604  are typically located inside of buildings, in some cases one or more indoor VRF units are located “outdoors” (i.e., outside of a building) for example to heat/cool a patio, entryway, walkway, etc. 
     One advantage of VRF system  600  is that some indoor VRF units  604  can operate in a cooling mode while other indoor VRF units  604  operate in a heating mode. For example, each of outdoor VRF units  602  and indoor VRF units  604  can operate in a heating mode, a cooling mode, or an off mode. Each building zone can be controlled independently and can have different temperature setpoints. In some embodiments, each building has up to three outdoor VRF units  602  located outside the building (e.g., on a rooftop) and up to 128 indoor VRF units  604  distributed throughout the building (e.g., in various building zones). Building zones may include, among other possibilities, apartment units, offices, retail spaces, and common areas. In some cases, various building zones are owned, leased, or otherwise occupied by a variety of tenants, all served by the VRF system  600 . 
     Many different configurations exist for VRF system  600 . In some embodiments, VRF system  600  is a two-pipe system in which each outdoor VRF unit  602  connects to a single refrigerant return line and a single refrigerant outlet line. In a two-pipe system, all of outdoor VRF units  602  may operate in the same mode since only one of a heated or chilled refrigerant can be provided via the single refrigerant outlet line. In other embodiments, VRF system  600  is a three-pipe system in which each outdoor VRF unit  602  connects to a refrigerant return line, a hot refrigerant outlet line, and a cold refrigerant outlet line. In a three-pipe system, both heating and cooling can be provided simultaneously via the dual refrigerant outlet lines. 
     Referring now to  FIG.  7   , a block diagram illustrating a VRF system  700  is shown, according to an exemplary embodiment. VRF system  700  is shown to include outdoor VRF unit  702 , several heat recovery units  706 , and several indoor VRF units  704 . Although  FIG.  7    shows one outdoor VRF unit  702 , embodiments including multiple outdoor VRF units  702  are also within the scope of the present disclosure. Outdoor VRF unit  702  may include a compressor  708 , a fan  710 , or other power-consuming refrigeration components configured convert a refrigerant between liquid, gas, and/or super-heated gas phases. Indoor VRF units  704  can be distributed throughout various building zones within a building and can receive the heated or cooled refrigerant from outdoor VRF unit  702 . Each indoor VRF unit  704  can provide temperature control for the particular building zone in which the indoor VRF unit  704  is located. Heat recovery units  706  can control the flow of a refrigerant between outdoor VRF unit  702  and indoor VRF units  704  (e.g., by opening or closing valves) and can minimize the heating or cooling load to be served by outdoor VRF unit  702 . 
     Outdoor VRF unit  702  is shown to include a compressor  708  and a heat exchanger  712 . Compressor  708  circulates a refrigerant between heat exchanger  712  and indoor VRF units  704 . The compressor  708  operates at a variable frequency as controlled by VRF Controller  714 . At higher frequencies, the compressor  708  provides the indoor VRF units  704  with greater heat transfer capacity. Electrical power consumption of compressor  708  increases proportionally with compressor frequency. 
     Heat exchanger  712  can function as a condenser (allowing the refrigerant to reject heat to the outside air) when VRF system  700  operates in a cooling mode or as an evaporator (allowing the refrigerant to absorb heat from the outside air) when VRF system  700  operates in a heating mode. Fan  710  provides airflow through heat exchanger  712 . The speed of fan  710  can be adjusted (e.g., by VRF Controller  714 ) to modulate the rate of heat transfer into or out of the refrigerant in heat exchanger  712 . 
     Each indoor VRF unit  704  is shown to include a heat exchanger  716  and an expansion valve  718 . Each of heat exchangers  716  can function as a condenser (allowing the refrigerant to reject heat to the air within the room or zone) when the indoor VRF unit  704  operates in a heating mode or as an evaporator (allowing the refrigerant to absorb heat from the air within the room or zone) when the indoor VRF unit  704  operates in a cooling mode. Fans  720  provide airflow through heat exchangers  716 . The speeds of fans  720  can be adjusted (e.g., by indoor unit controls circuits  722 ) to modulate the rate of heat transfer into or out of the refrigerant in heat exchangers  716 . 
     In  FIG.  7   , indoor VRF units  704  are shown operating in the cooling mode. In the cooling mode, the refrigerant is provided to indoor VRF units  704  via cooling line  724 . The refrigerant is expanded by expansion valves  718  to a cold, low pressure state and flows through heat exchangers  716  (functioning as evaporators) to absorb heat from the room or zone within the building. The heated refrigerant then flows back to outdoor VRF unit  702  via return line  726  and is compressed by compressor  708  to a hot, high pressure state. The compressed refrigerant flows through heat exchanger  712  (functioning as a condenser) and rejects heat to the outside air. The cooled refrigerant can then be provided back to indoor VRF units  704  via cooling line  724 . In the cooling mode, flow control valves  728  can be closed and expansion valve  730  can be completely open. 
     In the heating mode, the refrigerant is provided to indoor VRF units  704  in a hot state via heating line  732 . The hot refrigerant flows through heat exchangers  716  (functioning as condensers) and rejects heat to the air within the room or zone of the building. The refrigerant then flows back to outdoor VRF unit via cooling line  724  (opposite the flow direction shown in  FIG.  7   ). The refrigerant can be expanded by expansion valve  730  to a colder, lower pressure state. The expanded refrigerant flows through heat exchanger  712  (functioning as an evaporator) and absorbs heat from the outside air. The heated refrigerant can be compressed by compressor  708  and provided back to indoor VRF units  704  via heating line  732  in a hot, compressed state. In the heating mode, flow control valves  728  can be completely open to allow the refrigerant from compressor  708  to flow into heating line  732 . 
     As shown in  FIG.  7   , each indoor VRF unit  704  includes an indoor unit controls circuit  722 . Indoor unit controls circuit  722  controls the operation of components of the indoor VRF unit  704 , including the fan  720  and the expansion valve  718 , in response to a building zone temperature setpoint or other request to provide heating/cooling to the building zone. The indoor unit controls circuit  722  may also determine a heat transfer capacity required by the indoor VRF unit  704  and transmit a request to the outdoor VRF unit  702  requesting that the outdoor VRF unit  702  operate at a corresponding capacity to provide heated/cooled refrigerant to the indoor VRF unit  704  to allow the indoor VRF unit  704  to provide a desired level of heating/cooling to the building zone. 
     Each indoor unit controls circuit  722  is shown as communicably coupled to one or more sensors  750  and a user input device  752 . In some embodiments, the one or more sensors  750  may include a temperature sensor (e.g., measuring indoor air temperature), a humidity sensor, and/or a sensor measuring some other environmental condition of a building zone served by the indoor VRF unit  704 . In some embodiments, the one or more sensors include an occupancy detector configured to detect the presence of one or more people in the building zone and provide an indication of the occupancy of the building zone to the indoor unit controls circuit  722 . 
     Each user input device  752  may be located in the building zone served by a corresponding indoor unit  704 . The user input device  752  allows a user to input a request to the VRF system  700  for heating or cooling for the building zone and/or a request for the VRF system  700  to stop heating/cooling the building zone. According to various embodiments, the user input device  752  may include a switch, button, set of buttons, thermostat, touchscreen display, etc. The user input device  752  thereby allows a user to control the VRF system  700  to receive heating/cooling when desired by the user. 
     The indoor unit controls circuit  722  may thereby receive an indication of the occupancy of a building zone (e.g., from an occupancy detector of sensors  750  and/or an input of a user via user input device  752 ). In response, the indoor unit controls circuit  722  may generate a new request for the outdoor VRF unit  702  to operate at a requested operating capacity to provide refrigerant to the indoor unit  704 . The indoor unit controls circuit  722  may also receive an indication that the building zone is unoccupied and, in response, generate a signal instructing the outdoor VRF unit  702  to stop operating at the requested capacity. The indoor unit controls circuit  722  may also control various components of the indoor unit  704 , for example by generating a signal to turn the fan  720  on and off. 
     The outdoor unit controls circuit  714  may receive heating/cooling capacity requests from one or more indoor unit controls circuits  722  and aggregate the requests to determine a total requested operating capacity. Accordingly, the total requested operating capacity may be influenced by the occupancy of each of the various building zones served by various indoor units  704 . In many cases, a when a person or people first enter a building zone and a heating/cooling request for that zone is triggered, the total requested operating capacity may increase significantly, for example reaching a maximum operating capacity. Thus, the total request operating capacity may vary irregularly and unpredictably as a result of the sporadic occupation of various building zones. 
     The outdoor unit controls circuit  714  is configured to control the compressor  708  and various other elements of the outdoor unit  702  to operate at an operating capacity based at least in part on the total requested operating capacity. At higher operating capacities, the outdoor unit  702  consumes more power, which increases utility costs. In some embodiments, the VRF controller may be capable of 
     For an operator, owner, lessee, etc. of a VRF system, it may be desirable to minimize power consumption and utility costs to save money, improve environmental sustainability, reduce wear-and-tear on equipment, etc. In some cases, multiple entities or people benefit from reduced utility costs, for example according to various cost apportionment schemes for VRF systems described in U.S. Pat. Application No. 15/920,077 filed Mar. 13, 2018, incorporated by reference herein in its entirety. Thus, as described in detail below, the controls circuit  714  may be configured to manage the operating capacity of the outdoor VRF unit  702  to reduce utility costs while also providing comfort to building occupants. Accordingly, in some embodiments, the controls circuit  714  may be operable in concert with systems and methods described in P.C.T. Patent Application No. PCT/US2017/039937 filed Jun. 29, 2017, and/or U.S. Pat. Application No. 15/635,754 filed Jun. 28, 2017, both of which are incorporated by reference herein in their entireties. 
     Peer Analysis System 
     Referring generally to  FIGS.  8 - 19   , systems and methods for performing peer analysis to detect outlier systems or pieces of equipment are shown, according to some embodiments. Peer analysis is a process used to evaluate the performance of systems or pieces of equipment relative to their peers, using one or more peer metrics. Peer analysis begins with creating peer groups of systems or pieces of equipment based on one or more shared characteristics. One or more peer metrics can be generated based on system data for each of the systems and/or pieces of equipment in the peer group allowing for the systems to be compared. With outlier analysis, the peer metric(s) are analyzed using outlier detection methods to identify systems operating differently from other systems within the same peer group. Outlier detection is useful for determining both systems that are underperforming as compared to their peer group as well as systems that may be overperforming. Outliers may exist due to mechanical faults, changes in system behavior, human error, instrument error, or simply the result of natural deviations in a population. Detecting outliers early can identify faults before they become critical, or comparatively, allow for adjusting underperforming systems to match overperforming outliers without having to unnecessarily bear the lost efficiency of the underperforming systems. Outlier detection requires a sufficient amount of data to prove accurate. To account for circumstances where not enough data is available, or the available data is uncertain, the peer metric values can be weighted prior to the outlier detection process. The weights are based on the amount of data used to calculate the peer metric(s). Advantageously, weighting the peer metric values allows the outlier detection process to accurately identify outliers in small data sets. The systems and methods of  FIGS.  8 - 19    can be implemented using or as a part of a building management system or HVAC asset, for example HVAC asset  100  or VRF system  600 , according to some embodiments. 
     Referring now to  FIG.  8   , a block diagram of a building system  800  is shown, according to an exemplary embodiment. System  800  may include many of the same components as BMS  400  and BMS  500  as described with reference to  FIGS.  4 - 5   . For example, system  800  is shown to include building  10 , network  446 , and client devices  448 . Building  10  is shown to include connected equipment  810 , which can include any type of equipment used to monitor and/or control building  10 . Connected equipment  810  can include chillers  812 , AHUs  814 , VRF  816 , batteries  817 , or any other type of equipment in a building system (e.g., heaters, economizers, valves, actuators, dampers, cooling towers, fans, pumps, etc.) or building management system (e.g., lighting equipment, security equipment, refrigeration equipment, etc.). Connected equipment  810  can include any of the equipment of HVAC asset  100 , waterside system  200 , airside system  300 , BMS  400 , BMS  500 , VRF  600 , and/or VRF  700  as described with reference to  FIGS.  1 - 7   . 
     Connected equipment  810  can be outfitted with sensors to monitor various conditions of the connected equipment  810  (e.g., power consumption, on/off states, operating efficiency, temperature etc.). For example, chillers  812  can include sensors configured to monitor chiller variables such as chilled water temperature, condensing water temperature, and refrigerant properties (e.g., refrigerant pressure, refrigerant temperature, etc.) at various locations in the refrigeration circuit, and VRF sensors can be configured to monitor measured temperature, setpoint temperature, compressor on time, compressor off time, etc. In some embodiments, chiller  700  includes sensors that measure a set of monitored variables at various locations along the refrigeration circuit. Similarly, AHUs  814  can be outfitted with sensors to monitor AHU variables such as supply air temperature and humidity, outside air temperature and humidity, return air temperature and humidity, chilled fluid temperature, heated fluid temperature, damper position, etc. In general, connected equipment  810  can monitor and report variables that characterize the performance of the connected equipment  810 . Each monitored variable can be forwarded to BMS  806  as a data point including a point ID and a point value. 
     Monitored variables can include any measured or calculated values indicating the performance of and/or identifying connected equipment  810  and/or the components thereof. For example, monitored variables can include one or more measured or calculated temperatures (e.g., refrigerant temperatures, cold water supply temperatures, hot water supply temperatures, supply air temperatures, zone temperatures, etc.), pressures (e.g., evaporator pressure, condenser pressure, supply air pressure, etc.), flow rates (e.g., cold water flow rates, hot water flow rates, refrigerant flow rates, supply air flow rates, etc.), valve positions, resource consumptions (e.g., power consumption, water consumption, electricity consumption, etc.), control setpoints, model parameters (e.g., regression model coefficients), or any other time-series values that provide information about how the corresponding system, device, or process is performing. Monitored variables can be received from connected equipment  810  and/or from various components thereof. For example, monitored variables can be received from one or more controllers (e.g., BMS controllers, subsystem controllers, HVAC controllers, subplant controllers, AHU controllers, device controllers, etc.), BMS devices (e.g., chillers, cooling towers, pumps, heating elements, etc.), or collections of BMS devices. 
     Connected equipment  810  can also report equipment status information. Equipment status information can include, for example, the operational status of the equipment, an operating mode (e.g., low load, medium load, high load, etc.), an indication of whether the equipment is running under normal or abnormal conditions, the hours during which the equipment is running, a safety fault code, a manufacturer, or any other information that indicates the current status of connected equipment  810 . In some embodiments, each device of connected equipment  810  includes a control panel. Control panel  710  can be configured to collect monitored variables and equipment status information from connected equipment  810  and provide the collected data to BMS  806 . 
     Connected equipment  810  can provide monitored variables and equipment status information to BMS  806 . BMS  806  can include a building controller (e.g., BMS controller  366 ), a system manager (e.g., system manager  502 ), a network automation engine, or any other system or device of building  10  configured to communicate with connected equipment  810 . BMS  806  may include some or all of the components of BMS  400  or BMS  500 , as described with reference to  FIGS.  4 - 5   . In some embodiments, the monitored variables and the equipment status information are provided to BMS  806  as data points. Each data point can include a point ID and a point value. The point ID can identify the type of data point or a variable measured by the data point (e.g., condenser pressure, refrigerant temperature, power consumption, etc.). Monitored variables can be identified by name or by an alphanumeric code (e.g., Chilled_Water_Temp, 7694, etc.). The point value can include an alphanumeric value indicating the current value of the data point. 
     BMS  806  can broadcast the monitored variables and the equipment status information (i.e., operati data) as operation data  828  to a peer analysis system  802  via network  446 . In some embodiments, peer analysis system  802  is a component of BMS  806 . For example, peer analysis system  802  can be implemented as part of a METASYS® brand building automation system, as sold by Johnson Controls Inc. In other embodiments, peer analysis system  802  can be a component of a remote computing system or cloud-based computing system configured to receive and process data from one or more building management systems via network  446 . In other embodiments, peer analysis system  802  can be a component of a subsystem level controller (e.g., a HVAC controller, a VRF controller), a subplant controller, a device controller (e.g., AHU controller  330 , a chiller controller, etc.), a field controller, a computer workstation, a client device, or any other system or device that receives and processes monitored variables from connected equipment  810 . 
     Peer analysis system  802  may also receive system information (i.e., operation data) from additional BMSs, such as BMS  822  and BMS  824 , or any number of BMS systems connected to peer analysis system  802  via network  446 . Peer analysis system  802  may use the monitored variables and/or the equipment status information of operation data  828  provided to it to identify connected equipment  810 . In particular, the peer analysis system  802  may generate one or more peer groups of systems or pieces of connected equipment  810  and equipment and/or systems in other connected BMS systems, generate peer metrics for each member of the peer group, and perform weighted outlier detection processes using the peer metric(s) to detect one or more outliers in the peer groups. 
     Peer analysis system  802  may also send outlier(s)  818  to service technicians  820 . In some embodiments, service technicians  820  based on outlier(s)  818  may also be provided with and/or perform an automated action  834  to correct the operating performance of one of the plurality of HVAC assets. For example a maintenance request for a specific outlier unit. Still in other embodiments, automated action  834  generated by peer analysis system  802  may be automatically applied to BMS  806  and/or the other systems connected to peer analysis system  806 . For example, peer analysis system  802  may instruct BMS  802  to alter one or more monitored variables and/or setpoints for connected equipment  810  in building  10 . 
     Referring now to  FIG.  9   , a block diagram  900  illustrating peer analysis system  802  in greater detail is shown, according to some embodiments. Peer analysis system  802  is shown to include peer group generator  912 , peer metric generator  916 , weighted outlier detection module  920  and automated action generator  926  for detecting one or more outliers in a peer group, according to some embodiments. For example, peer analysis system  802  may detect underperforming VRF systems in a peer group of all VRF systems operated in a peer group of all VRF systems in a geographic region. 
     Peer analysis system  802  is shown to include a communications interface  928  and a processing circuit  906  having a processor  908  and memory  910 , according to some embodiments. In some embodiments, communications interface  928  facilitates communications between peer analysis system  802 , BMS  806 , connected equipment  810  (e.g., HVAC asset  100 , VRF system  600 , etc.). In some embodiments, connected equipment  810  includes one or more sensors for collecting operation data  828  as described above. The sensors may be temperature sensors, pressure sensors, infrared sensors, presence sensors, etc. In some embodiments, the sensors provide the operation data  828  to communications interface  928  and/or network  446 . In some embodiments, communication interface  804  facilities communications between peer analysis system  802  and a database connected to network  446 , containing operation data  828  for one or more systems in a network of systems (e.g., all buildings operated by a user, all VRF systems operated by a user, etc.), or for example shown in  FIG.  8    as BMS  822  and BMS  824 . 
     Communications interface  928  can be or include wired or wireless communications interfaces (e.g., jacks, antennas, transmitters, receivers, transceivers, wire terminals, etc.) for conducting data communications with devices included in connected equipment  810  or other external systems or devices or databases, according to some embodiments. In some embodiments, communications via communications interface  928  can be direct (e.g., local wired or wireless communications) or via a communications network (e.g., a WAN, the Internet, a cellular network, etc.). For example, communications interface  928  can include an Ethernet card and port for sending and receiving data via an Ethernet-based communications link or network. In another example, communications interface  928  can include a Wi-Fi transceiver for communicating via a wireless communications network. In yet another example, communications interface  928  can include cellular or mobile phone communications transceivers. 
     Still referring to  FIG.  9   , processing circuit  906  can be communicably connected to communications interface  928  such that processing circuit  906  and the various components thereof can send and receive data via communications interface  928 , according to some embodiments. Processor  908  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, according to some embodiments. 
     Memory  910  (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, according to some embodiments. In some embodiments, memory  910  can be or include volatile memory or non-volatile memory. Memory  910  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 some embodiments. According to some embodiments, memory  910  is communicably connected to processor  908  via processing circuit  906  and includes computer code for executing (e.g., by processing circuit  906  and/or processor  908 ) one or more processes described herein. 
     Still referring to  FIG.  9   , memory  910  is shown to include a peer group generator  912 , a peer metric generator  916 , a weighted outlier detection module  920  and an automated action generator  926 . In some embodiments, peer group generator  912  is configured to receive operation data  828  and/or directions from user device  830  via communications interface  928 , analyze the operation data  828 , generate using the operation data  828  one or more peer group(s)  914  of systems or pieces of equipment grouped together according to one or more common characteristics, and provide the peer group(s)  914  to peer metric generator  916 . Peer group(s)  914  indicates a way in which a plurality of systems or pieces of equipment can be grouped for peer analysis, according to some embodiments. Depending on the analytical need and data available, different types of peer groups can be defined according to different criteria (e.g., manufacturer, geographic area, business type, equipment type, equipment age, system operator, client, etc.). For example, separate peer groups can be generated for indoor units (IDUs), outdoor units (ODUs), refrigeration and cooling units (RCUs), and/or total systems for performing peer analysis. 
     In some embodiments, peer group generator  912  is configured to receive operation data  828  from communications interface  928  and analyze the operation data  828  to identify systems and/or pieces of equipment. The operation data  828  can include identifying information (e.g., model numbers, location, owner, operator, equipment type, etc.) and historical values (e.g., temperature set points, measured temperature values, compressor cycle times, etc.), according to some embodiments. In some embodiments, the operation data  828  includes additional data types or combinations of data types. 
     In some embodiments, peer group generator  912  is configured to identify one or more peer group(s)  814  according to user input  832  received via communications interface  928  from user device  830 . In some embodiments, user device  830  is the same and/or similar too client device  448 . User device  830  may be any type of device that allows a user to select peer groups to be identified. For example, user device  830  may be a smart phone, a laptop, a desktop computer, a smart watch, a tablet, etc. In some embodiments, user device  830  connects to a network interface that facilities communication between user device  830  and peer analysis system  802 , for example via network  446 . In some embodiments, user device  830  may communicate with Peer analysis system  802  vie the Internet, Wi-Fi, Bluetooth, etc. 
     Still in other embodiments, peer group generator  912  may be configured to identify predefined peer groups automatically. For example, peer group generator  912  can analyze operation data  828  and determine it includes data relating to VRF systems. Peer group generator  912  can then automatically identify IDU, ODU, and system peer groups for the VRF systems. In some embodiments, the predefined peer groups are defined by a user via user device  830  during commissioning of the Peer analysis system  802 . Peer analysis system  802  and/or network  446  may include information relating to common peer groups that should be automatically identified by peer group generator  912 . 
     Peer group generator  912  can be configured to output peer group(s)  914  to peer metric generator  916 , according to some embodiments. In some embodiments, peer group(s)  914  includes one or more peer groups. In some embodiments, the peer groups may overlap. For example, a peer group for indoor units in VRF systems may be generated, as well as a peer group for all VRF systems, which includes the indoor units as well as other units. In other embodiments, peer group(s)  914  includes a single peer group selected for analysis by a user. In some embodiments, peer group generator  912  outputs peer group(s)  914  and the operation data  828  used to generate the peer group(s)  914  to peer analysis system  802 . 
     Referring now to  FIG.  10   , an example of a peer group is shown, according to some embodiments.  FIG.  10    is shown to include a VRF peer group  1000 , according to some embodiments. In some embodiments, as shown VRF peer group  1000  includes VRF systems from multiple locations, such as Cork  1002 , Kadi  1004 , Chennai  1006 , Milwaukee  1008 , and Lyon  1010 . As shown, some locations may have more VRF systems included in the peer group than others. In some embodiments, the VRF systems in the peer group are all grouped based on being from the same manufacturer. In other embodiments, the VRF systems all belong to the same client. Any number of common characteristics or criterias based on the operation data  828  can be used to generate a peer group. While shown as a peer group of VRF systems, the peer groups can include other systems such as HVAC assets, lighting systems, etc. Still, in some embodiments the peer groups may function at an equipment level, and be composed of individual pieces of equipment. For example, a peer group may be generated including all indoor units in Cork. Referring still to  FIG.  10   , each VRF unit can be identified from the operation data  828  by the peer group generator  912 , according to some embodiments. 
     Referring back to  FIG.  9   , peer group(s)  914  is provided as an input to peer metric generator  916 , according to some embodiments. Peer metric generator  916  generates peer metric(s)  918  for each member of peer group(s)  914  and provides peer metric(s)  918  to weighted outlier detection module  920  which analyzes the peer metrics to detect one or more outliers, shown as outlier(s)  818 . In some embodiments, peer metric generator  916  is configured to collect peer group(s)  914 , and generate peer metric(s)  918 . In some embodiments, peer group(s)  914  includes the operation data  828 , from systems and/or pieces of equipment such as connected equipment  810 , used to generate peer group(s)  914 . In some embodiments, peer metric generator  916  is configured to generate peer metric(s)  918  for a predetermined period of time. For example, a peer metric for a peer group of all outdoor units located in Cork can be calculated for June through August of a given year. The period of time can be days, months, years, etc. In some embodiments, the peer metrics generated by peer metric generator  916  depends on characteristics of the peer group(s)  914 . For example, specific peer metrics may apply for a peer group of IDUs, a peer group of ODUs, a peer group of RCUs, etc. In some embodiments, only a single peer metric is generated, and outlier detection is performed on the values of the peer metric generated for that peer group. In some embodiments, multiple peer metrics are generated and outlier detection is done across using multiple peer metrics. In some embodiments, the different peer metrics are calculated based on the same data in operation data  828 . Still in other embodiments, the different peer metrics are based on different data in operation data  828 . For example, in univariate outlier detection process, only a single peer metric is generated and its values compared, whereas in a multivariate outlier detection process multiple peer metrics are generated and compared. 
     In some embodiments, weighted outlier detection module  920  performs weighted outlier detection process to account for small or uncertain data sets. In some embodiments, weighted outlier detection module  920  can perform peer analysis on a single peer metric using a univariate outlier detection process. A univariate outlier detection process analyzes a data set in a single feature space. In some embodiments, weighted outlier detection module  920  can perform peer analysis on two or more peer metrics using a multivariate outlier detection process. A multivariate outlier detection process can find outliers in a n-dimensional space of n features. While peer analysis system  802  is shown to generate outlier(s)  818  via weighted outlier detection module  920 , in some embodiments weighted outlier detection module  920  may not detect any outliers, and no outlier(s)  818  will be provided to automated action generator  926 . 
     Automated action generator  926  is shown to receive outlier(s)  818  from weighted outlier detection module  920 , according to some embodiments. In some embodiments, automated action generator  926  uses outlier(s)  818  to generate appropriate automated or corrective actions, shown as automated action  834 , for systems and/or pieces of equipment in peer group(s)  914 . In some embodiments, the automated action  834  is directed towards outlier(s)  818  of peer group(s)  914 . In other embodiments, the automated action generator  926  is directed towards systems and/or pieces of equipment in peer group(s)  914  apart from outlier(s)  818 . Still in other embodiments, the automated action be directed towards the entire peer group. automated action generator  926  can analyze the outlier(s)  818  to determine if any automated action should be initiated and if so what that action should be. An automated action can refer to any action taken to address an outlier system and/or piece of equipment, or the peer group the outlier is contained within. Automated action may include for example, providing and/or receiving maintenance to a system and/or device, distributing notifications/alerts to a user device to indicate to a user that an outlier is detected, operating systems and/or equipment (e.g., connected equipment  810 ), disabling systems and/or equipment (e.g., connected equipment  810 ), automatically scheduling a maintenance activity to be performed on systems and/or equipment, logging the outlier in a database, running a successive peer analysis, generating new peer groups, generating new peer metrics, etc. 
     Example Peer Metrics for a VRF System 
     The following are example peer metrics for a model VRF system, such as VRF system  600 , that can be generated by peer metric generator  916  for peer group(s)  914  using the operation data  828 , according to some embodiments. 
     In some embodiments, the operation data  828  includes measurement temperature (T m ) and set-point temperature (T s ) for calculating a temperature control error peer metric. Temperature control error describes how well a system and/or piece of equipment tracks a set-point temperature. Temperature control error (T e ) is represented as: 
     
       
         
           
             
               T 
               e 
             
             = 
             
               
                 
                   T 
                   m 
                 
                 − 
                 
                   T 
                   s 
                 
               
             
           
         
       
     
     In some embodiments, an average temperature control error and standard deviation of the temperature control error are considered as the peer metrics representing temperature control: 
     
       
         
           
             
               
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     In some embodiments, the operation data  828  includes the amount of change in an actuation signal, where the change is the change in a flow coefficient for calculating control effort peer metrics. A control effort peer metric represent the control effort required to track the set-point temperature over time. In some embodiments, to evaluate control effort, four peer metrics can be defined as follows, where C̅V̅ denotes CV average and T denotes the summation of time intervals during thermal-on status for the VRF system  600 . 
     
       
         
           
             
               
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                 C 
                 
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     The difference between the first two peer metrics is the value of the denominator or the normalization method. Each VRF system may have different expansion valve model, and the four control effort metrics are provided to accommodate the various valve models. Accordingly, CV values can be computed differently and different normalization metrics adjust the measured values, in some embodiments. In the peer metric of Eq. 4, the measured values are normalized by an average CV and in the metric of Eq. 5, the measured value is normalized by the maximum CV value. In addition, control effort peer metrics can include an average and standard deviation for CV shown in Eq. 6 and Eq. 7 respectively. 
     In some embodiments, the operation data  828  includes the absolute discharge pressure (P d ) and the absolute suction pressure (P s ) for calculating a compression ratio (CR) peer metric. The compression ratio can be used to represent compressor reliability and the coefficient of performance (COP) for a given system and/or piece of equipment. In some embodiments, the absolute suction pressure and the absolute discharge pressure are gauge pressures and atmospheric pressure should be added to have the absolute pressure. The compression ratio CR may be represented as: 
     
       
         
           
             C 
             R 
             = 
             
               
                 
                   P 
                   d 
                 
                 + 
                 0 
                 , 
                 1 
               
               
                 
                   P 
                   s 
                 
                 + 
                 0.1 
               
             
           
         
       
     
     In some embodiments, the operation data  828  includes the off time (OFT) and on time (OT) of one or more compressors, for calculating the cycle time (CT): 
     
       
         
           
             C 
             T 
             = 
             O 
             T 
             + 
             O 
             F 
             T 
           
         
       
     
     In some embodiments the operation data  828  also includes the number of off times in a period of time (NOF), the number of cycles (NC), and the number of cycles of a certain length of time (NCT) for one or more compressors for calculating a compressor cycle time peer metric. For example, the period of time can be five minutes, and the operation data  828  can include the number of off times within five minutes, and the length of time can be ten minutes, such that NCT is the number of cycle times less than ten minutes. The compressor cycle peer metrics defined below illustrate the Off Time Percentage (OFTP) and the Cycle Time Percentage (CTP): 
     
       
         
           
             O 
             F 
             T 
             P 
             = 
             100 
             ∗ 
             
               
                 N 
                 O 
                 F 
               
               
                 N 
                 C 
               
             
           
         
       
     
     
       
         
           
             C 
             T 
             P 
             = 
             100 
             ∗ 
             
               
                 N 
                 C 
                 T 
               
               
                 N 
                 C 
               
             
           
         
       
     
     Compressor cycle peer metrics can represent when one or more compressors are short cycling and/or other problems with compressors such as oversized systems, iced evaporator coils, clogged air filters, and/or low refrigerant. 
     In some embodiments, the operation data  828  includes the heat output for the compressor (Q) and the power supplied to the compressor (W) for calculating an efficiency or coefficient or performance (COP) for a VRF system. The value of the COP peer metric can be calculated for each system, as well as a value of a COP average peer metric and a COP standard deviation peer metric, as shown below: 
     
       
         
           
             C 
             O 
             P 
             = 
             
               
                 
                   Q 
                 
               
               W 
             
           
         
       
     
     
       
         
           
             
               
                 C 
                 O 
                 P 
               
               ¯ 
             
           
         
       
     
     
       
         
           
             σ 
             
               
                 C 
                 O 
                 P 
               
             
           
         
       
     
     In some embodiments, the peer metrics generated are based on the peer group(s)  914 . For example, for IDUs the temperature control error and control effort peer metrics can be includes, for ODUs the compression ratio and compressor cycling peer metrics can be calculated, and for system-level peer groups the system efficiency or COP peer metrics can be calculated. While only the above peer metrics are described, a person of ordinary skill in the art would understand that peer analysis can include other peer metrics for other systems and/or pieces of equipment without departing from the scope of the present disclosure. 
     Weighted Outlier Detection 
     Referring back to  FIG.  9   , weighted outlier detection module  920  is shown to receive peer metric(s)  918  and operation data  828  from peer metric generator  916 , according to some embodiments. Weighted outlier detection module  920  performs univariate and/or multivariate outlier detection processes to identify outliers in the peer group(s)  914 . Specifically, in some embodiments weighted outlier detection module  920  performs weighted outlier detection, wherein each peer metric value for each member of the peer group is assigned a weight based on the data and then outlier detection is performed using the set of weighted peer metric values and modified detection methods. Weighting the peer metric values reduces the effect of peer metric values based on uncertain or small amounts of data on the outlier detection process. In some embodiments, the weight assigned to peer metric values for a peer in the peer group is the same for each peer metric. For example, the amount of operation data obtained from Peer A in a peer group may be consistent across the various peer metrics, such that the weight calculated for each of the vlaues of the peer metrics of Peer A based on the operation data is also consistent across the types of peer metrics. Furthering the example, Peer B in the same peer group may have a different amount of operation data, and therefore the weight assigned to the values each peer metric for Peer B, while being the same across all peer metric types for Peer B, will differ from the weight assigned to Peer A. 
     Weighted outlier detection module  920  is shown to include univariate module  922  and multivariate module  924 . In some embodiments, weighted outlier detection module  920  receives the peer metric(s)  918  and passes peer metric(s)  918  to univariate module  922  and/or multivariate module  924 . In some embodiments, both univariate module  922  and multivariate module  924  process the peer metric(s)  918  and detect outlier(s)  818 . For example, univariate module  922  may perform univariate outlier detection for a temperature control error peer metric, and multivariate module  924  may perform univariate outlier detection outliers in the temperature control error and control effort space. In some embodiments, X is the set of values for the peer metrics available for outlier detection and is defined as: 
     
       
         
           
             
               X 
               
                 n 
                 × 
                 p 
               
             
             = 
             
               
                 
                   
                     
                       
                         
                           x 
                           
                             11 
                           
                         
                       
                     
                     
                       ⋯ 
                     
                     
                       
                         
                           x 
                           
                             1 
                             p 
                           
                         
                       
                     
                   
                   
                     
                       ⋮ 
                     
                     
                       ⋱ 
                     
                     
                       ⋮ 
                     
                   
                   
                     
                       
                         
                           x 
                           
                             n 
                             1 
                           
                         
                       
                     
                     
                       ⋯ 
                     
                     
                       
                         
                           x 
                           
                             n 
                             p 
                           
                         
                       
                     
                   
                 
               
             
           
         
       
     
      where n is the number of peers (i.e., HVAC assets) in the peer group and p is the number of peer metrics calculated per peer. 
     In some embodiments, univariate module  922  performs univariate outlier detection on X. When data is univariate, only a single peer metric per peer is tested such that p = 1, therefore X is defined as: 
     
       
         
           
             
               X 
               
                 n 
                 × 
                 1 
               
             
             = 
             
               
                 
                   
                     
                       
                         
                           x 
                           
                             11 
                           
                         
                       
                     
                   
                   
                     
                       ⋮ 
                     
                   
                   
                     
                       
                         
                           x 
                           
                             n 
                             1 
                           
                         
                       
                     
                   
                 
               
             
           
         
       
     
     In some embodiments, the univariate outlier detection test is a Generalized Extreme Studentized Deviate (GESD) test. Still in other embodiments, other univariate outlier tests may be used. For example, the Wilk’s Test and modified Wilk’s Test explained in further detail below can also be used to detect univariate outliers when using a univariate data set. 
     In general, a GESD test may detect one or more outliers in a univariate sample data set X nx1  for a peer metric that follows an approximately normal distribution, wherein the sample size n (i.e., number of values calculated, which in a univariate data set corresponds to the number of peers in the peer group) is greater than 25 and x i  is the i th  value of the peer metric in the peer group represented by X. In a GESD test a maximum number of outliers r is hypothesized, and a test statistic R i  based on a select value x i  is tested against a critical value λ i  to see if it is an outlier as compared to the other values of the peer metric of the same peer group. If the value x i  is an outlier, the value x i  is removed from the data set, and the procedure is then repeated with n-1 values, until the procedure is performed for r number of outliers. In other words, the GESD test performs r separate tests including a test for one outlier, a test for two outliers, a test for three outliers... up to a test for r outliers. The number of outliers is the largest i such that R i  &gt; λ i . 
     The maximum number of outliers can be defined as r, which in some embodiments may be determined as represented below, where n is the number of values for the peer metric in the set defined by the peer group: 
     
       
         
           
             r 
             = 
             floor 
             
               
                 
                   
                     n 
                     − 
                     1 
                   
                   2 
                 
               
             
           
         
       
     
     Still in other embodiments r may be computed using other methods of approximation. The test statistic R i  for a GESD test can be represented as shown below, where X̅ and s denote the sample mean and the sample standard deviation respectively: 
     
       
         
           
             
               X 
               ¯ 
             
             = 
             
               
                 
                   
                     ∑ 
                     
                       i 
                       = 
                       1 
                     
                     n 
                   
                   
                     
                       x 
                       i 
                     
                   
                 
               
               n 
             
           
         
       
     
     
       
         
           
             
               σ 
               2 
             
             = 
             
               
                 
                   
                     ∑ 
                     
                       i 
                       = 
                       1 
                     
                     n 
                   
                   
                     
                       
                         
                           
                             
                               x 
                               i 
                             
                             − 
                             
                               X 
                               ¯ 
                             
                           
                         
                       
                       2 
                     
                   
                 
               
               
                 n 
                 − 
                 1 
               
             
           
         
       
     
      such that the test statistic R i  is defined as: 
     
       
         
           
             
               R 
               i 
             
             = 
             
               
                 m 
                 a 
                 x 
                 
                   
                     
                       x 
                       i 
                     
                     − 
                     
                       
                         X 
                         ¯ 
                       
                       ¯ 
                     
                   
                 
               
               s 
             
           
         
       
     
     In the sequential steps of a GESD test, the peer metric value that maximizes |x i  - X̅| is removed, and a new test statistic R i+1  is computed with a new sample set of n-1 values, and the process repeats until r tests have been performed, generating a set of r test statistics {R 1  ... Rr }. Similarly, a number of r critical values can be computed for comparing to the test statistics. In some embodiments, the critical values are represented as follows for i = 1, 2, ... r: 
     
       
         
           
             
               λ 
               i 
             
             = 
             
               
                 
                   
                     n 
                     − 
                     i 
                   
                 
                 
                   t 
                   
                     p 
                     , 
                     n 
                     − 
                     i 
                     − 
                     1 
                   
                 
               
               
                 
                   
                     
                       
                         n 
                         − 
                         i 
                         − 
                         1 
                         + 
                         
                           t 
                           
                             p 
                             , 
                             n 
                             − 
                             i 
                             − 
                             1 
                           
                           2 
                         
                       
                     
                     
                       
                         n 
                         − 
                         i 
                         + 
                         1 
                       
                     
                   
                 
               
             
           
         
       
     
      where t p,n-i-1  is the percentage point from the t-distribution with a degree of freedom represented by n— - i - 1 and p is represented as shown below, with α as a significance level chosen for the GESD test: 
     
       
         
           
             p 
             = 
             1 
             − 
             
               α 
               
                 2 
                 
                   
                     n 
                     − 
                     i 
                     + 
                     1 
                   
                 
               
             
           
         
       
     
     The number of outliers is the largest i such that R i  &gt; λ i , with the outliers being the vlaues that at each step maximized |x i  - X̅| up to the largest such i. For example, referring to the example set of test statistics and critical values in the table below where r = 4: 
     
       
         
           
               
               
               
               
             
               
                 i 
                 Peer Metric Value max|x i  –  x̅ | (x i,extreme ) 
                 Test Statistic (R i  vs λ i ) 
                 Significant? 
               
             
            
               
                 1 
                 x 1,extreme 
 
                 R 1  &gt; λ 1 
 
                 Yes 
               
               
                 2 
                 x 2,extreme 
 
                 R 2  &gt; λ 2 
 
                 Yes 
               
               
                 3 
                 x 3,extreme 
 
                 R 3  &lt; λ 3 
 
                 No 
               
               
                 4 
                 x 4,extreme 
 
                 R 4  &gt; λ 4 
 
                 Yes 
               
            
           
         
       
     
     The table shows that at i = 4, R 4  &gt; λ 4  and therefore there are four outliers, the set of values that for each i up to i =4, {x 1,extreme , x 2,extreme , x 3,extreme , x 4,extreme } maximize |x i  - X̅|. As shown, although R 3  &lt; λ 3 , x 3,extreme  and is not considered significant, it is still considered an outlier because all values up to the largest i such that R i  &gt; λ i  are outliers in the GESD test, which as shown above is true of x 4,extreme . 
     Referring now to  FIG.  11 A , an example graph relating a number of VRF systems to a temperature control mean peer metric is shown, according to some embodiments. Graph  1100  can illustrate the outliers identified by univariate outlier detection process performed by univariate module  922  on a peer group of all VRF units in Chennai, Cork, Kadi, Lyon, Milwaukee, Norman, SMZ, Shanghai, Tochigi, and Wuxi. Outliers are shown in outlier zone  1102  of graph  1100 , which shows the outliers detected to be 9 VRF systems from the entire peer group. 
     Referring back to  FIG.  9   , in some embodiments, univariate module  922  performs a weighted GESD test. In the weighted GESD Test, the normal GESD test is modified to account for weights assigned to the values of the chosen peer metric based on the amount of data the values of the peer metrics are generated from. Similarly the test statistic is modified to account for the weights as shown below. A weighted GESD can be configured to more accurately identify outliers in circumstances where data collection methodologies lead to uncertain or limited data sets. 
     In some embodiments, the weighted GESD test statistic is modified to include weights for each value of the peer metric in the set x as shown below: 
     
       
         
           
             
               R 
               
                 w 
                 i 
               
             
             = 
             
               
                 max 
                 
                   
                     
                       w 
                       i 
                     
                     
                       
                         
                           x 
                           i 
                         
                         − 
                         
                           
                             
                               X 
                               w 
                             
                           
                           ¯ 
                         
                       
                     
                   
                 
               
               
                 
                   σ 
                   w 
                 
               
             
           
         
       
     
      where  
     
       
         
           
             
               
                 
                   X 
                   w 
                 
               
               ¯ 
             
           
         
       
     
     is the weighted mean and  
     
       
         
           
             
               σ 
               w 
               2 
             
           
         
       
     
     is weighted variance: 
     
       
         
           
             
               
                 
                   X 
                   w 
                 
               
               ¯ 
             
             = 
             
               
                 
                   w 
                   1 
                 
                   
                 
                   x 
                   1 
                 
                 + 
                 
                   w 
                   2 
                 
                   
                 
                   x 
                   2 
                 
                 + 
                 ⋯ 
                 + 
                 
                   w 
                   n 
                 
                   
                 
                   x 
                   n 
                 
               
               
                 
                   
                     ∑ 
                     
                       i 
                       = 
                       1 
                     
                     n 
                   
                   
                     
                       w 
                       i 
                     
                   
                 
               
             
           
         
       
     
     
       
         
           
             
               σ 
               w 
               2 
             
             = 
             
               
                 
                   ∑ 
                   
                     
                       w 
                       i 
                     
                       
                     
                       
                         
                           
                             
                               x 
                               i 
                             
                             − 
                             
                               
                                 
                                   X 
                                   w 
                                 
                               
                               ¯ 
                             
                           
                         
                       
                       2 
                     
                   
                 
               
               
                 
                   
                     n 
                     − 
                     1 
                   
                   n 
                 
                 
                   ∑ 
                   
                     
                       w 
                       i 
                     
                   
                 
               
             
           
         
       
     
      and where the weight w i  of a given value x i  can be represented in some embodiments as: 
     
       
         
           
             
               w 
               i 
             
             = 
             
               
                 number of available data for calculating  
                 
                   x 
                   i 
                 
               
               
                 mean 
                 
                   
                     number of available data for calculating  
                     x 
                   
                 
               
             
           
         
       
     
     In some embodiments, the weight w i  is bounded by the limit represented below, such that the weight w i  for a given value is the minimum between 1 and w i  as calculated in Eq. 26 such that: 
     
       
         
           
             
               w 
               i 
             
             
               
                 
                   w 
                   i 
                 
                 &gt; 
                 1 
               
             
             = 
             1 
           
         
       
     
     In some embodiments, the number of available data for calculating the value x i  is determined from operation data  828 . For example, considering an average temperature control error peer metric for a first VRF system in a peer group,  
     
       
         
           
             
               x 
               1 
             
             = 
             
               
                 
                   T 
                   = 
                 
               
               
                 e 
                 , 
                 1 
               
             
           
         
       
     
     may be computed based on  100  individual data points contained in the operation data  828  for the measurement temperature and the set-point temperature, where as  
     
       
         
           
             
               x 
               2 
             
             = 
             
               
                 T 
                 ¯ 
               
               
                 e 
                 , 
                 2 
               
             
           
         
       
     
     may be computed based on only 50 individual data points for the measurement temperature and the set-point temperature. In the example if x 1  and x 2  each relate to the only members in a peer group, the weight assigned to x 1  will be 
     
       
         
           
             
               w 
               1 
             
             = 
           
         
       
     
     
       
         
           
             
               
                 100 
               
               
                 75 
               
             
             = 
             1. 
             
               
                 33 
               
               ¯ 
             
             , 
               
             
               w 
               1 
             
             
               
                 
                   w 
                   1 
                 
                 &gt; 
                 1 
               
             
             = 
             1 
           
         
       
     
     therefore w 1  = 1 and the weight assigned to x 1  will be 
     
       
         
           
             
               w 
               2 
             
             = 
             
               
                 50 
               
               
                 75 
               
             
             = 
           
         
       
     
     
       
         
           
             0. 
             
               
                 66 
               
               ¯ 
             
           
         
       
     
     Therefore, when calculating the test statistic, x 1  will be weighted more than x 2 , and the modified GESD test can be performed as weighted towards x 1  to better account for the uncertainty in the underlying data. 
     Referring now to  FIG.  11 B , an example graph relating a temperature control standard deviation to a temperature control mean is shown, according to some embodiments. Graph  1110  can illustrate the outliers identified by a multivariate outlier detection process performed by multivariate module  924 . Particularly, outliers are shown as outliers  1112 . 
     Referring back to  FIG.  9   , in some embodiments, peer analysis system  802  performs multivariate outlier detection on X n×p  with multivariate module  924 . Multivariate outliers may not be detectable when each variable in the feature space is considered independently, and therefore multivariate analysis may be necessary in some embodiments to detect all outliers. For multiverse analysis values for multiple peer metrics are calculated and compared. In some embodiments, the plurality of peer metrics belong to the same dimension. For example, multivariate analysis can be performed using temperature control peer metrics and control effort peer metrics, as they each belong to the same dimension (i.e., IDUs). Comparatively, multivariate analysis using temperature control peer metrics and compressor cycling peer metrics will not be performed as they belong to different dimensions (i.e., temperature control relates to IDUs and compressor cycling relates to ODUs). It should be appreciated that in some embodiments the dimensions are based on characteristics of the peer group. 
     Multivariate module  924  may perform multivariate analysis using a Sequential Application of Wilk’s Multivariate Outlier Test (“Wilk’s Test”). In the Wilk’s Test, under the null hypothesis of no outliers all n observations are drawn from one multivariate normal distribution. Then Wilk’s Statistic is computed and compared to the critical value of Eq. 21 to find the extreme observations. First, the test statistic W j  is represented as: 
     
       
         
           
             
               W 
               j 
             
             = 
             
               
                 
                   
                     
                       A 
                       j 
                     
                   
                 
               
               
                 
                   A 
                 
               
             
           
         
       
     
      where A is the matrix of the sum of squares and products for the deviation scores of the values in data matrix X n×p  containing the multivariate set of values for the per metrics defined as: 
     
       
         
           
             A 
             = 
             
               
                 ∑ 
                 
                   i 
                   = 
                   1 
                 
                 n 
               
               
                 
                   
                     
                       x 
                       i 
                     
                     − 
                     
                       x 
                       ¯ 
                     
                   
                 
                 
                   
                     
                       x 
                       i 
                     
                     − 
                     
                       x 
                       ¯ 
                     
                   
                 
                 ′ 
               
             
           
         
       
     
      and A j  is the corresponding matrix with x j , eliminated from the sample giving a reduced sample of n-1 points. In some embodiments, the potential outlier, for example the value l, is the value which maximizes the test statistic W j . This can also be expressed as the value whose removal leads to the greatest reduction in |A|, which is the same as the value whose removal minimizes W j . Wilk’s Statistic D 1  can then be represented as: 
     
       
         
           
             
               D 
               1 
             
             = 
             
               
                 min 
               
               j 
             
             
               
                 
                   W 
                   j 
                 
               
             
             = 
             min 
             
               
                 
                   
                     
                       
                         
                           A 
                           l 
                         
                       
                     
                   
                   
                     
                       A 
                     
                   
                 
               
             
           
         
       
     
      which can be rewritten and simplified to be expressed as: 
     
       
         
           
             
               D 
               1 
             
             = 
             1 
             − 
             
               n 
               
                 n 
                 − 
                 1 
               
             
             ∗ 
             
               W 
               j 
             
             = 
             1 
             − 
             
               n 
               
                 n 
                 − 
                 1 
               
             
             
               
                 
                   x 
                   l 
                 
                 − 
                 
                   x 
                   ¯ 
                 
               
             
             ′ 
             
               A 
               
                 − 
                 1 
               
             
             
               
                 
                   x 
                   l 
                 
                 − 
                 
                   x 
                   ¯ 
                 
               
             
           
         
       
     
     In Wilk’s Test, the ratio W j  follows a beta distribution: 
     
       
         
           
             
               W 
               j 
             
             ~ 
             b 
             e 
             t 
             a 
             
               
                 
                   
                     
                       
                         n 
                         − 
                         p 
                         − 
                         1 
                       
                     
                   
                   2 
                 
                 , 
                 
                   p 
                   2 
                 
               
             
           
         
       
     
     Using the Bonferroni bound, the values of the peer metrics can be tested for significance level at  100α / n . These values can be obtained by using: 
     
       
         
           
             
               
                 
                   
                     1 
                     + 
                     
                       p 
                       
                         n 
                         − 
                         p 
                         − 
                         1 
                       
                     
                     
                       F 
                       
                         p 
                         , 
                         n 
                         − 
                         p 
                         − 
                         1 
                         , 
                         
                           
                             1 
                             − 
                             
                               
                                 α 
                                 n 
                               
                             
                           
                         
                       
                     
                   
                 
               
               
                 − 
                 1 
               
             
           
         
       
     
     Wilk’s Test can compute the above values for different confidence levels α. To check x l  is an outlier, the Wilk’s Statistic D 1  can be computed and compared to the critical value of Eq. 21. In some embodiments, if D 1  &lt; λ l , than x l  is an extreme observation and an outlier. Once the extreme observation has been identified, that observation is removed and the Wilks’s procedure is repeated for n - 1 observations. 
     In some embodiments, the Wilk’s Test above is modified to weight the values of the peer metrics for the peer group based on the amount of data each value is generated from to more accurately identify outliers in circumstances where data collection methodologies lead to uncertain or limited data sets. In some embodiments, the modified Sequential Application of Wilk’s Multivariate Outlier Test (“modified Wilk’s Test”) is modified as shown below in Eq. 34 - Eq. 37, with the test statistic W j  still defined as; 
     
       
         
           
             
               W 
               j 
             
             = 
             
               
                 
                   
                     
                       A 
                       w 
                     
                     
                         
                       j 
                     
                   
                 
               
               
                 
                   
                     
                       A 
                       w 
                     
                   
                 
               
             
           
         
       
     
      where A w  is now the sample sum of squares and products matrix for the deviation scores of the data matrix X n×p  containing the multivariate set of values for the peer metrics multiplied by the weights of each of the values of the peer metrics such that: 
     
       
         
           
             
               A 
               w 
             
             = 
             
               
                 ∑ 
                 
                   i 
                   = 
                   1 
                 
                 n 
               
               
                 
                   w 
                   i 
                 
                 
                   
                     
                       
                         
                           x 
                           i 
                         
                         − 
                         
                           x 
                           ¯ 
                         
                       
                     
                   
                   ′ 
                 
                 
                   
                     
                       x 
                       i 
                     
                     − 
                     
                       x 
                       ¯ 
                     
                   
                 
               
             
           
         
       
     
      with A w   j  being the corresponding matrix with x j , eliminated from the sample giving a reduced sample of n-1 points. As shown, x̅ W , is the mean vector of the set of values X and w i  is the weight calculated by Eqs. 26-27 above in reference to the weighted GESD test. As explained above, for multivariate analysis with values for multiple peer metrics per peer being calculated, in some embodiments the weights assigned to the values of different peer metrics will be the same, as they are based on the same underlying operation data. For example, the weights applied to values of a peer metric for Peer A in a peer group will be equal from one peer metric to another. In some embodiments, the potential outlier, is the value l (i.e., peer metric value) which when weighted maximizes the weighted test statistic, w * W j . This can also be expressed as the value whose removal when weighted leads to the greatest reduction in |A W |, which is the same as the point whose removal when weighted minimizes W j . The weighted Wilk’s Statistic D 1  can then be represented as: 
     
       
         
           
             
               D 
               1 
             
             = 
             
               
                 m 
                 i 
                 n 
               
               j 
             
             
               
                 
                   W 
                   j 
                 
               
             
             = 
             m 
             i 
             n 
             
               
                 
                   
                     
                       
                         
                           A 
                           w 
                         
                         
                             
                           l 
                         
                       
                     
                   
                   
                     
                       
                         
                           A 
                           w 
                         
                       
                     
                   
                 
               
             
           
         
       
     
      which can be rewritten and simplified to be expressed as: 
     
       
         
           
             
               D 
               1 
             
             = 
             1 
             − 
             
               n 
               
                 n 
                 − 
                 1 
               
             
             ∗ 
             
               D 
               1 
             
             = 
             1 
             − 
             
               n 
               
                 n 
                 − 
                 1 
               
             
             
               
                 
                   
                     
                       X 
                       l 
                     
                     − 
                     
                       X 
                       ¯ 
                     
                   
                 
               
               ′ 
             
             
               A 
               w 
             
             
                 
               
                 − 
                 1 
               
             
             
               
                 
                   X 
                   l 
                 
                 − 
                 
                   X 
                   ¯ 
                 
               
             
           
         
       
     
     To check x l  is an outlier, the weighted Wilk’s Statistic D 1  of the modified Wilk’s Test can be computed and compared to the critical value of Eq. 21. In some embodiments, if D 1  &lt; λ l , than x l  is an extreme observation and an outlier. Once the extreme observation has been identified, that observation is removed and the Wilks’s procedure is repeated for n - 1 observations. 
     Referring now to  FIG.  12   , examples graphs  1200  and  1210  relating a temperature control error standard deviation to a temperature control error mean along with the weighted ratio W j  are shown, with graph  1200  representing the results of the Wilk’s Test and graph  1210  representing the results of the modified Wilk’s Test, according to some embodiments. Graph  1200  is shown with 26 outliers in region  1202 , and graph  1210  is shown with only six outliers  1212 . In some embodiments, graphs  1200  and  1210  represent a peer group comprised of VRF systems, with the graphed observations corresponding to the calculated peer metrics for each member of the peer group. As shown, 25 of the 26 peer metrics values in the peer group identified as outliers by the Wilk’s Test were not indicated as outliers by the modified Wilk’s Test. The modified Wilk’s Test found only one outlier identified by the Wilk’s Test to be an outlier, and additionally found five outliers not identified by the Wilk’s Test. As shown, the modified Wilk’s Test significantly narrowed the set of identified outliers as compared to the unmodified Wilk’s Test. Still, in some embodiments, the modified Wilk’s Test may detect more outliers than the unmodified Wilk’s Test. 
     Referring now to  FIG.  13   , a flowchart of outlier detection process  1300  for use in peer analysis is shown, according to an exemplary embodiment. Process  1300  can be carried out by the peer analysis system  802 . Still in other embodiments, process  1300  can be carried out by BAS controller  366  of  FIG.  4   , and/or other components of BMS  400 . 
     Process  1300  begins at step  1302 , where the peer group generator  912  receives operation data  828  from communication interface  804 . In some embodiments, the operation data  828  is sourced from connected equipment  810  and/or network  446 . Operation data  828  may include any information pertaining to one or more systems and/or pieces of equipment, such as equipment type, system type, location, age, measured temperature, compressor on time, compressor off time, compressor cycle time, efficiency, power consumption, client, owner, operator, etc. Operation data  828  may include data relating to more than one type of system. For example, operation data  828  can include data for VRF systems  600  and waterside system  200 . In some embodiments, operation data  828  includes information for multiple BMS systems. 
     At step  1304 , the peer group generator  912  uses the operation data  828  to define one or more peer group(s)  914 . Peer group(s)  914  can be based on equipment type, manufacture, age, location, operator, owner, building type, and/or any other characteristic that may be commonly held by two or more systems and/or pieces of equipment. In some embodiments, a peer group contains systems and/or pieces of equipment also contained in another peer group. For example, a peer group of VRF systems can include the IDUs and ODUs for each VRF, where the IDUs may also be included in their own peer group of IDUs. In some embodiments, the peer groups are defined by a user providing a user input  832  from user device  830  via communications interface  928 . Still in other embodiments, the peer group(s)  914  are identified automatically by peer group generator  912  based on the operation data  828 . 
     At step  1306 , peer metric generator  916  receives the operation data  828  and the peer groups generated from it by peer group generator  912  and defines a peer analysis time period. The peer analysis time period limits the data from operation data  828  that peer metric generator  916  will analyze during the peer analysis process  1300 . For example, operation data  828  may include data ranging from 2000-2010, however the peer analysis time period may only extend from Jun. 1, 2019 to Sep. 1, 2019. Accordingly, only data within the peer analysis time period will be used by peer metric generator  916  to generate the peer metric(s)  918 . In some embodiments, the peer analysis time period is indicated by a user through user device  830  in user input  832 , provided to peer metric generator  916  via communications interface  928 . 
     At step  1308 , peer metric generator  916  generates one or more peer metric(s)  918  for each member of peer group(s)  914  during the peer analysis time period using the operation data  828 . In some embodiments, before peer metric generator  916  generates peer metric(s)  918 , peer metric generator  916  preprocesses the operation data  828 . In some embodiments, preprocessing includes removing missing data and/or filtering for data in the appropriate time range. In some embodiments, for a peer group of n members peer metric generator  916  will accordingly generate n values for each peer metric that is being generated, one for each member in the peer group, each value based on data in the operation data  828  corresponding to said member. Still in other embodiments, peer metric generator  916  generates values for peer metric(s)  918  only for members of peer group(s)  914  with a sufficient amount of data. In some embodiments, the amount of data in operation data  828  for one system in a peer group may not equal the amount of data in operation data  828  for another system in the same peer group. For example, consider a peer group of HVAC assets, one each in Chennai, Cork, Milwaukee, and Kadi for a time period of June 2010 to June 2011. Operation data  828  may have a complete 12 months of measured temperature and setpoint temperature data, such that the values of the average and standard deviation of the temperature control peer metrics shown in Eq. 4 and Eq. 5 are generated by peer metric generator  916  using the complete data set. Still referencing the same example, operation data  828  may only have 8 months of measured temperature and setpoint temperature data for Kadi, due to a malfunction in Kadi’s system resulting in no data being available for September 2010 through December 2010. Accordingly, the values of the temperature control peer metrics for Kadi may only be based on 8 months of data. 
     In some embodiments, the peer metric(s)  918  generated by peer metric generator  916  depend on the peer groups provided to peer metric generator  916  by peer group generator  912 . For example, for a peer group of IDUs peer metric generator  916  may be configured to generate temperature control error and control effort peer metrics, whereas for a peer group of ODUs peer metric generator  916  may be configured to generate compression ratio and compression cycling peer metrics. In some embodiments, the peer metric(s)  918  to be generated are chosen by a user via user input  832 . To enable a user to select the peer metric(s)  918 , peer metric generator  916  may provide the peer metric(s)  918  corresponding to a particular peer group(s)  914  to user device  830  such that a user can select what peer metric(s)  918  to generate. Peer metric generator  916  and/or peer analysis system  802  may include functionality to generate graphical user interfaces (GUIs) that indicate the available peer group(s)  914  and peer metric(s)  918  and such GUIs can be displayed to a user on user device  830 . Still in some embodiments, the peer group(s)  914  and/or peer metric(s)  918  are presented to the user via a website accessible by the user device  830 . In some embodiments, the peer group(s)  914  and/or peer metric(s)  918  are presented on an application installed and/or otherwise accessible by user device  830 . It should be appreciated that the peer group(s)  914  and/or peer metric(s)  918  (as well as the outlier(s)  818  as explained in further detail below) can be provided to the user via user device  830  in any appropriate fashion for obtaining peer group(s)  914  and/or peer metric  918  selections. 
     At step  1310 , the weighted outlier detection module  920  receives the peer metric(s)  918  (i.e., the values for the selected peer metric(s)) from peer metric generator  916  and performs a weighted outlier detection process using peer metric(s)  918  to detect one or more outlier values corresponding to one or more outlier systems and/or pieces of equipment. In some embodiments, the outlier detection process is a weighted univariate outlier detection process such as a weighted GESD test performed by univariate module  922  and/or a weighted multivariate outlier detection process such as modified Wilk’s Method performed by multivariate module  924 , explained above with reference to  FIG.  9   . In some embodiments, the weights for the outlier detection processes are generated per value for each member of the peer group(s)  914 , based on the number of data used to generate the peer metric value divided by the total number of data used to generate all of the values of all the peer metric(s)  918  for the entire peer group(s)  914 , such as shown in Eqs. 26-27. 
     In some embodiments, weighted outlier detection module  920  receives user input  832  indicating what kind of outlier detection process to perform. In some embodiments, the weighted outlier detection process used depends on the peer metric(s)  918 . In some embodiments, only a single peer metric  918  is generated by peer metric generator  916  and therefore multivariate module  924  will not perform a multivariate outlier detection analysis, and instead only univariate module  922  will perform a univariate outlier detection process. In some embodiments, multiple peer metric(s)  918  will be provided to weighted outlier detection module  920 , and multivariate module  924  can be configured to perform a weighted multivariate outlier detection process using the peer metric(s)  918 . Still in other embodiments, multiple peer metric(s)  918  are provided and both univariate and multivariate outlier detection processes are performed. 
     At step 1312, automated action generator  926  receives outlier(s)  818  from weighted outlier detection module  920  and operates HVAC equipment (i.e., connected equipment  810 ) based on the outlier(s)  818 . In some embodiments, automated action generator  926  generates an automated action  834  based on outlier(s)  818 . In some embodiments, the automated action  834  relates to the detected outlier. For example, the values of the compressor cycling peer metrics for a VRF may be identified as outliers by weighted outlier detection module  920 , and automated action generator  926  can schedule a maintenance request for the VRF unit. Still in other embodiments, the automated action  834  can be directed towards the non-outlier members of the peer group. For example, a peer group of VRF systems in a heavily polluted location may suffer from widespread air filter clogging, and the outlier(s)  818  detected by weighted outlier detection module  920  may be VRF systems in the peer group that are actually overperforming, for example due to said systems not having a clogged air filter. Accordingly, automated action generator  926  may initiate an automated action to schedule maintenance for all VRF systems not detected as outliers, as the outlier detection processes identified over performers not under performers. In some embodiments, other automated action  834  may be initiated, such as though explained above with reference to  FIG.  9    above. For example automated action generator  926  can be configured to use outlier(s)  818  for fault adaptive model predictive control of systems and/or pieces of equipment in peer group(s)  914 . In some embodiments, outlier  818  can be can be used as an input in a model predictive control system for one or more BMSs. In some embodiments, the automated action  834  generated from outlier  818  in a model predictive control system can include selecting an optimal control scheme based on the outlier and adjust the operation of one or more HVAC equipment in the peer group based on the optimal control scheme, as described in U.S. Pat. Application No. 17/388,776, filed Jul. 29, 2021, the entire disclosure of which is incorporated by reference herein. 
     Referring now  FIG.  14   , a flowchart of process  1400  for outlier detection using weighted outlier statistics is shown, according to some embodiments. Process  1400  can be carried out by the peer analysis system  802 . Still in other embodiments, process  1400  can be carried out by BAS controller  366  of  FIG.  4   , and/or other components of BMS  400  or a remote computing system connected to network  446 . Process  1400  illustrates the processes performed by weighted outlier detection module  920  as it generates outlier(s)  818  based on the values of peer metric(s)  918 . In some embodiments, process  1400  illustrates univariate outlier detection performed by univariate module  922  of weighted outlier detection module  920 . In some embodiments, process  1400  illustrates a multivariate outlier detection process performed by multivariate module  924  of weighted outlier detection module  920 . 
     Process  1400  begins at step  1402 , where peer analysis system  802  defines the maximum number of r outliers to be tested in one or more outlier detection processes. As explained above, for both univariate and multivariate outlier detection, the processes are sequential and performed starting at i = 1 until i = r where r is the maximum number of outliers to be tested. In some embodiments, r is determined as shown in Eq. 17. In some embodiments, a user of user device  830  selects r and provides the selection to peer analysis system  802  via user input  832 . Still in other embodiments, r is determined automatically based on Eq. 17 and/or other methods of approximation. 
     At step  1404 , peer analysis module defines a significance level α. In some embodiments, the significance level α is input by a user of user device  830  and provided to weighted outlier detection module  920  in user input  832  via communication interface  804 . In some embodiments, significance level α is selected from significance levels with tabulated Wilk’s values. 
     At step  1406 , process  1400  is shown to proceed in the outlier detection process so long as i is less than or equal to r. If i &gt; r, then the outlier detection process of process  1400  has iterated for every i up to the maximum number of outliers r and process  1400  proceeds to step  1418  and ends. Once i &gt; r, the outliers are all values, x i , up the largest i such that the test statistic is less than the critical value. If at step  1406  i is less than or equal to r, process  1400  is shown proceeding to step  1408 . 
     At step  1408 , weighted outlier detection module  920  computes the weighted outlier statistics. In some embodiments step  1408  includes univariate module  922  of weighted outlier detection module  920  performing a weighted GESD test as described above with reference to  FIG.  9   . The weighted outlier statistics can include the weighted mean  X     w    of Eq. 24, the weighted variance σ w   2  represented by Eq. 25, the weighted test statistic R wi  represented by Eq. 23, and the critical values λ i  represented in Eq. 21 for the peer metric x j  that is farthest from the weighted mean, or in other words maximizes  
     
       
         
           
             
               
                 
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     Still in other embodiments other test statistics may be computed for other univariate outlier detection methods. As described above with reference to  FIG.  9   , weighted outlier detection module  920  is configured to perform a weighted outlier detection wherein the values of peer metric(s)  918  are assigned a weight w i  based on the number of data used to calculate the peer metrics compared to the number of data used to calculate all of the values of the peer metric for the peer group, as represented in Eqs. 26 and 27. 
     Still in other embodiments steps  1408  includes multivariate module  924  of weighted outlier detection module  920  performing a multivariate outlier detection process as described above with reference to  FIG.  9   . The weighted outlier statistics computed by multivariate module  924  calculate at step  1410  can include the weighted matrix A w  of sum of squares and cross products for the deviation scores of the values of the peer metrics as represented by Eq. 35, the weighted test statistic W j  as represented by Eq. 34, the outlier x j  is that whose removal leads to the greatest reduction in |A w | when weighted which is the value that minimizes W j  as represented in Eq. 36, the weighted Wilk’s Statistic as represented in Eq. 37, and the critical value as represented by Eq. 21. Still in other embodiments one or more other outlier detection statistics referenced herein may be computed in step  1408  of process  1400 . 
     At step  1410  weighted outlier detection module  920  compares the weighted Wilk’s Statistic from step  1408  against the computed critical value from step  1408  to determine if the test statistic for the i is significant. In some embodiments, wherein univariate module  922  of weighted outlier detection module  920  performs univariate outlier detection using a GESD test, the test statistic is significant when greater than the critical value, R i  &gt; λ i . In other embodiments, wherein multivariate module  924  of weighted outlier detection module  920  performs a weighted outlier detection process the test statistic is significant when the weighted Wilk’s Statistic, for example represented in some embodiments by Eq. 37, is less than the critical value. 
     At step  1410  if the test statistic of the chosen weighted outlier detection process is not significant, process  1400  advances to step  1416  and i is advanced by 1 such that i = i + 1 before returning back to step  1406  to proceed with the next sequential step of the outlier detection process. At step  1410 , if the test statistic of the chosen weighted outlier detection process is significant, process  1400  advances to step  1412 . 
     At step  1412 , weighted outlier detection module  920  saves i as an outlier, and then removes the value of the peer metric x i  from the set x of values in preparation for the next sequential step. As described above with reference to  FIGS.  9  and  10   , once an outlier value is discovered the outlier value is removed from the set of values and new statistics are calculated based on the set of values x i-1 . 
     At step  1416 , i is advanced by 1 such that i = i + 1, and process  1400  is shown returning back to step  1406  to repeat until i &gt; r. Once i &gt; r process  1400  is shown ending at step  1418 . In some embodiments, the set of outliers determined by process  1400  includes all outliers identified in step  1412  and all peer metrics x i  tested for i up to the largest i that was found significant at step  1410 . 
     Outlier Detection User Interfaces 
     Referring generally to  FIGS.  15 - 19   , various illustrations of user interface displays that can be displayed to a user interface are shown. The user interface can be displayed on various devices, such as for example, a computer, a tablet, a phone, etc. Information displayed to the user interface can be gathered by one or more systems and/or pieces of equipment such as building  10  or BMS  400 . In some embodiments, the information displayed to the user interface is gathered from sensors in building  10  such as temperature sensors, pressure sensors, light sensors, presence sensors, etc. For example, a building such as building  10  may integrate a peer analysis detection process performed by peer group generator  912  and peer analysis system  802  to provide outlier detection to the building based on operation data collected by building  10 . In some embodiments, the information displayed to the user interface can be gathered by another system external to the a building and/or BMS configured to monitor the building and/or BMS. Still in some embodiments, operation data from multiple systems is displayed to the user interface. Therefore, information regarding the systems and/or pieces of equipment connected to peer analysis system  802  and/or other controllers, and peer analysis system  802  can be displayed to a user such that a user can be provided means for detecting outliers. In some embodiments, the user interface allows a user to interact with the outlier detection process performed by peer analysis system  802 . For example, the user interface may allow a user to select a peer analysis time period, select a confidence level for the peer analysis, select a peer group for the peer analysis to be performed on, select the outlier detection method (i.e., univariate GESD, multivariate Wilk’s, etc.), select the peer metrics to be analyzed by the outlier detection method, or adjust any other variable or input discussed above with regards to outlier detection process. 
     Referring now to  FIG.  15   , an illustration  1500  of a user interface for interacting with peer analysis system  802  is shown, according to some embodiments. Illustration  1500  is shown to display peer analysis main page that can be displayed to a user for initiating an outlier detection process using peer analysis system  802 . Illustration  1500  is shown to include peer group selector  1502 . In some embodiments, peer group selector contains the peer group(s)  914  generated by peer group generator  912  from operation data  828 . In some embodiments, the peer group(s)  914  are contained in a drop-style menu, allowing a user to select which peer group to perform peer analysis including outlier detection on. 
     Illustration  1500  is shown to include outlier methods selector  1504 . In some embodiments, outlier methods selector  1504  allows a user to select whether a univariate outlier detection process should be performed or a multivariate outlier detection process. In some embodiments, the options represented by outlier methods selector  1504  are limited by the peer group selected in peer group selector  1502 . For example, if a peer group with only a single peer metric included within it is selected, only a univariate method will be available in peer group selector  1502 . 
     Illustration  1500  is shown to include metrics selector  1506 . In some embodiments, metrics selector  1506  displays the peer metric(s)  918  generated by peer metric generator  916 . In some embodiments, the peer metric displayed depend on the peer group selected in peer group selector  1502 . For example, peer group selector  1502  is shown to select an IDU Group in  FIG.  15   , and outlier methods selector is shown to select the univariate method. Accordingly, metrics selector  1506  displays peer metric for IDU systems, such as temperate control effort mean, temperature control standard deviation, control effort normalization with mean, control effort normalization with capacity, control effort mean, and control effort standardization. Metrics selector  1506  allows a user to select which peer metrics should be analyzed in a univariate method for outliers. 
     Illustration  1500  is shown to include peer group member information  1508 . In some embodiments, the peer group member information  1508  displayed is related to the peer group selected by peer group selector  1502 . The peer group member information  1508  can include the number of sites included in the peer group, the number of pieces of equipment such as IDUs, the number of systems, and the number of ODUs. Still in other embodiments the peer group member information  1508  can include information relevant to other systems such as number of chillers in the group, number of boilers in the group, number of batteries, etc. 
     Illustration  1500  is shown to include index  1510 . In some embodiments, index  1510  displays the systems and/or pieces of equipment that compose the peer group selected in peer group selector  1502 . For example as shown in  FIG.  15   , index  1510  includes IDU index identifiers for systems in Chennai, and Cork, and all other systems in the group. 
     Illustration  1500  is shown to include peer analysis runtime indicator  1512 . As shown in greater detail below with reference to  FIGS.  17 - 19   , in some embodiments a user can select the peer analysis time period that the outlier detection process should be performed in. As explained above, the peer analysis time period limits what data peer analysis system  802  analyzes when generating peer metric(s)  918 . 
     Illustration  1500  is shown to include outlier results  1514 . In some embodiments, outlier results  1514  displays the values for peer metric(s)  918  for the peer metrics selected in metrics selector  1506  for the peer group selected by peer group selector  1502 . As shown, in the example illustration  1500  two peer metrics were discovered for the temperature control mean of the VRF systems in the peer group named IDU Group. In some embodiments, the outlier results  1514  lists the site, system number, equipment type, equipment number, outlier detection method, and the value of the peer metric for each outlier that is listed in outlier results  1514 . As shown in illustration  1500  the outlier results are also displayed in outlier results graph  1516 . Outlier results graph  1516  plots the distribution of the values for the selected peer metric to the number of the system and/or piece of equipment in the peer group. For example, the systems in IDU Group are shown graphed according to their peer group number on the x-axis and their temperature control mean peer metric on the y-axis. The outlier values identified by outlier results  1514  are shown as solid points in outlier results graph  1516 . Below outlier results graph  1516  in illustration  1500  there is shown the x-axis control  1518  and the y-axis control  1520 . In some embodiments, the x-axis control is not available to be adjusted by a user. For example, when the univariate method is selected in outlier methods selector  1504 , only one peer metric is analyzed, and the x-axis is reserved for the peer group number. Still in other embodiments, the x-axis control  1518  allows a user to select which peer metric appears on the x-axis of outlier results graph  1516 , as shown in  FIG.  16   . 
     Illustration  1500  is shown to include a legend  1522 . In some embodiments legend  1522  identifies which sites in the selected peer group correspond to which values displayed in outlier results graph  1516 . Illustration  1500  is shown to include excluded group member list  1524 . In some embodiments, some members of the selected peer group do not have enough corresponding data, for example in operation data  828 , to be included in the outlier detection process. For example, the systems may be new, or may not have been operating during the selected peer analysis time period. 
     Referring now to  FIG.  16   , an illustration  1600  of a user interface for interacting with peer analysis system  802  is shown, according to some embodiments. In illustration  1600  a multivariate outlier detection method is selected in outlier methods selector  1504  and the x-axis control  1518  and y-axis control  1520  allow a user to select which peer metrics from metrics selector  1506  are displayed on outlier results graph  1516 . As shown in outlier results  1514 , more outliers were detected in the multivariate process comparing the temperature control mean to the control effort normalization with mean peer metric then by the univariate process for the temperature control mean on its own as shown in  FIG.  15   . 
     Referring now to  FIG.  17   , an illustration  1700  of a user interface for adjusting aspects of the peer analysis process performed by peer analysis system  802  is shown, according to some embodiments. Illustration  1700  is shown to display a peer analysis test run page  1724  of a user interface presented to a user. Illustration  1700  is also shown to include a peer analysis time period selector  1726 . In some embodiments, the peer analysis time period selector  1726  allows a user to select the time period for performing the peer analysis that is displayed in  FIGS.  15  and  16    in the peer analysis runtime indicator  1512 . 
     Illustration  1700  is shown to include peer group quantity selector  1728 . In some embodiments, a user can perform outlier detection on a single peer group. Still in other embodiments, a user can perform outlier selection on more than one peer group. For example, a user may perform outlier detection on a peer group of VFDs in Chennai and a peer group of VFDs in York. 
     Illustration  1700  is shown to include site selector  1730 , system selector  1730 , and unit selector  1734 . In some embodiments, a user interface allows a user to select which sites and/or systems and/or units should be included in the peer analysis. For example, a user may wish to perform peer analysis on all systems with IDUs. In some embodiments, a user can deselect a site and/or system or piece of equipment, and the site and/or system will not be included in a peer group and no peer metrics or outliers will be generated based on its data. 
     Illustration  1700  is shown to include outlier method selection panel  1736 . In some embodiments, outlier method selection panel  1736  allows a user to select between a univariate and a multi variate outlier detection method. In some embodiments, outlier method selection panel  1736  also allows a user to set a confidence level represented as alpha. In some embodiments, the outlier method selection panel  1736  also includes options for which peer metrics should be included in the peer analysis. In some embodiments, all available metrics for a system will be selected, and each will be calculated for display in outlier results  1514  and outlier results graph  1516  of  FIGS.  15  and  16   . 
     Illustration  1700  is shown to include a metrics list  1738 . In some embodiments, metrics list  1738  lists the peer metrics selected by a user in the outlier method selection panel  1736  for a more detailed display. In some embodiments, metric list  1738  allows a user to select individual peer metrics for calculation. For example, all peer metrics associated with IDUs may be selected in outlier method selection panel  1736 , and metrics list  1738  lists the IDU peer metrics such as temperature control mean, temperature control standard deviation, control effort normalization with mean, control effort normalization with capacity, etc. and allows a user to select specific peer metrics for inclusion/exclusion of the outlier detection process. 
     Referring now to  FIG.  18   , an illustration  1800  of a user interface for selecting the peer analysis time period of a peer analysis process performed by peer analysis system  802  is shown, according to some embodiments. Illustration  1800  includes peer a start date selector  1740  and an end date selector  1742  allowing a user to set the peer analysis time period for the peer analysis process performed by peer analysis system  802 . 
     Referring now to  FIG.  19   , an illustration  1900  of an example peer analysis project is shown, according to some embodiments. As shown in illustration  1900  the peer analysis time period in peer analysis time period selector  1726  runs from Jan. 15, 2019 to Jan. 15, 2020. Additionally as shown in site selector  1730  only the Chennai site is selected for peer analysis. As shown in outlier method selection panel  1736  a multivariate outlier detection method is to be perform with an alpha, or confidence level of 0.1 for all IDU metrics. Metrics list  1738  only temperature control mean and the temperature control standard deviation peer metrics are selected for analysis. In some embodiments, for multivariate analysis only peer metrics from the same dimension can be compared. For example, as the temperature control mean and the temperature control standard deviation peer metrics are selected for analysis peer metrics relating to the ODU metrics and system metrics may not be available for selection. 
     Configuration of Exemplary Embodiments 
     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 can 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, calculation steps, processing steps, comparison steps, and decision steps. 
     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 can be reversed or otherwise varied and the nature or number of discrete elements or positions can 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 can be varied or re-sequenced according to alternative embodiments. Other substitutions, modifications, changes, and omissions can be made in the design, operating conditions and arrangement of the exemplary embodiments without departing from the scope of the present disclosure. 
     As used herein, the term “circuit” may include hardware structured to execute the functions described herein. In some embodiments, each respective “circuit” may include machine-readable media for configuring the hardware to execute the functions described herein. The circuit may be embodied as one or more circuitry components including, but not limited to, processing circuitry, network interfaces, peripheral devices, input devices, output devices, sensors, etc. In some embodiments, a circuit may take the form of one or more analog circuits, electronic circuits (e.g., integrated circuits (IC), discrete circuits, system on a chip (SOCs) circuits, etc.), telecommunication circuits, hybrid circuits, and any other type of “circuit.” In this regard, the “circuit” may include any type of component for accomplishing or facilitating achievement of the operations described herein. For example, a circuit as described herein may include one or more transistors, logic gates (e.g., NAND, AND, NOR, OR, XOR, NOT, XNOR, etc.), resistors, multiplexers, registers, capacitors, inductors, diodes, wiring, and so on). 
     The “circuit” may also include one or more processors communicably coupled to one or more memory or memory devices. In this regard, the one or more processors may execute instructions stored in the memory or may execute instructions otherwise accessible to the one or more processors. In some embodiments, the one or more processors may be embodied in various ways. The one or more processors may be constructed in a manner sufficient to perform at least the operations described herein. In some embodiments, the one or more processors may be shared by multiple circuits (e.g., circuit A and circuit B may comprise or otherwise share the same processor which, in some example embodiments, may execute instructions stored, or otherwise accessed, via different areas of memory). Alternatively or additionally, the one or more processors may be structured to perform or otherwise execute certain operations independent of one or more co-processors. In other example embodiments, two or more processors may be coupled via a bus to enable independent, parallel, pipelined, or multi-threaded instruction execution. Each processor may be implemented as one or more general-purpose processors, application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), digital signal processors (DSPs), or other suitable electronic data processing components structured to execute instructions provided by memory. The one or more processors may take the form of a single core processor, multi-core processor (e.g., a dual core processor, triple core processor, quad core processor, etc.), microprocessor, etc. In some embodiments, the one or more processors may be external to the apparatus, for example the one or more processors may be a remote processor (e.g., a cloud based processor). Alternatively or additionally, the one or more processors may be internal and/or local to the apparatus. In this regard, a given circuit or components thereof may be disposed locally (e.g., as part of a local server, a local computing system, etc.) or remotely (e.g., as part of a remote server such as a cloud based server). To that end, a “circuit” as described herein may include components that are distributed across one or more locations. The present disclosure contemplates methods, systems and program products on any machine-readable media for accomplishing various operations. The embodiments of the present disclosure can 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. 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.