Patent Publication Number: US-11032172-B2

Title: Asynchronous wireless data transmission system and method for asynchronously transmitting samples of a measured variable by a wireless sensor

Description:
CROSS REFERENCE TO RELATED PATENT APPLICATION 
     This application is a continuation of U.S. patent application Ser. No. 15/619,203 filed Jun. 9, 2017, now U.S. Pat. No. 10,333,810, the entire disclosure of which is incorporated by reference herein. 
    
    
     BACKGROUND 
     The present disclosure relates generally to a control system for a building and more particularly to a control system which transmits measurements from a wireless sensor to a controller. 
     To increase battery life in wireless feedback control applications (e.g., zone temperature control) fixed and dynamic sampling strategies have recently been developed. These strategies are able to significantly reduce the wireless sensor radio transmission rate while providing near equal closed loop performance when compared to traditional deterministic sampling schemes. While these new sampling strategies work well for feedback control, they may not be ideal for supporting data-based analytics (e.g., monitoring, fault detection, causal analysis, etc.) since much less data is provided to the analytics and the data intervals may not be uniform. 
     SUMMARY 
     One implementation of the present disclosure is an asynchronous wireless data transmission system. The system includes a wireless sensor and a data recipient device. The wireless sensor includes a measurement device configured to collect a plurality of samples of a measured variable at a plurality of different sampling times, a transmission generator configured to generate a compressed data object containing the plurality of samples of the measured variable, and a wireless radio configured to transmit the compressed data object at a transmission time asynchronous with at least one of the sampling times. The data recipient device includes an object decompressor configured to extract the plurality of samples of the measured variable from the compressed data object. 
     In some embodiments, the wireless sensor includes a measurement database and a measurement logger configured to store the plurality of samples in the measurement database. Each sample stored in the measurement database may include a time attribute indicating the sampling time at which the sample is collected and a value attribute indicating a value of the measured variable at the sampling time. 
     In some embodiments, the transmission generator is configured to use the time attributes of the samples stored in the measurement database to identify the sampling time at which each sample was collected and select each of the samples that were collected between the transmission time and a previous transmission time for inclusion in the compressed data object. 
     In some embodiments, an amount of time that elapses between consecutive samples of the measured variable defines a sampling period and an amount of time that elapses between consecutive transmissions from the wireless sensor to the data recipient device defines a transmission period. The sampling period may be substantially shorter than the transmission period such that multiple samples of the measured variable are collected within a single transmission period. 
     In some embodiments, the wireless sensor includes a transmission timing controller configured to set the transmission period to an integer multiple of the sampling period and set the transmission time to be synchronous with an end of the transmission period 
     In some embodiments, the wireless sensor includes a transmission timing controller configured to identify a value of the measured variable associated with each of the plurality of samples and dynamically set the transmission time based on one or more of the identified values of the measured variable. In some embodiments, the transmission timing controller is configured to calculate a delta value upon collecting each sample of the measured variable. The delta value may indicate an amount by which a current value of the measured variable deviates from a most recent value of the measured variable transmitted to the data recipient device. The transmission timing controller can cause the compressed data object to be generated and transmitted to the data recipient device in response to a determination that the delta value exceeds a threshold value. 
     In some embodiments, the compressed data object includes each sample of the measured variable collected since a previous transmission time at which a previous compressed data object was sent from the wireless sensor to the data recipient device. 
     In some embodiments, the data recipient device includes a measurement database and the object decompressor is configured to store the extracted samples of the measured variable in the measurement database. Each sample stored in the measurement database may include a time attribute indicating the sampling time at which the sample is collected, a value attribute indicating a value of the measured variable at the sampling time, and a key attribute identifying the wireless sensor that collected the sample. 
     In some embodiments, the data recipient device is configured to use the extracted samples of the measured variable to perform data-based analytics comprising at least one of fault detection and diagnostics, system identification, and noise estimation. 
     In some embodiments, at least one of the wireless sensor and the data recipient device includes a deadband filter configured to filter each sample of the measured variable by adjusting the value of the measured variable based on whether the value of the measured variable is within a deadband range. In some embodiments, the deadband filter is configured to set the value of the measured variable equal to a setpoint for the measured variable in response to a determination that the value of the measured variable is within the deadband range. 
     In some embodiments, the deadband filter is configured to subtract a predetermined amount from the value of the measured variable in response to a determination that the value of the measured variable exceeds a maximum of the deadband range. In some embodiments, the deadband filter is configured to add the predetermined amount to the value of the measured variable in response to a determination that the value of the measured variable is less than a minimum of the deadband range. 
     Another implementation of the present disclosure is a method for asynchronously transmitting samples of a measured variable from a wireless sensor to a data recipient device. The method includes collecting a plurality of samples of the measured variable at the wireless sensor at a plurality of different sampling times and generating, by the wireless sensor, a compressed data object containing the plurality of samples of the measured variable. The method includes transmitting the compressed data object from the wireless sensor to the data recipient device via a wireless radio at a transmission time asynchronous with at least one of the sampling times, and extracting the plurality of samples of the measured variable from the compressed data object at the data recipient device. 
     In some embodiments, the method includes storing the plurality of samples in a measurement database within the wireless sensor. Each sample stored in the measurement database may include a time attribute indicating the sampling time at which the sample is collected and a value attribute indicating a value of the measured variable at the sampling time. 
     In some embodiments, the method includes using the time attributes of the samples stored in the measurement database to identify the sampling time at which each sample was collected and selecting each of the samples that were collected between the transmission time and a previous transmission time for inclusion in the compressed data object. 
     In some embodiments, an amount of time that elapses between consecutive samples of the measured variable defines a sampling period, an amount of time that elapses between consecutive transmissions from the wireless sensor to the data recipient device defines a transmission period, and the sampling period is substantially shorter than the transmission period such that multiple samples of the measured variable are collected within a single transmission period. 
     In some embodiments, the method includes setting the transmission period to an integer multiple of the sampling period and setting the transmission time to be synchronous with an end of the transmission period 
     In some embodiments, the method includes identifying a value of the measured variable associated with each of the plurality of samples and dynamically setting the transmission time based on one or more of the identified values of the measured variable. 
     In some embodiments, the method includes calculating a delta value indicating an amount by which a current value of the measured variable deviates from a most recent value of the measured variable transmitted to the data recipient device. The delta value may be calculated upon collecting each sample of the measured variable. The method may include causing the compressed data object to be generated and transmitted to the data recipient device in response to a determination that the delta value exceeds a threshold value. 
     In some embodiments, the compressed data object includes each sample of the measured variable collected since a previous transmission time at which a previous compressed data object was sent from the wireless sensor to the data recipient device. 
     In some embodiments, the method includes storing the extracted samples of the measured variable in a measurement database within the data recipient device. Each sample stored in the measurement database may include a time attribute indicating the sampling time at which the sample is collected, a value attribute indicating a value of the measured variable at the sampling time, and a key attribute identifying the wireless sensor that collected the sample. 
     In some embodiments, the method includes filtering each sample of the measured variable using a deadband filter. The filtering may include adjusting the value of the measured variable based on whether the value of the measured variable is within a deadband range. In some embodiments, the filtering includes setting the value of the measured variable equal to a setpoint for the measured variable in response to a determination that the value of the measured variable is within the deadband range. 
     In some embodiments, the filtering includes subtracting a predetermined amount from the value of the measured variable in response to a determination that the value of the measured variable exceeds a maximum of the deadband range. In some embodiments, the filtering includes adding the predetermined amount to the value of the measured variable in response to a determination that the value of the measured variable is less than a minimum of the deadband range. 
     In some embodiments, the method includes using the extracted samples of the measured variable to perform data-based analytics comprising at least one of fault detection and diagnostics, system identification, and noise estimation. 
     Those skilled in the art will appreciate that the summary is illustrative only and is not intended to be in any way limiting. Other aspects, inventive features, and advantages of the devices and/or processes described herein, as defined solely by the claims, will become apparent in the detailed description set forth herein and taken in conjunction with the accompanying drawings. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a drawing of a building equipped with a HVAC system, according to some embodiments. 
         FIG. 2  is a block diagram of a waterside system that may be used in conjunction with the building of  FIG. 1 , according to some embodiments. 
         FIG. 3  is a block diagram of an airside system that may be used in conjunction with 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. 
         FIG. 6  is a block diagram of control system with asynchronous wireless data transmission from a wireless sensor to a controller, according to some embodiments. 
         FIG. 7  is a block diagram of a system in which the wireless sensor and controller of  FIG. 6  can be implemented, according to some embodiments. 
         FIG. 8  is a block diagram of another system in which the wireless sensor and controller of  FIG. 6  can be implemented, according to some embodiments. 
         FIG. 9  is a block diagram of another system in which the wireless sensor and controller of  FIG. 6  can be implemented, according to some embodiments. 
         FIG. 10  is a block diagram illustrating the wireless sensor and controller of  FIG. 6  in greater detail, according to some embodiments. 
         FIG. 11  is a flowchart illustrating a send-on-delta (SOD) process which can be performed by the wireless sensor of  FIG. 6 , according to some embodiments. 
         FIG. 12  is a graph illustrating the operation of a deadband filter to filter measurements collected by the wireless sensor of  FIG. 6 , according to some embodiments. 
         FIG. 13  is a graph of raw data illustrating the temperature of a building space over time, according to some embodiments. 
         FIG. 14  is a graph of the temperature sampled every minute from the raw data of  FIG. 13 , according to some embodiments. 
         FIG. 15  is a graph of the temperature data collected as shown in  FIG. 13  transmitted using the send-on-delta technique, according to some embodiments. 
         FIG. 16  is a graph of the data transmitted with the asynchronous transmission technique used by the wireless sensor of  FIG. 6 , according to some embodiments. 
         FIG. 17  is a flowchart of an asynchronous data transmission process which can be performed by the wireless sensor and/or the controller of  FIG. 6 , according to some embodiments. 
     
    
    
     DETAILED DESCRIPTION 
     Overview 
     Referring generally to the FIGURES, a control system with asynchronous wireless data transmission and components thereof are shown, according to various exemplary embodiments. To increase battery life in wireless feedback control applications (e.g., zone temperature control) fixed and dynamic sampling strategies have recently been developed. An example of such a dynamic sampling strategy is described in detail in U.S. patent application Ser. No. 15/618,492 filed Jun. 9, 2017, the entire disclosure of which is incorporated by reference herein. These strategies are able to significantly reduce the wireless sensor radio transmission rate while providing near equal closed loop performance when compared to traditional deterministic sampling schemes. While these new sampling strategies work well for feedback control, they may not be ideal for supporting data-based analytics (e.g., monitoring, fault detection, causal analysis, etc.) since much less data is provided to the analytics and the data intervals may not be uniform. 
     The systems and methods described herein can be used to reduce the battery power consumption of a wireless sensor while preserving the original data for data-based analytics. For example, a control system described herein includes a wireless sensor, a controller, and a plant. The wireless sensor can be configured to collect samples of the measured variable y p  at regular intervals. The length of time between measurements collected by the wireless sensor is referred to herein as the measurement period and/or the measurement interval. The wireless sensor can store multiple measurements y p  in memory contained within the wireless sensor. In some embodiments, the wireless sensor includes a filter (e.g., a deadband filter) configured to filter the measurements y p  collected by the measurement device. The wireless sensor can use the filter to convert the measurements y p  into filtered measurements y w . In other embodiments, the filter may be a component of the controller. 
     The wireless sensor may include a wireless radio configured to wirelessly transmit measurements to the controller. The measurements transmitted to the controller can include the raw measurements y p  and/or the filtered measurements y w . The length of time between transmissions to the controller is referred to herein as the transmission period and/or the transmission interval. The transmission interval can be a regular interval (e.g., one transmission every ten minutes) or an irregular or dynamic interval (e.g., transmit when the measured variable y p  changes by a threshold amount, etc.). The transmission interval may be significantly longer than the measurement interval such that multiple measurements are collected within each transmission interval. In some embodiments, the transmission timing is controlled by a transmission timing controller within the wireless sensor  602 . The transmission timing is described in greater detail below. 
     In some embodiments, the wireless sensor transmits multiple measurements to the controller as part of a single transmission or message. For example, the wireless sensor can generate a compressed data object that includes multiple measurements. In some embodiments, the compressed data object includes all of the measurements that have been collected since the previous transmission to the controller. The compressed data object can then be transmitted to the controller via the wireless radio. Advantageously, this allows the wireless sensor to conserve battery power and reduce network traffic by reducing the number of transmissions while still preserving the data sampled at the faster measurement interval. 
     The controller can be configured to decompress the compressed data objects received from the wireless sensor and extract the multiple measurements. The controller can use the measurements as input to a feedback control process to calculate the controlled variable u c  (e.g., a setpoint, a control signal, etc.). In various embodiments, the controller may be a proportional controller, a proportional-integral (PI) controller, a proportional-integral-derivative (PID) controller, a model predictive controller (MPC), and/or any other type of controller configured to generate an input u c  to the plant as a function of the feedback received from the wireless sensor. 
     In some embodiments, the controller stores the extracted measurements in a measurement database along with a timestamp indicating a time at which the measurement was collected. Over time, the controller may receive and store all of the measurements collected by the wireless sensor at the faster measurement interval as part of compressed data objects transmitted at the slower transmission interval. This allows the controller and/or other systems or devices to use the full set of measurements to perform analytics that would not be possible if only one measurement were received in each transmission. For example, the controller can use the full set of measurements to analyze noise levels, calculate the frequency of oscillations of the measurements, or perform other analytics that require measurements collected at the faster measurement interval. These and other features of the control system are described in greater detail below. 
     Building HVAC Systems and Building Management Systems 
     Referring now to  FIGS. 1-5 , several building management systems (BMS) and HVAC systems 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 system  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 . 
     Building and HVAC System 
     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 system, a security system, a lighting system, a fire alerting system, any other system that is capable of managing building functions or devices, or any combination thereof. 
     The BMS that serves building  10  includes a HVAC system  100 . HVAC system  100  can include a plurality of HVAC devices (e.g., heaters, chillers, air handling units, pumps, fans, thermal energy storage, etc.) configured to provide heating, cooling, ventilation, or other services for building  10 . For example, HVAC system  100  is shown to include a waterside system  120  and an airside system  130 . Waterside system  120  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 . An exemplary waterside system and airside system which can be used in HVAC system  100  are described in greater detail with reference to  FIGS. 2-3 . 
     HVAC system  100  is shown to include a chiller  102 , a boiler  104 , and a rooftop air handling unit (AHU)  106 . Waterside system  120  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. 
     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 system  100  or can be implemented separate from HVAC system  100 . When implemented in HVAC system  100 , waterside system  200  can include a subset of the HVAC devices in HVAC system  100  (e.g., boiler  104 , chiller  102 , pumps, valves, etc.) and 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 system  100  or can be implemented separate from HVAC system  100 . When implemented in HVAC system  100 , airside system  300  can include a subset of the HVAC devices in HVAC system  100  (e.g., AHU  106 , VAV units  116 , ducts  112 - 114 , fans, dampers, etc.) and can be located in or around building  10 . Airside system  300  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 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 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  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 system  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 system  100 , a security system, a lighting system, waterside system  200 , etc.) via a communications link  370  according to like or disparate protocols (e.g., LON, BACnet, etc.). In various embodiments, AHU controller  330  and 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 system  100 , its subsystems, and/or devices. Client device  368  can be a computer workstation, a client terminal, a remote or local interface, or any other type of user interface device. Client device  368  can be a stationary terminal or a mobile device. For example, client device  368  can be a desktop computer, a computer server with a user interface, a laptop computer, a tablet, a smartphone, a PDA, or any other type of mobile or non-mobile device. Client device  368  may communicate with BMS controller  366  and/or AHU controller  330  via communications link  372 . 
     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 with reference to  FIGS. 2-3 . 
     Each of building subsystems  428  can include any number of devices, controllers, and connections for completing its individual functions and control activities. HVAC subsystem  440  can include many of the same components as HVAC system  100 , as described with reference to  FIGS. 1-3 . For example, HVAC subsystem  440  can include a chiller, a boiler, any number of air handling units, economizers, field controllers, supervisory controllers, actuators, temperature sensors, and other devices for controlling the temperature, humidity, airflow, or other variable conditions within building  10 . Lighting subsystem  442  can include any number of light fixtures, ballasts, lighting sensors, dimmers, or other devices configured to controllably adjust the amount of light provided to a building space. Security subsystem  438  can include occupancy sensors, video surveillance cameras, digital video recorders, video processing servers, intrusion detection devices, access control devices and servers, or other security-related devices. 
     Still referring to  FIG. 4 , 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 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  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&#39;s application, desired comfort level, particular building equipment, or based on other concerns. For example, the demand response policy definitions can specify which equipment can be turned on or off in response to particular demand inputs, how long a system or piece of equipment should be turned off, what setpoints can be changed, what the allowable set point adjustment range is, how long to hold a high demand setpoint before returning to a normally scheduled setpoint, how close to approach capacity limits, which equipment modes to utilize, the energy transfer rates (e.g., the maximum rate, an alarm rate, other rate boundary information, etc.) into and out of energy storage devices (e.g., thermal storage tanks, battery banks, etc.), and when to dispatch on-site generation of energy (e.g., via fuel cells, a motor generator set, etc.). 
     Integrated control layer  418  can be configured to use the data input or output of building subsystem integration layer  420  and/or demand response later  414  to make control decisions. Due to the subsystem integration provided by building subsystem integration layer  420 , integrated control layer  418  can integrate control activities of the subsystems  428  such that the subsystems  428  behave as a single integrated supersystem. In 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 system  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 (TOM)  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 . 
     Control System with Asynchronous Wireless Data Transmission 
     Referring now to  FIG. 6 , a block diagram of control system  600  with asynchronous wireless data transmission is shown, according to an exemplary embodiment. To increase battery life in wireless feedback control applications (e.g., zone temperature control) fixed and dynamic sampling strategies have recently been developed. An example of such a dynamic sampling strategy is described in detail in U.S. patent application Ser. No. 15/618,492 filed Jun. 9, 2017, the entire disclosure of which is incorporated by reference herein. These strategies are able to significantly reduce the wireless sensor radio transmission rate while providing near equal closed loop performance when compared to traditional deterministic sampling schemes. While these new sampling strategies work well for feedback control, they may not be ideal for supporting data-based analytics (e.g., monitoring, fault detection, causal analysis, etc.) since much less data is provided to the analytics and the data intervals may not be uniform. Control system  600  is configured to reduce the battery power consumption of a wireless sensor while preserving the original data for data-based analytics. 
     Control system  600  is shown to include a wireless sensor  602 , a controller  604 , and a plant  606 . A plant in control theory is the combination of a process and one or more mechanically-controlled outputs. In some embodiments, plant  606  includes one or more controllable HVAC components (e.g., chillers, heaters, actuators, fans, AHUs, RTUs, valves, etc.) that operate to affect an environmental condition within a building space. For example, plant  606  can include an air handling unit configured to control temperature within a building space via one or more mechanically-controlled actuators and/or dampers. Plant  606  can include any of the HVAC equipment described with reference to  FIGS. 1-5 . In various embodiments, plant  606  can include a chiller operation process, a damper adjustment process, a mechanical cooling process, a ventilation process, a refrigeration process, or any other process in which an input variable to plant  606  (i.e., controlled variable u c ) is adjusted to affect an output from plant  606  (i.e., measured variable y p ). Several examples of plant  606  are described with reference to  FIGS. 7-9 . 
     Wireless sensor  602  can be configured to record measurements of measured variable y p  and transmit measurements to controller  604 . Wireless sensor  602  may include a measurement device (e.g., a temperature sensor, humidity sensor, enthalpy sensor, pressure sensor, lighting sensor, flow rate sensor, voltage sensor, valve position sensor, etc.) configured to collect samples of measured variable y p  from plant  606 . In some embodiments, wireless sensor  602  includes multiple measurement devices, each configured to measure a different variable (e.g., temperature, humidity, pressure, etc.). In other embodiments, wireless sensor  602  includes a single measurement device configured to measure a single measured variable y p . 
     Wireless sensor  602  may include an internal power source (e.g., a battery) configured to power the electronic components of wireless sensor  602 . Wireless sensor  602  can draw power from the internal power source and use the power to transmit measurements to controller  604 . In some embodiments, wireless sensor  602  receives power from an external power source such as an electric grid, a wireless charging source, radio frequency waves, or other external power sources. In other embodiments, wireless sensor  602  is powered exclusively by the internal power source. 
     Wireless sensor  602  can be configured to collect samples of the measured variable y p  at regular intervals. For example, wireless sensor  602  may obtain a temperature measurement in a particular zone of a building every minute. The length of time between measurements collected by wireless sensor  602  is referred to herein as the measurement period and/or the measurement interval. Wireless sensor  602  can store multiple measurements y p  in memory contained within wireless sensor  602 . In some embodiments, wireless sensor  602  includes a filter (e.g., a deadband filter) configured to filter the measurements y p  collected by the measurement device. Wireless sensor  602  can use the filter to convert the measurements y p  into filtered measurements y w . In other embodiments, the filter may be a component of controller  604 . 
     Wireless sensor  602  may include a wireless radio configured to wirelessly transmit measurements to controller  604 . The measurements transmitted to controller  604  can include the raw measurements y p  and/or the filtered measurements y w . The length of time between transmissions to controller  604  is referred to herein as the transmission period and/or the transmission interval. The transmission interval can be a regular interval (e.g., one transmission every ten minutes) or an irregular or dynamic interval (e.g., transmit when the measured variable y p  changes by a threshold amount, etc.). The transmission interval may be significantly longer than the measurement interval such that multiple measurements are collected within each transmission interval. In some embodiments, the transmission timing is controlled by a transmission timing controller within wireless sensor  602 . The transmission timing is described in greater detail with reference to  FIG. 10 . 
     In some embodiments, wireless sensor  602  transmits multiple measurements to controller  604  as part of a single transmission or message. For example, wireless sensor  602  can generate a compressed data object that includes multiple measurements. In some embodiments, the compressed data object includes all of the measurements that have been collected since the previous transmission to controller  604 . The compressed data object can then be transmitted to controller  604  via the wireless radio. Advantageously, this allows wireless sensor  602  to conserve battery power and reduce network traffic by reducing the number of transmissions while still preserving the data sampled at the faster measurement interval. 
     Controller  604  can be configured to decompress the compressed data objects received from wireless sensor  602  and extract the multiple measurements. Controller  604  can use the measurements as input to a feedback control process to calculate the controlled variable u c  (e.g., a setpoint, a control signal, etc.). In various embodiments, controller  604  may be a proportional controller, a proportional-integral (PI) controller, a proportional-integral-derivative (PID) controller, a model predictive controller (MPC), and/or any other type of controller configured to generate an input u c  to plant  606  as a function of the feedback received from wireless sensor  602 . 
     In some embodiments, controller  604  stores the extracted measurements in a measurement database along with a timestamp indicating a time at which the measurement was collected. Over time, controller  604  may receive and store all of the measurements collected by wireless sensor  602  at the faster measurement interval as part of compressed data objects transmitted at the slower transmission interval. This allows controller  604  and/or other systems or devices to use the full set of measurements to perform analytics that would not be possible if only one measurement were received in each transmission. For example, controller  604  can use the full set of measurements to analyze noise levels, calculate the frequency of oscillations of the measurements, or perform other analytics that require measurements collected at the faster measurement interval. 
     Referring now to  FIG. 7 , an example of a system  700  in which wireless sensor  602  and controller  604  can be implemented is shown, according to an exemplary embodiment. System  700  is shown to include a building zone  702 . Building zone  702  may include one or more rooms, offices, lobbies, and/or other areas of a building that may require heating, cooling, and/or other types of environmental control. 
     Wireless sensor  602  is shown to be located within zone  702 . In some embodiments, wireless sensor  602  is part of a thermostat, remote sensor, Wi-Fi sensor, Zigbee Sensor, or other device configured to measure a variable state or condition within building zone  702 . Wireless sensor  602  is shown to include processing circuit  714  and wireless radio  712 . Processing circuit  714  may perform one or more operations causing wireless radio  712  to transmit measurements y w  to controller  604 . Processing circuit  714  may include one or more processors and/or memory devices, as described with reference to  FIG. 10 . 
     Controller  604  can be any building controller or other device that can cause HVAC device  716  to affect an environmental condition in zone  702  (e.g., AHU controller  330  and/or BMS controller  366 ). HVAC device  716  may be a residential outdoor unit, a furnace, a heat pump, an air conditioner, a variable air volume (VAV) unit (e.g., VAVs  116 ), a boiler (e.g., boiler  104 ), a chiller (e.g., chiller  102 ), an air handler and/or roof top unit (e.g., AHU  106 ) and/or any other HVAC device described herein. Controller  604  is shown to include wireless radio  704  and a processing circuit  706  both of which are described with reference to  FIG. 10 . 
     Controller  604  may communicate wirelessly with wireless sensor  602  via wireless radios  704  and  712 . Wireless radio  704  can be configured to receive measured temperature values, humidity values, and/or other types of measurements y w  from wireless radio  712  of sensor  602 . Wireless radio  712  can be configured to send commands to wireless radio  704  such as historical read commands to read historical data (e.g., measured temperature values measured over a time horizon) that processing circuit  706  can be configured to store. Processing circuit  706  of controller  604  can be configured to generate control signals u c  for HVAC device  716 . In some embodiments, wireless radio  704  receives measured temperature values from wireless sensor  602  and causes HVAC device  716  to cause a change in the environmental conditions of zone  702  based on the measured temperature values. 
     Referring now to  FIG. 8 , another example of a system  800  in which wireless sensor  602  and controller  604  can be implemented is shown, according to an exemplary embodiment. In system  800 , controller  604  is shown controlling a variable air volume (VAV) unit  802 . Wireless sensor  602  may record measurements of measured variable y p  and send measurements to controller  604 . Controller  604  may use the measurements to generate control signals u c  for VAV unit  802 . In some embodiments, based on data received from wireless sensor  602 , (e.g., measured temperature values of zone  702 ) controller  604  can control VAV unit  802  to cause environmental conditions of zone  702  to meet an environmental setpoint. VAV unit  802  may be a device that includes a damper that controls the airflow into zone  802 . In this respect, controller  604  can be configured to generate control signals for the damper of VAV unit  802  and/or any other actuator of VAV unit  802 . In some embodiments, VAV unit  802  is a VAV such as one of VAVs  116  as described with further reference to  FIG. 1 . 
     Referring now to  FIG. 9 , another example of a system  900  in which wireless sensor  602  and controller  604  can be implemented is shown, according to an exemplary embodiment. In system  900 , controller  604  is shown generating control signals u c  for an indoor variable refrigerant flow (VRF) unit  902 . Wireless sensor  602  may record measurements of measured variable y p  and send measurements to controller  604 . Wireless sensor  602  can be configured to send temperatures, setpoints, and other data associated with zone  702  to controller  604 . Based on the information received from wireless sensor  602 , controller  604  can be configured to generate control signals u c  for indoor VRF unit  902 . Indoor VRF unit  902  may send and receive refrigerant from outdoor VRF unit  904  via refrigerant conduits  906 . Based on the refrigerant received from outdoor VRF unit  904 , indoor VRF unit  902  can be configured to heat and/or cool zone  702  with the refrigerant. Indoor VRF unit  902  and outdoor VRF unit  904  may be part of a VRF system for zone  702  and the building that includes zone  702  (e.g., building  10 ). Based on the data received from wireless sensor  602 , controller  604  can cause indoor VRF unit  902  to affect the environmental conditions of zone  702 . 
     Wireless Sensor and Controller 
     Referring now to  FIG. 10 , a block diagram illustrating wireless sensor  602  and controller  604  in greater detail is shown, according to an exemplary embodiment. Wireless sensor  602  is shown to include a measurement device  732 , a wireless radio  712 , a processing circuit  714 , and a battery  704 . Battery  704  can be configured to power the electronic components of wireless sensor  602 . For example, battery  704  is shown providing battery power to measurement device  732 , wireless radio  712 , and processing circuit  714 . Processing circuit  714  can be configured to conserve battery power by reducing the number and/or frequency of wireless data transmissions sent to controller  604 . 
     Measurement device  732  can include any type of transducer, sensor, or other measurement device (e.g., a temperature sensor, humidity sensor, enthalpy sensor, pressure sensor, lighting sensor, flow rate sensor, voltage sensor, valve position sensor, etc.) configured to collect samples of measured variable y p  from plant  606 . In some embodiments, measurement device  732  is a temperature sensor configured to measure the temperature of building zone  702 . For example, measurement device  732  may include a thermocouple, a thermistor, a resistance temperature detector and/or any combination thereof. In some embodiments, wireless sensor  602  includes multiple measurement devices  732 , each configured to measure a different variable (e.g., temperature, humidity, pressure, etc.). In other embodiments, wireless sensor  602  includes a single measurement device  732  configured to measure a single measured variable y p . In some embodiments, measurement device  732  includes an analog-to-digital converter or other electronics for converting measurements y p  to digital values. 
     Wireless radio  712  can be configured to send data to controller  604 . Specifically, wireless radio  712  can be configured to communicate with wireless radio  704  of controller  604 . In various embodiments, wireless radio  712  can be configured to communicate via Wi-Fi, Zigbee (e.g., Zigbee IP, Zigbee Pro Green Power), Bluetooth, 2G, 3G, LTE, a local area network (LAN), a metropolitan area network (MAN), a wide area network (WAN) (e.g., the Internet), ad hoc wireless communication, and/or any other type of wireless communications protocol or network. As shown in  FIG. 10 , the data sent to controller  604  may include a compressed data object containing multiple measurements. The compressed data object may include multiple samples of the measured variable y p  and/or multiple filtered measurements y w . The generation of the compressed data object and the timing of transmissions sent by wireless radio  712  may be controlled by processing circuit  714 . 
     Processing circuit  714  is shown to include a processor  718  and memory  720 . Processor  718  can be a general purpose or specific purpose processor, an application specific integrated circuit (ASIC), one or more field programmable gate arrays (FPGAs), a group of processing components, or other suitable processing components. Processor  718  may be configured to execute computer code and/or instructions stored in memory  720  or received from other computer readable media (e.g., CDROM, network storage, a remote server, etc.). 
     Memory  720  can include one or more devices (e.g., memory units, memory devices, storage devices, etc.) for storing data and/or computer code for completing and/or facilitating the various processes described in the present disclosure. Memory  720  can include random access memory (RAM), read-only memory (ROM), hard drive storage, temporary storage, non-volatile memory, flash memory, optical memory, or any other suitable memory for storing software objects and/or computer instructions. Memory  720  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 disclosure. Memory  720  can be communicably connected to processor  718  via processing circuit  714  and can include computer code for executing (e.g., by processor  718 ) one or more processes described herein. 
     Wireless sensor  602  is shown to include a measurement logger  722  and a measurement database  724 . Measurement logger  722  can be configured to log the measurements y p  obtained by measurement device  732  in measurement database  724 . In some embodiments, measurement device  732  obtains measurements y p  at a regular interval, referred to herein as the measurement interval. For example, measurement device  732  may collect a measurement y p  once per minute, once per second, or at any other measurement interval. In some embodiments, measurement logger  722  converts the measurements y p  to data objects that include attributes describing the measurements y p . For example, measurement logger  722  can generate a data object for each measurement y p . Each data object may include a key attribute indicating the unique ID of wireless sensor  602  (e.g., “ConferenceRoom4_ZN-T”), a time attribute indicating the time at which the corresponding measurement y p  was collected (e.g., “2017-05-22; 08:00:00”), and a value attribute indicating the value of the measured variable y p  (e.g., 74° F.). Measurement logger  722  can store the measurements y p  and/or the data objects based on the measurements y p  in measurement database  724 . 
     Wireless sensor  602  is shown to include a transmission timing controller  728 . Transmission timing controller  728  can be configured to control (i.e., schedule) the times at which transmissions are sent to controller  604 . The amount of time that elapses between transmissions to controller  604  is referred to herein as the transmission interval. The transmission interval may be a regular interval (e.g., once every ten minutes, once every hour, etc.) or an irregular interval (i.e., an interval that changes dynamically based on the values of the measured variable y p ). In some embodiments, the transmission interval is longer than the measurement interval such that multiple measurements y p  are obtained by measurement device  732  and logged by measurement logger  722  during each transmission interval. 
     In some embodiments, the times at which transmissions are sent to controller  604  are dynamically determined by transmission timing controller  728  based on the values of measured variable y p . For example, transmission timing controller  728  may determine that a transmission should be sent to controller  604  in response to a determination that the value of the measured variable y p  has changed by a threshold amount since the last transmission was sent. This transmission timing strategy is referred to as send-on-delta (SOD) and is described in detail in U.S. patent application Ser. No. 15/618,492. The SOD strategy is summarized briefly in the following paragraphs. 
     Each time a sample of the measured variable y p  is obtained, transmission timing controller  728  may compare the current value of the measured variable y p,cur  to the most recent value of the measured variable y p,prev  transmitted to controller  604 . If the difference between y p,cur  and y p,prev  is greater than a threshold value δ (i.e., |y p,cur −y p,prev |&gt;δ), transmission timing controller  728  may determine that a new transmission should be sent to controller  604 . However, if the difference between y p,cur  and y p,prev  not greater than the threshold value δ (i.e., |y p,cur −y p,prev |≤δ), transmission timing controller  728  may determine that a new transmission should not be sent to controller  604 . 
     In some embodiments, transmission timing controller  728  schedules transmissions to controller  604  such that the transmission interval Δt w  is maintained between a minimum allowable transmission interval Δt w   min  and a maximum allowable transmission interval Δt w   max  (i.e., Δt w   min ≤Δt w ≤Δt w   max ). For example, each time a sample of the measured variable y p  is obtained, transmission timing controller  728  may compare the time at which the sample of the measured variable y p  is obtained (i.e., the current time t cur ) with the time at which the previous transmission was sent to controller  604  (i.e., the previous transmission time t prev ). Transmission timing controller  728  may calculate the difference Δt w  between t cur  and t prev  (i.e., Δt w =t cur −t prev ) and compare the difference Δt w  with Δt w   min  and Δt w   max . 
     If Δt w  is less than Δt w   min  (i.e., Δt w &lt;Δt w   min ), transmission timing controller  728  may determine that a new transmission should not be sent to controller  604 , regardless of the value of the measured variable y p . This ensures that at least a minimum time Δt w   min  elapses between transmissions to controller  604 . If Δt w  is greater than Δt w   min  (i.e., Δt w &gt;Δt w   max ), transmission timing controller  728  may determine that a new transmission should be sent to controller  604 , regardless of the value of the measured variable y p . This ensures that at most a maximum time Δt w   max  elapses between transmissions to controller  604 . If Δt w  is between Δt w   min  and Δt w   max  (i.e., Δt w   min ≤Δt w ≤Δt w   max ), transmission timing controller  728  may compare the value of y p,cur  with y p,prev  to determine whether a new transmission should be sent. For example, transmission timing controller  728  may determine that a new transmission should be sent to controller  604  if the difference between y p,cur  and y p,prev  is greater than a threshold value δ (i.e., |y p,cur −y p,prev |&gt;δ), as previously described. 
     Referring now to  FIG. 11 , a flowchart illustrating the SOD process  1100  is shown, according to an exemplary embodiment. SOD process  1100  is shown to include obtaining a sample of the measured variable y p,cur  at time t cur  (step  1102 ) and calculating the amount of time Δt w =t cur −t prev  that has elapsed since the last transmission to controller  604  (step  1104 ). If Δt w  is less than a minimum time threshold Δt w   min  (i.e., the result of step  1106  is “yes”), a new transmission is not sent (step  1112 ). However, if Δt w  is not less than the minimum time threshold Δt w   min  (i.e., the result of step  1106  is “no”), the elapsed time Δt w  is compared to a maximum time threshold Δt w   min  (step  1108 ). 
     If Δt w  is greater than the maximum time threshold Δt w   max  (i.e., the result of step  1108  is “yes”), a new transmission is sent (step  1114 ). However, if Δt w  is not greater than the maximum time threshold Δt w   max  (i.e., the result of step  1108  is “no”), the SOD process  1100  calculates a change in the measured variable Δy p =|y p,cur −y p,prev | since the last transmission (step  1110 ). If the change in the measured variable Δy p  is greater than a threshold value δ (i.e., the result of step  1116  is “yes”), a new transmission is sent (step  1114 ). However, if the change in the measured variable Δy p  is not greater than the threshold value δ (i.e., the result of step  1116  is “no”), then a new transmission is not sent (step  1112 ). 
     It should be noted that SOD technique is merely one example of a transmission timing technique which can be used to determine the times at which transmissions are sent to controller  604 . It is contemplated that the systems and methods of the present disclosure can be used in combination with any sensor that obtains and transmits measurements asynchronously. For example, the systems and methods described herein can be used to transmit data from any sensor that obtains measurements at a rate faster than the sensor transmits measurements. Accordingly, the sensor may record multiple measurements between each transmission. Each transmission may include multiple measurements obtained since the previous transmission. The multiple measurements can be packaged into a single compressed data object and sent to controller  604  in a single transmission. 
     Referring now to  FIGS. 10 and 12 , wireless sensor  602  is shown to include a deadband filter  726 . In some embodiments, deadband filter  726  is a component of wireless sensor  602 . In other embodiments, deadband filter  726  is a component of controller  604 . Deadband filter  726  can be configured to filter one or more of the measurements y p  collected by wireless sensor  602  to generate one or more filtered measurements y w . In some embodiments, deadband filter  726  determines whether each measurement y p  is within a deadband range centered around a setpoint r for the measured variable y p . The setpoint r can be provided as an input to wireless sensor  602  (e.g., if wireless sensor  602  is part of a thermostat) and/or controller  604 . 
     If the measurement y p  is within the deadband range 
               (       i   .   e   .     ,       r   -     DB   2       ≤     y   p     ≤     r   +     DB   2           )     ,         
deadband filter  726  may set the filtered measurement y w  equal to the setpoint r. However, if the measurement y p  is outside the deadband range
 
               (       i   .   e   .     ,       y   p     &lt;     r   -       DB   2     ⁢           ⁢   or   ⁢           ⁢     y   p         &gt;     r   +     DB   2           )     ,         
deadband filter  726  may add or subtract the deadband threshold
 
             DB   2         
from the measurement y p  to bring the filtered measurement y w  closer to the setpoint r. The following equation illustrates the calculation which may be performed by deadband filter  726  to generate each filtered measurement y w  as a function of the corresponding raw measurement y p :
 
     
       
         
           
             
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     A graph  1200  illustrating the operation of deadband filter  726  is shown in  FIG. 12 . The horizontal axis of graph  1200  represents the measurement y p  provided as an input to deadband filter  726 , whereas the vertical axis of graph  1200  represents the filtered measurement y w  provided as an output of deadband filter  726 . The center point  1206  of graph  1200  is equal to the setpoint r for measured variable y p . For example, if measured variable y p  is a room temperature, and the setpoint r for the room temperature is 70° F., the center point  1206  of graph  1200  may have a value of 70° F. 
     Graph  1200  is shown to have two sections: a slope section  1202  and a deadband section  1204 . Deadband section  1204  has a range of 
             ±     DB   2           
on either side of the setpoint r. If the input y p  to deadband filter  726  falls within deadband section  1204 
 
               (       i   .   e   .     ,       r   -     DB   2       ⁢           ≤           ⁢     y   p     ≤     r   +     DB   2           )     ,         
the output y w  of deadband filter  726  is equal to the setpoint r. However, if the input y p  to deadband filter  726  falls within slope section  1202 ,
 
               (       i   .   e   .     ,       y   p     &lt;     r   -       DB   2     ⁢           ⁢   or   ⁢           ⁢     y   p         &gt;     r   +     DB   2           )     ,         
the output y w  of deadband filter is a linear function of the input y p  and is shifted closer to the setpoint r by an amount equal to the deadband threshold
 
               DB   2     .         
For example, if the input y p  falls within slope section  1202  and is less than the setpoint r, then the output y w  is equal to
 
               y   p     +       DB   2     .           
However, if the input y p  falls within slope section  1202  and is greater than the setpoint r, then the output y w  is equal to
 
     
       
         
           
             
               y 
               p 
             
             + 
             
               
                 DB 
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     Advantageously, deadband filter  726  operates to reduce the integrated error of the measured variable y p  relative to the setpoint r by establishing a deadband section  1204  around the setpoint r 
               (       i   .   e   .     ,     r   ±     DB   2         )     .         
If the measurement y p  falls within deadband section  1204 , the filtered measurement y w  will be equal to the setpoint r and the error e=r−y w  will be equal to zero. This ensures that controller  604  will not accumulate a large integrated error (e.g., Σ i=1   n e i ) over time for persistent values of y p  within deadband section  1204 .
 
     In some embodiments, various components of wireless sensor  602  operate using the filtered measurements y w  instead of the raw measurements y p . For example, transmission timing controller  728  may use the filtered measurements y w  instead of the raw measurements y p  to determine whether a new transmission should be sent to controller  604 . Similarly, measurement logger  722  and measurement database  724  can be configured to log and store the filtered measurements y w . It is contemplated that these and other components of wireless sensor  602  can use the filtered measurements y w  in place of the raw measurements y p  or in addition to the raw measurements y p  to perform the functions described herein with respect to each component of wireless sensor  602 . 
     For embodiments in which deadband filter  726  is a component of controller  604 , deadband filter  726  may receive the raw measurements y p  transmitted by wireless sensor  602 . For example, deadband filter  726  can receive one or more of the raw measurements y p  extracted from a compressed data object transmitted to controller  604  from wireless sensor  602 . Deadband filter  726  can process the raw measurements y p  to generate filtered measurements y w  and can provide the filtered measurements y w  to feedback controller  740  for use in generating the control signal u c  for plant  606 . The operation of controller  604  is described in greater detail below. 
     Referring again to  FIG. 10 , wireless sensor  602  is shown to include a transmission generator  730 . Transmission generator  730  can be configured to generate a compressed data object for transmission to controller  604 . The compressed data object may contain multiple measurements, which may include the raw measurements y p  and/or the filtered measurements y w . For ease of explanation, the operation of transmission generator  730  will be described assuming that the filtered measurements y w  are used to generate the compressed data object. However, it should be understood that transmission generator  730  may operate using the raw measurements y p  in addition to or in place of the filtered measurements y w  in various embodiments. 
     Transmission generator  730  is shown receiving a transmission time from transmission timing controller  728 . The transmission time may indicate a time at which to generate and send a transmission to controller  604 . The transmission time may indicate a future time at which to generate and send a compressed data object or may include a command to generate and transmit a compressed data object at the current time t cur . Upon receiving the transmission time from transmission timing controller  728 , transmission generator  730  may identify all of the measurements y w  that have been obtained or generated since the previous time t prev  at which a transmission was sent to controller  604 . The set of measurements y w  identified by transmission generator  730  may include all measurements y w  obtained by wireless sensor  602  that have not yet been transmitted to controller  604 . For example, if the previous transmission to controller  604  occurred at time t 0  (i.e., t prev =t 0 ) and the current time is t 5  (i.e., t cur =t 5 ), transmission generator  730  may identify all of the measurements y w  obtained or generated after time t 0  up to and including time t 5  (e.g., measurements y w  obtained at times t 1 , t 2 , t 3 , t 4 , and t 5 ). 
     Transmission generator  730  can generate a compressed data object that includes multiple measurements y w . In some embodiments, each measurement y w  is stored in the compressed data object as a key-time-value triplet. For example, each measurement y w  may include a key indicating the unique ID of the sensor which recorded the measurement (e.g., wireless sensor  602 ), a value indicating the value of the measurement (e.g., 72° F.), and a time indicating the time at which the measurement was obtained (e.g., 2017-05-22; 08:00:00). Transmission generator  730  can be configured to compress the set of measurements y w  included in the compressed data object and provide the compressed data object to wireless radio  712 . Wireless radio  712  can transmit the compressed data object to controller  604 . 
     Still referring to  FIG. 10 , controller  604  is shown to include a wireless radio  704  and a processing circuit  706 . Wireless radio  704  may be configured to receive data transmitted wirelessly from wireless sensor  602 . Specifically, wireless radio  704  may receive the compressed data object from wireless sensor  602  via wireless radio  712 . Wireless radio  704  may be the same or similar to wireless radio  712 . In various embodiments, wireless radio  704  can be configured to communicate via Wi-Fi, Zigbee (e.g., Zigbee IP, Zigbee Pro Green Power), Bluetooth, 2G, 3G, LTE, a local area network (LAN), a metropolitan area network (MAN), a wide area network (WAN) (e.g., the Internet), ad hoc wireless communication, and/or any other type of wireless communications protocol or network. 
     Processing circuit  706  is shown to include a processor  736  and memory  738 . Processor  736  can be a general purpose or specific purpose processor, an application specific integrated circuit (ASIC), one or more field programmable gate arrays (FPGAs), a group of processing components, or other suitable processing components. Processor  736  may be configured to execute computer code and/or instructions stored in memory  738  or received from other computer readable media (e.g., CDROM, network storage, a remote server, etc.). 
     Memory  738  can include one or more devices (e.g., memory units, memory devices, storage devices, etc.) for storing data and/or computer code for completing and/or facilitating the various processes described in the present disclosure. Memory  738  can include random access memory (RAM), read-only memory (ROM), hard drive storage, temporary storage, non-volatile memory, flash memory, optical memory, or any other suitable memory for storing software objects and/or computer instructions. Memory  738  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 disclosure. Memory  738  can be communicably connected to processor  736  via processing circuit  706  and can include computer code for executing (e.g., by processor  736 ) one or more processes described herein. 
     Controller  604  is shown to include an object decompressor  742  and a measurement database  744 . Object decompressor  742  can be configured to receive and decompress the compressed data object provided by wireless sensor  602 . Object decompressor  742  can extract multiple measurements y w  from the compressed data object and identify the key-time-value triplet associated with each measurement y w . Object decompressor  742  can use the key of each measurement y w  to identify the sensor from which the measurement y w  was received. Similarly, object decompressor  742  can use the time attribute of each measurement y w  to identify the time at which the measurement y w  was obtained, and can use the value attribute of each measurement y w  to identify the measured value. Object decompressor  742  can store the extracted measurements y w  in measurement database  744 , provide the measurements y w  to feedback controller  740 , and/or send the measurements y w  to an external system or device (e.g., a remote analytics system, a supervisory controller, etc.). 
     Feedback controller  740  can be configured to use one or more of the measurements y w  extracted from the compressed data object to generate a control signal u c  for plant  606 . In various embodiments, controller  740  can use a proportional control technique, a proportional-integral (PI) control technique, a proportional-integral-derivative (PID) control technique, a model predictive control (MPC) technique, an extremum-seeking control technique, or any other type of feedback control technique to generate the control signal u c  as a function of the measurements y w . Feedback controller  740  can provide the control signal u c  to plant  606  for use in controlling one or more devices of plant  606 . Plant  606  may operate in accordance with the control signal u c  to adjust the value of measured variable y p . 
     In some embodiments, feedback controller  740  includes an adaptive tuner configured to automatically tune controller  740 . For example, if controller  740  is a PI controller, the adaptive tuner can be configured to adjust the proportional gain parameter and integral time parameter of the PI controller. In some embodiments, controller  740  is a pattern recognition adaptive controller (PRAC) with an integrated tuner. Several example of PRACs which can be used as controller  740  are described in detail in U.S. Pat. No. 5,506,768 filed Aug. 16, 1994, and U.S. Pat. No. 5,355,305 filed Oct. 29, 1992. The entire disclosures of these patents are incorporated by reference herein. 
     Example Graphs 
     Referring now to  FIGS. 13-16 , several graphs  1300 - 1600  illustrating the operation wireless sensor  602  are shown, according to an exemplary embodiment.  FIG. 13  is a graph  1300  of raw data illustrating the temperature of a building space over time. Line  1302  is a continuous depiction of the temperature and represents the information that would be obtained from a sensor that continuously measures the temperature of the building space. Line  1302  has a moderate amount of noise representing the measurement noise of wireless sensor  602  and/or the process noise of plant  606 . 
       FIG. 14  is a graph  1400  of the temperature sampled every minute. Each sample  1402  is a sample of the continuous temperature data shown in graph  1300 . The sampling period used to collect samples  1402  is one minute. In other words, each sample  1402  is obtained one minute after the previous sample. Over the fifty-minute sampling window, fifty samples  1402  may be collected (i.e., one sample each minute). 
       FIG. 15  is a graph  1500  of the temperature data transmitted using the send-on-delta technique. Each of the samples  1502  shown in graph  1500  represents the temperature of the building space at the corresponding time. The time between transmissions in graph  1500  is irregular. At the beginning of the fifty-minute window, the transmission period is approximately two minutes. However, as the temperature begins to approach steady-state, the amount of time required for the temperature to change by an amount exceeding the send-on-delta threshold δ increases, which results in a greater amount of time between transmissions. Near the end of the fifty-minute window, the temperature is not changing by an amount exceeding the threshold δ, but transmissions are sent when the amount of time since the previous transmission Δt w  reaches the maximum time threshold Δt w   max . In graph  1500 , only nine samples  1502  are transmitted over the duration of the fifty-minute time window. Compared with the samples  1402  collected every minute shown in graph  1400 , a significant amount of information is lost. 
       FIG. 16  is a graph  1600  of the data transmitted with the asynchronous transmission technique used by wireless sensor  602 . Each of the vertical dotted lines in graph  1600  indicate a time at which a transmission is sent to controller  604 . As shown, transmissions are sent at times t 1 , t 2 , t 3 , t 4 , t 5 , t 6 , t 7 , t 8 , and t 9 . The transmission at time t 1  includes only a single sample collected at time t 1 . The transmission at time t 2  occurs two minutes after t 1  and includes two samples (i.e., the sample collected at time t 2  and the sample collected between times t 1  and t 2 ) packaged into a single compressed data object. Similarly, the transmission at time t 3  occurs two minutes after t 2  and includes two samples (i.e., the sample collected at time t 3  and the sample collected between times t 2  and t 3 ) packaged into a single compressed data object. 
     As the temperature begins to approach steady-state, the amount of time required for the temperature to change by an amount exceeding the send-on-delta threshold δ increases, which results in a greater amount of time between transmissions. For example, the transmission at time t 6  occurs six minutes after the transmission at time t 5  and includes six samples of the temperature (i.e., the sample  1602  collected at time t 6  and the five samples  1604  collected between times t 5  and t 6 ). As the temperature change slows even further, the amount of time required for the temperature to change by an amount exceeding the send-on-delta threshold δ further increases, which leads to a longer time between transmissions. Each transmission may include a compressed data object containing the sample collected at the transmission time, as well as the samples collected since the previous transmission. For example, the transmission at time t 7  may include a compressed data file containing the sample collected at time t 7  as well as the samples collected between times t 6  and t 7 . 
     Advantageously, the asynchronous data transmission used by wireless sensor  602  collects multiple samples between transmissions and sends multiple samples as part of a single transmission. The reduced number of transmissions reduces network traffic and reduces the amount of power required to transmit the samples to controller  604 , which extends battery life. The complete data set sampled at the faster measurement interval (e.g., each minute) is preserved and sent to controller  604  asynchronously. This allows controller  604  to store the complete data set for use in data-based analytics, fault detection and diagnostics, system identification, noise estimation, and other applications that require the complete data set. 
     Asynchronous Data Transmission Process 
     Referring now to  FIG. 17 , a flowchart of an asynchronous data transmission process  1700  is shown, according to an exemplary embodiment. Process  1700  can be performed by one or more components of control system  600 . For example, process  1700  can be performed by wireless sensor  602  and/or controller  604  as described with reference to  FIGS. 6-16 . 
     Process  1700  is shown to include collecting a plurality of samples of a measured variable at a wireless sensor at a plurality of different sampling times (step  1702 ). In some embodiments, an amount of time that elapses between each sampling time defines a sampling period. The sampling period may be regular (e.g., one sample per minute, one sample per second, etc.) or irregular (e.g., sample when requested, sample at random times, etc.). In some embodiments, the measured variable is an environmental variable of a building space (e.g., measured temperature, measured humidity, measured pressure, etc.) or a measured variable associated with the operation of building equipment (e.g., measured refrigerant temperature, measured compressor speed, measured airflow rate, etc.). 
     Process  1700  is shown to include storing the plurality of samples of the measured variable in a measurement database associated with the wireless sensor (step  1704 ). Each sample of the measured variable may include a key attribute, a value attribute, and/or a time attribute. The key attribute may identify the sensor that collected the sample and/or the measured variable associated with the sample. The time attribute may identify the time at which the sample was collected. The value attribute may identify the value of the measured variable at the corresponding sampling time. 
     Process  1700  is shown to include determining a transmission time asynchronous with at least one of the sampling times (step  1706 ). In other words, the transmission time may occur substantially later than at least one of the sampling times (e.g., several minutes after the sample is collected). In some embodiments, the transmission time is determined by transmission timing controller  728 , as previously described. For example, the transmission time can be determined using a send-on-delta (SOD) technique. The SOD technique may include comparing each sample of the measured variable to the most recent value of the measured variable transmitted to the controller. If the difference between the current value of the measured variable and the most recently transmitted value exceeds a threshold, step  1706  may include triggering a transmission to controller  604 . 
     Each time a sample of the measured variable y p  is obtained, transmission timing controller  728  may compare the current value of the measured variable y p,cur  to the most recent value of the measured variable y p,prev  transmitted to controller  604 . If the difference between y p,cur  and y p,prev  is greater than a threshold value δ (i.e., |y p,cur −y p,prev |&gt;δ), transmission timing controller  728  may determine that a new transmission should be sent to controller  604 . However, if the difference between y p,cur  and y p,prev  is not greater than the threshold value δ (i.e., |y p,cur −y p,prev |≤δ), transmission timing controller  728  may determine that a new transmission should not be sent to controller  604 . 
     In some embodiments, transmission timing controller  728  schedules transmissions to controller  604  such that the transmission interval Δt w  is maintained between a minimum allowable transmission interval Δt w   min  and a maximum allowable transmission interval Δt w   max  (i.e., Δt w   min ≤Δt w ≤Δt w   max ) For example, each time a sample of the measured variable y p  is obtained, transmission timing controller  728  may compare the time at which the sample of the measured variable y p  is obtained (i.e., the current time t cur ) with the time at which the previous transmission was sent to controller  604  (i.e., the previous transmission time t prev ). Transmission timing controller  728  may calculate the difference Δt w  between t cur  and t prev  (i.e., Δt w =t cur −t prev ) and compare the difference Δt w  with Δt w   min  and Δt w   max . 
     If Δt w  is less than Δt w   min  (i.e., Δt w &lt;Δt w   min ), transmission timing controller  728  may determine that a new transmission should not be sent to controller  604 , regardless of the value of the measured variable y p . This ensures that at least a minimum time Δt w   min  elapses between transmissions to controller  604 . If Δt w  is greater than Δt w   min  (i.e., Δt w &gt;Δt w   max ), transmission timing controller  728  may determine that a new transmission should be sent to controller  604 , regardless of the value of the measured variable y p . This ensures that at most a maximum time Δt w   max  elapses between transmissions to controller  604 . If Δt w  is between Δt w   min  and Δt w   max  (i.e., Δt w   min ≤Δt w ≤Δt w   max ), transmission timing controller  728  may compare the value of y p,cur  with y p,prev  to determine whether a new transmission should be sent. For example, transmission timing controller  728  may determine that a new transmission should be sent to controller  604  if the difference between y p,cur  and y p,prev  is greater than a threshold value δ (i.e., |y p,cur −y p,prev |&gt;δ), as previously described. 
     Still referring to  FIG. 17 , process  1700  is shown to include generating a compressed data object containing the plurality of samples of the measured variable (step  1708 ). In some embodiments, step  1708  is performed at the transmission time determined in step  1706 . The compressed data object may include each sample of the measured variable collected since the most recent transmission from wireless sensor  602  to controller  604 . For example, step  1708  may include identifying all of the samples that have been collected since the previous time t prev  at which a transmission was sent to controller  604 . The set of samples identified in step  1708  may include all samples obtained by wireless sensor  602  that have not yet been transmitted to controller  604 . For example, if the previous transmission to controller  604  occurred at time t 0  (i.e., t prev =t 0 ) and the current time is t 5  (i.e., t cur =t 5 ), step  1708  may include identifying all of the measurements y w  obtained or generated after time t 0  up to and including time t 5  (e.g., measurements y w  obtained at times t 1 , t 2 , t 3 , t 4 , and t 5 ). 
     In some embodiments, each sample in the compressed data object contains a key-time-value triplet. For example, each sample may include a key indicating the unique ID of the sensor which recorded the measurement (e.g., wireless sensor  602 ), a value indicating the value of the measurement (e.g., 72° F.), and a time indicating the time at which the measurement was obtained (e.g., 2017-05-22; 08:00:00). Step  1708  can include compressing the set of samples included in the compressed data object and providing the compressed data object to wireless radio  712 . 
     Process  1700  is shown to include transmitting the compressed data object from the wireless sensor to the controller at the transmission time (step  1710 ) and extracting the plurality of samples of the measured variable from the compressed data object at the controller (step  1712 ). In some embodiments, step  1712  includes identifying the key-time-value triplet associated with each sample Step  1712  can include using the key of each sample to identify the sensor from which the sample was received. Similarly, step  1712  can include using the time attribute of each sample to identify the time at which the sample was obtained, and using the value attribute of each sample to identify the measured value. In various embodiments, step  1712  can include storing the extracted samples in measurement database  744 , providing the samples to feedback controller  740 , and/or sending the samples to an external system or device (e.g., a remote analytics system, a supervisory controller, etc.). 
     Process  1700  is shown to include using one or more of the extracted samples at the controller to modulate a control signal for a plant (step  1714 ). Step  1714  can include using a proportional control technique, a proportional-integral (PI) control technique, a proportional-integral-derivative (PID) control technique, a model predictive control (MPC) technique, an extremum-seeking control technique, or any other type of feedback control technique to generate the control signal u c  as a function of the samples. Step  1714  can include providing the control signal u c  to plant  606  for use in controlling one or more devices of plant  606 . Plant  606  may operate in accordance with the control signal u c  to adjust the value of measured variable y p . 
     Configuration of Exemplary Embodiments 
     The construction and arrangement of the systems and methods as shown in the various exemplary embodiments are illustrative only. Although only a few embodiments have been described in detail in this disclosure, many modifications are possible (e.g., variations in sizes, dimensions, structures, shapes and proportions of the various elements, values of parameters, mounting arrangements, use of materials, colors, orientations, etc.). For example, the position of elements 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. 
     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. 
     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, processing steps, comparison steps and decision steps.