Patent Publication Number: US-2021173360-A1

Title: Augmented deep learning using combined regression and artificial neural network modeling

Description:
CROSS-REFERENCE TO RELATED PATENT APPLICATIONS 
     This application is a divisional of U.S. patent application Ser. No. 16/054,805, filed Aug. 3, 2018, which claims the benefit of and priority to U.S. Provisional Patent Application No. 62/540,749, filed Aug. 3, 2017, both of which are incorporated herein by reference in their entireties. 
    
    
     BACKGROUND 
     The present disclosure relates generally to the field of heating, ventilation and air conditioning (HVAC) control systems. The present disclosure relates more particularly to systems and methods for performing augmented deep learning (ADL) predictions using combined regression and artificial neural network (ANN) modeling techniques. 
     Building management systems may utilize models to predict the relationships between physical plant inputs and outputs. These relationships may be utilized in physical plant optimization, control optimization, fault detection and diagnosis, and various other building management analytics. Regression and ANN modeling techniques have complementary advantages and disadvantages when utilized to predict these relationships. For example, regression model predictions can be made immediately upon startup of the physical plant before a large set of operational data is collected, but tend to be less accurate than ANN model predictions. ANN model predictions require access to a larger data set, but result in more accurate predictions once the data set has been collected. A building management system that leverages the advantages of both modeling techniques would therefore be useful. 
     SUMMARY 
     One implementation of the present disclosure is a building management system. The building management system includes a database, a trust region identifier configured to perform a cluster analysis technique to identify trust regions, and a regression model predictor configured to utilize a regression model technique to calculate a regression model prediction. The building management system further includes a distance metric calculator configured to calculate a distance metric, an artificial neural network model predictor configured to utilize an artificial neural network model technique to calculate an artificial neural network model prediction, and a combined prediction calculator configured to determine a combined prediction based on the distance metric, the regression model prediction, and the artificial neural network model prediction. 
     In some embodiments, the combined prediction calculator uses a weighted average or a Kalman filter to determine the combined prediction. 
     In some embodiments, the distance metric calculator is configured to calculate the distance metric using plant input data and a cluster distribution mean or a cluster centroid. In other embodiments, the cluster distribution mean is identified using a Gaussian mixture model technique. In other embodiments, the cluster centroid is identified using a k-means technique. 
     In some embodiments, the plant input data includes manufacturing data and offsite data. 
     Another implementation of the present disclosure is a method for operating a building management system for a physical plant. The method includes creating a regression model using pre-operation data during a pre-operational stage of physical plant, identifying multiple data clusters generated by physical plant data during an operational stage, and determining whether the multiple data clusters exceeds a first data sufficiency threshold. If the multiple data clusters exceeds the first data sufficiency threshold, the method includes creating a first artificial neural network model using the multiple data clusters and determining whether new physical plant data meets a first similarity criterion of at least one of the data clusters. If the new plant data meets a first similarity criterion, the method includes making a first artificial neural network prediction using the first artificial neural network model and modifying a characteristic of the physical plant according to the first artificial neural network prediction. 
     In some embodiments, the pre-operation data includes manufacturing data and offsite data. In some embodiments, the physical plant data includes plant input data or plant output data. 
     In some embodiments, the first data sufficiency threshold is based on a quantity of physical plant data. 
     In some embodiments, the method further includes making a regression model prediction using the regression model in response to a determination that the multiple data clusters does not exceed the first data sufficiency threshold and utilizing the regression model prediction to perform a fault detection task, a fault diagnosis task, or a control task. 
     In some embodiments, the method further includes utilizing the first artificial neural network prediction as an input to the regression model. The first artificial neural network prediction is configured to improve a quality of the regression model. 
     In some embodiments, the method further includes determining whether the multiple data clusters exceeds a second data sufficiency threshold and creating a second artificial neural network model using the multiple data clusters in response to a determination that the multiple data clusters exceeds the second data sufficiency threshold. The method further includes determining whether new physical plant data meets a second similarity criterion of at least one of the multiple data clusters, and in response to a determination that the new plant data meets the second similarity criterion, making a second artificial neural network prediction using the second artificial neural network model. In other embodiments, the method further includes determining a combined prediction based on the first artificial neural network prediction and the second artificial neural network prediction. 
     Yet another implementation of the present disclosure is a method of making an augmented deep learning model prediction. The method includes receiving plant input data and plant output data from a physical plant, performing a cluster analysis technique to identify trust regions, calculating a regression model prediction using a regression model technique based on plant input data and plant output data, and calculating a distance metric. The method further includes calculating an artificial neural network prediction using an artificial neural network technique based on plant input data, plant output data, and the distance metric, determining a combined prediction based on the distance metric and at least one of the regression model prediction or the artificial neural network prediction, modifying a characteristic of the physical plant according to the combined prediction. 
     In some embodiments, determining the combined prediction includes use of a weighted average or a Kalman filter. 
     In some embodiments, calculating the distance metric includes use of plant input data and a cluster distribution mean or a cluster centroid. In other embodiments, the cluster distribution mean is identified using a Gaussian mixture model technique. In other embodiments, the cluster centroid is identified using a k-means technique. 
     In some embodiments, the plant input data includes manufacturing data or offsite data. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a perspective view of a building served by a building management system (BMS), according to some embodiments. 
         FIG. 2  is a block diagram of a waterside system which may be used in conjunction with the BMS of  FIG. 1 , according to some embodiments. 
         FIG. 3  is a block diagram of an airside system which may be used in conjunction with the BMS of  FIG. 1 , according to some embodiments. 
         FIG. 4  is a block diagram of the BMS of  FIG. 1 , according to some embodiments. 
         FIG. 5  is a block diagram of a BMS configured to perform ADL predictions using combined regression and ANN modeling techniques, according to some embodiments. 
         FIG. 6  is a flow diagram illustrating a process for performing ADL predictions using combined regression and ANN modeling techniques, according to some embodiments. 
         FIG. 7  is a plot of data clusters used to identify trust regions, according to some embodiments. 
         FIG. 8  is a plot of ADL predictions for trust regions identified using Gaussian Mixture Modeling (GMM) techniques, according to some embodiments. 
         FIG. 9  is a plot of ADL predictions for trust regions identified using cluster centroid (k-means) techniques, according to some embodiments. 
     
    
    
     DETAILED DESCRIPTION 
     Overview 
     Before turning to the FIGURES, which illustrate the exemplary embodiments in detail, it should be understood that the disclosure is not limited to the details or methodology set forth in the description or illustrated in the figures. It should also be understood that the terminology is for the purpose of description only and should not be regarded as limiting. 
     Referring generally to the FIGURES, various systems and methods for making augmented deep learning (ADL) predictions using combined regression and artificial neural network (ANN) modeling techniques in the operation of a building management system are shown. The combination of the modeling techniques leverages the advantages of both: predictions resulting from regression models are utilized in early operational stages when a lack of sufficient data makes ANN predictions impossible or inadvisable, while more accurate ANN predictions are utilized once a sufficient body of operational data has been collected. In some cases, regression model predictions are provided as input to the ANN model and vice versa, increasing the quality of the predictions from both the regression and ANN models. 
     Building Management System and HVAC System 
     Referring now to  FIGS. 1-4 , an exemplary building management system (BMS) and HVAC system in which the systems and methods of the present invention may be implemented are shown, according to an exemplary embodiment. Referring particularly to  FIG. 1 , a perspective view of a building  10  is shown, according to an exemplary embodiment. Building  10  is serviced by a building management system including a HVAC system  100 . HVAC system  100  may 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 may 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  may 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 may be heated in boiler  104  or cooled in chiller  102 , depending on whether heating or cooling is required in building  10 . Boiler  104  may add heat to the 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  may 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 may 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  may include one or more fans or blowers configured to pass the airflow over or through a heat exchanger containing the working fluid. The working fluid may then return to 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  may 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  may 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. 
     Referring now to  FIG. 2 , a block diagram of a waterside system  200  is shown, according to an exemplary embodiment. In various embodiments, waterside system  200  may supplement or replace waterside system  120  in HVAC system  100  or may be implemented separate from HVAC system  100 . When implemented in HVAC system  100 , waterside system  200  may include a subset of the HVAC devices in HVAC system  100  (e.g., boiler  104 , chiller  102 , pumps, valves, etc.) and may operate to supply a heated or chilled fluid to AHU  106 . The HVAC devices of waterside system  200  may be located within building  10  (e.g., as components of waterside system  120 ) or at an offsite location such as a central plant. 
     In  FIG. 2 , waterside system  200  is shown as a central plant having a plurality of subplants  202 - 212 . Subplants  202 - 212  are shown to include a heater subplant  202 , a heat recovery chiller subplant  204 , a chiller subplant  206 , a cooling tower subplant  208 , a hot thermal energy storage (TES) subplant  210 , and a cold thermal energy storage (TES) subplant  212 . Subplants  202 - 212  consume resources (e.g., water, natural gas, electricity, etc.) from utilities to serve the thermal energy loads (e.g., hot water, cold water, heating, cooling, etc.) of a building or campus. For example, heater subplant  202  may 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  may 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  may 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 may be delivered to individual zones of building  10  to serve the thermal energy loads of building  10 . The water then returns to subplants  202 - 212  to receive further heating or cooling. 
     Although subplants  202 - 212  are shown and described as heating and cooling water for circulation to a building, it is understood that any other type of working fluid (e.g., glycol, CO2, etc.) may be used in place of or in addition to water to serve the 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 invention. 
     Each of subplants  202 - 212  may 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 may 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  may include more, fewer, or different types of devices and/or subplants based on the particular configuration of waterside system  200  and the types of loads served by waterside system  200 . 
     Referring now to  FIG. 3 , a block diagram of an airside system  300  is shown, according to an exemplary embodiment. In various embodiments, airside system  300  may supplement or replace airside system  130  in HVAC system  100  or may be implemented separate from HVAC system  100 . When implemented in HVAC system  100 , airside system  300  may 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 may 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  may 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  may be exhausted from AHU  302  through exhaust damper  316  as exhaust air  322 . 
     Each of dampers  316 - 320  may be operated by an actuator. For example, exhaust air damper  316  may be operated by actuator  324 , mixing damper  318  may be operated by actuator  326 , and outside air damper  320  may 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 may 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 may be collected, stored, or used by actuators  324 - 328 . AHU controller  330  may 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  may 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  may 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  may 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  may be controlled by an actuator. For example, valve  346  may be controlled by actuator  354  and valve  352  may be controlled by actuator  356 . Actuators  354 - 356  may communicate with AHU controller  330  via 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  may 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  may be separate (as shown in  FIG. 3 ) or integrated. In an integrated implementation, AHU controller  330  may be a software module configured for execution by a processor of BMS controller  366 . 
     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  may 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  may be a computer workstation, a client terminal, a remote or local interface, or any other type of user interface device. Client device  368  may be a stationary terminal or a mobile device. For example, client device  368  may be a desktop computer, a computer server with a user interface, a laptop computer, a tablet, a smartphone, a PDA, or any other type of mobile or non-mobile device. Client device  368  may communicate with BMS controller  366  and/or AHU controller  330  via communications link  372 . 
     Referring now to  FIG. 4 , a block diagram of a building management system (BMS)  400  is shown, according to an exemplary embodiment. BMS  400  may 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  may include any number of devices, controllers, and connections for completing its individual functions and control activities. HVAC subsystem  440  may include many of the same components as HVAC system  100 , as described with reference to  FIGS. 1-3 . For example, HVAC subsystem  440  may include a chiller, a boiler, any number of air handling units, economizers, field controllers, supervisory controllers, actuators, temperature sensors, and other devices for controlling the temperature, humidity, airflow, or other variable conditions within building  10 . Lighting subsystem  442  may 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  may include occupancy sensors, video surveillance cameras, digital video recorders, video processing servers, intrusion detection devices, access control devices and servers, or other security-related devices. 
     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  may 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 WiFi transceiver for communicating via a wireless communications network. In another example, one or both of interfaces  407 ,  409  may 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  may 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.) may include one or more devices (e.g., RAM, ROM, Flash memory, hard disk storage, etc.) for storing data and/or computer code for completing or facilitating the various processes, layers and modules described in the present application. Memory  408  may be or include volatile memory or non-volatile memory. Memory  408  may include database components, object code components, script components, or any other type of information structure for supporting the various activities and information structures described in the present application. According to an exemplary embodiment, memory  408  is communicably connected to processor  406  via processing circuit  404  and includes computer code for executing (e.g., by processing circuit  404  and/or processor  406 ) one or more processes described herein. 
     In some embodiments, BMS controller  366  is implemented within a single computer (e.g., one server, one housing, etc.). In various other embodiments BMS controller  366  may 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  may 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  may be configured to receive inputs from building subsystems  428  and other data sources, determine optimal control actions for building subsystems  428  based on the inputs, generate control signals based on the optimal control actions, and provide the generated control signals to building subsystems  428 . The following paragraphs describe some of the general functions performed by each of layers  410 - 420  in BMS  400 . 
     Enterprise integration layer  410  may 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  may 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  may 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  may 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 may 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 may 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 an exemplary embodiment, demand response layer  414  includes control logic for responding to the data and signals it receives. These responses can include communicating with the control algorithms in integrated control layer  418 , changing control strategies, changing setpoints, or activating/deactivating building equipment or subsystems in a controlled manner. Demand response layer  414  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 may 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 may 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 may 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 may 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  may be configured to use the data input or output of building subsystem integration layer  420  and/or demand response later  414  to make control decisions. Due to the subsystem integration provided by building subsystem integration layer  420 , integrated control layer  418  can integrate control activities of the subsystems  428  such that the subsystems  428  behave as a single integrated supersystem. In an exemplary embodiment, integrated control layer  418  includes control logic that uses inputs and outputs from a plurality of building subsystems to provide greater comfort and energy savings relative to the comfort and energy savings that separate subsystems could provide alone. For example, integrated control layer  418  may 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  may 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  may 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  may 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  may 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  may 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  may 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  may 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 may 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  may be configured to output a specific identification of the faulty component or cause of the fault (e.g., loose damper linkage) using detailed subsystem inputs available at building subsystem integration layer  420 . In other exemplary embodiments, FDD layer  416  is configured to provide “fault” events to integrated control layer  418  which executes control strategies and policies in response to the received fault events. According to an exemplary embodiment, FDD layer  416  (or a policy executed by an integrated control engine or business rules engine) 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  may 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  may 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. 
     Augmented Deep Learning Techniques 
     Turning now to  FIG. 5 , a block diagram of a BMS  500  configured to perform augmented deep learning (ADL) predictions using combined regression and artificial neural networks (ANN) modeling techniques is shown. BMS  500  is shown to include a physical plant  502 . Physical plant  502  represents the physical process, processes, or physics that convert plant inputs into outputs. Plant inputs may be represented mathematically as a vector [u i ] with length i, while plant outputs may be represented as a vector [y j ] with length j. For example, if physical plant  502  is representative of a central plant, the lengths i and j of the input vector [u i ] and output vector [y j ] may exceed 50. 
     Inputs [u i ] and outputs [y j ] may originate from a variety of sources, including detailed physics-based simulations (e.g., based on manufacturer data or equipment design models), historical data, and typical values either known from experience or obtained from similar plants. In various embodiments, the model representing physical plant  502  may be linear or non-linear, and in some arrangements, the model may be of high order. If the physical plant  502  operates dynamically, then the output of physical plant  502  will include estimated time constants so that alignment between steady state inputs and outputs can be obtained. The objective of the ADL prediction is to predict the relationship between the inputs [u i ] and outputs [y i ] of the physical plant  502 . 
     The process of making an ADL prediction may be performed by BMS controller  366 , described above with reference to  FIGS. 3-4 . BMS controller  366  is shown to include, among other components, a database  504 , modules related to regression modeling techniques, modules related to ANN modeling techniques, and a combined prediction calculator  518  that utilizes the combined outputs of the regression modeling modules and the ANN modeling modules. Database  504  is configured to store plant inputs [m] and outputs [y i ]. In some embodiments, database  504  is additionally configured to store labeled data for identified data clusters or trust regions (see trust region identifier  506 , described in further detail below). Labeled data for identified trust regions may include labels, statistics, and centroid locations of trust regions. In some embodiments, historical data related to physical plant  502  is available and can be used as plant input data for filling database  504 . In some embodiments, this historical or pre-operation data for the physical plant  502  includes equipment manufacturer data or data collected from physical plants other than physical plant  502 . In other embodiments, database  504  is initially empty upon initiation of the ADL prediction process. 
     Modules of BMS controller  366  related to regression modeling may include a regression model parameter identifier  508  and a regression model predictor  510 . Regression modeling is a statistical process for estimating the relationship among variables. Typically, regression modeling involves minimization of the L 2  norm so that estimated model parameters will minimize the sum of the prediction errors squared. An advantage of regression models is that, when designed properly, low order models may be used effectively for both interpolation and extrapolation. Another advantage is that regression models are effective at modeling dominant relationships between inputs and outputs even when minimal data is available to estimate parameters. However, regression modeling techniques generally have lower predictive power when compared with a fully trained ANN model. 
     In various embodiments, the regression model may use a combination of a deterministic model and a stochastic model. In short, a deterministic model is one in which every set of variable states is uniquely determined by parameters in the model and by sets of previous states of these variables. In other words, a deterministic model always performs the same way for a given set of initial conditions. By contrast, in a stochastic model, which may be alternatively referred to as a statistical model, variable states are not described by unique values, but by probability distributions. Because the stochastic model portion of the regression model operates to drive the error in the model to zero, regression modeling yields good predictions even when relatively little data is available to the model. (As described in further detail below, ANN modeling yields better predictions when more data is available to the model.) Further details of a method for using a combination of deterministic and stochastic models in regression modeling may be found in U.S. patent application Ser. No. 14/717,593 filed May 20, 2015. The entire disclosure of U.S. patent application Ser. No. 14/717,593 is incorporated by reference herein. 
     Regression model parameter identifier  508  is configured to estimate the regression model parameters (i.e., [e]) and in some cases pre-process the plant inputs [u i ] to remove non-significant inputs (i.e., a stepwise regression technique) and/or reduce the number of model inputs by creating linear combinations of the original inputs (i.e., a latent variable technique) or by performing input transformations (i.e., a principal components analysis (PCA) technique). Further details of a PCA technique may be found in U.S. patent application Ser. No. 14/744,761 filed Jun. 19, 2015. The entire disclosure of U.S. patent application Ser. No. 14/744,761 is incorporated by reference herein. The purpose of the pre-processing performed by regression model parameter identifier  508  is to yield either a low-order physics-based model, also known as a “grey box” model, or a low order empirical model that has good predictive power for both interpolation and extrapolation. 
     Regression model predictor  510  is configured to predict a current output (i.e., [y j ] pred,reg ) calculated from the plant inputs [u i ], or the modified inputs determined by regression model parameter identifier  508 . Regression model predictor  510  may utilize any suitable regression modeling technique to yield [y j ] pred,reg . For example, if a prediction of chiller power is desired, a reduced order physics-based model (e.g., a Gordon-Ng Universal Chiller Model) may be utilized. Alternatively, since chiller power is a function of both load and lift, a linear bi-quadratic empirical model may be utilized. In some embodiments, the parameters estimated by regression model parameter identifier  508  and the predictions executed by regression model predictor  510  are performed simultaneously and independently from the ANN predictions executed by the ANN modules. In other embodiments, the predictions from the regression model predictor  510  are provided as input to the ANN modules to improve the ANN predictions. Similarly, ANN predictions may be provided as input to the regression modules to improve the regression model predictions. 
     Modules of BMS controller  366  related to ANN modeling may include trust region identifier  506 , distance metric calculator  512 , ANN trainer  514 , and ANN model predictor  516 . ANNs, also referred to as “deep learning” or connectionist systems, are computing systems inspired by biological neural networks. A typical ANN consists of thousands of interconnected artificial neurons, which are stacked sequentially in rows known as layers, forming millions of connections. ANNs may be applied to provide non-linear mapping between inputs and labeled data, and they are very effective at modeling complex non-linear relationships even if the modeler has no understanding of the process being modeled. However, ANNs have several implementation and operational disadvantages. One disadvantage is that a large volume of training data, including labeled data, is required and significant computational resources are required for training. Significant resources may also be involved if a human is required to label the data. Further disadvantages of ANNs include the fact that they do not provide a causal explanation of the relationship between the inputs and the resulting predictions. They are also unsuitable for extrapolation and interpolation in regions where training data was sparse or absent since predicted outputs can often have little relationship with the physical reality. 
     Trust region identifier  506  may be configured to receive stored plant inputs [u i ] from database  504  and employ cluster analysis to identify data clusters within the i dimensional hyperspace. Options for the cluster analysis technique include, but are not limited to, Gaussian Mixture Model (GMM) and k-means techniques. In various embodiments, trust region identifier  506  is configured to give each identified cluster or trust region a label so that it can be uniquely identified. Regardless of the cluster analysis technique utilized, over time and as additional data becomes available, new trust regions will be created, older trust regions will consolidate, and voids in the i dimensional hyperspace will be reduced until eventually the entire hyperspace is spanned by a single trust region. In some embodiments, trust region identifier  506  operates independently of the application consuming the ADL predictions. 
     GMMs are composed of multiple multivariate normal density functions. For each cluster, the GMM provides both an i dimensional mean vector and an i×i dimensional covariance matrix that are useful for understanding both the cluster location and how the data is distributed within the cluster. In some embodiments, GMMs utilize posterior probabilities to determine member in a cluster. The “best” number of clusters within the i dimensional hyperspace can be determined using a variety of techniques, including Principal Component Analysis (PCA) or Akaike Information Criterion (AIC). By contrast, k-means clustering determines membership by minimizing distances from points to the mean or median location of its assigned cluster. For each cluster, the k-means technique provides the centroid location. In addition, the total sum of the distances may be utilized to determine the ideal number of clusters to be identified. 
     Still referring to  FIG. 5 , ANN trainer  512  may be configured to train the ANN for each identified trust region. Periodically, new data previously identified as belonging to a trust region (i.e., by trust region identifier  506 ) may be utilized to provide additional training data for the associated ANNs. This additional training data allows the predictive power of the ANNs to increase over time. In some embodiments, ANN trainer  514  operates offline and independently with respect to the application consuming the ADL predictions. 
     Distance metric calculator  514  may be configured to calculate a distance metric between a given input [u i ] and a nearby trust region. For example, the distance metric may be between an input [u i ] and a cluster distribution mean identified via a GMM technique or a cluster centroid identified via a k-means technique. The distance metric may be calculated using any suitable technique (e.g., a Euclidean distance, a Mahalanobis distance). ANN model predictor  516  is configured to calculate ANN predictions [y j ] pred,ANN  of nearby trust regions. Classification of “nearby” may be determined based on the distance metric determined by the distance metric calculator  514 , or, in the case of trust regions identified by a GMM technique, the distance metric and co-variance information. In some embodiments, both distance metric calculator  514  and ANN model predictor  516  are configured to operate synchronously with the application consuming the ADL predictions. 
     Combined prediction calculator  518  is configured to determine ADL predictions based on the regression model prediction input (i.e., [y j ] pred,reg ) received from regression model predictor  510  and the ANN model prediction input (i.e., [y j ] pred,ANN ) received from ANN model predictor  516 . Any suitable technique may be utilized to combine the regression and ANN model predictions. For example, combination techniques may include, but are not limited to, Kalman filtering, linear combinations, and non-linear combinations. The combined prediction calculator  518  may be configured to operate synchronously with the application consuming the ADL predictions. Further details regarding the combined ADL predictions are included below with reference to  FIGS. 8-9 . 
     Referring now to  FIG. 6 , a flow diagram illustrating a process  600  for using augmented deep learning techniques to make modeling predictions is shown. In some embodiments, process  600  is performed by BMS controller  366  of BMS  400 . Process  600  is shown to begin with step  602 , in which database  504  receives physical outputs from the physical plant  502 . In some embodiments, the physical outputs are a vector [y j ] with length j. At step  604 , trust region identifier  506  performs cluster analysis methods to identify trust regions (e.g., via a GMM or a k-factors technique). 
     At step  606 , regression model parameter identifier  508  estimates the parameters for the regression model. In some embodiments, step  606  includes removing non-significant inputs or reducing the number of model inputs via linear combinations of the original inputs and/or input transformations. Continuing with step  608 , regression model predictor  510  calculates regression model predictions (i.e., [y j ] pred,reg ) based on parameter input received from regression model parameter identifier  508 . In various embodiments, the regression model prediction includes both a deterministic and a stochastic component and may be calculated via a variety of regression model techniques (e.g., a reduced order physics-based model, a linear quadratic empirical model). 
     At step  610 , ANN trainer  512  receives inputs from database  504  and trust region identifier  506  to train the ANN for each identified trust region. Periodically providing ANN trainer  512  with new data may increase the predictive power of the ANN model over time. In some embodiments, step  610  is not performed until one or more data sufficiency thresholds is exceeded. In various embodiments, the data sufficiency threshold may be based on the amount of data received from the database  504 . The amount of data stored in the database  504  (e.g., plant input data, plant output data) may be related to the amount of time the physical plant  502  has been operational. Continuing with step  612 , the distance metric calculator  514  calculates a distance metric (e.g., a Euclidean distance, a Mahalanobis distance) between the inputs [u i ] and the cluster distribution means (i.e., if a GMM technique has been utilized to identify the trust regions) or the cluster centroids (i.e., if a k-means technique has been utilized to identify the trust regions). At step  614 , ANN model predictor  516  makes an ANN model prediction based on input received from ANN trainer  512  and distance metric calculator  514 . As described above, in some embodiments, the ANN model prediction may be utilized as an input to the regression model predictor  510  to improve the quality of the regression model predictions. 
     In some embodiments, the steps comprising the regression model prediction (i.e., steps  606  and  608 ) occur simultaneously with the steps comprising the ANN model prediction (i.e., steps  610 - 614 ). In other embodiments, the steps comprising the regression model prediction are performed during a pre-operational stage of the physical plant  502  and before sufficient data has been collected from the physical plant  502  to perform the steps comprising the ANN model prediction. 
     Process  600  concludes at step  616  as combined prediction calculator  518  utilizes the regression model prediction (i.e., [y j ] pred,reg ) and the ANN model prediction (i.e., [y j ] pred,ANN ) to determine an ADL prediction. In various embodiments, combined prediction calculator  518  uses any suitable technique (e.g., Kalman filters, linear combinations, non-linear combinations) to determine the combined prediction from the regression model prediction and the ANN model prediction. In various embodiments, the combined prediction calculator  518  may utilize the regression model prediction, one ANN model prediction, multiple ANN predictions, or any combination thereof to determine the ADL prediction. The ADL prediction may be utilized to modify an operating characteristic of the physical plant  502 . For example, the ADL prediction may be used to optimize control of the equipment in HVAC system  100 , waterside system  200 , or airside system  300 . In other embodiments, the ADL prediction can be used to perform fault detection tasks, fault diagnostic tasks, or other tasks related to analytics. 
     Referring now to  FIG. 7 , a plot  700  of trust regions as identified by a k-means technique is depicted, according to some embodiments. As described above, trust regions may include regions of i dimensional hyperspace (represented in plot  700  by axes  702 ,  704 , and  706 ) where input [u i ] is sufficiently dense to train an ANN. As shown in the plot  700 , data is clustered in four discrete regions  708 ,  710 ,  712 , and  714 , which may lead to the identification of four trust regions. In some embodiments, the k-means technique is performed by trust region identifier  506 . 
     Turning now to  FIG. 8 , a plot  800  of ADL predictions for trust regions identified using Gaussian Mixture Modeling (GMM) techniques is shown, according to some embodiments. As shown, plot  800  depicts a first trust region  802  and a second trust region  804  plotted along first axis  806  and second axis  808 . The trust regions  802  and  804  may be utilized as similarity criteria to determine whether one or more ANN model predictions should be utilized in whole or in part in the ADL prediction. For example, if input [u i ] is located within the 68% confidence limits of the nearest trust region (i.e., Point A, represented by  810 ), then the ANN model prediction is used exclusively without regard to the regression model prediction: 
       [ y   j ] pred,ADL ( u   i )=[ y   j ] pred,ANN ( u   i ) 
     By contrast, if input [u i ] is located outside of the 99% confidence limits of the nearest trust region (i.e., Point B, represented by  812 ), then the regression model prediction is used exclusively without regard to the ANN model prediction: 
       [ y   j ] pred,ADL ( u   i )=[ y   j ] pred,reg ( u   i ) 
     If, however, input [u i ] is located between the 68% and 99% confidence limits of the nearest trust region (i.e., Point C, represented by  814 ), then the ADL model prediction is a continuous function of both the ANN model prediction and the regression model prediction: For example, in some embodiments, the continuous function includes a weighted average or a Kalman filter. 
       [ y   j ] pred,ADL ( u   i )= f ([ y   j ] pred,ANN ( u   i ),[ y   j ] pred,reg ( u   i )) 
     Referring now to  FIG. 9 , a plot  900  of ADL predictions for trust regions identified using cluster centroid (k-means) techniques is shown, according to some embodiments. As shown, plot  900  depicts a first trust region  902  and a second trust region  904  plotted along first axis  906  and second axis  908 . The trust regions  902  and  904  may be utilized as similarity criteria to determine whether one or more ANN model predictions should be utilized in whole or in part in the ADL prediction. If input [u i ] is located within a predetermined distance from the nearest trust region centroid (i.e., inside the region  910  bounded by the vertically-oriented ellipse), then the ANN prediction is used exclusively without regard to the regression model prediction: 
       [ y   j ] pred,ADL ( u   i )=[ y   j ] pred,ANN ( u   i ) 
     Conversely, if input [u i ] is located outside the second predetermined distance from the nearest trust region centroid (i.e., outside the region  912  bounded by the horizontally-oriented ellipse), then the regression model prediction is used exclusively without regard to the ANN model prediction: 
       [ y   j ] pred,ADL ( u   i )=[ y   j ] pred,reg ( u   i ) 
     If, however, input [u i ] is located in the region between the bounded trust regions (i.e., between regions  910  and  912 ), then the ADL model prediction is a continuous function of both the ANN model prediction and the regression model prediction based on the distance of the input [u i ] from the cluster centroid: 
       [ y   j ] pred,ADL ( u   i )= f ([ y   j ] pred,ANN ( u   i ),[ y   j ] pred,reg ( u   i ),distance) 
     Although the systems and methods described above have been described exclusively with reference to control of the environmental conditions of a building via a building management system (e.g., making predictions of a required chiller power), ADL predictions made from a combination of regression and ANN model predictions may be utilized in a variety of applications. For example, the ADL prediction techniques described herein may be useful in the fields of video processing, image recognition, object identification, threat modeling, fault detection, and industrial configuration optimization. 
     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. For example, the position of elements may be reversed or otherwise varied and the nature or number of discrete elements or positions may be altered or varied. Accordingly, all such modifications are intended to be included within the scope of the present disclosure. The order or sequence of any process or method steps may be varied or re-sequenced according to alternative embodiments. Other substitutions, modifications, changes, and omissions may be made in the design, operating conditions and arrangement of the exemplary embodiments without departing from the scope of the present disclosure. 
     The present disclosure contemplates methods, systems and program products on any machine-readable media for accomplishing various operations. The embodiments of the present disclosure may be implemented using existing computer processors, or by a special purpose computer processor for an appropriate system, incorporated for this or another purpose, or by a hardwired system. Embodiments within the scope of the present disclosure include program products comprising machine-readable media for carrying or having machine-executable instructions or data structures stored thereon. Such machine-readable media can be any available media that can be accessed by a general purpose or special purpose computer or other machine with a processor. By way of example, such machine-readable media can comprise RAM, ROM, EPROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to carry or store desired program code in the form of machine-executable instructions or data structures and which can be accessed by a general purpose or special purpose computer or other machine with a processor. Combinations of the above are also included within the scope of machine-readable media. Machine-executable instructions include, for example, instructions and data which cause a general purpose computer, special purpose computer, or special purpose processing machines to perform a certain function or group of functions. 
     Although the figures show a specific order of method steps, the order of the steps may differ from what is depicted. Also two or more steps may be performed concurrently or with partial concurrence. Such variation will depend on the software and hardware systems chosen and on designer choice. All such variations are within the scope of the disclosure. Likewise, software implementations could be accomplished with standard programming techniques with rule based logic and other logic to accomplish the various connection steps, processing steps, comparison steps and decision steps.