CONTROLLER FOR A BUILDING CONTROL SYSTEM AND METHOD FOR OPERATING EQUIPMENT OF SUCH SYSTEM - Patent application

Abstract

A controller for a building control system includes a processor and a memory storing instructions that, when executed by the processor, cause the processor to perform operations including identifying zones within a building, analyzing data associated with the zones, and generating zone groupings based on the data associated with the zones. The zone groupings each define a zone group and specify which of the zones are grouped together to form each of the zone groups. The operations also include identifying a particular zone grouping from the zone groupings based on the data associated with the zones, and generating control signals for operating equipment of the building control system to provide heating or cooling to the zones using the particular zone grouping.

Description

[Technical field]

[0001] CROSS-REFERENCE TO RELATED PATENT APPLICATIONS This application claims the benefit of and priority to U.S. patent application Ser. No. 16 / 442,103, filed Jun. 14, 2019, the entire disclosure of which is incorporated herein by reference. [Background technology]

[0002] The present disclosure generally relates to variable refrigerant flow (VRF) systems. A VRF system typically includes one or more outdoor VRF units that consume power to heat and / or cool a refrigerant. A VRF system also typically includes multiple indoor VRF units located in various spaces of a building, each of which receives a refrigerant from the outdoor VRF unit and uses the refrigerant to transfer heat to or from a particular space. Summary of the Invention

[0003] One implementation of the disclosure is a controller for a building control system. The controller includes a processor and a memory storing instructions that, when executed by the processor, cause the processor to perform operations including identifying zones in a building, analyzing data associated with the zones, and generating zone groupings based on the data associated with the zones. The zone groupings each define a zone group and specify which of the zones are grouped together to form each of the zone groups. The operations also include identifying a particular zone grouping from the zone groupings based on the data associated with the zones, and generating control signals to operate equipment of the building control system to provide heating or cooling to the zones using the particular zone grouping.

[0004] In some embodiments, the data associated with the zone includes a historical zone temperature set point for the zone and a historical energy consumption value for the zone.

[0005] In some embodiments, analyzing the data associated with the zones includes grouping each type of data included in the data for each zone over a predetermined time period to generate a data block for each zone, and selecting significant features associated with the data block.

[0006] In some embodiments, the operations further include generating a zone grouping, the zone grouping being a particular zone grouping that defines a plurality of zone groups and specifies which of the plurality of zones are grouped together to form each of the plurality of zone groups.

[0007] In some embodiments, analyzing the data associated with the zones includes selecting a clustering algorithm for identifying clusters of data associated with the zones and generating a particular zone grouping based on the clusters of data associated with the zones. In some embodiments, analyzing the data associated with the zones includes pre-clustering the zones into clusters based on location data of the zones to generate a pre-clustering of the zones, comparing the pre-clustering of the zones to the clusters of data associated with the zones to determine a similarity between the pre-clustering of the zones and the clusters, and generating the particular zone grouping based on the similarity between the pre-clustering of the zones and the clusters.

[0008] Another implementation of the disclosure is a controller for a building control system that operates equipment of the building control system. The controller includes a zone identifier that identifies zones in the building, a zone data collector that collects data associated with the zones in the building, a zone grouping generator module, and a control signal generator. The zone grouping generator module generates zone groupings that define zone groups and specify which of the zones identified by the zone identifier are grouped together to form each of the zone groups, analyzes the data collected by the zone data collector, and identifies a particular zone grouping from the zone groupings based on the data associated with the zones. The control signal generator generates control signals for operating equipment of the building control system to provide heating or cooling to the zones using the particular zone groupings determined by the zone grouping generator module.

[0009] In some embodiments, the zone data collector collects data associated with zones within a building, including historical zone temperature set points for the zones and energy consumption values ​​for the zones.

[0010] In some embodiments, the zone grouping generator module blocks each type of data included in the data for each of the zones over a predetermined period of time to generate data blocks for each of the zones and selects significant features associated with the data blocks.

[0011] In some embodiments, the zone grouping generator module is further configured to generate a zone grouping, the zone grouping being a particular zone grouping that defines a plurality of zone groups and specifies which of the plurality of zones are grouped together to form each of the plurality of zone groups.

[0012] In some embodiments, the zone grouping generator module selects a clustering algorithm for identifying clusters of data associated with the zones and generates particular zone groupings based on the clusters of data associated with the zones.

[0013] In some embodiments, the zone grouping generator pre-clusters the zones into clusters based on the location data of the zones to generate a pre-clustering of zones, compares the pre-clustering of zones to the clusters of data associated with the zones to determine a similarity between the pre-clustering of zones and the clusters, and generates a particular zone grouping based on the similarity between the pre-clustering of zones and the clusters.

[0014] Yet another implementation of the present disclosure is a method for operating equipment of a building control system to provide heating or cooling to zones, the method including identifying zones within a building, analyzing data associated with the zones, generating zone groupings based on the data associated with the zones that define zone groups and specify which of the zones are grouped together to form each of the zone groups, identifying a particular zone grouping from the zone groupings based on the data associated with the zones, and generating control signals to operate equipment of the building control system to provide heating or cooling to the zones using the particular zone grouping.

[0015] In some embodiments, analyzing the data associated with the zones includes grouping each type of data included in the data for each of the zones over a predetermined time period to generate a data block for each of the zones and selecting significant features associated with the data block.

[0016] In some embodiments, the method further includes generating a zone grouping, the zone grouping being a particular zone grouping that defines a plurality of zone groups and specifies which of the zones are grouped together to form each of the plurality of zone groups.

[0017] In some embodiments, analyzing the data associated with the zones includes selecting a clustering algorithm for identifying clusters of data associated with the zones and generating particular zone groupings based on the clusters of data associated with the zones.

[0018] In some embodiments, analyzing the data associated with the zones further includes pre-clustering the zones into clusters based on the location data of the zones to generate a pre-clustering of zones, comparing the pre-clustering of zones to the clusters of data associated with the zones to determine a similarity between the pre-clustering of zones and the clusters, and generating a particular zone grouping based on the similarity between the pre-clustering of zones and the clusters.

[0019] Those skilled in the art will appreciate that the summary is merely illustrative and is not intended to be in any way limiting. Other aspects, 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 in conjunction with the accompanying drawings. [Brief description of the drawings]

[0020] [Figure 1A] FIG. 1 is a first diagram of a variable refrigerant flow system for a building in accordance with an exemplary embodiment. [Figure 1B] FIG. 2 is a second diagram of a variable refrigerant flow system for a building in accordance with an exemplary embodiment. [Diagram 2]FIG. 2 is a detailed diagram of a variable refrigerant flow system for a building in accordance with an illustrative embodiment. [Diagram 3] FIG. 3 is a block diagram of a VRF controller implemented with the variable refrigerant flow system of FIG. 2 according to an exemplary embodiment. [Figure 4] 4 is a block diagram of a zone grouping generator implemented with the VRF controller of FIG. 3 according to an example embodiment. [Figure 5A] 5 is a first example of a zone grouping generated by the zone grouping generator of FIG. 4 according to an example embodiment. [Figure 5B] 5 is a second example of a zone grouping generated by the zone grouping generator of FIG. 4 according to an exemplary embodiment. [Figure 6] 1 is a flowchart of a general VRF control process including zone grouping generation according to an example embodiment. [Figure 7] 7 is a flowchart of a zone grouping generation process implemented in the VRF control process of FIG. 6 according to an example embodiment. [Figure 8] 8 is a flowchart illustrating a data analysis process implemented in the zone grouping generation process of FIG. 7 in accordance with an exemplary embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0021] overview Before turning to the figures which show in detail certain exemplary embodiments, it is to be understood that the disclosure is not limited to the details or methodology described in the specification or shown in the figures. It is also to be understood that the terminology used herein is for the purpose of description only and should not be regarded as limiting.

[0022] With general reference to the figures, a variable refrigerant flow (VRF) system and its components are shown, according to some embodiments. The VRF system includes a VRF controller configured to operate equipment included in the VRF system, according to some embodiments. The VRF controller is configured to operate the equipment included in the VRF system using control signals to provide heating and / or cooling loads to various spaces included in the structure. According to some embodiments, to simplify the control algorithms used by the VRF controller, various spaces included in the structure are grouped together to form one or more zone groups. When creating one or more zone groups, a user may not know the optimal grouping of zones with respect to operation of the VRF equipment associated with each zone group.

[0023] To aid in the generation of the one or more zone groups, the VRF controller, according to some embodiments, is configured to identify each zone included in the structure, analyze data associated with each zone, generate zone groupings based on the analyzed data, and output the one or more zone groupings for use in VRF control. Advantageously, the VRF controller aids a user in commissioning a VRF system by reducing the number of zone grouping iterations performed by the user.

[0024] Variable Refrigerant Flow System 1A-1B, a variable refrigerant flow (VRF) system 100 is shown, according to some embodiments. The VRF system 100 is shown to include one or more outdoor VRF units 102, and multiple indoor VRF units 104. The outdoor VRF units 102 may be located outside of a building and may operate to heat or cool a refrigerant. The outdoor VRF units 102 may consume electricity to convert the refrigerant between a liquid phase, a vapor phase, and / or a superheated vapor phase. The indoor VRF units 104 may be distributed throughout various building zones within the building and may receive heated or cooled refrigerant from the outdoor VRF units 102. Each indoor VRF unit 104 may provide temperature control for the particular building zone in which the indoor VRF unit 104 is located. Although the term "indoor" is used to indicate that the indoor VRF units 104 are typically located inside a building, in some cases, one or more indoor VRF units are located "outdoors" (i.e., outside of a building) to, for example, heat / cool a patio, a doorway, a walkway, etc.

[0025] One advantage of the VRF system 100 is that some indoor VRF units 104 can operate in a cooling mode while other indoor VRF units 104 operate in a heating mode. For example, each of the outdoor VRF units 102 and the indoor VRF units 104 can operate in a heating mode, a cooling mode, or an off mode. Each building zone can be independently controlled and can have a different temperature set point. In some embodiments, each building has up to three outdoor VRF units 102 located on the exterior of the building (e.g., on the rooftop) and up to 128 indoor VRF units 104 distributed throughout the building (e.g., various building zones). Building zones can include dwelling units, offices, retail spaces, and common areas, among other possibilities. In some cases, the various building zones are owned, rented, or otherwise occupied by different tenants who are all serviced by the VRF system 100.

[0026] There are many different configurations for the VRF system 100. In some embodiments, the VRF system 100 is a two-pipe system where each outdoor VRF unit 102 connects to a single refrigerant return line and a single refrigerant outlet line. In a two-pipe system, all of the outdoor VRF units 102 may operate in the same mode since only one of the heated or cooled refrigerant may be provided through a single refrigerant outlet line. In other embodiments, the VRF system 100 is a three-pipe system where each outdoor VRF unit 102 connects to a refrigerant return line, a hot refrigerant outlet line, and a cold refrigerant outlet line. In a three-pipe system, both heating and cooling may be provided simultaneously through dual refrigerant outlet lines. An example of a three-pipe VRF system is described in more detail with reference to FIG. 2.

[0027] Referring now to FIG. 2, a block diagram illustrating a VRF system 200 is shown, according to an exemplary embodiment. The VRF system 200 is shown to include an outdoor VRF unit 202, a number of heat recovery units 206, and a number of indoor VRF units 204. Although FIG. 2 shows one outdoor VRF unit 202, embodiments including multiple outdoor VRF units 202 are also within the scope of this disclosure. The outdoor VRF unit 202 may include a compressor 208, a fan 210, or other power-consuming refrigeration components configured to convert refrigerant between liquid, vapor, and / or superheated vapor phases. The indoor VRF units 204 may be distributed throughout various building zones within a building and may receive heated or cooled refrigerant from the outdoor VRF units 202. Each indoor VRF unit 204 may provide temperature control for the particular building zone in which the indoor VRF unit 204 is located. The heat recovery unit 206 can control the flow of refrigerant between the outdoor VRF unit 202 and the indoor VRF unit 204 (e.g., by opening and closing a valve) to minimize the heating or cooling load supplied by the outdoor VRF unit 202.

[0028] The outdoor VRF unit 202 is shown to include a compressor 208 and a heat exchanger 212. The compressor 208 circulates a refrigerant between the heat exchanger 212 and the indoor VRF unit 204. The compressor 208 operates at a variable frequency as controlled by a VRF controller 214. At higher frequencies, the compressor 208 provides greater heat transfer capacity to the indoor VRF unit 204. The power consumption of the compressor 208 increases proportionally to the compressor frequency.

[0029] Heat exchanger 212 can function as a condenser (allowing the refrigerant to reject heat to the outside air) when VRF system 200 operates in a cooling mode, or as an evaporator (allowing the refrigerant to absorb heat from the outside air) when VRF system 200 operates in a heating mode. Fan 210 provides airflow through heat exchanger 212. The speed of fan 210 can be adjusted (e.g., by VRF controller 214) to regulate the rate of heat transfer to or from the refrigerant in heat exchanger 212.

[0030] Each indoor VRF unit 204 is shown to include a heat exchanger 216 and an expansion valve 218. Each of the heat exchangers 216 can function as a condenser (allowing the refrigerant to reject heat to the air in a room or zone) when the indoor VRF unit 204 operates in a heating mode, or as an evaporator (allowing the refrigerant to absorb heat from the air in a room or zone) when the indoor VRF unit 204 operates in a cooling mode. A fan 220 provides airflow through the heat exchanger 216. The speed of the fan 220 can be adjusted (e.g., by the indoor unit control circuit 222) to regulate the rate of heat transfer to or from the refrigerant in the heat exchanger 216.

[0031] In FIG. 2, the indoor VRF unit 204 is shown operating in a cooling mode. In the cooling mode, refrigerant is provided to the indoor VRF unit 204 via cooling line 224. The refrigerant is expanded by expansion valve 218 to a low temperature, low pressure state and flows through heat exchanger 216 (acting as an evaporator) to absorb heat from a room or zone within the building. The heated refrigerant then flows via return line 226 back to the outdoor VRF unit 202 and is compressed to a high temperature, high pressure state by compressor 208. The compressed refrigerant flows through heat exchanger 212 (acting as a condenser) to reject heat to the outside air. The cooled refrigerant may then be returned to the indoor VRF unit 204 via cooling line 224. In the cooling mode, the flow control valve 228 may be closed and the expansion valve 230 may be fully opened.

[0032] In heating mode, refrigerant is provided to the indoor VRF unit 204 in a hot state via heating line 232. The hot refrigerant flows through heat exchanger 216 (acting as a condenser) and rejects heat to the air in the room or zone of the building. The refrigerant then flows through cooling line 224 (opposite the flow direction shown in FIG. 2) back to the outdoor VRF unit. The refrigerant can be expanded to a lower temperature, lower pressure state by expansion valve 230. The expanded refrigerant flows through heat exchanger 212 (acting as an evaporator) and absorbs heat from the outside air. The heated refrigerant can be compressed by compressor 208 and returned to the indoor VRF unit 204 in a hot compressed state via heating line 232. In heating mode, flow control valve 228 can be fully opened to allow refrigerant from compressor 208 to flow to heating line 232.

[0033] 2, each indoor VRF unit 204 includes an indoor unit control circuit 222. The indoor unit control circuit 222 controls the operation of the components of the indoor VRF unit 204, including the fan 220 and the expansion valve 218, in response to a building zone temperature setpoint or other request to provide heating / cooling to the building zone. The indoor unit control circuit 222 may also determine a heat transfer capacity required by the indoor VRF unit 204 and send a request to the outdoor VRF unit 202 to request the outdoor VRF unit 202 to operate at a corresponding capacity to provide heated / cooled refrigerant to the indoor VRF unit 204 to enable the indoor VRF unit 204 to provide the desired level of heating / cooling to the building zone.

[0034] Each indoor unit control circuit 222 is shown communicatively coupled to one or more sensors 250 and a user input device 252. In some embodiments, the one or more sensors 250 may include a temperature sensor (e.g., measuring indoor air temperature), a humidity sensor, and / or a sensor that measures some other environmental condition of the building zone serviced by the indoor VRF unit 204. In some embodiments, the one or more sensors include an occupancy detector configured to detect the presence of one or more people in the building zone and provide an indication of occupancy of the building zone to the indoor unit control circuit 222.

[0035] Each user input device 252 may be located within a building zone served by a corresponding indoor unit 204. The user input device 252 allows a user to input a request to the VRF system 200 for heating or cooling of the building zone and / or a request for the VRF system 200 to stop heating / cooling the building zone. According to various embodiments, the user input device 252 may include a switch, a button, a set of buttons, a thermostat, a touch screen display, or the like. The user input device 252 thereby allows a user to control the VRF system 200 to receive heating / cooling when the user desires.

[0036] The indoor unit control circuit 222 may thereby receive an indication of occupancy of the building zone (e.g., from an occupancy detector of the sensor 250 and / or user input via the user input device 252). In response, the indoor unit control circuit 222 may generate a new request for the outdoor VRF unit 202 to operate at the requested operating capacity to provide refrigerant to the indoor unit 204. The indoor unit control circuit 222 may also receive an indication that the building zone is unoccupied and in response generate a signal instructing the outdoor VRF unit 202 to stop operating at the requested capacity. The indoor unit control circuit 222 may also control various components of the indoor unit 204, for example, by generating signals to turn the fan 220 on and off.

[0037] The outdoor unit control circuit 214 may receive heating / cooling capacity requests from one or more indoor unit control circuits 222 and aggregate the requests to determine a total requested operating capacity. The total requested operating capacity may therefore be affected by the occupancy of each of the various building zones serviced by the various indoor units 204. In many cases, the total requested operating capacity may increase significantly, e.g., reaching a maximum operating capacity, when a person or people first enter a building zone and trigger the heating / cooling request for that zone. Thus, the total requested operating capacity may vary erratically and unpredictably as a result of sporadic occupancy of the various building zones.

[0038] The outdoor unit control circuitry 214 is configured to control the compressor 208, and various other elements of the outdoor unit 202, to operate at an operating capacity based at least in part on the requested total operating capacity. At a higher operating capacity, the outdoor unit 202 consumes more power, increasing utility costs. In some embodiments, the VRF controller may be capable of:

[0039] It may be desirable for operators, owners, renters, etc. of VRF systems to minimize power consumption and utility costs to save money, improve environmental sustainability, and reduce wear and tear on equipment. In some cases, multiple entities or people benefit from reduced utility costs, for example, according to various cost allocation schemes of VRF systems described in U.S. Patent Application No. 15 / 920,077, filed March 13, 2018, which is incorporated herein by reference in its entirety. Thus, as described in more detail below, the control circuitry 214 may be configured to manage the operating capacity of the outdoor VRF unit 202 to reduce utility costs while also providing comfort to the building occupants. Thus, in some embodiments, the control circuitry 214 may be operable in concert with systems and methods described in PCT Patent Application No. PCT / US2017 / 039937, filed June 29, 2017, and / or U.S. Patent Application No. 15 / 635,754, filed June 28, 2017, both of which are incorporated herein by reference in their entireties.

[0040] VRF system using zone grouping method 3-8, systems and methods for generating zone groupings and using the zone groupings to control equipment in a building control system are shown, according to some embodiments. The systems and methods of FIGS. 3-8, according to some embodiments, may be implemented using or as part of a building management system or HVAC system, such as the VRF system 100 of FIG. 1A. For ease and clarity of explanation, the following description of FIGS. 3-8 will refer to elements of the VRF system 100. However, it should be noted that various embodiments of the present disclosure may be implemented in a variety of HVAC systems, BMS, and combinations thereof, and are not limited to VRF systems.

[0041] 3, a detailed block diagram 300 of the VRF controller 214 is shown, according to some embodiments. The VRF controller 214 is shown, according to some embodiments, to include a communication interface 301 and a processing circuit 303 having a processor 305 and a memory 307. In some embodiments, the communication interface 301 facilitates communication between the VRF controller 214 and devices included in the VRF system 200 (e.g., the compressor 208, the indoor unit 204, etc.) to enable user control, monitoring, and adjustment of the devices included in the VRF system 100.

[0042] The communication interface 301, according to some embodiments, can be or include a wired or wireless communication interface (e.g., a jack, antenna, transmitter, receiver, transceiver, wired terminal, etc.) for data communication with devices included in the VRF system 200 or other external systems or devices. In some embodiments, communication via the communication interface 301 may be direct (e.g., local wired or wireless communication) or over a communication network (e.g., a WAN, the Internet, a cellular network, etc.). For example, the communication interface 301 may include an Ethernet card and port for transmitting and receiving data over an Ethernet-based communication link or network. In another example, the communication interface 301 may include a Wi-Fi transceiver for communicating over a wireless communication network. In yet another example, the communication interface 301 may include a cellular or mobile phone communication transceiver.

[0043] 3, processing circuitry 303, according to some embodiments, may be communicatively coupled to communications interface 301 such that processing circuitry 303 and its various components may transmit and receive data via communications interface 301. Processor 305, according to some embodiments, may be implemented as a general purpose processor, an application specific integrated circuit (ASIC), one or more field programmable gate arrays (FPGAs), a group of processing components, or other suitable electronic processing components.

[0044] Memory 307 (e.g., memory, memory unit, storage device, etc.), according to some embodiments, 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 herein. In some embodiments, memory 307 may be or include volatile or non-volatile memory. Memory 307, according to some embodiments, 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 herein. According to some embodiments, memory 307 is communicatively coupled to processor 305 via processing circuitry 303 and includes computer code for executing (e.g., by processing circuitry 303 and / or processor 305) one or more processes described herein.

[0045] 3, memory 307 is shown to include a zone grouping generator 302, a system identification module 306, and a control signal generator 310, according to some embodiments. In some embodiments, zone grouping generator 302 is configured to collect zone data, analyze the zone data, generate zone groupings based on the analyzed zone data, and provide an output of zone groupings 304. Zone groupings 304, according to some embodiments, illustrates how multiple building zones may be grouped for system identification and building control purposes. In some embodiments, all of the building zones in a single zone group are treated as a single unit when generating system models and control signals. For example, a different system model may be generated for each zone group (using data from all of the zones in that zone group), and the system model for the zone group may be used to generate control signals for all of the zones in that zone group. The functionality and methodology of zone grouping generator 302 are described in more detail below. Zone grouping generator 302, according to some embodiments, may be configured to output zone groupings 304 to system identification module 306. In some embodiments, the zone groupings 304 include one or more zone groupings, while in other embodiments, the zone groupings 304 include an individual zone that is identified as the best zone grouping.

[0046] As shown in FIG. 3, the zone groupings 304 are sent as input to a system identification module 306, according to some embodiments. The system identification module 306 is configured to perform a system identification process to generate a model of the system, according to some embodiments. Possible system identification processes that may be performed by the system identification module 306 are described in U.S. Pat. No. 9,235,657, issued Jan. 12, 2016, which is incorporated by reference herein in its entirety. Generally, system identification is a process of identifying unspecified system parameters in a model of a building, for example, by minimizing prediction errors. In some embodiments, the system identification module 306 may be configured to perform a system identification process for each of the zone groups in the zone groupings 304 generated by the zone grouping generator 302.

[0047] Each system identification process uses input and output data associated with all of the building zones in a particular zone group to generate a system model 308 for that zone group, according to some embodiments. For example, the system identification module 306 may use the zone grouping 304 to identify all of the building zones in a first zone group to generate a system model associated with the first zone group of the zone grouping 304. In some embodiments, the system identification module 306 obtains training data (e.g., input and output data) associated with the first group of building zones and uses the training data to train a system model for the first zone group. The system identification module 306 repeats this process for each of the zone groups in the zone grouping 304 to generate a different system model for each zone group in the zone grouping 304, according to some embodiments. According to some embodiments, the system identification module 306 provides each system model 308 to the control signal generator 310.

[0048] The control signal generator 310 is shown receiving the system model 308 from the system identification module 306, according to some embodiments. In some embodiments, the control signal generator 310 uses the system model 308 to generate appropriate control signals for the VRF equipment 314 that operates to control the environment of one or more zone groups. Each system model 308 may correspond to a particular zone group defined by the zone groupings 304 generated by the zone grouping generator 302 and may be used to generate control signals for the corresponding zone group. In some embodiments, the control signal generator 310 uses any of a variety of model-based control methodologies (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 generate the control signals 312. The control signal generator 310 is shown outputting the control signals 312 to the VRF equipment 314, according to some embodiments. In some embodiments, the control signals 312 include commands to operate the VRF equipment 314.

[0049] Zone Grouping Generator 4, a block diagram 400 is shown illustrating the zone grouping generator 302 in more detail, according to some embodiments. The zone grouping generator 302 is shown to include a zone identifier 402, a zone data collector 404, and a zone grouping generator module 406 to determine the zone groupings 304 as output, according to some embodiments.

[0050] 4, the zone identifier 402 is configured to identify types of zones within a building, according to some embodiments. For example, the zone identifier 402 may be tasked with identifying the types and / or number of zones within a building that serves as a school. The types of zones within a school may include classrooms, laboratories, restrooms, and hallways. Additional types of zones may be identified and are not limited to the types of zones listed above.

[0051] 4, the zone data collector 404 is configured to collect zone data for the zones identified by the zone identifier 402, according to some embodiments. In some embodiments, the collected zone data includes historical values ​​(e.g., temperature set points, indoor air temperatures, etc.). In some embodiments, the collected zone data includes zone characteristics (e.g., the operating mode of the zone, the relative position of each zone within a zone group, etc.). In some embodiments, the collected data includes additional data types, or any combination of data types.

[0052] In some embodiments, the zone data collector 404 is configured to continuously collect zone data and monitor changes in the collected zone data over a predetermined period of time. For example, the zone data collector 404 may continuously collect values ​​of temperature set points associated with the zones identified by the zone identifier 402 and monitor changes in the temperature set points associated with each zone over a one-week period. In some such embodiments, the zone data collector 404 is configured to determine the need to generate new zone groupings based on changes in the zone data and automatically trigger a new zone grouping process to the zone grouping generator 302. For example, the zone data collector 404 may detect a change in the temperature of a zone outside of an acceptable threshold range over a one-week period and automatically send a signal to the zone grouping generator 302 to generate a new zone grouping. In some embodiments, the acceptable threshold range is input by a user. For example, a user may input an acceptable threshold range for a temperature set point associated with a particular zone, which may be ±2°F of the temperature set point.

[0053] The zone grouping generator module 406 is shown to be included in the zone grouping generator 302 according to some embodiments. In some embodiments, the zone grouping generator module 406 may be configured to generate a plurality of different zone groupings. Each zone grouping, according to some embodiments, defines one or more zone groups and specifies one or more building zones within each zone group. Thus, each zone grouping defines various ways in which a plurality of building zones may be organized into zone groups. An example of two different zone groupings that may be generated for the same set of building zones is described in more detail with reference to FIG. 5A and FIG. 5B. In some embodiments, the zone grouping generator module 406 combines zones into two or more groups to form a zone group. In some embodiments, the zone grouping generator module 406 generates a single zone as a group. As described in more detail below, the zone grouping generator module 406, according to some embodiments, is configured to perform a cluster analysis of the zone data collected by the zone data collector 404 associated with one or more zones identified by the zone identifier 402 to generate one or more different zone groupings.

[0054] 5A and 5B, various examples of zone groupings are shown according to some embodiments. FIG. 5A and 5B are shown including a structure 502 that functions as a school according to some embodiments. In some embodiments, the structure 502 is shown including laboratories 506a and 506b (collectively referred to as laboratories 506), interior classrooms 510a, 510b, and 510c (collectively referred to as interior classrooms 510), restrooms 514a, 514b, and 514c (collectively referred to as restrooms 514), hallways 518a, 518b, and 518c (collectively referred to as hallways 518), and exterior classrooms 522a, 522b, and 522c (collectively referred to as exterior classrooms 522). In some embodiments, the zones within the structure 502 were grouped using the zone grouping generator 302. Each of the individual zones within the structure 502 was identified using the zone identifier 402 according to some embodiments. In some embodiments, the zone grouping generator module 406 generated multiple zone groupings. In some embodiments, only two distinct (e.g., combinations of zone groups) zone groupings were generated by the zone grouping generator module 406. In some embodiments, three or more distinct zone groupings were generated by the zone grouping generator module 406.

[0055] With particular reference to FIG. 5A , a first zone grouping 500 is shown to include zone group 1 504 including a laboratory 506, zone group 2 508 including a classroom 510, zone group 3 512 including a restroom 514, zone group 4 516 including a hallway 518, and zone group 5 520 including a classroom 522, according to some embodiments.

[0056] 5B, a second zone grouping 550 is shown according to some embodiments. The second zone grouping 550 is shown to include zone group 1 554 including restrooms 514a, classroom 510b, and south hallway 518c, zone group 2 558 including north hallway 518a, restrooms 514c, and classroom 522c, zone group 3 562 including laboratory 506a and classroom 510c, zone group 4 566 including classroom 510a, classroom 522a, and laboratory 506b, and zone group 5 570 including center hallway 518b, classroom 522b, and restrooms 514b, according to some embodiments.

[0057] Prior to generating the first zone grouping 500 and the second zone grouping 550, each zone in the structure 502 was identified by the zone identifier 402, according to some embodiments. Following the identification of each zone in the structure 502, according to some embodiments, zone data for each zone in the structure 502 is collected by the zone data collector 404. The zone grouping generator module 406, according to some embodiments, performed one or more clustering algorithms using the zone data collected by the zone data collector 404. The clustering algorithms performed by the zone grouping generator module 406 generated the first zone grouping 500 and the second zone grouping 550 for use in VRF control. In some embodiments where more than one zone grouping is generated, a user selects which zone groupings to use in VRF control.

[0058] A method for VRF control using zone grouping generation Referring to FIG. 6, a process 600 for generating a system model and using the system model to control a variable refrigerant flow system is shown according to some embodiments. The process 600 begins with step 602 according to some embodiments. Step 602 includes generating zone groupings using the zone grouping generator 302 according to some embodiments. In step 602, the zone grouping generator 302 is configured to generate one or more zone groupings and provide an output of the zone groupings to the system identification module 306 according to some embodiments. In some embodiments, all of the building zones in a single zone group are treated as a single unit when generating the system model and control signals. For example, a different system model may be generated for each zone group (using data from all of the zones in that zone group), and the system model for the zone group may be used to generate control signals for all of the zones in that zone group. Methods for generating zone groupings are described in more detail below.

[0059] Process 600 continues with step 604, according to some embodiments. Step 604 includes, according to some embodiments, performing system identification using the system identification module 306 to generate a model of the zone grouping generated in step 602. In step 604, the system identification module 306 is configured, according to some embodiments, to perform a system identification process to generate a model of the system. In some embodiments, the system identification module 306 obtains training data (e.g., input data and output data) associated with the first group of building zones and uses the training data to train a system model for the first zone group. In some embodiments, the system identification module 306 repeats this process for each of the zone groups in the zone grouping to generate a different system model for each zone group. In some embodiments, the system identification module 306 is configured to perform a system identification process for each of the zone groups in the zone grouping generated by the zone grouping generator 302 in step 602.

[0060] Process 600 continues to step 606, according to some embodiments. In some embodiments, step 606 includes using the model generated in 604 to generate control signals for the VRF equipment using control signal generator 310. In step 606, control signal generator 310 is configured to receive one or more system models generated by system identification module 306 in step 604, according to some embodiments. In some embodiments, control signal generator 310 uses the system models generated by system identification module 306 in step 604 to generate appropriate control signals for VRF equipment 314 that operate to control the environment of one or more zone groups. In some embodiments, control signal generator 310 uses any of a variety of model-based control methodologies (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 generate the control signals in step 606.

[0061] Process 600 is shown continuing at step 608, according to some embodiments. Step 608 includes transmitting the control signals generated by control signal generator 310 at step 606 to various VRF equipment, according to some embodiments. In some embodiments, the generated control signals are transmitted to various indoor VRF units located within a particular zone or zone group configured to control one or more environmental variables (e.g., temperature, humidity, etc.) for one or more zones within the particular zone group. In some embodiments, the generated control signals are transmitted to various outdoor VRF units configured to provide refrigerant to various indoor VRF units located within a particular zone or zone group.

[0062] How to generate zone groupings 7, a process 700 for generating zone groupings is shown, according to some embodiments. Process 700 begins with step 702, according to some embodiments. In step 702, the zone identifier 402, according to some embodiments, is configured to identify the type and / or number of zones in the structure. For example, step 702 may include the zone identifier 402 identifying the type and number of zones in the structure that functions as a school. In some embodiments, the types of zones in a school include classrooms, laboratories, restrooms, and hallways. Additional types of zones may be identified and are not limited to the types of zones listed above.

[0063] 7, process 700 is shown continuing to step 704, according to some embodiments. In some embodiments, step 704 includes a zone data collector 404 collecting zone data for the zones identified by the zone identifier 402 in step 702. In some embodiments, the data collected includes historical values ​​(e.g., temperature set points for the zones, energy consumption by the zones, etc.). In some embodiments, the data collected includes zone characteristics (e.g., the relative position of each zone within a zone group, etc.). In some embodiments, the data collected includes additional data types, or any combination of data types.

[0064] In some embodiments, step 704 includes the zone data collector 404 continuously collecting data and monitoring changes in the collected data over a predetermined period of time. In some such embodiments, step 704 includes the zone data collector 404 determining the need to generate new zone groupings based on changes in the zone data and automatically triggering a new zone grouping process to the zone grouping generator 302. For example, if the zone data collector 404 receives indoor air temperature data that includes a value outside a predetermined acceptable threshold range of indoor air temperature values ​​for a particular zone in a zone group, the zone data collector 404 may send a signal to the zone grouping generator 302 to perform a new zone grouping process.

[0065] Process 700 is shown continuing to step 706 according to some embodiments. As described in more detail below with reference to FIG. 8, in some embodiments, step 706 includes the zone grouping generator module 406 using the zone data collected by the zone data collector 404 in step 704 in one or more clustering algorithms to determine optimal zone groupings based on the collected zone data. Further, in some embodiments, step 706 includes analyzing location data for each zone in the structure. In some such embodiments, analyzing the location data for each zone in the structure further includes a pre-clustering process configured to cluster each zone into zone groups based on the location data. For example, a floor plan of a building including zones for use in process 700 may be converted to grid coordinates to determine a location value based on the grid coordinates. The location value is used in one or more clustering algorithms to generate zone groupings based on the relative location of each zone, according to some embodiments. In some embodiments, step 708 includes the zone grouping generator module 406 outputting the zone groupings based on the analyzed data of step 706 to the system identification module 306 to generate a system model associated with the zone groupings.

[0066] 8, a process 800 for analyzing zone data and determining zone groupings is shown, according to some embodiments. Process 800 may be executed to accomplish step 706 of process 700. In some embodiments, process 800 is configured to use raw data (e.g., historical temperature set points, historical indoor air temperatures, etc.) collected over a period of time (e.g., days, weeks, months, etc.). In some embodiments, the raw data used in process 800 is the zone data collected by zone data collector 404 in step 704 of process 700. In some embodiments, process 800 includes zone grouping generator module 406 analyzing the zone data and generating zone groupings.

[0067] Various types of raw data provided as inputs to process 800 are shown to include historical indoor air temperatures, historical temperature set points, historical outdoor air temperatures, and historical energy used by the HVAC. Some of the types of input data may be specific to a particular zone, while other types of input data may apply to an entire building or campus. For example, the historical indoor air temperatures, historical temperature set points, and historical energy used by the HVAC may be zone specific, while the historical outdoor air temperatures may apply to an entire building or campus. For any zone-specific data type, the input data to process 800 may include a separate data set for each zone. For example, the input data may include a set of historical temperature set points for zone A, a set of historical temperature set points for zone B, and so on. The same is true for the historical indoor air temperatures and the historical energy used by the HVAC.

[0068] Each set of input data to process 800 may include a time series of values. Each time series of values ​​may include multiple samples of a particular point or variable (e.g., indoor air temperature in zone A, indoor air temperature in zone B, temperature setpoint for zone A, temperature setpoint for zone B, outdoor air temperature, etc.). Each sample may include both a data value indicating the value of the sample (e.g., 75°F) and a timestamp indicating the time the sample was measured or generated (e.g., 2018-07-05-18:00:00). Thus, the input data to process 800 may include a set of time series, with each time series corresponding to a particular point or variable. Some of the time series (and corresponding points or variables) may be specific to a particular zone, while other time series may apply to an entire building or campus.

[0069] In some embodiments, the input data to process 800 includes multiple attributes that may be used to group the input data. Examples of such attributes include data type (e.g., indoor air temperature, temperature setpoint, outdoor air temperature, energy used by HVAC, etc.), the corresponding zone to which the data applies (e.g., Zone A, Zone B, Zone C, etc.), zone group (if the corresponding zone is assigned to a zone group), timestamp, data value, unit of measure, or other attributes that provide context or semantic information for the raw data value.

[0070] In some embodiments, input data to process 800 is grouped into a set of samples by building zone. Each sample may include a timestamp, an attribute indicating the building zone to which the sample applies, and values ​​for each of the various data types. For example, a particular sample may correspond to "Zone A" and may include values ​​for the historical indoor air temperature, historical temperature setpoint, historical outdoor air temperature, and historical energy used by the HVAC at a particular time. Thus, each sample may include among other relevant zone data (i.e., values ​​for each input data type) for a particular zone at a particular time.

[0071] Step 802, according to some embodiments, involves grouping each type of zone data collected by the zone data collector 404 into a particular block of data for each type of zone data over a predefined time period for each particular zone. For example, a week's data collection effort including seven data samples of historical indoor air temperature for Zone A (one data sample taken for each day of the week) may be blocked together into a single block. In some embodiments, the predefined time period for which the data samples are grouped together is configurable based on user input. For example, a user may choose to block a month's worth of samples or a year's worth of samples.

[0072] In some embodiments, step 802 includes grouping the samples of zone data for zone i=1:N into K blocks of size T, where N is the total number of zones in the structure, K is the total number of blocks of data samples for each zone i, and T is the total number of data samples in each block collected per zone. In some embodiments, each data sample t includes multiple types of zone data points associated with each zone i. For example, data sample t may include a setpoint temperature value for zone i, an indoor air temperature for zone i, and energy usage by zone i. In some embodiments, a user inputs a desired number of blocks K. Additionally, in some embodiments, a user inputs a desired number of data samples T to be included in each block k. Given blocks k=1:K, and block k includes T data samples, the set of samples t assigned to block k may be determined by the following formula: t=(k*T):((k+1)*T)-1

[0073] For example, using the previous formula, assume the user has input that each block contains two data samples (so that T=2). Data samples t=2:3 (i.e., the second and third samples) are blocked into block k=1 for each zone. Thus, data samples t=4:5 (i.e., the fourth and fifth samples) for each zone are blocked into block k=2 for each zone.

[0074] The K data blocks containing the T data samples generated in step 802 are each averaged in step 804 according to some embodiments. In some embodiments, each type of zone data contained in each block K is averaged to generate an average of each type of zone data for each zone. For example, assume that data sample t includes values ​​for setpoint temperature zone i, indoor air temperature for zone i, and energy usage by zone i. Each distinct value for zone i may be averaged for each block K using the following formula:

number

[0075] In the previous formula,

number

[0076] Process 800 proceeds to step 806, according to some embodiments. In some embodiments, step 806 includes selecting one or more important features of each zone for use in the clustering algorithm of step 808. The one or more important features selected for each zone in step 808 are used to identify correlations between various types of data included in one or more block averages calculated for each zone in step 804, according to some embodiments. The selected important features may include one or more block averages calculated in step 804 and / or further calculations using various block averages calculated in step 804. For example, the important features selected in step 806 may include subtracting an average historical temperature set point calculated in step 804 from an average historical indoor air temperature calculated in step 804 to calculate a difference between the average historical temperature and the average historical air temperature. In some embodiments, selecting important features in step 808 identifies correlations between one or more block averages calculated in step 804 and the operating conditions of one or more zones. For example, zone A may be identified as always operating in a heating mode when the historical outdoor air temperature is at or below 45°F.

[0077] Another example may include collecting block averages of energy used by an HVAC device to operate the HVAC device based on the average historical temperature setpoints within one or more zones. In some embodiments, the selected key features include historical operating modes determined by the energy consumption of each zone. For example, the zone data may include a Q HVAC,i In another example, if the zone data includes an energy consumption value for zone i greater than 1, such that Q HVAC,i If the energy usage value for zone i is less than −1, such that Q<−1, then zone i may be identified as having historically operated in cooling mode. HVAC,iIn yet another example, where =0, zone i may be identified as historically operating in an "off" mode, such that no energy is consumed by zone i. The examples of selected important characteristics are not intended to be limiting. In some embodiments, a user inputs one or more selected important characteristics in step 806.

[0078] Step 806, according to some embodiments, is depicted as outputting one or more selected important features for use in step 808. In step 808, according to some embodiments, a clustering algorithm is applied to the zone data contained in the selected important features to generate zone groups. Step 808, according to some embodiments, is also depicted as receiving as input a desired number of groups (e.g., the number of groups of zones). In some embodiments, the number of groups is input by a user. For example, a user may input four groups into which the N zones should be grouped.

[0079] In some embodiments, step 808 includes the zone grouping generator module 406 generating zone groupings by applying any of a variety of clustering algorithms to the selected important features identified and / or calculated in step 806. Generally, a clustering algorithm collects a set of data samples into a predetermined number of groups that contain relatively similar data samples. Some well-known clustering algorithms include hierarchical clustering, k-means clustering, and simple expected maximum, according to some embodiments. In some embodiments, one clustering algorithm is applied to a single type of zone data (e.g., zone temperature set points, zone indoor air temperatures, etc.) to generate zone groupings. In some embodiments, two or more clustering algorithms are applied to a single type of zone data to generate multiple zone groupings.

[0080] Configuration of an Exemplary Embodiment The configurations and arrangements of the systems and methods as shown in the various exemplary embodiments are merely exemplary. Although only a few embodiments have been described in detail in this disclosure, many modifications are possible (e.g., differences in size, dimensions, structure, shape, and proportions of various elements, parameter values, mounting configurations, use of materials, colors, orientations, etc.). For example, the positions of elements may be reversed or otherwise changed, and the nature or number or positions of separate elements may be altered or changed. Accordingly, all such modifications are intended to be included within the scope of this disclosure. The order or sequence of any process or method steps may be changed or rearranged according to alternative embodiments. Other substitutions, modifications, changes, and omissions are possible in the design, operating conditions, and arrangements of the exemplary embodiments without departing from the scope of this disclosure.

[0081] The present disclosure contemplates methods, systems, and program products on any machine-readable medium for accomplishing various operations. The embodiments of the present disclosure can be implemented using existing computer processors, or by dedicated computer processors for suitable systems incorporated for this or another purpose, or by hardwired systems. An embodiment within the scope of the present disclosure includes a program product including a machine-readable medium for carrying or having machine-executable instructions or data structures stored thereon. Such machine-readable media can be any available medium accessible by a general-purpose or special-purpose computer or other machine with a processor. By way of example, such machine-readable media can include RAM, ROM, EPROM, EEPROM, CD-ROM, or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of machine-executable instructions or data structures and that 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 that cause a general-purpose computer, a special-purpose computer, or a special-purpose processing machine to perform a certain function or group of functions.

[0082] Although the diagram shows a particular order of method steps, the order of steps may differ from that depicted. Also, two or more steps may be performed simultaneously or partially simultaneously. Such variations depend on the software and hardware systems selected, as well as designer choice. All such variations are within the scope of this disclosure. Similarly, software implementations may be accomplished by standard programming techniques with rule-based logic and other logic to accomplish the various connection, processing, comparison, and decision steps.

Claims

1. 1. A controller for a building control system, comprising: The controller comprises one or more processors and a memory storing instructions; The instructions, when executed by the one or more processors, cause the one or more processors to: Identifying a plurality of zones within a building; analyzing data associated with the plurality of zones; generating one or more zone groupings based on the data associated with the plurality of zones, each of the one or more zone groupings defining a plurality of zone groups specifying which of the plurality of zones are grouped together to form each of the plurality of zone groups; identifying a particular zone grouping from the one or more zone groupings based on the data associated with the plurality of zones; generating a zone grouping, the particular zone grouping defining a plurality of zone groups to specify which of the plurality of zones are grouped together to form each of the plurality of zone groups; generating a model for a particular zone group defined by the particular zone grouping, the particular zone group including more than one of the plurality of zone groups; and generating one or more control signals configured to operate equipment of the building control system to provide heating or cooling to the plurality of zones using a model for the particular zone group defined by the particular zone grouping.

2. The data associated with the plurality of zones includes: one or more hysteretic zone temperature set points for the plurality of zones; one or more historical zone operating modes for the plurality of zones; and one or more historical energy consumption values ​​of the plurality of zones.

3. Analyzing the data associated with the plurality of zones comprises: grouping each type of data included in the data for each of the plurality of zones over a predetermined time period to generate a plurality of data blocks for each of the plurality of zones; and selecting one or more significant features associated with the plurality of data blocks.

4. The controller of claim 1 , wherein the operations further include a system identification process for generating a model for the particular zone group.

5. A controller for a building control system, comprising: The controller comprises one or more processors and a memory storing instructions; The instructions, when executed by the one or more processors, cause the one or more processors to: Identifying a plurality of zones within a building; analyzing data associated with the plurality of zones; generating one or more zone groupings based on the data associated with the plurality of zones, each of the one or more zone groupings defining a plurality of zone groups specifying which of the plurality of zones are grouped together to form each of the plurality of zone groups; identifying a particular zone grouping from the one or more zone groupings based on the data associated with the plurality of zones; generating a model for a particular zone group defined by the particular zone grouping, the particular zone group including more than one of the plurality of zone groups; generating one or more control signals configured to operate equipment of the building control system to provide heating or cooling to the plurality of zones using a model for the particular zone group defined by the particular zone grouping; Analyzing the data associated with the plurality of zones comprises: selecting one or more clustering algorithms to identify one or more clusters comprising data associated with the plurality of zones; generating the particular zone grouping based on the one or more clusters including data associated with the plurality of zones.

6. Analyzing the data associated with the plurality of zones comprises: pre-clustering the plurality of zones into one or more clusters based on the location data of the plurality of zones to generate a zone pre-clustering that includes the plurality of zones; comparing the pre-clustering of zones to the one or more clusters including the data associated with the plurality of zones to determine at least one similarity between the pre-clustering of zones and the one or more clusters; and generating the particular zone grouping based on the at least one similarity between the pre-clustering of zones and the one or more clusters.

7. 1. A controller for a building control system configured to operate equipment of the building control system, comprising: a zone identifier configured to identify a plurality of zones within a building; a zone data collector configured to collect data associated with the plurality of zones within the building; a zone grouping generator module, comprising: analyzing the data collected by the zone data collector; and generating one or more zone groupings, each of the one or more zone groupings defining a plurality of zone groups to specify which of the plurality of zones identified by the zone identifier are grouped together to form each of the plurality of zone groups; identifying a particular zone grouping from the one or more zone groupings based on the data associated with the plurality of zones; generating a zone grouping, the particular zone grouping defining a plurality of zone groups and specifying which of the plurality of zones are grouped together to form each of the plurality of zone groups; a zone grouping generator module configured to generate a model for a particular zone group defined by the particular zone grouping, the particular zone group including more than one of the plurality of zone groups; a control signal generator configured to use the model for the particular zone group generated by the zone grouping generator module to generate one or more control signals for operating equipment of the building control system to provide heating or cooling to the plurality of zones.

8. The zone data collector is configured to collect data associated with the plurality of zones within the building, the data comprising: one or more hysteretic zone temperature set points for the plurality of zones; one or more historical operating models of the plurality of zones; and one or more energy consumption values ​​of the plurality of zones.

9. The zone grouping generator module includes: blocking each type of data included in the data for each of the plurality of zones over a predetermined period of time to generate a plurality of data blocks for each of the plurality of zones; The controller of claim 7 , further configured to: select one or more significant features associated with the plurality of data blocks.

10. The controller of claim 7 , further comprising a system identification module configured to generate a model for the particular zone group.

11. A controller for a building control system configured to operate equipment of the building control system, comprising: a zone identifier configured to identify a plurality of zones within a building; a zone data collector configured to collect data associated with the plurality of zones within the building; a zone grouping generator module, comprising: analyzing the data collected by the zone data collector; and generating one or more zone groupings, each of the one or more zone groupings defining a plurality of zone groups to specify which of the plurality of zones identified by the zone identifier are grouped together to form each of the plurality of zone groups; identifying a particular zone grouping from the one or more zone groupings based on the data associated with the plurality of zones; a zone grouping generator module configured to generate a model for a particular zone group defined by the particular zone grouping, the particular zone group including more than one of the plurality of zone groups; a control signal generator configured to use the model for the particular zone group generated by the zone grouping generator module to generate one or more control signals for operating equipment of the building control system to provide heating or cooling to the plurality of zones; The zone grouping generator module includes: selecting one or more clustering algorithms configured to identify one or more clusters comprising the data associated with the plurality of zones; generating the particular zone grouping based on the one or more clusters that include the data associated with the plurality of zones.

12. The zone grouping generator module includes: pre-clustering the plurality of zones into one or more clusters based on the location data of the plurality of zones to generate a zone pre-clustering that includes the plurality of zones; comparing the pre-clustering of zones to the one or more clusters including the data associated with the plurality of zones to determine at least one similarity between the pre-clustering of zones and the one or more clusters; and generating the particular zone grouping based on the at least one similarity between the pre-clustering of zones and the one or more clusters.

13. 1. A method of operating equipment in a building control system to provide heating or cooling to a plurality of zones, comprising the steps of: identifying said plurality of zones within a building; analyzing data associated with the plurality of zones; generating one or more zone groupings based on the data associated with the plurality of zones, each of the zone groupings defining a plurality of zone groups specifying which of the plurality of zones are grouped together to form each of the plurality of zone groups; and identifying a particular zone grouping from the one or more zone groupings based on the data associated with the plurality of zones; generating a zone grouping, the particular zone grouping defining a plurality of zone groups and specifying which of the plurality of zones are grouped together to form each of the plurality of zone groups; generating a model for a particular zone group defined by the particular zone grouping, the particular zone group including more than one of the plurality of zone groups; and generating one or more control signals configured to operate equipment of the building control system to provide heating or cooling to the plurality of zones using a model for the particular zone group defined by the particular zone grouping.

14. Analyzing the data associated with the plurality of zones comprises: grouping each type of data included in the data for each of the plurality of zones over a predetermined time period to generate a plurality of data blocks for each of the plurality of zones; and selecting one or more significant features associated with the plurality of data blocks.

15. The method of claim 13 further comprising performing a system identification process to generate a model for the particular zone group.

16. A method of operating equipment in a building control system to provide heating or cooling to a plurality of zones, comprising: identifying said plurality of zones within a building; analyzing data associated with the plurality of zones; generating one or more zone groupings based on the data associated with the plurality of zones, each of the zone groupings defining a plurality of zone groups specifying which of the plurality of zones are grouped together to form each of the plurality of zone groups; identifying a particular zone grouping from the one or more zone groupings based on the data associated with the plurality of zones; generating a model for a particular zone group defined by the particular zone grouping, the particular zone group including more than one of the plurality of zone groups; generating one or more control signals configured to operate equipment of the building control system to provide heating or cooling to the plurality of zones using a model for the particular zone group defined by the particular zone grouping; Analyzing the data associated with the plurality of zones comprises: selecting one or more clustering algorithms to identify one or more clusters comprising data associated with the plurality of zones; generating a zone grouping for the particular zone based on the one or more clusters that include data associated with the plurality of zones.

17. Analyzing the data associated with the plurality of zones comprises: pre-clustering the plurality of zones into one or more clusters based on the location data of the plurality of zones to generate a zone pre-clustering that includes the plurality of zones; comparing the pre-clustering of zones to the one or more clusters including the data associated with the plurality of zones to determine at least one similarity between the pre-clustering of zones and the one or more clusters; 17. The method of claim 16, further comprising: generating the particular zone grouping based on the at least one similarity between the pre-clustering of zones and the one or more clusters.

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