The present disclosure relates generally to a building management system (BMS) and more particularly to a BMS with enterprise management and reporting. A BMS is, in general, a system of devices configured to control, monitor, and manage equipment in or around a building or building area. A BMS can include, for example, a HVAC system, a security system, a lighting system, a fire alerting system, any other system that is capable of managing building functions or devices, or any combination thereof.
One implementation of the present disclosure is a plant room management system including one or more memory devices configured to store instructions thereon that, when executed by one or more processors, cause the one or more processors to obtain steady-state values of data samples generated by plant room equipment and generate key performance indicators based on the steady-state values; and generate a dashboard that displays the generated key performance indicators. The dashboard may include a plant efficiency and building cooling load widget that displays plant efficiency and building cooling load; a chiller efficiency and chiller cooling load widget that displays chiller efficiencies and chiller cooling load for a chiller; and a plant energy consumption widget that displays energy consumption grouped by equipment type.
In some embodiments, the one or more processors generate the key performance indicators based only on the steady-state values.
In some embodiments, the steady-state values are associated with a period of time in which values generated by the plant room equipment remain within a predetermined range.
In some embodiments, the one or more processors obtain the steady-state values of the generated data samples by obtaining the generated data samples; identifying values of the generated data samples that are associated with successive time stamps; determining that the identified values are within a threshold of each other; and based on the determination that the values are within the threshold of each other, determining that the identified values are associated with a steady-state condition of operation.
In some embodiments, one or more processors obtain the steady-state values of the generated data samples by obtaining the generated data samples; identifying values of generated data samples that are associated with successive time stamps; determining that the identified values are not within a threshold of each other; and based on the determination that the identified values are not within the threshold of each other, disregarding the identified values.
In some embodiments, the one or more processors obtain the steady-state values of the generated data samples by determining whether values of the generated data samples are associated with times that the plant room is in a running condition; and disregarding any values that are not associated with times that the plant room equipment is in the running condition.
In some embodiments, the dashboard further includes a leaving water temperature widget that displays a minimum and a maximum leaving water temperature over time for a chiller.
In some embodiments, the dashboard further includes a delta temperature widget that displays a minimum and a maximum delta water temperature over time for a chiller.
In some embodiments, the dashboard further includes a chiller supply temperature and chiller active power widget that displays temperature and demand over time for a chiller; a cooling tower leaving temperature and cooling tower active power widget that displays temperature and demand over time for a cooling tower; and a building cooling load and outside air temperature widget that displays total cooling load and outside air temperature over time.
In another implementation of the present disclosure, a method for monitoring and controlling a plant room is described. The method may include obtaining values of data samples generated by plant room equipment that is operating in a running condition and generating key performance indicators based on the values; and generating a dashboard that displays the generated key performance indicators. The dashboard includes a plant efficiency and building cooling load widget that displays plant efficiency and building cooling load; a chiller efficiency and chiller cooling load widget that displays chiller efficiencies and chiller cooling load for a chiller; and a plant energy consumption widget that displays energy consumption grouped by equipment type.
In some embodiments, generating the key performance indicators includes generating the key performance indicators based only on values generated by plant room equipment that is operating in the running condition.
In some embodiments, the running condition is associated with times in which the plant room equipment is turned on.
In some embodiments, the dashboard further includes a run hours comparison widget that displays run hours for a plurality of equipment units.
In some embodiments, the dashboard further includes a chiller supply temperature and chiller active power widget that displays temperature and demand over time for a chiller; a cooling tower leaving temperature and cooling tower active power widget that displays temperature and demand over time for a cooling tower; and a building cooling load and outside air temperature widget that displays total cooling load and outside air temperature over time.
In some embodiments, obtaining values of data samples generated by the plant room equipment that are operating in the running condition further includes determining which values are associated with a steady-state condition; and disregarding any values that are not associated with the steady-state condition.
Yet another implementation of the present disclosure is a plant room management system including one or more memory devices configured to store instructions thereon that, when executed by one or more processors, cause the one or more processors to obtain steady-state values of data samples generated by plant room equipment that is operating in a running condition and generate key performance indicators based on the steady-state values; and generate a dashboard that displays the generated key performance indicators. The dashboard may include a plant efficiency and building cooling load widget that displays plant efficiency and building cooling load; a chiller efficiency and chiller cooling load widget that displays chiller efficiencies and chiller cooling load for a chiller; and a plant energy consumption widget that displays energy consumption grouped by equipment type.
In some embodiments, the one or more processors generate the key performance indicators based only on the steady-state values that are generated by plant room equipment that is operating in the running condition.
In some embodiments, the dashboard further includes a run hours comparison widget that displays run hours for a plurality of equipment units.
In some embodiments, the one or more processors obtain the steady-state values of the generated data samples by obtaining the generated data samples; identifying values of generated data samples that are associated with successive time stamps; determining that the values are within a threshold of each other; and, based on the determination that the values are within the threshold of each other, determining that the values are associated with a steady-state condition of operation.
In some embodiments, the one or more processors generate key performance indicators by disregarding any generated data samples that are not associated with times that the plant room equipment is operating in the running condition.
Those skilled in the art will appreciate that the summary is illustrative only and is not intended to be in any way limiting. Other aspects, inventive features, and advantages of the devices and/or processes described herein, as defined solely by the claims, will become apparent in the detailed description set forth herein and taken in conjunction with the accompanying drawings.
Referring now to
Referring particularly to
The BMS that serves building 10 includes a HVAC system 100. HVAC system 100 can include a plurality of HVAC devices (e.g., heaters, chillers, air handling units, pumps, fans, thermal energy storage, etc.) configured to provide heating, cooling, ventilation, or other services for building 10. For example, HVAC system 100 is shown to include a waterside system 120 and an airside system 130. Waterside system 120 may provide a heated or chilled fluid to an air handling unit of airside system 130. Airside system 130 may use the heated or chilled fluid to heat or cool an airflow provided to building 10. An exemplary waterside system and airside system which can be used in HVAC system 100 are described in greater detail with reference to
HVAC system 100 is shown to include a chiller 102, a boiler 104, and a rooftop air handling unit (AHU) 106. Waterside system 120 may use boiler 104 and chiller 102 to heat or cool a working fluid (e.g., water, glycol, etc.) and may circulate the working fluid to AHU 106. In various embodiments, the HVAC devices of waterside system 120 can be located in or around building 10 (as shown in
AHU 106 may place the working fluid in a heat exchange relationship with an airflow passing through AHU 106 (e.g., via one or more stages of cooling coils and/or heating coils). The airflow can be, for example, outside air, return air from within building 10, or a combination of both. AHU 106 may transfer heat between the airflow and the working fluid to provide heating or cooling for the airflow. For example, AHU 106 can include one or more fans or blowers configured to pass the airflow over or through a heat exchanger containing the working fluid. The working fluid may then return to chiller 102 or boiler 104 via piping 110.
Airside system 130 may deliver the airflow supplied by AHU 106 (i.e., the supply airflow) to building 10 via air supply ducts 112 and may provide return air from building 10 to AHU 106 via air return ducts 114. In some embodiments, airside system 130 includes multiple variable air volume (VAV) units 116. For example, airside system 130 is shown to include a separate VAV unit 116 on each floor or zone of building 10. VAV units 116 can include dampers or other flow control elements that can be operated to control an amount of the supply airflow provided to individual zones of building 10. In other embodiments, airside system 130 delivers the supply airflow into one or more zones of building 10 (e.g., via supply ducts 112) without using intermediate VAV units 116 or other flow control elements. AHU 106 can include various sensors (e.g., temperature sensors, pressure sensors, etc.) configured to measure attributes of the supply airflow. AHU 106 may receive input from sensors located within AHU 106 and/or within the building zone and may adjust the flow rate, temperature, or other attributes of the supply airflow through AHU 106 to achieve setpoint conditions for the building zone.
Referring now to
In
Hot water loop 214 and cold water loop 216 may deliver the heated and/or chilled water to air handlers located on the rooftop of building 10 (e.g., AHU 106) or to individual floors or zones of building 10 (e.g., VAV units 116). The air handlers push air past heat exchangers (e.g., heating coils or cooling coils) through which the water flows to provide heating or cooling for the air. The heated or cooled air can be delivered to individual zones of building 10 to serve thermal energy loads of building 10. The water then returns to subplants 202-212 to receive further heating or cooling.
Although subplants 202-212 are shown and described as heating and cooling water for circulation to a building, it is understood that any other type of working fluid (e.g., glycol, CO2, etc.) can be used in place of or in addition to water to serve thermal energy loads. In other embodiments, subplants 202-212 may provide heating and/or cooling directly to the building or campus without requiring an intermediate heat transfer fluid. These and other variations to waterside system 200 are within the teachings of the present disclosure.
Each of subplants 202-212 can include a variety of equipment configured to facilitate the functions of the subplant. For example, heater subplant 202 is shown to include a plurality of heating elements 220 (e.g., boilers, electric heaters, etc.) configured to add heat to the hot water in hot water loop 214. Heater subplant 202 is also shown to include several pumps 222 and 224 configured to circulate the hot water in hot water loop 214 and to control the flow rate of the hot water through individual heating elements 220. Chiller subplant 206 is shown to include a plurality of chillers 232 configured to remove heat from the cold water in cold water loop 216. Chiller subplant 206 is also shown to include several pumps 234 and 236 configured to circulate the cold water in cold water loop 216 and to control the flow rate of the cold water through individual chillers 232.
Heat recovery chiller subplant 204 is shown to include a plurality of heat recovery heat exchangers 226 (e.g., refrigeration circuits) configured to transfer heat from cold water loop 216 to hot water loop 214. Heat recovery chiller subplant 204 is also shown to include several pumps 228 and 230 configured to circulate the hot water and/or cold water through heat recovery heat exchangers 226 and to control the flow rate of the water through individual heat recovery heat exchangers 226. Cooling tower subplant 208 is shown to include a plurality of cooling towers 238 configured to remove heat from the condenser water in condenser water loop 218. Cooling tower subplant 208 is also shown to include several pumps 240 configured to circulate the condenser water in condenser water loop 218 and to control the flow rate of the condenser water through individual cooling towers 238.
Hot TES subplant 210 is shown to include a hot TES tank 242 configured to store the hot water for later use. Hot TES subplant 210 may also include one or more pumps or valves configured to control the flow rate of the hot water into or out of hot TES tank 242. Cold TES subplant 212 is shown to include cold TES tanks 244 configured to store the cold water for later use. Cold TES subplant 212 may also include one or more pumps or valves configured to control the flow rate of the cold water into or out of cold TES tanks 244.
In some embodiments, one or more of the pumps in waterside system 200 (e.g., pumps 222, 224, 228, 230, 234, 236, and/or 240) or pipelines in waterside system 200 include an isolation valve associated therewith. Isolation valves can be integrated with the pumps or positioned upstream or downstream of the pumps to control the fluid flows in waterside system 200. In various embodiments, waterside system 200 can include more, fewer, or different types of devices and/or subplants based on the particular configuration of waterside system 200 and the types of loads served by waterside system 200.
Referring now to
In
Each of dampers 316-320 can be operated by an actuator. For example, exhaust air damper 316 can be operated by actuator 324, mixing damper 318 can be operated by actuator 326, and outside air damper 320 can be operated by actuator 328. Actuators 324-328 may communicate with an AHU controller 330 via a communications link 332. Actuators 324-328 may receive control signals from AHU controller 330 and may provide feedback signals to AHU controller 330. Feedback signals can include, for example, an indication of a current actuator or damper position, an amount of torque or force exerted by the actuator, diagnostic information (e.g., results of diagnostic tests performed by actuators 324-328), status information, commissioning information, configuration settings, calibration data, and/or other types of information or data that can be collected, stored, or used by actuators 324-328. AHU controller 330 can be an economizer controller configured to use one or more control algorithms (e.g., state-based algorithms, extremum seeking control (ESC) algorithms, proportional-integral (PI) control algorithms, proportional-integral-derivative (PID) control algorithms, model predictive control (MPC) algorithms, feedback control algorithms, etc.) to control actuators 324-328.
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Cooling coil 334 may receive a chilled fluid from waterside system 200 (e.g., from cold water loop 216) via piping 342 and may return the chilled fluid to waterside system 200 via piping 344. Valve 346 can be positioned along piping 342 or piping 344 to control a flow rate of the chilled fluid through cooling coil 334. In some embodiments, cooling coil 334 includes multiple stages of cooling coils that can be independently activated and deactivated (e.g., by AHU controller 330, by BMS controller 366, etc.) to modulate an amount of cooling applied to supply air 310.
Heating coil 336 may receive a heated fluid from waterside system 200 (e.g., from hot water loop 214) via piping 348 and may return the heated fluid to waterside system 200 via piping 350. Valve 352 can be positioned along piping 348 or piping 350 to control a flow rate of the heated fluid through heating coil 336. In some embodiments, heating coil 336 includes multiple stages of heating coils that can be independently activated and deactivated (e.g., by AHU controller 330, by BMS controller 366, etc.) to modulate an amount of heating applied to supply air 310.
Each of valves 346 and 352 can be controlled by an actuator. For example, valve 346 can be controlled by actuator 354 and valve 352 can be controlled by actuator 356. Actuators 354-356 may communicate with AHU controller 330 via communications links 358-360. Actuators 354-356 may receive control signals from AHU controller 330 and may provide feedback signals to controller 330. In some embodiments, AHU controller 330 receives a measurement of the supply air temperature from a temperature sensor 362 positioned in supply air duct 312 (e.g., downstream of cooling coil 334 and/or heating coil 336). AHU controller 330 may also receive a measurement of the temperature of building zone 306 from a temperature sensor 364 located in building zone 306.
In some embodiments, AHU controller 330 operates valves 346 and 352 via actuators 354-356 to modulate an amount of heating or cooling provided to supply air 310 (e.g., to achieve a setpoint temperature for supply air 310 or to maintain the temperature of supply air 310 within a setpoint temperature range). The positions of valves 346 and 352 affect the amount of heating or cooling provided to supply air 310 by cooling coil 334 or heating coil 336 and may correlate with the amount of energy consumed to achieve a desired supply air temperature. AHU 330 may control the temperature of supply air 310 and/or building zone 306 by activating or deactivating coils 334-336, adjusting a speed of fan 338, or a combination of both.
Still referring to
In some embodiments, AHU controller 330 receives information from BMS controller 366 (e.g., commands, setpoints, operating boundaries, etc.) and provides information to BMS controller 366 (e.g., temperature measurements, valve or actuator positions, operating statuses, diagnostics, etc.). For example, AHU controller 330 may provide BMS controller 366 with temperature measurements from temperature sensors 362-364, equipment on/off states, equipment operating capacities, and/or any other information that can be used by BMS controller 366 to monitor or control a variable state or condition within building zone 306.
Client device 368 can include one or more human-machine interfaces or client interfaces (e.g., graphical user interfaces, reporting interfaces, text-based computer interfaces, client-facing web services, web servers that provide pages to web clients, etc.) for controlling, viewing, or otherwise interacting with HVAC system 100, its subsystems, and/or devices. Client device 368 can be a computer workstation, a client terminal, a remote or local interface, or any other type of user interface device. Client device 368 can be a stationary terminal or a mobile device. For example, client device 368 can be a desktop computer, a computer server with a user interface, a laptop computer, a tablet, a smartphone, a PDA, or any other type of mobile or non-mobile device. Client device 368 may communicate with BMS controller 366 and/or AHU controller 330 via communications link 372.
Referring now to
Each of building subsystems 428 can include any number of devices, controllers, and connections for completing its individual functions and control activities. HVAC subsystem 440 can include many of the same components as HVAC system 100, as described with reference to
Still referring to
Interfaces 407, 409 can be or include wired or wireless communications interfaces (e.g., jacks, antennas, transmitters, receivers, transceivers, wire terminals, etc.) for conducting data communications with building subsystems 428 or other external systems or devices. In various embodiments, communications via interfaces 407, 409 can be direct (e.g., local wired or wireless communications) or via a communications network 446 (e.g., a WAN, the Internet, a cellular network, etc.). For example, interfaces 407, 409 can include an Ethernet card and port for sending and receiving data via an Ethernet-based communications link or network. In another example, interfaces 407, 409 can include a Wi-Fi transceiver for communicating via a wireless communications network. In another example, one or both of interfaces 407, 409 can include cellular or mobile phone communications transceivers. In one embodiment, communications interface 407 is a power line communications interface and BMS interface 409 is an Ethernet interface. In other embodiments, both communications interface 407 and BMS interface 409 are Ethernet interfaces or are the same Ethernet interface.
Still referring to
Memory 408 (e.g., memory, memory unit, storage device, etc.) can include one or more devices (e.g., RAM, ROM, Flash memory, hard disk storage, etc.) for storing data and/or computer code for completing or facilitating the various processes, layers and modules described in the present application. Memory 408 can be or include volatile memory or non-volatile memory. Memory 408 can include database components, object code components, script components, or any other type of information structure for supporting the various activities and information structures described in the present application. According to some embodiments, memory 408 is communicably connected to processor 406 via processing circuit 404 and includes computer code for executing (e.g., by processing circuit 404 and/or processor 406) one or more processes described herein.
In some embodiments, BMS controller 366 is implemented within a single computer (e.g., one server, one housing, etc.). In various other embodiments BMS controller 366 can be distributed across multiple servers or computers (e.g., that can exist in distributed locations). Further, while
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Enterprise integration layer 410 can be configured to serve clients or local applications with information and services to support a variety of enterprise-level applications. For example, enterprise control applications 426 can be configured to provide subsystem-spanning control to a graphical user interface (GUI) or to any number of enterprise-level business applications (e.g., accounting systems, user identification systems, etc.). Enterprise control applications 426 may also or alternatively be configured to provide configuration GUIs for configuring BMS controller 366. In yet other embodiments, enterprise control applications 426 can work with layers 410-420 to optimize building performance (e.g., efficiency, energy use, comfort, or safety) based on inputs received at interface 407 and/or BMS interface 409.
Building subsystem integration layer 420 can be configured to manage communications between BMS controller 366 and building subsystems 428. For example, building subsystem integration layer 420 may receive sensor data and input signals from building subsystems 428 and provide output data and control signals to building subsystems 428. Building subsystem integration layer 420 may also be configured to manage communications between building subsystems 428. Building subsystem integration layer 420 translate communications (e.g., sensor data, input signals, output signals, etc.) across a plurality of multi-vendor/multi-protocol systems.
Demand response layer 414 can be configured to optimize resource usage (e.g., electricity use, natural gas use, water use, etc.) and/or the monetary cost of such resource usage in response to satisfy the demand of building 10. The optimization can be based on time-of-use prices, curtailment signals, energy availability, or other data received from utility providers, distributed energy generation systems 424, from energy storage 427 (e.g., hot TES 242, cold TES 244, etc.), or from other sources. Demand response layer 414 may receive inputs from other layers of BMS controller 366 (e.g., building subsystem integration layer 420, integrated control layer 418, etc.). The inputs received from other layers can include environmental or sensor inputs such as temperature, carbon dioxide levels, relative humidity levels, air quality sensor outputs, occupancy sensor outputs, room schedules, and the like. The inputs may also include inputs such as electrical use (e.g., expressed in kWh), thermal load measurements, pricing information, projected pricing, smoothed pricing, curtailment signals from utilities, and the like.
According to some embodiments, demand response layer 414 includes control logic for responding to the data and signals it receives. These responses can include communicating with the control algorithms in integrated control layer 418, changing control strategies, changing setpoints, or activating/deactivating building equipment or subsystems in a controlled manner. Demand response layer 414 may also include control logic configured to determine when to utilize stored energy. For example, demand response layer 414 may determine to begin using energy from energy storage 427 just prior to the beginning of a peak use hour.
In some embodiments, demand response layer 414 includes a control module configured to actively initiate control actions (e.g., automatically changing setpoints) which minimize energy costs based on one or more inputs representative of or based on demand (e.g., price, a curtailment signal, a demand level, etc.). In some embodiments, demand response layer 414 uses equipment models to determine an optimal set of control actions. The equipment models can include, for example, thermodynamic models describing the inputs, outputs, and/or functions performed by various sets of building equipment. Equipment models may represent collections of building equipment (e.g., subplants, chiller arrays, etc.) or individual devices (e.g., individual chillers, heaters, pumps, etc.).
Demand response layer 414 may further include or draw upon one or more demand response policy definitions (e.g., databases, XML files, etc.). The policy definitions can be edited or adjusted by a user (e.g., via a graphical user interface) so that the control actions initiated in response to demand inputs can be tailored for the user's application, desired comfort level, particular building equipment, or based on other concerns. For example, the demand response policy definitions can specify which equipment can be turned on or off in response to particular demand inputs, how long a system or piece of equipment should be turned off, what setpoints can be changed, what the allowable set point adjustment range is, how long to hold a high demand setpoint before returning to a normally scheduled setpoint, how close to approach capacity limits, which equipment modes to utilize, the energy transfer rates (e.g., the maximum rate, an alarm rate, other rate boundary information, etc.) into and out of energy storage devices (e.g., thermal storage tanks, battery banks, etc.), and when to dispatch on-site generation of energy (e.g., via fuel cells, a motor generator set, etc.).
Integrated control layer 418 can be configured to use the data input or output of building subsystem integration layer 420 and/or demand response later 414 to make control decisions. Due to the subsystem integration provided by building subsystem integration layer 420, integrated control layer 418 can integrate control activities of the subsystems 428 such that the subsystems 428 behave as a single integrated supersystem. In some embodiments, integrated control layer 418 includes control logic that uses inputs and outputs from a plurality of building subsystems to provide greater comfort and energy savings relative to the comfort and energy savings that separate subsystems could provide alone. For example, integrated control layer 418 can be configured to use an input from a first subsystem to make an energy-saving control decision for a second subsystem. Results of these decisions can be communicated back to building subsystem integration layer 420.
Integrated control layer 418 is shown to be logically below demand response layer 414. Integrated control layer 418 can be configured to enhance the effectiveness of demand response layer 414 by enabling building subsystems 428 and their respective control loops to be controlled in coordination with demand response layer 414. This configuration may advantageously reduce disruptive demand response behavior relative to conventional systems. For example, integrated control layer 418 can be configured to assure that a demand response-driven upward adjustment to the setpoint for chilled water temperature (or another component that directly or indirectly affects temperature) does not result in an increase in fan energy (or other energy used to cool a space) that would result in greater total building energy use than was saved at the chiller.
Integrated control layer 418 can be configured to provide feedback to demand response layer 414 so that demand response layer 414 checks that constraints (e.g., temperature, lighting levels, etc.) are properly maintained even while demanded load shedding is in progress. The constraints may also include setpoint or sensed boundaries relating to safety, equipment operating limits and performance, comfort, fire codes, electrical codes, energy codes, and the like. Integrated control layer 418 is also logically below fault detection and diagnostics layer 416 and automated measurement and validation layer 412. Integrated control layer 418 can be configured to provide calculated inputs (e.g., aggregations) to these higher levels based on outputs from more than one building subsystem.
Automated measurement and validation (AM&V) layer 412 can be configured to verify that control strategies commanded by integrated control layer 418 or demand response layer 414 are working properly (e.g., using data aggregated by AM&V layer 412, integrated control layer 418, building subsystem integration layer 420, FDD layer 416, or otherwise). The calculations made by AM&V layer 412 can be based on building system energy models and/or equipment models for individual BMS devices or subsystems. For example, AM&V layer 412 may compare a model-predicted output with an actual output from building subsystems 428 to determine an accuracy of the model.
Fault detection and diagnostics (FDD) layer 416 can be configured to provide on-going fault detection for building subsystems 428, building subsystem devices (i.e., building equipment), and control algorithms used by demand response layer 414 and integrated control layer 418. FDD layer 416 may receive data inputs from integrated control layer 418, directly from one or more building subsystems or devices, or from another data source. FDD layer 416 may automatically diagnose and respond to detected faults. The responses to detected or diagnosed faults can include providing an alert message to a user, a maintenance scheduling system, or a control algorithm configured to attempt to repair the fault or to work-around the fault.
FDD layer 416 can be configured to output a specific identification of the faulty component or cause of the fault (e.g., loose damper linkage) using detailed subsystem inputs available at building subsystem integration layer 420. In other exemplary embodiments, FDD layer 416 is configured to provide “fault” events to integrated control layer 418 which executes control strategies and policies in response to the received fault events. According to some embodiments, FDD layer 416 (or a policy executed by an integrated control engine or business rules engine) may shut-down systems or direct control activities around faulty devices or systems to reduce energy waste, extend equipment life, or assure proper control response.
FDD layer 416 can be configured to store or access a variety of different system data stores (or data points for live data). FDD layer 416 may use some content of the data stores to identify faults at the equipment level (e.g., specific chiller, specific AHU, specific terminal unit, etc.) and other content to identify faults at component or subsystem levels. For example, building subsystems 428 may generate temporal (i.e., time-series) data indicating the performance of BMS 400 and the various components thereof. The data generated by building subsystems 428 can include measured or calculated values that exhibit statistical characteristics and provide information about how the corresponding system or process (e.g., a temperature control process, a flow control process, etc.) is performing in terms of error from its setpoint. These processes can be examined by FDD layer 416 to expose when the system begins to degrade in performance and alert a user to repair the fault before it becomes more severe.
Referring now to
BMS 500 provides a system architecture that facilitates automatic equipment discovery and equipment model distribution. Equipment discovery can occur on multiple levels of BMS 500 across multiple different communications busses (e.g., a system bus 554, zone buses 556-560 and 564, sensor/actuator bus 566, etc.) and across multiple different communications protocols. In some embodiments, equipment discovery is accomplished using active node tables, which provide status information for devices connected to each communications bus. For example, each communications bus can be monitored for new devices by monitoring the corresponding active node table for new nodes. When a new device is detected, BMS 500 can begin interacting with the new device (e.g., sending control signals, using data from the device) without user interaction.
Some devices in BMS 500 present themselves to the network using equipment models. An equipment model defines equipment object attributes, view definitions, schedules, trends, and the associated BACnet value objects (e.g., analog value, binary value, multistate value, etc.) that are used for integration with other systems. Some devices in BMS 500 store their own equipment models. Other devices in BMS 500 have equipment models stored externally (e.g., within other devices). For example, a zone coordinator 508 can store the equipment model for a bypass damper 528. In some embodiments, zone coordinator 508 automatically creates the equipment model for bypass damper 528 or other devices on zone bus 558. Other zone coordinators can also create equipment models for devices connected to their zone busses. The equipment model for a device can be created automatically based on the types of data points exposed by the device on the zone bus, device type, and/or other device attributes. Several examples of automatic equipment discovery and equipment model distribution are discussed in greater detail below.
Still referring to
In some embodiments, system manager 502 is connected with zone coordinators 506-510 and 518 via a system bus 554. System manager 502 can be configured to communicate with zone coordinators 506-510 and 518 via system bus 554 using a master-slave token passing (MSTP) protocol or any other communications protocol. System bus 554 can also connect system manager 502 with other devices such as a constant volume (CV) rooftop unit (RTU) 512, an input/output module (TOM) 514, a thermostat controller 516 (e.g., a TEC5000 series thermostat controller), and a network automation engine (NAE) or third-party controller 520. RTU 512 can be configured to communicate directly with system manager 502 and can be connected directly to system bus 554. Other RTUs can communicate with system manager 502 via an intermediate device. For example, a wired input 562 can connect a third-party RTU 542 to thermostat controller 516, which connects to system bus 554.
System manager 502 can provide a user interface for any device containing an equipment model. Devices such as zone coordinators 506-510 and 518 and thermostat controller 516 can provide their equipment models to system manager 502 via system bus 554. In some embodiments, system manager 502 automatically creates equipment models for connected devices that do not contain an equipment model (e.g., IOM 514, third party controller 520, etc.). For example, system manager 502 can create an equipment model for any device that responds to a device tree request. The equipment models created by system manager 502 can be stored within system manager 502. System manager 502 can then provide a user interface for devices that do not contain their own equipment models using the equipment models created by system manager 502. In some embodiments, system manager 502 stores a view definition for each type of equipment connected via system bus 554 and uses the stored view definition to generate a user interface for the equipment.
Each zone coordinator 506-510 and 518 can be connected with one or more of zone controllers 524, 530-532, 536, and 548-550 via zone buses 556, 558, 560, and 564. Zone coordinators 506-510 and 518 can communicate with zone controllers 524, 530-532, 536, and 548-550 via zone busses 556-560 and 564 using a MSTP protocol or any other communications protocol. Zone busses 556-560 and 564 can also connect zone coordinators 506-510 and 518 with other types of devices such as variable air volume (VAV) RTUs 522 and 540, changeover bypass (COBP) RTUs 526 and 552, bypass dampers 528 and 546, and PEAK controllers 534 and 544.
Zone coordinators 506-510 and 518 can be configured to monitor and command various zoning systems. In some embodiments, each zone coordinator 506-510 and 518 monitors and commands a separate zoning system and is connected to the zoning system via a separate zone bus. For example, zone coordinator 506 can be connected to VAV RTU 522 and zone controller 524 via zone bus 556. Zone coordinator 508 can be connected to COBP RTU 526, bypass damper 528, COBP zone controller 530, and VAV zone controller 532 via zone bus 558. Zone coordinator 510 can be connected to PEAK controller 534 and VAV zone controller 536 via zone bus 560. Zone coordinator 518 can be connected to PEAK controller 544, bypass damper 546, COBP zone controller 548, and VAV zone controller 550 via zone bus 564.
A single model of zone coordinator 506-510 and 518 can be configured to handle multiple different types of zoning systems (e.g., a VAV zoning system, a COBP zoning system, etc.). Each zoning system can include a RTU, one or more zone controllers, and/or a bypass damper. For example, zone coordinators 506 and 510 are shown as Verasys VAV engines (VVEs) connected to VAV RTUs 522 and 540, respectively. Zone coordinator 506 is connected directly to VAV RTU 522 via zone bus 556, whereas zone coordinator 510 is connected to a third-party VAV RTU 540 via a wired input 568 provided to PEAK controller 534. Zone coordinators 508 and 518 are shown as Verasys COBP engines (VCEs) connected to COBP RTUs 526 and 552, respectively. Zone coordinator 508 is connected directly to COBP RTU 526 via zone bus 558, whereas zone coordinator 518 is connected to a third-party COBP RTU 552 via a wired input 570 provided to PEAK controller 544.
Zone controllers 524, 530-532, 536, and 548-550 can communicate with individual BMS devices (e.g., sensors, actuators, etc.) via sensor/actuator (SA) busses. For example, VAV zone controller 536 is shown connected to networked sensors 538 via SA bus 566. Zone controller 536 can communicate with networked sensors 538 using a MSTP protocol or any other communications protocol. Although only one SA bus 566 is shown in
Each zone controller 524, 530-532, 536, and 548-550 can be configured to monitor and control a different building zone. Zone controllers 524, 530-532, 536, and 548-550 can use the inputs and outputs provided via their SA busses to monitor and control various building zones. For example, a zone controller 536 can use a temperature input received from networked sensors 538 via SA bus 566 (e.g., a measured temperature of a building zone) as feedback in a temperature control algorithm. Zone controllers 524, 530-532, 536, and 548-550 can use various types of control algorithms (e.g., state-based algorithms, extremum seeking control (ESC) algorithms, proportional-integral (PI) control algorithms, proportional-integral-derivative (PID) control algorithms, model predictive control (MPC) algorithms, feedback control algorithms, etc.) to control a variable state or condition (e.g., temperature, humidity, airflow, lighting, etc.) in or around building 10.
Referring now to
Some BEMSs monitor data collected from multiple facilities or buildings within a portfolio. The buildings or facilities may be located in disparate geographies. The type of operations and energy consumption these buildings may vary greatly. Aspects of a BEMS application can include comparing buildings and facilities within the portfolio and benchmarking. However, some metrics to deliver this insight provide inaccurate information or compare buildings or facilities that are not reasonably similar. One type of performance comparison between buildings and facilities is an energy consumption comparison. Such a comparison may include classifying buildings or facilities with the highest energy consumption as being worst performing and classifying buildings or facilities with the lowest energy consumption as being best performing. However, this type of comparison may be inaccurate at comparing and peer to peer benchmarking. Additionally, some BEMSs that include fault detection and diagnostics for building equipment do not utilize the information drawn from the fault engine to further supplement this comparison and benchmarking activity.
The concept of introducing a three-dimensional analysis for facility/building energy and equipment performance may provide an accurate comparison across different facilities/buildings within a portfolio by first normalizing the values down to a comparable common factor and then applying relative comparison against each other. Advantageously, even if the building operations type may vary, they can be accurately compared against the others within that portfolio by arriving at a common denominator using the three-dimensional analysis. The resulting comparisons may truly reflect the potential of an enterprise management application.
The three-dimensional analysis may include calculating three different dimensions both across energy and fault information and arriving at a common denominator to provide an output that normalizes the impact of different operations of the buildings. The addition of a third dimension provides a more accurate picture across the portfolio relative to analyses that make use of only one or two of the dimensions. In some embodiments, the three-dimensional analysis concept is implemented as part of an enterprise management product, such as Metasys Enterprise Management by Johnson Controls.
Referring specifically to
Based on these three dimensions, the visualization shown in
When a building management system is deployed in a newly commissioned site, the second dimension may be absent for the first year, In this case, the first dimension can be applied across both the length 602 and breadth 603 of the cell 601, 606-608. When a building management system is deployed in an existing site, all three dimensions can be individually determined as previously described. When available data is more than a year, the second dimension may be based on average data for a given building. The average data may thereby form a benchmark specifically for that building and may increase the accuracy of the three-dimensional analysis
Referring now to
Based on these three dimensions, the visualization shown in
When a building management system is deployed in a newly commissioned site, a new third dimension can be calculated. The new third dimension may be the ratio of fault count to fault hours and can be used for determining the color of cells. The lower this ratio the better a building is performing with respect to its equipment performance. The scale color can be automatically based on the relative ratios calculated in this particular scenario. When a building management system is deployed in an existing site, all three dimensions can be individually determined as previously described. When available data is more than a year, the second dimension may be based on average data for a given building. The average data may thereby form a benchmark specifically for that building and may increase the accuracy of the three-dimensional analysis. The rule engine may have the ability to reprocess historical data to return fault results, making the analysis more feasible.
Referring now to
The left branch 806 may perform on-demand roll ups to aggregate energy information from different subspaces of a building (step 808) to generate energy consumption for each building 809. Energy consumption for each building 809 can then be divided by the net lettable area of that building (NLA) (step 810). For each building, the first dimension can then be calculated as energy consumption per NLA per day (step 811). The second dimension may be calculated similarly, using consumption and NLA information for the same timeline the previous year (step 812). The third dimension may then be found as a percentage deviation between the first dimension and the second dimension (step 813). The left branch 806 thereby provides energy performance comparison across buildings (step 814).
The right branch 807 may utilize fault information of each building 815, total fault hours 816, and the total fault count 817. The first dimension can be defined by total fault count (step 817). The second dimension can be calculated as the fault hours divided by fault count for the same period last year (step 818). The third dimension may be defined by a percentage deviation of current fault count per fault hours with respect to the same period last year (step 819). The right branch 807 thereby provides equipment performance comparison across buildings 820.
Referring now to
A building management system may include various meters along with points. Data from the meters can be monitored to determine the consumption and demand of spaces where the meters are located. In some embodiments, a building management system can perform automated roll-ups of the meters. However, if the meters serving a particular space are removed or added, it can be difficult to account for this change through a dynamic process initiated by the user through the UI. Changes to the meter configuration can be implemented on the backend, which may trigger recalculations. In some instances, the recalculations can take days to complete. Advantageously, the on-demand roll-ups of meters described herein may be based on linking or delinking of meters from the roll-up. Accordingly, changes to the meters may take effect immediately.
Referring particularly to
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In some building enterprise management solutions, it can be cumbersome to understand in one single snapshot how the building is faring with respect to its energy and equipment performance. For example, a user may be required to navigate to various visualizations within the software and collate the data manually to arrive at any logical conclusion. The scorecard described herein may include essential information in building enterprise management solutions that provides a holistic view on the overall energy performance of buildings and how critical equipment are performing, all from one single view.
The key performance indicators (KPIs) included in the scorecard can be derived from data from different sources and can be displayed as valuable insights which are logically arranged in a sequential manner which would make most sense to a building owner or facility manager. For example, the scorecard may fully leverage the advantage of a sub-metering investment made within the various sub-spaces of the building. The scorecard may account for critical equipment such as chillers and whether they are performing within desired limits. It may be desirable to view this information from one single user interface.
Referring particularly to
The widget “Building Energy Overview” 1301 may automatically roll-up the consumption of the subspaces within the building (e.g., floors, wings and rooms) and show the normalized energy consumption of the entire building as a single entity for the selected time period. Time selection 1302 may be available on the upper right corner and can be switched easily between one week, one month, three months, six months, one year, or any other custom timeline (e.g., yesterday, last 24 hours, last week, last 30 days, last 6 months, or any custom range). In some embodiments, the peak demand registered within the building is shown within this widget
The widget “Consumption By Commodity” 1303 may breakdown the entire building's consumption by the different types of commodity being used within the building. The widget “Energy Density By Space” 1304 may be derived from an automated calculation of kWh/unit area/day for all the subspaces within the building. The subspaces within the widget may be automatically arranged in a descending fashion by highlighting the spaces which have the highest energy density within the building. The “Consumption By Space” widget 1305 may arrange the subspaces in a descending fashion by highlighting the spaces which register the highest consumption on the top of the list.
All these derived outputs may be supplemented by two visual quest in the form of a “Consumption by Load Type” widget 1306 and a “Peak Demand By Space” widget 1307. The consumption by load type widget may breakdown the energy consumption of the building into various categories (e.g., HVAC, chillers, lighting, hot water, pump and auxiliary, plug load, etc.). The peak demand by space widget 1307 provides a thermal map where the cell size is determined by the peak demand value and the cell color is automatically derived from the relative values registered from various spaces in terms of demand.
This entire view as one entity becomes an unique demonstration of building's energy performance and is termed as “Building Energy Performance Scorecard.” This entire set of data can be extracted in .csv format and can be submitted for any auditory purposes or for earning credits for achieving various environmental benchmark (LEED, NABERS, etc.).
Referring now to
The “Chiller Fault Indicator” widget 1401 may display an open number of faults with respect to individual chillers. This is followed by two highly valued pieces of information provided in the Chiller Leave Water Temperature (LWT) with Respect to Time” widget 1402 and the “Chiller Load Pattern” widget 1403. Both these widgets may be a combination of heat map and bin analysis and provide powerful insights in determining in which range of temperature and load pattern each individual chiller is spending most of its time.
The scorecard may include two other bin analysis widgets including a “Chiller LWT Min and Max Range” widget 1404 and a “Delta Temperature Min and Max Range” widget 1405, which indicate the chiller leaving water temperature minimum and maximum temperature range and the minimum and maximum temperature differential when the chillers are operational.
The “Chiller Start Count” widget 1406 may indicate chiller start counts which efficiently lets a facility manager or any chiller expert understand the fact if chillers are catering to the load as per sequencing logics which have been implemented at site.
All these put together provide a unique scorecard for chillers. A single snapshot lets a user understand whether these critical equipment are performing as intended or are there any major deviations from ideal conditions. This scorecard concept may be provided as part of enterprise analytics solutions and can be used as part of any dashboard based analytics platform.
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Similarly, the building equipment scorecard may use space information 1601, energy data through metering or virtual meters 1602, chiller fault information, supply and return water temperature information, and operational status information 1603 to generate the information shown in the scorecard. This information may include chiller fault count 1604, chiller start count 1605, bin analysis with respect to leaving temperature and load pattern 1606, bin analysis with respect to minimum and maximum range of supply temperature 1607, and bin analysis with respect to maximum and minimum range of delta between supply and return temperatures 1608.
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In some building enterprise management solutions, it can be cumbersome to understand in one single snapshot how the building is faring with respect to its energy and equipment performance. For example, a user may be required to navigate to various visualizations within the software and collate the data manually to arrive at any logical conclusion. The plant room dashboard described herein provides a holistic view on the overall energy performance and operational efficiency of how critical plant room equipment are performing, all from one single section.
The plant room dashboard 1700 may include information available from macro to micro levels with respect to the plant room's critical equipment. The plant room dashboard 1700 may also correlate energy performance of these equipment sets with their performance index in terms of operational efficiency and other parameters which have a direct correlation to energy. The different parameters in the plant room equipment can be compared, which provides invaluable insights for a building owner or a facility manager. The plant room dashboard 1700 may provide a comprehensive and complete view of the plant room as an entity in the form of key information with respect to its performance and optimization opportunities.
In some embodiments, the plant room dashboard 1700 is implemented as part of an enterprise management and analytics offering. The plant room dashboard 1700 can be used by large universities and central plants to provide additional value, and can also be used by central plant optimization (CPO) jobs where monitoring the outcomes of applying CPO techniques is a key aspect. The savings and performance improvements as a result can directly be reflected in this dedicated section. Informed decisions can be made on further improvements.
Referring specifically to
Advantageously, the arrangement and positioning of widgets 1702-1706 of plant room dashboard 1700 may provide for a one-stop view of how a building is operating. Based on the positioning of each widget 1702-1706, users may easily correlate data between widgets without going to different pages that are specific to each widget. For example, the “Plant Efficiency and Building Cooling Load” widget 1702 may have a direct link to “Run Hours Comparison” widget 1703 because various equipment such as primary pumps, secondary pumps, etc., may run longer as a result of a higher cooling load in the building. Similarly, the “Run Hours Comparison” widget 1703 may be correlated with “Plant Energy Consumption” widget 1705 because longer run time of the equipment may result in higher plant energy consumption.
In another example, a user may correlate data displayed in the “Plant Efficiency and Building Cooling Load” widget 1702 with data displayed in the “Plant Energy Consumption” widget 1705. A plant room that is operating efficiently as shown in the “Plant Efficiency and Building Cooling Load” widget 1702 may consume less energy, which may be shown in the “Plant Energy Consumption” Widget 1705. A plant room that is not operating efficiently may consume more energy. As discussed below, the “Plant Energy Consumption” widget 1705 may show energy consumption by equipment type (e.g., cooling tower fans, condenser pumps, etc.). In some embodiments, energy consumption may be shown as a percentage of the total energy consumption of the plant room. In an example case, a user may notice that the plant room is not operating efficiently based on data of the “Plant Energy Consumption” widget 1705. The user may view the energy consumption of various equipment types such as cooling tower fans or condenser pumps of the plant room in the “Plant Energy Consumption” widget 1705 on the same interface to quickly see which equipment needs to be repaired or replaced to increase the plant room's overall efficiency. In some embodiments, the user may cause the plant room to use other equipment of the plant room that is operating more efficiently.
Another correlation that a user may make based on data in the plant room dashboard 1700 is a correlation based on data of the “Chiller Efficiency and Chiller Cooling Load” widget 1704 and data of the “Chiller Consumption Baseline” widget 1706. The “Chiller Efficiency and Chiller Cooling Load” widget 1704 may display how efficiently the chillers are operating based on various cooling loads. The “Chiller Consumption Baseline” widget 1706 may display a chiller energy consumption over time compared to an expected chiller consumption baseline. A user may view a correlation between the chiller efficiency, the chiller cooling load, and the expected chiller consumption to better understand how a chiller is operating.
Clicking on the second radio button 1707 may cause the view shown in
Each of widgets 1801-1803 may be related to each other. For example, a high chiller supply temperature of the “Chiller Supply Temperature and Chiller Active Power” widget 1801 may be correlated to an increase in cooling tower leaving temperature of the “Cooling Tower Leaving Temperature and Cooling Tower Active Power” widget 1802. The correlation may be a result of a condenser not being able to dissipate heat. The building cooling load of the “Building Cooling Load and Outdoor Air Temperature (OAT)” widget 1803 may increase because of the high chiller supply temperature. A user may easily view such a correlation between the high chiller supply temperature, the increase in the cooling tower leaving temperature, and the building cooling load in the plant room dashboard 1700 because the information is in one view of the plant room dashboard 1700. Based on such information, the user may easily identify problems in how equipment in the plant room is operating and fix them accordingly.
Referring now to
A time selection 1904 is available on the upper right corner and can be switched between one week, one month, three months, six months, one year, or any other custom timeline (e.g., yesterday, last 24 hours, last week, last 30 days, last 6 months, or any custom range). A user can view the data in grid format by clicking on the grid toggle 1905.
In some embodiments, the cooling load and the plant efficiency of the “Plant Efficiency and Building Cooling Load” widget 1702 is determined based on data that is collected when the plant room (or individual equipment of the plant room) is in a “running” (e.g., operating, turned on, etc.) condition. In some embodiments, the building management system collects all data from the plant room and only uses data associated with times that the plant room (or individual equipment of the plant room) is running. The building management system may obtain indications that the plant room or equipment of the plant room is running from the plant room equipment or sensors associated with the plant room. For example, if a plant room is running for 12 hours a day for a month, the building management system associated with the plant room may collect data associated with 12 daily hours that the plant room is running while discarding or disregarding any other data. In this way, the building management system may ensure that the data is not skewed with data that may not be relevant to users viewing the plant room dashboard 1700 such as, for example, when the plant room is not running and therefore not consuming any energy.
In some embodiments, the cooling load and the plant room efficiency of the “Plant Efficiency and Building Cooling Load” widget 1702 is determined based on data that is collected when the plant room (or individual equipment of the plant room) is running in a steady-state condition. Equipment may be running in the steady-state condition when it is associated with values that remain within a predetermined range over a period of time. The building management system may determine when the plant room (or individual equipment of the plant room) is running in the steady-state condition by analyzing values associated with times that the plant room or equipment is in the running condition, or, in some cases, each of the values that are produced by equipment of the plant room. The values may fluctuate initially and eventually achieve a steady-state. In some embodiments, the building management system only displays values in the “Plant Efficiency and Building Cooling Load” widget 1702 and, in some cases, each widget of the plant room dashboard 1700, that are associated with the times the plant room is operating in the steady-state condition, in the running condition, or both the steady-state and the running condition. In some embodiments, the building management substantially only displays such values on the plant room dashboard 1700 (e.g., 95% or more of the values may be associated with the steady-state condition, the running condition, or both). Any percentage of the values shown on the plant room dashboard 1700 may be associated with the steady-state condition, the running condition, or both.
To determine if the plant room is in the steady-state condition, the building management system may analyze times of values in increments of a threshold number of time stamps, such as 10 sequential time stamps. The building management system may determine that the plant room is in the steady-state condition if there is no swing in values between the 10 successive time stamps. In various embodiments, a swing may be defined as a variation from a previous value of at least a threshold amount. For example, for the cooling load, in some embodiments, a swing may be defined as an increase or decrease of any more than 5% of the value of the previous cooling load value. For the plant room efficiency, in some embodiments, the swing may be defined as an increase or decrease of any more than 0.1% of the previous plant room efficiency value. For each of the cooling load and the plant room efficiency, swings may be defined by comparisons to any value. Consequently, data shown in the “Plant Efficiency and Building Cooling Load” widget 1702 may not include values that are associated with times that the plant room or equipment of the plant room is warming up after recently being turned on or is otherwise operating erratically. It should be noted that all or any portion of the information shown on the plant room dashboard 1700 may be associated with times that equipment is operating in the steady-state condition and/or the running condition.
Referring now to
A time selection 2002 is available on the upper right corner and can be switched between one week, one month, three months, six months, one year, or any other custom timeline (e.g., yesterday, last 24 hours, last week, last 30 days, last 6 months, or any custom range). A user can view the data in grid format by clicking on the grid toggle 2003.
In some embodiments, similar to the data shown in “Plant Efficiency and Building Cooling Load” widget 1702, data shown in the “Run Hours Comparison” widget 1703 only includes data that is associated with times that pumps (e.g., primary pumps, secondary pumps, cooling tower pumps, etc.) and any condensers in the plant room are running. The building management system may determine the pumps or the condensers are running based on data from the pumps or condensers themselves or sensors associated with them. The building management system may discard or disregard any data that is not associated with times that the pumps or the condensers are operating. Consequently, users may view an accurate representation of the number of hours that the pumps and/or condensers of the plant room are running.
In some embodiments, the run hours of the “Run Hours Comparison” widget 1703 is determined based on data that is collected when the plant room (or individual equipment of the plant room) is running in a steady-state condition. The building management system may determine when the plant room (or individual equipment of the plant room) is running in the steady-state condition by analyzing each value associated with times that the plant room is in the running condition, or, in some cases, each of the values that are produced by equipment of the plant room. The values may fluctuate initially and eventually achieve a steady-state. The building management system may only display values in the “Run Hours Comparison” widget 1703 that are associated with the times the plant room is operating in the steady-state condition.
Referring now to
A time selection 2104 is available on the upper right corner and can be switched between one week, one month, three months, six months, one year, or any other custom timeline (e.g., yesterday, last 24 hours, last week, last 30 days, last 6 months, or any custom range). A user can view the data in grid format by clicking on the grid toggle 2105.
In some embodiments, similar to the data shown in the “Plant Efficiency and Building Cooling Load” widget 1702, data shown in the “Chiller Efficiency and Chiller Cooling Load” widget 1704 only includes data that is associated with times that the chillers are running. The building management system may determine the chillers are running based on data from the chillers themselves (e.g., from data of a controller of a smart chiller) or from sensors associated with the chillers. The building management system may discard or disregard any data that is not associated with times that the chillers are operating. Consequently, users may view an accurate representation of the efficiency and/or cooling load of the chillers without data being skewed by instances where the chillers are not running.
In some embodiments, the chiller efficiency and cooling loads are calculated based on values when the chillers are operating in a steady-state condition. For example, when a chiller initially begins operating, there may be large spikes and/or drops in how efficiently the chiller operates and/or the cooling load of the chiller. However, over time the chiller may settle and operate in the steady-state condition where its efficiency and/or cooling load may remain relatively constant (e.g., within a range as determined by an administrator). The building management system may only show values in the “Chiller Efficiency and Chiller Cooling Load” widget 1704 that are associated with the times that the chiller is operating in the steady-state condition.
The building management system may determine when the chiller is operating in the steady-state condition by analyzing consecutive sets of a threshold number of timestamps, such as 10 sequential timestamps, for one or each of the cooling load and the efficiency of the chiller. The building management system may determine that the chiller is operating in the steady-state condition if there is no swing in values between the threshold number of timestamps. For example, for the cooling load, a swing may be defined as an increase or decrease of any more than 5% of the value of the immediate previous cooling load value. For the chiller efficiency, in some embodiments, the swing may be defined as an increase or decrease of any more than 0.1% of the immediate previous chiller efficiency value. For each of the cooling load and the chiller efficiency, swings may be defined by comparisons to any value. Consequently, data shown in the “Chiller Efficiency and Chiller Cooling Load” widget 1704 may not include values that are associated with times that the plant room or equipment of the plant room is warming up after recently being turned on or is otherwise operating erratically.
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A time selection 2201 is available on the upper right corner and can be switched between one week, one month, three months, six months, one year, or any other custom timeline (e.g., yesterday, last 24 hours, last week, last 30 days, last 6 months, or any custom range). A user can view the data in grid format by clicking on the grid toggle 2202.
In some embodiments, similar to the data shown in the “Plant Efficiency and Building Cooling Load” widget 1702, data shown in the “Plant Energy Consumption” widget 1705 only includes data that is associated with times that the individual components or equipment types of the plant room are running. For example, the building management system may determine times that one or more of individual chiller pumps, the chiller, or the cooling tower are running. The building management system may only use data from the determined times to determine a total energy consumption of each component or equipment type. Consequently, the building management system may avoid using data that may be collected while the components are not operating. For example, the building management system may avoid using data gathered based on current leakage of equipment when the equipment is not running. The building management system may discard (e.g., remove from memory or not use in any calculations) or disregard any data that is associated with times that the various pieces of equipment are not operating. By discarding such data, the plant room dashboard 1700 may show a more accurate representation of the total energy consumption of various equipment types in the “Plant Energy Consumption” widget 1705.
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Time resolution operation for this widget may be different than previously described. Any timeline selected may display for the past period. For example, selecting the one month option on the time selection 2305 may display the month's deviation and impact. Selecting the three month option may display the three month deviation and impact and so on. Calculations may be defined on a complete month basis, which may depend on the baseline provided. The month's baseline can be broken down to days for the applicable resolutions (e.g., month by day, week by day, etc.). A user can view the data in grid format by clicking on the grid toggle 2306.
Chiller baseline consumption may be subtracted from the chiller actual consumption to derive the deviation and absolute savings. This calculation may be performed based on the selected timeline. The deviation can be defined as:
Deviation=((Actual Consumption−Baseline Consumption)/Baseline Consumption)*100=value expressed as a percentage deviation
Savings=Actual Consumption−Baseline Consumption.
In some embodiments, as with the data shown in each of the widgets 1702-1705, data shown in the “Chiller Consumption Baseline” widget 1706 only includes data that is associated with times that the chillers are running. Consequently, users may view an accurate representation of the total energy consumption of the chillers versus their baseline without data being skewed by instances where the chillers are not running.
Further, in some embodiments, the “Chiller Consumption Baseline” widget 1706 only includes data generated when the chillers are operating in the steady-state condition. To determine when the chillers are operating in the steady-state condition, the building management system may analyze times of values in increments of a threshold number of timestamps, such as 10 sequential timestamps, for the energy consumption of the chillers. The building management system may determine that the chillers are operating in the steady-state condition if there is no swing in the values between any of the threshold number of timestamps. For example, in some embodiments, a swing may be defined as an increase or decrease of any more than 1% of an energy consumption value between successive timestamps. Swings may be defined by comparisons to any value. Consequently, data shown in the “Chiller Consumption Baseline” widget 1706 may not include values associated with times when the chillers are warming up after recently being turned on or are otherwise operating erratically.
Referring now to
A time selection 2404 is available on the upper right corner and can be switched between one week, one month, three months, six months, one year, or any other custom timeline (e.g., yesterday, last 24 hours, last week, last 30 days, last 6 months, or any custom range). A user can view the data in grid format by clicking on the grid toggle 2405.
In some embodiments, as with the data shown in each of the widgets 1702-1706, data shown in the “Chiller Supply Temperature and Chiller Active Power” widget 1801 only includes data that is associated with times that the chillers are running. Consequently, users may view an accurate representation of the chiller supply temperature and the corresponding energy demand of the chiller without data being skewed by instances where the chiller is not running.
In some embodiments, the chiller supply temperature and the chiller active power are calculated based on values when the chillers are operating in a steady-state condition. For example, when a chiller initially begins operating, there may be large spikes and/or drops in the supply temperature and/or the active power of the chiller. However, over time the chiller may settle and operate in the steady-state condition where its supply temperature and/or active power may remain relatively constant (e.g., within a range as determined by an administrator). In some embodiments, the building management system only shows values in the “Chiller Supply Temperature and Chiller Active Power” widget 1801 that are associated with the times that the chillers are operating in the steady-state condition.
The building management system may determine when the chillers are operating in the steady-state condition by analyzing times of values in increments of a threshold number of timestamps, such as 10 sequential timestamps, for the chiller supply temperature and the active power of the chillers. Any threshold may be used. The building management system may determine that the chillers are operating in the steady-state condition if there is no swing in the values between any of the threshold number of timestamps. For example, for the chiller supply temperature, in some embodiments, a swing may be defined as an increase or decrease of any more than 1% of a value if the value is in Fahrenheit and 0.1% if the value is in Celsius. For chiller active power, in some embodiments, a swing may be defined as an increase or decrease of more than 1% between values. For each of the chiller supply temperature and the chiller active power, swings may be defined by comparisons to any value. Consequently, data shown in the “Chiller Supply Temperature and Chiller Active Power” widget 1801 may not include values that are associated with times that the chillers are warming up after recently being turned on or are otherwise operating erratically.
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A time selection 2503 is available on the upper right corner and can be switched between one week, one month, three months, six months, one year, or any other custom timeline (e.g., yesterday, last 24 hours, last week, last 30 days, last 6 months, or any custom range). A user can view the data in grid format by clicking on the grid toggle 2504.
Referring now to
A time selection 2603 is available on the upper right corner and can be switched between one week, one month, three months, six months, one year, or any other custom timeline (e.g., yesterday, last 24 hours, last week, last 30 days, last 6 months, or any custom range). A user can view the data in grid format by clicking on the grid toggle 2604.
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In some embodiments, the only data that is shown in the chart 4202 is data associated with times that the chiller is operating in a steady-state condition. To determine if the chiller is operating in the steady-state condition, the building management system may analyze values in increments of a threshold number of time stamps, such as 10 sequential time stamps. The building management system may determine that the chiller is operating in the steady-state condition if there is no swing in the values between any of the threshold number of timestamps. For example, in some embodiments, the building management system may determine that the chiller is operating in the steady-state condition when values of the leaving water temperature stay within 0.1% of each other in 10 successive samples. The building management system may use any threshold and any number of samples to determine when the chiller is operating in the steady-state condition. The building management system may use values from the 10 successive samples and/or examples thereafter to generate chart 4202. Advantageously, the data shown in chart 4202 may not be skewed by leaving water temperature values that were generated when the chiller just turned on, was warming up, or was otherwise operating erratically.
Referring now to
In some embodiments, the only data that is shown in the chart 4302 is data associated with times that the chiller is running and/or operating in a steady-state condition. To determine if the chiller is operating in the steady-state condition, the building management system may analyze values of the delta water temperature in increments of a threshold number of time stamps, such as 10 sequential time stamps. The building management system may determine that the chiller is operating in the steady-state condition if there is no swing in the values between any of the threshold number of timestamps. For example, in some embodiments, the building management system may determine that the chiller is operating in the steady-state condition when values of the delta water temperature are within 10 degrees Celsius or 14 degrees Fahrenheit of each other between 10 successive samples. The building management system may use any number of samples and compare the values of the samples to any threshold to determine that the chiller is operating in the steady-state condition. Advantageously, the data shown in the chart 4302 may not be skewed by delta water temperature values that were generated when the chiller was just turned on, was warming up, or was otherwise operating erratically.
Fault Visualization with Parent-Child Relationships
Referring now to
In fault detection, diagnostics, and visualization systems, visualizations may be based on when the fault has occurred, how long the fault has occurred, and in which equipment the fault has occurred. It may also be important to determine the root cause of faults. The fault visualization interfaces described herein may highlight what's happening upstream (in parent equipment) and/or downstream (in child equipment) to allow the user to easily view the root cause of a fault.
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This helps the user to understand that even though valve is stuck, the boiler may not be providing the sufficient heating. When the boiler fault is clicked, the fault visualization interface may indicate that the delta temperature is low. For example,
Advantageously, the fault visualization interface allows a user to easily identify the root cause of a fault, instead of requiring the user to search for faults across the equipment. The visual indication is a clear indicator for the user to focus & resolve the fault.
Global Rule Library with Crowdsourcing Framework
Referring now to
Building enterprise management solutions (BEMS) typically involves monitoring and identifying optimization opportunities to improve the overall building performance and achieve energy savings. One component of any such enterprise analytics platforms is fault detection and diagnostics (FDD). Some analytics platforms perform FDD using rule based fault detection. The rule may be a mathematical expression which correlates various parameters within the boundaries of an equation and drives outputs (i.e., faults) when the boundaries are violated.
Some FDD products are based on a standard library of rules which gets deployed when the FDD product is installed on site. However, this fixed library may not be sufficient to cover varying requirements around the globe. For example, situations may arise in which a majority of the rules within the library are not applicable to a building because either the library does not encompass the equipment types within the building or the existing equipment do not have the required process point to trigger those rules.
This results in two situations. From the customer's perspective, the investment in the FDD product is not fully utilized as most of the available rules cannot be put to use or further investment needs to be made on the instrumentation infrastructure just to utilize the benefits of the FDD product for which they have already borne the cost. From the FDD provider's perspective, it becomes increasingly difficult to maintain different versions of rules to suit each customer's requirement. Due to the complexity of defining rules, customers may not be allowed to define rules, which increases the burden on the FDD provider.
The systems and methods described herein use a permission-based rule editor for customers to define rules using a user friendly editor as well as crowdsourcing the approved rules to be a part of a global rule library for the FDD product. The concept around such a framework may follow a permission-based hierarchy which would enable a customer to define his own rules. Once approved, the rules can be made available to other customers subscribed to the services of the product, thereby expanding the standard rule library so that it can automatically encompass more scenarios. The framework ensures a healthy collaboration between customers and product organization as both can work together to improve the capabilities of the FDD system.
A customer can subscribe to services of an enterprise analytics software and accept the terms and conditions of participating in the crowdsourcing program. When the customer feels that he needs more rules to suffice his requirements around measuring equipment and energy performance, the customer can define his own rules which will be tagged to the category of internal rule. A team of experts from the FDD product organization may be notified of the new rule and they can validate the authenticity of such a rule and approve it. Approved rules may be immediately applicable to the specific customer only and may also be automatically tagged to the global library so that they can be used by other customers based upon their choice to do so. Accordingly, this will not only ensure customer participation and appreciating the software capability but will establish an ever increasing global library which can encompass more scenarios with time.
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For a new subscriber to the services of enterprise analytics software, a global (standard) rule library both for faults and diagnostics may be made available and applied (step 3403). The customer can choose which rules he wants to run for applicable equipment groups. The customer-selected fault rules may come with pre-mapped diagnostic rules, which may be auto applied from the diagnostic library (step 3104). Upon application, these set of rules may automatically start executing on the process points and return valuable insights in terms of equipment performance. The customer may also chose to apply additional diagnostic rules from the available library to the customer-selected fault rules (step 3405).
Any rule which is a part of the global library falls under the category of external rules. All rules may be supported by an appropriate description which states what the intended purpose of the rule and what problem it seeks to identify. This becomes helpful for customers to understand which rules are applicable for their site and use them accordingly. Any external rule equation may not be displayed to any customer except for the customer who has contributed to the external rule as a part of the framework (described in greater detail below) to maintain confidentiality.
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In the first scenario (i.e., the customer is signed up for the crowdsourcing program), when a custom fault/diagnostic rule is defined and saved (step 4101), an auto generated email notification 4102 may be sent to the organization that created the FDD product (e.g., Johnson Controls). The notification may be sent to a team of subject matter experts (SMEs) within the organization (step 4103). The SME team may validate the rule using the test fault feature, which ensures that the rule is supported in the existing framework and would return appropriate results.
Once the rule has been approved, the rule may activate specifically for the customer who defined it (step 4104). The rule may also become a part of the global rule library and may be added to the external rule list for all other customers who are subscribed to the services of the enterprise analytics software (step 4105). In some embodiments, the rule equation can only be viewed by the enterprise software provider and the customer who has defined it to maintain confidentiality of individual customer information. Since each rule is also supported by a description, it will be easy for other customers who benefit from this crowdsourcing mechanism to understand if the rule is applicable for their site and use it accordingly. The customer who contributed in defining this custom rule can view and edit the equation.
Once edited, the rule may be subject to the same approval process. However, this time the SME team may choose to save it as a new rule in the existing global rule library if there are deviations in output or overwrite the old rule as it may suggest enhancement in detection. A customer who is signed up for the crowdsourcing program may have the benefits of receiving updates to the global rule library as a part of other customers contributing to the library through their respective custom rule section (step 4106).
In the second scenario (i.e., the customer is not signed up for the crowdsourcing program), when a custom fault/diagnostic rule is defined and saved (step 4101), an auto generated email notification 4102 may be sent to the SME team (step 4103). The SME team may validate the rule using the test fault feature which ensures that the rule is supported in the existing framework and would return appropriate results.
Once the rule has been approved, the rule may activate specifically for the customer who defined it (step 4104). However, the rule does not become a part of the global rule library as the customer has chosen to keep it confidential by not participating in the program. The rule equation can only be viewed by the enterprise software provider and the customer who has defined it to maintain confidentiality of individual customer information (step 4107). The customer who has contributed in defining this custom rule can view and edit the equation
Once edited, the rule may be subject to the same approval process. The rule (fault/diagnostic) will only be applicable to the specific customer site. A customer who is not signed up for the crowdsourcing program may not have the benefits of receiving updates to the global rule library as a part of other customers contributing to the library through their respective custom rule section (step 4108).
The construction and arrangement of the systems and methods as shown in the various exemplary embodiments are illustrative only. Although only a few embodiments have been described in detail in this disclosure, many modifications are possible (e.g., variations in sizes, dimensions, structures, shapes and proportions of the various elements, values of parameters, mounting arrangements, use of materials, colors, orientations, etc.). For example, the position of elements can be reversed or otherwise varied and the nature or number of discrete elements or positions can be altered or varied. Accordingly, all such modifications are intended to be included within the scope of the present disclosure. The order or sequence of any process or method steps can be varied or re-sequenced according to alternative embodiments. Other substitutions, modifications, changes, and omissions can be made in the design, operating conditions and arrangement of the exemplary embodiments without departing from the scope of the present disclosure.
The present disclosure contemplates methods, systems and program products on any machine-readable media for accomplishing various operations. The embodiments of the present disclosure can be implemented using existing computer processors, or by a special purpose computer processor for an appropriate system, incorporated for this or another purpose, or by a hardwired system. Embodiments within the scope of the present disclosure include program products comprising machine-readable media for carrying or having machine-executable instructions or data structures stored thereon. Such machine-readable media can be any available media that can be accessed by a general purpose or special purpose computer or other machine with a processor. By way of example, such machine-readable media can comprise RAM, ROM, EPROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to carry or store desired program code in the form of machine-executable instructions or data structures and which can be accessed by a general purpose or special purpose computer or other machine with a processor. Combinations of the above are also included within the scope of machine-readable media. Machine-executable instructions include, for example, instructions and data which cause a general purpose computer, special purpose computer, or special purpose processing machines to perform a certain function or group of functions.
Although the figures show a specific order of method steps, the order of the steps may differ from what is depicted. Also two or more steps can be performed concurrently or with partial concurrence. Such variation will depend on the software and hardware systems chosen and on designer choice. All such variations are within the scope of the disclosure. Likewise, software implementations could be accomplished with standard programming techniques with rule based logic and other logic to accomplish the various connection steps, processing steps, comparison steps and decision steps.
Number | Date | Country | Kind |
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201741026688 | Jul 2017 | IN | national |
201921051144 | Dec 2019 | IN | national |
This application is a continuation-in-part of U.S. patent application Ser. No. 15/821,604, entitled “Building Management System with Central Plantroom Dashboards,” filed Nov. 22, 2017, which claims the benefit of priority to Indian Provisional Patent Application No. 201741026688 filed Jul. 27, 2017. This application also claims the benefit of priority to Indian Provisional Patent Application No. 201921051144 filed Dec. 11, 2019. The entire disclosure of each of the aforementioned applications is incorporated by reference herein.
Number | Date | Country | |
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Parent | 15821604 | Nov 2017 | US |
Child | 16805436 | US |