BUILDING MANAGEMENT SYSTEM WITH AIR QUALITY OCCUPANT IMPACT ASSESSMENT
A building system for a building. The building system can include one or more memory devices. The one or more memory device can store instructions. The instructions, when executed by one or more processors, can cause the one or more processors to receive, from one or more sensors, air quality measurements associated with one or more spaces of the building, and generate, using the air quality measurements, a productivity score for at least one of the building or at least one space of the one or more spaces of the building. The productivity score can be based on a predicted productivity or effect on productivity of one or more occupants of the building in view of the air quality measurements.
This application claims the benefit of and priority to U.S. Provisional Patent Application No. 63/329,198 filed Apr. 8, 2022, the entirety of which is incorporated by reference herein. The following application are incorporated by reference herein in their entireties: U.S. Provisional Patent Application No. 63/252,050 filed Oct. 4, 2021; U.S. Provisional Patent Application No. 63/230,608 filed Aug. 6, 2021; U.S. patent application Ser. No. 17/459,963 filed Aug. 27, 2021; U.S. patent application Ser. No. 17/541,119 filed Dec. 2, 2021; International Patent Application No. PCT/US2020/041770 filed Jul. 13, 2020; U.S. patent application Ser. No. 17/013,273 filed Sep. 4, 2020; U.S. Provisional Patent Application No. 63/255,347 filed Oct. 13, 2021; U.S. Provisional Patent Application No. 63/281,409 filed Nov. 19, 2021, U.S. patent application Ser. No. 17/354,583 filed Jun. 22, 2021, U.S. patent application Ser. No. 17/404,799 filed Aug. 17, 2021, and U.S. Provisional Patent Application 63/113,019 filed Nov. 12, 2020.
SUMMARYAt least one embodiment is directed to a building system for a building. The building system can include one or more memory devices. The one or more memory devices can store instructions. The instructions, when executed by one or more processors, can cause the one or more processors to receive, from one or more sensors, air quality measurements associated with one or more spaces of the building, generate, using the air quality measurements, a productivity score for at least one of the building or at least one space of the one or more spaces of the building, the productivity score can be based on a predicted productivity or effect on productivity of one or more occupants of the building in view of the air quality measurements, and initiate, based on the productivity score, one or more actions to improve at least one of the productivity score or an air quality of at least one of the building or at least one space of the one or more spaces of the building.
In some embodiments, the productivity score can indicate a predicted work output for at least one first occupant of the one or more occupants of the building, and the instructions can further cause the one or more processors to predict a number of absences for the at least one first occupant, and predict an impact to the productivity score based on the predicted number of absences for the at least one first occupant.
In some embodiments, the air quality measurements can include one or more air quality metrics, and generating the productivity score can include assigning weight values to the one or more air quality metrics to generate one or more air quality metrics, and executing a model to generate the productivity score using the one or more weight air quality metrics. The model can be trained using data correlating different values for the one or more air quality metrics to productivity.
In some embodiments, the instructions can further cause the one or more processors to generate, responsive to generating the productivity score, one or more recommendations for actions to impact the productivity score, generate a report including the productivity score and the one or more recommendations, the report can indicate at least one of a predicted amount of absences, a predicted amount of tasks completed, or a predicted impact associated with implementing the one or more recommendations, cause a user device to display a user interface, the user interface including the report, receive, from the user device, a user input indicating a selected recommendation of the one or more recommendations, and implement, responsive to receiving the user input, the selected recommendation of the one or more recommendations.
In some embodiments, the one or more spaces can comprise a plurality of spaces and the one or more sensors can comprise a plurality of sensors that can provide the air quality measurements for the plurality of spaces, and the instructions can further cause the one or more processors to generate, responsive to receiving the air quality measurements associated with the plurality of spaces of the building, air quality profiles for the plurality of spaces of the building, the air quality profiles can be generated based on the air quality measurements associated with the plurality of spaces of the building, update, responsive to receiving second air quality measurements associated with the plurality of spaces of the building, the air quality profiles to reflect the second air quality measurements, and generate, using the air quality profiles for the plurality of spaces of the building, one or more trends pertaining to air quality of the building.
In some embodiments, the one or more spaces can comprise a plurality of spaces and the one or more sensors can comprise a plurality of sensors that can provide the air quality measurements for the plurality of spaces, and the instructions can further cause the one or more processors to generate, using information describing the plurality of spaces of the building, a plurality of space hierarchies, a first space hierarchy of the plurality of space hierarchies can associate a first space of the plurality of spaces of the building with a second space of the plurality of spaces of the building, generate, using the plurality of space hierarchies and a plurality of air quality profiles, a plurality of productivity scores for the plurality of space hierarchies, and determine, using the plurality of productivity scores for the plurality of space hierarchies, whether at least one productivity score of the plurality of productivity scores for the plurality of space hierarchies is outside of a predetermined range.
In some embodiments, the instructions can further cause the one or more processors to determine whether the productivity score is within a predetermined range, cause, in response to determining that the productivity score is not within the predetermined range, a building management system to take action to adjust the productivity score, and maintain, in response to adjusting the productivity score, the productivity score within the predetermined range.
In some embodiments, the one or more actions to improve at least one of the productivity score or the air quality of at least one of the building or the at least one space of the one or more spaces can include one more control strategies for controlling equipment of the building and the instructions can further cause the one or more processors to transmit, to a building management system, control signals causing the building management system to implement at least one control strategy of the one or more control strategies, and detect, responsive to transmitting the control signals, an improvement to the productivity score or an improvement to the air quality of at least one of the building or the at least one space of one or more spaces.
In some embodiments, the one or more spaces can comprise a plurality of spaces, the productivity score can include an aggregated value of a plurality of productivity scores of the plurality of spaces, and the instructions can further cause the one or more processors to determine, using the plurality of productivity scores of the plurality of spaces, a difference between a first productivity score of the plurality of productivity scores and a second productivity score of the plurality of productivity scores, the first productivity score of the plurality of productivity scores can pertain to a first space of the plurality of spaces and the second productivity score of the plurality of productivity scores can pertain to a second space of the plurality of spaces, determine, using the difference between the first productivity score of the plurality of productivity scores and the second productivity score of the plurality of productivity scores, whether the difference exceeds a predetermined range, and generate, responsive to determining that the difference exceeds the predetermined range, one or more recommendations for actions that impact the productivity score, wherein the one or more recommendations indicate that occupants move from the first space of the plurality of spaces to the second space of the plurality of spaces or that occupants move from the second space of the plurality of spaces to first space of the plurality of spaces. The occupants moving can cause the difference between the first productivity score of the plurality of productivity scores and the second productivity score of the plurality of productivity scores to be within the predetermined range.
In some embodiments, the air quality measurements can be at least one of total volatile organic compounds (TVOC), carbon dioxide (CO2), carbon monoxide (CO), ozone, particulates, or formaldehyde.
At least one embodiment is directed to a method for predicting productivity of one or more occupants of a building. The method can include receiving, by one or more processors from one or more sensors, air quality measurements associated with one or more spaces of the building, generating, by the one or more processors using the air quality measurements, a productivity score for at least one of the building or at least one space of the one or more spaces of the building, the productivity score can be based on a predicted productivity or effect on productivity of the one or more occupants of the building in view of the air quality measurements, and initiating, by the one or more processors based on the productivity score, one or more actions to improve at least one of the productivity score or an air quality of at least one of the building or at least one space of the one or more spaces of the building.
In some embodiments, the productivity score can indicate a predicted work output for at least one first occupant of the one or more occupants of the building, and the method can further include predicting, by the one or more processors, a number of absences for the at least one first occupant, and predicting, by the one or more processors, an impact to the productivity score based on the predicted number of absences for the at least one first occupant.
In some embodiments, the air quality measurements can include one or more air quality metrics, and generating the productivity score can include assigning, by the one or more processors, weight values to the one or more air quality metrics to generate one or more weight air quality metrics, and executing, by the one or more processors, a model to generate the productivity score using the one or more weighted air quality metrics as inputs to the model. The model can be trained using data correlating different values for the one or more air quality metrics to productivity.
In some embodiments, the method can further include generating, by the one or more processors responsive to generating the productivity score, one or more recommendations for actions to impact the productivity score, generating, by the one or more processors, a report including the productivity score and the one or more recommendations, the report can indicate at least one of a predicted amount of absences, a predicted amount of tasks completed, or a predicted impact associated with implementing the one or more recommendations, causing, by the one or more processors, a user device to display a user interface, the user interface can include the report, receiving, by the one or more processors from the user device, a user input indicating a selected recommendation of the one or more recommendations, and implementing, by the one or more processors responsive to receiving the user input, the selected recommendation of the one or more recommendations.
In some embodiments, the one or more spaces can comprise a plurality of spaces and the one or more sensors can comprise a plurality of sensors that can provide the air quality measurements for the plurality of spaces, and the method can further include generating, by the one or more processors responsive to receiving the air quality measurements associated with the plurality of spaces of the building, air quality profiles for the plurality of spaces of the building, the air quality profiles can be generated based on the air quality measurements associated with the plurality of spaces of the building, updating, by the one or more processors responsive to receiving second air quality measurements associated with the plurality of spaces of the building, the air quality profiles to reflect the second air quality measurements, and generating, by the one or more processors using the air quality profiles for the plurality of spaces of the building, one or more trends pertaining to air quality of the building.
In some embodiments, the one or more spaces can comprise a plurality of spaces and the one or more sensors can comprise a plurality of sensors that can provide the air quality measurements for the plurality of spaces, and the method can further include generating, by the one or more processors using information describing the plurality of spaces of the building, a plurality of space hierarchies, a first space hierarchy of the plurality of space hierarchies can associate a first space of the plurality of spaces of the building with a second space of the plurality of spaces of the building, generating, by the one or more processors using the plurality of space hierarchies and a plurality of air quality profiles, a plurality of productivity scores for the plurality of space hierarchies, and determining, by the one or more processors using the plurality of productivity scores for the plurality of space hierarchies, whether at least one productivity score of the plurality of productivity scores for the plurality of space hierarchies is outside of a predetermined range.
In some embodiments, the method can further include determining, by the one or more processors, whether the productivity score is within a predetermined range, causing, by the one or more processors in response to determining that the productivity score is not within the predetermined range, a building management system to take action to adjust the productivity score, and maintaining, by the one or more processors in response to adjusting the productivity score, the productivity score within the predetermined range.
At least one embodiment is directed to one or more non-transitory storage medium. The one or more non-transitory storage medium can store instructions. The instructions, when executed by one or more processors, can cause the one or more processors to implement operations comprising receiving, from one or more sensors, air quality measurements associated with one or more spaces of a building, generating, using the air quality measurements, a productivity score for at least one of the building or at least one space of one or more spaces of the building, the productivity score based on a predicted productivity or effect on productivity of one or more occupants of the building in view of the air quality measurements, and initiating, based on the productivity score, one or more actions to improve at least one of the productivity score or an air quality of at least one of the building or at least one space of the one or more spaces of the building.
In some embodiments, the productivity score can indicate a predicted work output for at least one first occupant of the one or more occupants of the building, and the operations can further comprise predicting a number of absences for the at least one first occupant, and predicting an impact to the productivity score based on the predicted number of absences for the at least one first occupant.
In some embodiments, the air quality measurements can include one or more air quality metrics, and generating the productivity score can include assigning weight values to the one or more air quality metrics to generate one or more weight air quality metrics, and executing a model to generate the productivity score using the one or more weighted air quality metrics as inputs to the model. The model can be trained using data correlating different values for the one or more air quality metrics to productivity.
Various objects, aspects, features, and advantages of the disclosure will become more apparent and better understood by referring to the detailed description taken in conjunction with the accompanying drawings, in which like reference characters identify corresponding elements throughout. In the drawings, like reference numbers generally indicate identical, functionally similar, and/or structurally similar elements.
Referring generally to the Figures, systems and methods are provided by monitoring air quality in a building with multiple spaces. According to various example embodiments, sensors may be deployed into multiple spaces and used over a period of time to collect data regarding the air quality in the spaces. In some embodiments, the sensors may be deployed temporarily (e.g., as a service) and removed at the end of the monitoring/test period. In other embodiments, the sensors may be permanently installed. The collected data may be used to generate one or more air quality occupant impact assessments of the spaces and actions that may be taken to improve the air quality occupant impact assessments. While the present disclosure discusses various examples in the context of office buildings, it should be noted that the features of the present disclosure are equally applicable to any type of building or group of buildings having multiple spaces into which sensors may be temporarily or permanently installed.
Referring now to
The BMS that serves building 10 includes an HVAC system 100. HVAC system 100 can include 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 can provide a heated or chilled fluid to an air handling unit of airside system 130. Airside system 130 can 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 can use boiler 104 and chiller 102 to heat or cool a working fluid (e.g., water, glycol, etc.) and can 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 can 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 can 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 can then return to chiller 102 or boiler 104 via piping 110.
Airside system 130 can deliver the airflow supplied by AHU 106 (i.e., the supply airflow) to building 10 via air supply ducts 112 and can 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 can receive input from sensors located within AHU 106 and/or within the building zone and can 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
Each of building subsystems 228 can include any number of devices, controllers, and connections for completing its individual functions and control activities. HVAC subsystem 240 can include many of the same components as HVAC system 100, as described with reference to
Still referring to
Interfaces 207, 209 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 228 or other external systems or devices. In various embodiments, communications via interfaces 207, 209 can be direct (e.g., local wired or wireless communications) or via a communications network 246 (e.g., a WAN, the Internet, a cellular network, etc.). For example, interfaces 207, 209 can include an Ethernet card and port for sending and receiving data via an Ethernet-based communications link or network. In another example, interfaces 207, 209 can include a Wi-Fi transceiver for communicating via a wireless communications network. In another example, one or both of interfaces 207, 209 can include cellular or mobile phone communications transceivers. In one embodiment, communications interface 207 is a power line communications interface and BAS interface 209 is an Ethernet interface. In other embodiments, both communications interface 207 and BAS interface 209 are Ethernet interfaces or are the same Ethernet interface.
Still referring to
Memory 208 (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 208 can be or include volatile memory or non-volatile memory. Memory 208 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 an exemplary embodiment, memory 208 is communicably connected to processor 206 via processing circuit 204 and includes computer code for executing (e.g., by processing circuit 204 and/or processor 206) one or more processes described herein.
In some embodiments, BAS controller 202 is implemented within a single computer (e.g., one server, one housing, etc.). In various other embodiments BAS controller 202 can be distributed across multiple servers or computers (e.g., that can exist in distributed locations). Further, while
Still referring to
Enterprise integration layer 210 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 226 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 226 can also or alternatively be configured to provide configuration GUIs for configuring BAS controller 202. In yet other embodiments, enterprise control applications 226 can work with layers 210-220 to optimize building performance (e.g., efficiency, energy use, comfort, or safety) based on inputs received at interface 207 and/or BAS interface 209.
Building subsystem integration layer 220 can be configured to manage communications between BAS controller 202 and building subsystems 228. For example, building subsystem integration layer 220 can receive sensor data and input signals from building subsystems 228 and provide output data and control signals to building subsystems 228. Building subsystem integration layer 220 can also be configured to manage communications between building subsystems 228. Building subsystem integration layer 220 translate communications (e.g., sensor data, input signals, output signals, etc.) across multi-vendor/multi-protocol systems.
Demand response layer 214 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 224, from energy storage 227, or from other sources. Demand response layer 214 can receive inputs from other layers of BAS controller 202 (e.g., building subsystem integration layer 220, integrated control layer 218, 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 can also include inputs such as electrical use (e.g., expressed in kWh), thermal load measurements, pricing information, projected pricing, smoothed pricing, curtailment signals from utilities, and the like.
According to an exemplary embodiment, demand response layer 214 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 218, changing control strategies, changing setpoints, or activating/deactivating building equipment or subsystems in a controlled manner. Demand response layer 214 can also include control logic configured to determine when to utilize stored energy. For example, demand response layer 214 can determine to begin using energy from energy storage 227 just prior to the beginning of a peak use hour.
In some embodiments, demand response layer 214 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 214 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 can represent collections of building equipment (e.g., subplants, chiller arrays, etc.) or individual devices (e.g., individual chillers, heaters, pumps, etc.).
Demand response layer 214 can 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 setpoint 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 218 can be configured to use the data input or output of building subsystem integration layer 220 and/or demand response later 214 to make control decisions. Due to the subsystem integration provided by building subsystem integration layer 220, integrated control layer 218 can integrate control activities of the subsystems 228 such that the subsystems 228 behave as a single integrated supersystem. In an exemplary embodiment, integrated control layer 218 includes control logic that uses inputs and outputs from 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 218 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 220.
Integrated control layer 218 is shown to be logically below demand response layer 214. Integrated control layer 218 can be configured to enhance the effectiveness of demand response layer 214 by enabling building subsystems 228 and their respective control loops to be controlled in coordination with demand response layer 214. This configuration can reduce disruptive demand response behavior relative to conventional systems. For example, integrated control layer 218 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 218 can be configured to provide feedback to demand response layer 214 so that demand response layer 214 checks that constraints (e.g., temperature, lighting levels, etc.) are properly maintained even while demanded load shedding is in progress. The constraints can 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 218 is also logically below fault detection and diagnostics layer 216 and automated measurement and validation layer 212. Integrated control layer 218 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 212 can be configured to verify that control strategies commanded by integrated control layer 218 or demand response layer 214 are working properly (e.g., using data aggregated by AM&V layer 212, integrated control layer 218, building subsystem integration layer 220, FDD layer 216, or otherwise). The calculations made by AM&V layer 212 can be based on building system energy models and/or equipment models for individual BAS devices or subsystems. For example, AM&V layer 212 can compare a model-predicted output with an actual output from building subsystems 228 to determine an accuracy of the model.
Fault detection and diagnostics (FDD) layer 216 can be configured to provide on-going fault detection for building subsystems 228, building subsystem devices (i.e., building equipment), and control algorithms used by demand response layer 214 and integrated control layer 218. FDD layer 216 can receive data inputs from integrated control layer 218, directly from one or more building subsystems or devices, or from another data source. FDD layer 216 can automatically diagnose and respond to detected faults. The responses to detected or diagnosed faults can include providing an alarm 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 216 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 220. In other exemplary embodiments, FDD layer 216 is configured to provide “fault” events to integrated control layer 218 which executes control strategies and policies in response to the received fault events. According to an exemplary embodiment, FDD layer 216 (or a policy executed by an integrated control engine or business rules engine) can 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 216 can be configured to store or access a variety of different system data stores (or data points for live data). FDD layer 216 can 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 228 can generate temporal (i.e., time-series) data indicating the performance of BAS 200 and the various components thereof. The data generated by building subsystems 228 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 216 to expose when the system begins to degrade in performance and alarm a user to repair the fault before it becomes more severe.
Referring now to
Each sensor of the temporary air quality sensors 302 can measure one or multiple air quality metrics, e.g., can include one sensors or a set of sensors. For example, the sensors 302 can measure ventilation for a space, occupancy for a space, CO2 for a space, particulate matter PM1, particulate matter PM10 for a space, particulate matter PM2.5 for a space, volatile organic compounds (VOC) for the space, total volatile organic compound (TVOC) for the space, thermal measurements for the space, temperature for the space, relative humidity for the space, dew point for the space, ozone for the space, carbon monoxide (CO) for the space, formaldehyde for the space, acetone for the space, mold/mildew for the space, pollen for the space, bacteria for the space, microbial flora (including allergens), viruses for the space and/or any other types of metrics/characteristics for the space. In some embodiments, the sensors 302 are permanent sensors that are installed in a permanent manner. In this regard, if the sensors 302 are permanent, the air quality occupant impact assessments and/or recommendations can be generated over a requested period of time, e.g., a particular day, week, year, etc.
The measurements of the sensors 302 can be communicated to a cloud platform that can perform an analysis on the air quality measurements of the various spaces of the office building 301. For example, the sensors 302 can be wireless sensors (or wired sensors) that communicate across a network 314 which may include local networks within the office building 301 and/or external networks. For example, various routers, switches, servers, cellular towers, LAN networks, WAN networks, Wi-Fi networks, etc. can be included within the network 314 and can communicate the measurements of the sensors 302 to the building analysis system 304.
In some embodiments one or more canisters may be used to collect the air quality metrics. For example, the canisters may collect data regarding ventilation for a space, occupancy for a space, CO2 for a space, particulate matter PM1, particulate matter PM10 for a space, particulate matter PM2.5 for a space, volatile organic compounds (VOC) for the space, total volatile organic compound (TVOC) for the space, thermal measurements for the space, temperature for the space, relative humidity for the space, dew point for the space, ozone for the space, carbon monoxide (CO) for the space, formaldehyde for the space, acetone for the space, mold/mildew for the space, pollen for the space, bacteria for the space, microbial flora (including allergens), viruses for the space and/or any other types of metrics/characteristics for the space. Additionally, the contents of the canisters may be provided to the temporary air quality sensors 302. In some embodiments, the temporary air quality sensors 302 may take measurements of the contents of the canisters. The measurements relating to the contents of the canisters may be analyzed by communicating the measurements to a remote (e.g., on-premises or off-premises) system, such as the cloud platform described herein. In some embodiments, the measurements relating to the contents of the canisters may be communicated to the building analysis system 304.
Furthermore, information describing physical characteristics of the office building 301 and various spaces of the office building 301 can be provided to the building analysis system 304 via a mobile application of a user device 312, a web browser of the user device 312, and/or any another application of the user device 312. The information can be manually collected site data, photos of the office building 301, equipment information of the office building 301, schematic diagrams of the office building 301, user information, desired metrics, desired building performance, floor plans of the spaces assessed via the sensors 302, AHU zone maps indicating each AHU and the spaces the AHUs serve, an AHU list/schedule indicating lists of AHUs with sizes and service information, etc. The user device 312 can be a smartphone, a tablet, a laptop computer, a desktop computer, etc. The user device 312 can communicate with the building analysis system 304 via the network 314.
The building analysis system 304 can be a cloud based system, a remote system, a local on-premises system with the office building 301, etc. The building analysis system 304 can include processors 306 and/or memory devices 308. Processors 306 can be implemented as a general purpose processor, an application specific integrated circuit (ASIC), one or more field programmable gate arrays (FPGAs), a group of processing components, or other suitable electronic processing components.
Memory devices 308 (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 devices 308 can be or include volatile memory or non-volatile memory. Memory devices 308 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 an exemplary embodiment, memory devices 308 is communicably connected to processors 306 via the processors 306 and includes computer code for executing (e.g., by the processors 306) one or more processes described herein.
The building analysis system 304 includes a space air quality analyzer 311, a recommendation generator 307, and an air quality occupant impact assessment generator 310. The space air quality analyzer 311 can record measurements of the various sensors 302 and create air quality profiles of the various spaces of the office building 301. For example, the space air quality analyzer 311 can record air quality for the spaces and generate a trend over time (e.g., timeseries data or other time correlated data) in which the temporary sensors 302 are installed, e.g., over the two weeks that the sensors 302 are installed. The trends created by the space air quality analyzer 311 are described with further reference to U.S. Provisional Application No. 63/230,608 filed Aug. 6, 2021, the entirety of which is incorporated by reference herein. The trends created by the space air quality analyzer 311 can be provided to the recommendation generator 307 and the air quality occupant impact assessment generator 310.
In some embodiments, the space air quality analyzer 311 can generate space hierarchy air quality information. For example, rooms, hallways, and closets may be basic units of space in the office building 301. However, a hierarchy of spaces can be built from the basic space unit. For example, a group of rooms could form a zone of a floor. A group of zones could form a floor of a building. A group of floors could form a building of a campus. The space air quality analyzer 311 can generate higher level space air quality metrics for a particular space based on the basic space units that make up the particular space. For example, a CO2 metric for a floor could be generated by averaging the CO2 metrics for all rooms that make up the floor. Similarly, the CO2 metrics for a building could be made based on averaging CO2 metrics for all the floors of the building. In some embodiments, the metrics may be used to generate productivity scores for the spaces (e.g., the rooms themselves, floors/buildings/campuses that include the rooms, etc.). In some embodiments, the space productivity scores may be specific to air quality. In some embodiments, the metrics may be used in combination with other metrics to generate an overall space productivity score. In some embodiments, the air quality metrics may be used in combination to generate a combined air quality productivity score, and that score may in turn be used as a component score to generate an overall space/building productivity score that includes air quality as a component. Examples of such features that may be used in conjunction with the features of the present disclosure can be found in U.S. patent application Ser. No. 17/354,583, filed Jun. 22, 2021, and Ser. No. 17/354,565, filed Jun. 22, 2021, both of which are incorporated herein by reference in their entireties. For example, in some embodiments, the productivity score may be presented within similar interfaces and/or could be a component score of a healthy person score and/or overall building health score as described in accordance with various example embodiments therein. Similarly, in some embodiments the recommendations may be provided using a framework similar to that shown therein.
In some embodiments, the productivity scores may be generated by taking the air quality measurements and comparing the air quality measurements to stored data correlating the air quality measurements to predictive occupant productivity. In some embodiments, correlating the air quality measurements to predictive occupant productivity may be done by way of a lookup table. In some embodiments, a mechanism, that directly correlates the air quality measurements to predictive occupant productivity, may involve using weighted combinations of the air quality measurements and/or the predictive occupant productivity. In some embodiments, a model and/or other type of algorithm may be used that takes the air quality measurements as inputs. In some embodiments, the model may be generated using historical data for the building and/or other buildings (e.g., sharing similar characteristics).
In some embodiments, the productivity scores may be occupant productivity scores. Additionally, an occupant productivity score may provide an expected occupant productivity of one or more occupants (e.g., a relative expected occupant productivity, such as on a relative scale, e.g., via a normalized score such as from 1-10). In some embodiments, occupant productivity may be determined by an occupant's work output in a given timeframe. For example, occupant productivity may be the total number of tasks an occupant completed within one work day, wherein the number of completed tasks may be the work output and one work day may be the given timeframe. Additionally, if the occupant productivity were to increase, the number of tasks completed within one work day may increase. Similarly, if occupant productivity were to decrease, the number of tasks completed within one work day may decrease.
The air quality occupant impact assessment generator 310 may generate reports that summarize the air quality trends of the spaces of the building and/or include recommendations. The report generated by the air quality occupant impact assessment generator 310 may provide the report to the user device 312 for review by a user. The report may further indicate areas of the office building 301, recommendations for improving indoor air quality (IAQ), recommendations for saving energy in the office building 301, etc. In some embodiments, the report is a user interface including various charts, graphs, trends, recommendations, or other information. The interface may be displayed on a display device of the user device 312.
The air quality occupant impact assessment generator 310 can generate a report including recommendations generated by the recommendation generator 307 indicating actionable data that can be implemented by the building analysis system 304 and/or a BMS system of the office building 301 (e.g., the BMS system described in
The report generated by the air quality occupant impact assessment generator 310 can also include a detailed building data summary report that indicates building size and use, recent renovation, special use areas, number of AHU's, filtration type and schedule, air supply system type, and specific areas of concern. The report can indicate a technicians visual inspection of representative AHU's, fan coil units, induction units, filter type/installation/condition, air supply diffusers, exhaust systems, and/or return air grilles. The report can indicate whether air systems of the office building 301 are under proper control, sequence of operations is being followed, and all controls are operating per the desired setpoint and schedule.
The report generated by the air quality occupant impact assessment generator 310 may include an expected amount of occupant absences. The expected amount of occupant absences may be based on the air quality measurements. The expected amount of occupant absences may be given an occupant absence value. The occupant absence value may be compared to a predetermined occupant absence value range. In some embodiments, the productivity scores may be impacted by the occupant absence value. The occupant absence value may be above, below, and/or within the predetermined occupant absence value range. In some embodiments, the productivity scores may decrease as a result of the occupant absence value being above the predetermined occupant absence value range. In some embodiments, the productivity scores may increase as a result of the occupant absence value being above the predetermined absence value range. In some embodiments, the productivity scores may decrease as a result of the occupant absence value being below the predetermined occupant absence value range. In some embodiments the productivity scores may increase as a results of the occupant absence value being below the predetermined occupant absence value range.
The report can include air quality tests of the sensors 302, e.g., CO2, TVOC, CO, PM 1, PM2.5, PM 10, viruses, bacteria, acetone, mold/mildew, pollen, microbial flora, temperature, relative humidity, NO2, SO2, O3, VOC's, airflow vectors, air pressure differentials, etc. The report can indicate a ventilation assessment indicating the results of testing that ensures outside air intake, supply air fan, and/or ventilation system is supplying minimum outdoor air ventilation rate detailed by ASHRAE 62.1-2016. Ventilation needs based on space type, square footage, and occupancy. The report can indicate an infection risk assessment indicating DNA-tagged bioaerosolstracers safely simulate respiratory emissions to identify potential infection hotspots, verify ventilation and filtration system performance for mitigating airborne exposures, and optimize enhancements.
The recommendations generated by the recommendation generator 307 and included within the report generated by the air quality occupant impact assessment generator 310 can further include recommendations to investigate ventilation rates of rooms of the office building 301 with CO2 levels above a particular level (e.g., 1100 ppm). The recommendations can indicate a current ventilation rate of a space along with comparisons to other ventilation rates of other spaces, inconsistencies can indicate that a user should consider adjusting the ventilation rates of the spaces. If all of the ventilation rates are similar, the recommendation can recommend changing a ventilation policy for the entire office building 301. The recommendations could further be to analyze a source of TVOC for a space where TVOC is above a particular amount, investigate a source of VOCs in a space with TVOCs above a particular amount, etc.
The recommendations in some embodiments, can include recommendations to improve ventilation, e.g., diluting dirty air with clean air as available from outside the office building 301. This recommendation can ensure the delivery of ASHRAE required ventilation rates. The recommendations can be recommendations to improve filtration for spaces. Filtration may mechanically remove particles from the air of the space. The recommendation can be a recommendation to increase particle collection with options with filters such as Koch filters, MAC-10 fan filter units, enviro portable HEPA filtration units, etc.
The recommendations can include recommendations for improving disinfection for a space, e.g., deactivating bacteria and/or viruses in the space. The recommendations can be recommendations to install and/or operate disinfectant systems such as disinfectant light systems (e.g., ultraviolet (UV), ultraviolet-C (UVC), etc.). The recommendations can be recommendations to implement isolation of certain spaces of the office building 301. For example, cause one space to be an isolated space that contains particles and prevents the particles going elsewhere in the office building 301. This can be implemented through creating a negative-pressure isolation environments. The recommendations can be recommendations for performing monitoring and maintenance of equipment, e.g., to inspect equipment at a particular frequently and/or track results for maintenance and monitoring to maintain clean air.
In some embodiments, the CO2 measurements of the sensors 302 can be used by the recommendation generator 307 to determine how well a space is being ventilated. If the CO2 levels are higher than particular amounts, a recommendation to increase ventilation can be generated and/or implemented. The TVOC measurements can indicate how safe a space is for human beings and/or animals. If TVOC is above a particular level, an alert can be generated to evacuate the space and/or address the high TVOC level. The PM2.5 levels can indicate how well filtering equipment is operating. If PM2.5 is greater than a particular amount, this may indicate that the space is not being properly filtered and that a filter of equipment serving the space needs to be replaced and/or changed to a higher quality filter.
In some embodiments, the recommendation generator 307 can perform an analysis on equipment type for the spaces. For example, the recommendation generator 307 could analyze that spaces with low PM2.5 use unit ventilators while spaces with high PM2.5 use VAVs. This improvement in performance of the unit ventilators vs. the VAVs can be used in a recommendation for the recommendation generator 307 to recommend that unit ventilators replace the VAVS in the office building 301.
In some embodiments, the recommendation generator 307 could recommend that persons with allergies be assigned to areas of a building with low VOC, TVOC, PM2.5, PM10 levels, pollen, and/or allergens. This may allow the allergenic persons to avoid having an asthma attack or other breathing problems. In some embodiments, scheduling can be set up and/or recommended by the building analysis system 304 such that occupants are not assigned spaces with high VOC, TVOC, PM2.5 levels for a long duration.
In some embodiments, the air quality occupant impact assessment generator 310 may generate one or more productivity scores for the entire office building 301 or one or more spaces or zones of the office building 301. The productivity scores may be generated using any possible combination of one or more of the air quality metrics described herein. For example, the productivity scores may be generated by using the CO2 metric. Similarly, the productivity score may be generated by using the CO2 metric in combination with the TVOC metric. Similarly, the productivity scores may be generated by using the CO2 metric in combination with the measured ventilation metric. Similarly, the productivity score may be generated by using the CO2 metric in combination with the PM1 metric. Similarly, the productivity score may be generated by using the CO2 metric in combination with the PM2.5 metric. Similarly, the productivity score may be generated by using the CO2 metric in combination with the PM10 metric. Similarly, the productivity score may be generated by using the CO2 metric in combination with the VOC metric. Similarly, the productivity score may be generated by using the CO2 metric in combination with the thermal metric. Similarly, the productivity score may be generated by using the CO2 metric in combination with the temperature metric. Similarly, the productivity score may be generated by using the CO2 metric in combination with the relative humidity metric. Similarly, the productivity scores may be generated by using the CO2 metric in combination with the dew point metric. Similarly, the productivity score may be generated by using the CO2 metric in combination with the Ozone metric. Similarly, the productivity score may be generated by using the CO2 metric in combination with the CO metric. Similarly, the productivity score may be generated by using the CO2 metric in combination with the formaldehyde metric.
In some embodiments, the productivity scores may be generated by adding or removing metrics used to generate the productivity scores. For example, the productivity score may be generated by using the CO2 metric in combination with the TVOC metric and the ventilation metric. Similarly, the productivity score may be generated by using the CO2 metric in combination with the PM1 metric and the ventilation metric. Similarly, the productivity score may be generated by using the CO2 metric in combination with the PM2.5 metric and the ventilation metric. Similarly, the productivity score may be generated by using the CO2 metric in combination with the PM10 metric and the ventilation metric. Similarly, the productivity score may be generated by using the CO2 metric in combination with the VOC metric and the ventilation metric. Similarly, the productivity score may be generated by using the CO2 metric in combination with the thermal metric and the ventilation metric. Similarly, the productivity score may be generated by using the CO2 metric in combination with the temperature metric and the ventilation metric. Similarly, the productivity score may be generated by using the CO2 metric in combination with the relative humidity metric and the ventilation metric. Similarly, the productivity scores may be generated by using the CO2 metric in combination with the dew point metric and the ventilation metric. Similarly, the productivity score may be generated by using the CO2 metric in combination with the Ozone metric and the ventilation metric. Similarly, the productivity score may be generated by using the CO2 metric in combination with the CO metric and the ventilation metric. Similarly, the productivity score may be generated by using the CO2 metric in combination with the formaldehyde metric and the ventilation metric. Similarly, the productivity scores may be generated in combination of one or more of the air quality metrics described herein. While the present disclosure discusses various combinations of air quality metrics that may be used to generate the productivity scores, it should be noted that the features of the present disclosure are equally applicable to any one or more air quality metrics and/or any one or more combination of air quality metrics.
In some embodiments, the productivity scores may be generated by applying one or more weights to the air quality metrics described herein. For example, a weight value of X may be applied to the CO2 metric. Additionally, a weight value of Y may be applied to the PM1 metric. The value of X may be equal to the value of Y. Additionally, the value of X may be less than or equal to the value of Y. Additionally, the value of X may be greater than or equal to the value of Y. Similarly, the value of Y may be equal to the value of X. Additionally, the value of Y may be less than or equal to the value of X. Additionally, the value of Y may be greater than or equal to the value of X. Similarly, the weight value of X and/or the weight value of Y may be applied to the CO2 metric. While the present disclosure discusses various combinations of applying weight values to air quality metrics that may be used to generate the productivity scores, it should be noted that the features of the present disclosure are equally applicable to any one or more possible combination of applying weight values to air quality metrics.
In some embodiments, the applied weight values may be used to increase the priority of the air quality metrics when generating the productivity scores. For example, the ventilation metric may have a weight value of 2 applied. Additionally, the CO2 metric may have a weight value of 1 applied. The ventilation metric may contribute to the productivity scores at twice the proportion to that of the CO2 metric.
In some embodiments, the air quality metrics may have the same weight value applied which may result in the air quality metrics having the same priority when generating the productivity scores. For example, the CO2 metric, the ventilation metric, and the VOC metric may each have a weight value of 1 applied. The CO2 metric, the ventilation metric and the VOC metric may now contribute equally to the productivity scores.
In some embodiments, the applied weight values may be used to decrease the priority of the air quality metrics when generating the productivity scores. For example, the ventilation metric may have a weight value of 2 applied. Additionally, the CO2 metric may have a weight value of 1 applied. The ventilation metric may now contribute to the productivity scores at half the proportion to that of the CO2 metric.
In some embodiments, the productivity scores may be generated by normalizing the air quality metrics described herein. For example, the CO2 metric and the acetone metric may be normalized by determining a ratio of CO2 to acetone. Similarly in some embodiments, the CO2 metric and the acetone metric may be normalized by determining a ratio of acetone to CO2. While the present disclosure discusses various combinations of normalizing the air quality metrics that may be used to generate the productivity scores, it should be noted that the features of the present disclosure are equally applicable to any one or more possible combination of normalizing air quality metrics.
In some embodiments, the recommendation generator 307 may perform an analysis of the productivity scores. The recommendation generator 307 may determine that the productivity scores are within one or more acceptable productivity scores ranges. For example, the office building 301 may have one or more predetermined productivity scores. Additionally, the office building 301 may have one or more productivity scores above and/or below the predetermined productivity scores that may create the acceptable productivity scores ranges. Additionally, the recommendation generator 307 may determine if any action is recommended to maintain or improve the productivity scores. Similarly, the recommendation generator 307 may determine that the productivity scores are not within the acceptable productivity scores ranges. Additionally, the recommendation generator 307 may determine if any action is recommended to adjust the productivity scores.
In some embodiments, the recommendation generator 307 may determine that the one or more air quality metrics described herein may be controlled in order to maintain the productivity scores. The building analysis system 304 and/or a BMS system of the office building 301 (e.g., the BMS system described in
In some embodiments, the recommendation generator 307 may determine that the one or more air quality metrics described herein may be controlled in order to adjust the productivity scores. The building analysis system 304 and/or the BMS system may then control the one or more air quality metrics described herein in order to adjust the productivity scores. For example, the recommendation generator 307 may determine that the CO2 metric may be controlled in order to adjust the productivity scores. The building analysis system 304 and/or the BMS system may then control the CO2 metric in order to adjust the productivity score. Similarly, the recommendation generator 307 may determine that the VOC metric may be controlled in order to adjust the productivity scores. The building analysis system 304 and/or the BMS system may then control the VOC metric. Similarly, the recommendation generator 307 may determine that the CO2 metric and/or the VOC metric may be controlled in order to adjust the productivity scores. The building analysis system 304 and/or the BMS system may then control the CO2 metric and/or the VOC metric in order to adjust the productivity scores. While the present disclosure discusses various combinations of using one or more air quality metrics described herein to adjust the productivity scores, it should be noted that the features of the present disclosure are equally applicable to any one or more possible combinations of controlling the air quality metrics described herein in order to adjust the productivity scores.
In some embodiments, the air quality metrics described herein may be controlled by performing one or more control operations. For example, the air quality metrics described herein may be controlled by using one or more ventilation rates. Similarly, the air quality metrics described herein may be controlled by using one or more filtration rates. Additionally, the filtration rates may involve Air-handler filtration, in zone filtration, and/or any combination of thereof. Similarly, the air quality metrics described herein may be controlled by using one or more disinfection rates. Additionally, the disinfection rates may involve Air-handler disinfection, in zone disinfection, and/or any combination thereof. Similarly, the air quality metrics described herein may be controlled by using the ventilation rates in combination with the filtration rates.
In some embodiments, the recommendation generator 307 may determine that the air quality metrics described herein may be controlled by performing one or more control operations described herein. For example, the recommendation generator 307 may determine that the CO2 metric may be controlled by adjusting the ventilation rates of the spaces within the office building 301. The building analysis system 304 and/or the BMS system may then control the CO2 metric by adjusting the ventilation rates of the office spaces. Similarly, the recommendation generator 307 may determine that the VOC metric may be controlled by adjusting the ventilation rates of the spaces within the office building 301. The building analysis system 304 and/or the BMS system may then control the VOC metric by adjusting the ventilation rates of the office spaces. Similarly, the recommendation generator 307 may determine that the CO2 metric and/or the VOC metric may be controlled by adjusting the ventilation rates of the spaces within the office building 301. The building analysis system 304 and/or the BMS system may then control the CO2 metric and/or the VOC metric by adjusting the ventilation rates of the office spaces. While the present disclosure discusses various combinations of the air metrics described herein that may be controlled by performing the actions described herein, it should be noted that the features of the present disclosure are equally applicable to any one or more possible combinations of the air quality metrics that may be controlled by performing the actions described herein.
In some embodiments, the recommendation generator 307 may determine that one or more control operations may be performed in order to control the air quality metrics described herein. For example, the recommendation generator 307 may determine that adjusting the filtration rate of the office spaces may control the CO2 metric. The building analysis system 304 and/or the BMS system may then adjust the filtration rate of the office spaces in order to control the CO2 metric. Similarly, the recommendation generator 307 may determine that adjusting the air disinfection rate may control the CO2 metric. The building analysis system 304 and/or the BMS system may then adjust the air disinfection rate in order to control the CO2 metric. Similarly, the recommendation generator 307 may determine that adjusting the filtration rate and/or adjusting the air disinfection rate of the office spaces may control the CO2 metric. The building analysis system 304 and/or the BMS system may then adjust the filtration rate and/or adjust the air disinfection rate of the office spaces in order to control the CO2 metric. Similarly, the recommendation generator 307 may determine that adjusting the filtration rate and/or adjusting the air disinfection rate of the office spaces may control the TVOC metric and/or the PM1 metric. The building analysis system 304 and/or the BMS system may then adjust the filtration rate and/or adjust the air disinfection rate of the office spaces in order to control the TVOC metric and/or the PM1 metric. While the present disclosure discusses various combinations of one or more actions that may be performed in order to control the air quality metrics described herein, it should be noted that the features of the present disclosure are equally applicable to any one or more possible combinations of the actions that may be performed in order to control the air quality metrics described herein.
In some embodiments the air quality metrics described herein may be controlled if occupants occupy a different location within the office building 301. In some embodiments, the air quality metrics described herein may be controlled if one or more occupants occupy a location that is not the office building 301.
In some embodiments, the recommendation generator 307 may determine that the air quality metrics described herein may be controlled by recommending that one or more occupants occupy a different location within the office building 301. For example, the recommendation generator 307 may determine that the CO2 metric may be controlled by adjusting occupant location within the office building 301. The recommendation generator 307 may then control the CO2 metric by recommending one or more locations within the office building 301 that the occupants may occupy. Similarly, the recommendation generator 307 may determine that the VOC metric may be controlled by adjusting occupant location within the office building 301. The recommendation generator 307 may then control the VOC metric by recommending one or more locations within the office building 301 that the occupants may occupy. Similarly, the recommendation generator 307 may determine that the CO2 metric and/or the VOC metric may be controlled by adjusting occupant location within the office building 301. The recommendation generator 307 may then control the CO2 metric and/or the VOC metric by recommending one or more locations within the office building 301 that the occupants may occupy. While the present disclosure discusses various combinations of the air quality metrics described herein that may be controlled by adjusting occupant location within an office building, it should be noted that the features of the present disclosure are equally applicable to any one or more possible combinations of the air quality metrics that may be controlled by adjusting occupant location within an office building.
In some embodiments, the recommendation generator 307 may determine that the air quality metrics described herein may be controlled by recommending that one or more occupants work from the occupants' home. For example, the recommendation generator 307 may determine that the CO2 metric may be controlled by having one or more occupants work from home. The recommendation generator 307 may then control the CO2 metric by recommending the occupants work from home. Similarly, the recommendation generator 307 may determine that the VOC metric may be controlled by having the occupants work from home. The recommendation generator 307 may then control the VOC metric by recommending the occupants work from home. Similarly, the recommendation generator 307 may determine that the CO2 metric and/or the VOC metric may be controlled by having the occupants work from home. The recommendation generator 307 may then control the CO2 metric and/or the VOC metric by recommending the occupants work from home. While the present disclosure discusses various combinations of the air quality metrics described herein that may be controlled by having the occupants work from home, it should be noted that the features of the present disclosure are equally applicable to any one or more possible combinations of the air quality metrics that may be controlled by adjusting occupant location.
In some embodiments, the recommendation generator 307 may compare the air quality metrics of the office building 301 with the air quality metrics of one or more occupants' homes. The recommendation generator 307 may determine that the productivity scores may be higher if the occupant works from home. The recommendation generator 307 may recommend that the occupants work from the occupants' homes. In some embodiments, the recommendation generator 307 may compare expected air quality metrics of the office building 301 with the expected air quality metrics of the occupants' homes. The recommendation generator 307 may determine using the expected air quality metrics which location may have the higher productivity score. The recommendation generator may then recommend that the occupants occupy the location with the higher productivity score.
In some embodiments, the recommendation generator 307 may use factors other than the air quality metrics of the office building 301 and the air quality metrics of the occupants' homes. In some embodiments, the recommendation generator 307 may compare expected distractions of the office building 301 with the expected distractions of the occupants' home. The recommendation generator 307 may recommend that the occupants work from the location with the least amount of distractions.
In some embodiments, the recommendation generator 307 may determine that the productivity scores of the office building 301 are lower than the productivity scores of one or more locations not within the office building 301. Additionally, the recommendation generator 307 may recommend that the occupants work from the locations with the higher productivity score.
In some embodiments, the recommendation generator 307 may determine that the productivity scores of the office building 301 are the same as the productivity scores of one or more locations not within the office building 301. Additionally, the recommendation generator 307 may recommend that the occupants work at a location not within the office building 301. Similarly, in some embodiments, the recommendation generator 307 may recommend that the occupants work from a location within the office building 301.
In some embodiments, the recommendation generator 307 may determine that the productivity scores of the office building 301 may decrease if the occupants work in the office building 301. Additionally, the recommendation generator 307 may recommend that the occupants work from a location not within the office building 301.
In some embodiments, the recommendation generator 307 may determine that the productivity scores of the office building may decrease if the occupants work in the office building 301. Additionally, the recommendation generator 307 may recommend that the occupant work from a location not within the office building 301.
In some embodiments, various data discussed herein may be stored in, retrieved from, or processed in the context of digital twins. In some such embodiments, the digital twins may be provided within an infrastructure such as those described in U.S. patent application Ser. No. 17/134,661 filed Dec. 28, 2020, 63/289,499 filed Dec. 14, 2021, and Ser. No. 17/537,046 filed Nov. 29, 2021, the entireties of each of which are incorporated herein by reference.
In some embodiments, various data discussed herein may be processed at (e.g., processed using models executed at) a cloud or other off-premises computing system/device or group of systems/devices, an edge or other on-premises system/device or group of systems/devices, or a hybrid thereof in which some processing occurs off-premises and some occurs on-premises. In some example implementations, the data may be processed using systems and/or methods such as those described in U.S. patent application Ser. No. 17/710,458 filed Mar. 31, 2022, which is incorporated herein by reference in its entirety.
Referring now to
In step 402, the building analysis system 304 may connect to the temporary air quality sensors 302 via the network 314, the temporary air quality sensors 302 being installed by a technician in the office building 301 on a temporary basis (e.g., for two weeks, three weeks, etc.). Connecting to the sensors 302 may include sending a message to the sensors 302 requesting a response, receiving an indication from the sensors 302 indicating that the sensors 302 are online, receiving measurements from the sensors 302 for a first time, creating a data point to store measurements of the sensor in, etc. In step 404, the air quality measurements may be received by the building analysis system 304.
In step 406, the building analysis system 304 may generate one or more air quality occupant impact assessments and/or recommendations for the air quality occupant impact assessments of the office building 301. The air quality occupant impact assessments may summarize air quality for various spaces of the office building 301. The recommendations may be included within the air quality occupant impact assessments and may indicate control operations for implementation for various spaces (e.g., new ventilation rates, air flow rates, air change rates, etc.). The recommendations may recommend investigation into various sources of TVOCs, VOCs, etc. in various places of the office building 301, etc. The one or more air quality occupant impact assessments and/or recommendations may be used to generate one or more productivity scores for the office building 301 and/or space/spaces within the office building 301 in step 408.
In some embodiments, the air quality occupant impact assessments may include infection risk for the office building 301, spaces of the office building 301, and/or occupants of the office building 301. The infection risk may be a risk level of contracting an infectious disease present in a population (e.g., COVID19, influenza, the bird influenza, etc.). The infection risk may be based on current ventilation rates, filter performance, etc. which may be derived from the air quality measurements of the sensors 302. The air quality occupant impact assessment may indicate a control profile, e.g., guidelines for implementing physical control of AHUs, VAVs, unit ventilators, temporary space filters, etc. The guidelines may be ranges for operating settings, recommended operating settings, specific control algorithms to be used, etc. This control profile may operate equipment to reduce the infection risk. In some embodiments, the control profile may operate to provide energy savings. The control profile may be used to determine operating settings that are implemented at a time after the sensors 302 are disconnected from and/or uninstalled. In some embodiments, features described in U.S. patent application Ser. Nos. 16/927,759 and 16/927,318, both filed Jul. 13, 2020 and both incorporated by reference herein in their entireties, may be utilized in conjunction with the features of the present disclosure. For example, in some embodiments, the infection risk may be estimated using the readings collected by the sensors 302 and processed using the Wells-Reilly equation as described in detail in the aforementioned applications.
In step 410, the productivity scores may be analyzed in order to determine if the productivity scores are within one or more predetermined productivity scores ranges. Analysis that may be done during this step may be found herein.
In step 412, the building analysis system 304 may disconnect from the sensors 302 as the sensors 302 are to be removed and uninstalled by a technician. Disconnecting from the sensors 302 can include sending a shutdown message to the sensor 302, sending a disconnect message from the sensors 302, not receiving new data from the sensors 302, etc. The sensors 302 can be uninstalled by the technician and disconnected from at the end of the temporary installation period. After the sensors are disconnected from, the building analysis system 304 and/or the BMS system may begin operating with operating settings and/or control algorithms based on the control profile generated by the building analysis system 304. While
Referring now to
In step 502 the productivity scores are provided for analysis. In step 504, the productivity scores are compared to the predetermined productivity scores ranges. If the productivity scores are within the predetermined productivity scores ranges then the process moves to step 512. If the productivity scores are not within the predetermined productivity scores ranges the process moves to step 506.
In step 506, the building analysis system 304 determines if action will be taken to adjust the productivity scores. If no action will be taken then the process moves to step 508. If action will be taken then the process moves to step 510. In step 508, the building analysis system 304 does not to take any action. The process will return to step 504.
In step 510, one or more control operations may be performed in order to adjust the productivity scores. Once the control operations are performed the process will return to step 504. In step 512, the building analysis system 304 determines if action will be taken to maintain the productivity scores. If no action will be taken then the process moves to step 514. If action will be taken then the process will move to step 516.
In step 514, the building analysis system 304 does not take any action. The process will return to step 504. In step 516, the building analysis system 304 will perform one or more control operations. Once the control operation are performed the process will return to step 504. The process 500 may be performed one or more times. While the present disclosure discusses various combinations of one or more steps that may be performed while analyzing the productivity scores, it should be noted that the features of the present disclosure are equally applicable to any one or more possible combinations of steps that may be performed in order to analyze the productivity scores.
Pareto Optimization TechniquesIn some embodiments, the building analysis system 304 and/or the BMS system may use Pareto optimization techniques to determine if one or more control operations may be performed in order to control the air quality metrics describe herein. The building analysis system may determine that the one or more of the control operations may be capable of adequately controlling the air quality metrics. Additionally, the building analysis system 304 and/or the BMS system may then use Pareto optimization techniques to determine if the one or more of the control operations contains one or more additional benefits. For example, the building analysis system 304 and/or the BMS system may determine that the ventilation control operation and/or the filtration operation may adequately control the air quality metrics. Additionally, the building analysis system 304 and/or the BMS system may then use Pareto optimization techniques to determine if the ventilation control operation and/or the filtration control operations may have additional benefits. A Pareto optimization controller and Pareto optimization techniques are described with further reference to U.S. patent application Ser. No. 17/483,078 filed Sep. 23, 2021, the entirety of which is incorporated by reference herein. In some embodiments, the BAS controller 202 may be similar to the Pareto optimization controller described therein.
In some embodiments, the control operations may be used as decision variables when performing Pareto optimization. In some embodiments, one or more possible combination of the control operations may be used as decision variables. In some embodiments, building occupancy may be used as decision variables. In some embodiments, time of day may be used as decision variables. In some embodiments, external temperature may be used as decision variables.
In some embodiments, the productivity scores and energy cost may be used as objective functions. In some embodiments, the productivity scores and sustainability may be used as objective functions. In some embodiments, the productivity scores and duration of time may be use as objective functions. In some embodiments, the productivity scores and carbon emissions may be used as objective functions. In some embodiments, the productivity scores and infection risk may be use as objective functions. In some embodiments, the productivity scores and occupant comfort may be used as objective functions. While the present disclosure discusses various combinations of objective functions, it should be noted that the features of the present disclosure are equally applicable to any one or more possible combinations of objective functions. Additionally, in some embodiments one or more variables may be used as objective functions.
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Process 600 includes obtaining multiple sets of values of control decision variables, each set of values including a different combination of the control decision variables (step 602), according to some embodiments. In some embodiments, the control decision variables include control operations describe herein. In some embodiments, the control decision variables include operating parameters or control decisions of the ventilation rates or the filtration rate. It should be understood that the control decision variables described herein are not limited to only two variables, and may include any number of variables. In some embodiments, each set of the values of the control decision variables is a unique combination of different values of the control decision variables. Similarly, regardless of a number of control decision variables, each set of the values of the control decision variables may be a unique combination, according to some embodiments.
Process 600 includes performing a simulation for each set of the values of the control decision variables to determine sets of values of energy cost and productivity scores (step 1704), according to some embodiments. In some embodiments, the simulations are performed by the building analysis system 304 and/or the BMS system, or processing circuitry thereof. In some embodiments, the simulations are performed for a future time horizon to generate predicted or simulated values for the energy cost and productivity score. In some embodiments, the simulations are performed for a previous or historical time period to determined values of the energy cost and productivity score for analysis (e.g., for comparison with actual historical data of the energy cost and productivity score). In some embodiments, each of the sets of values of control decisions (e.g., as obtained in step 602) is used for a separate simulation to determine a corresponding set of performance variables (e.g., the values of energy cost and productivity score). In some embodiments, the simulations are performed subject to one or more constraints.
Process 600 includes determining which of the sets of values of energy cost and productivity scores are infeasible and which are feasible (step 606), according to some embodiments. In some embodiments, step 606 is performed by the building analysis system 304 and/or the BMS system. In some embodiments, step 606 is performed using one or more constraints. The one or more constraints can be minimum or maximum allowable values of either of the energy cost and productivity scores, according to some embodiments. For example, if one of the sets of values of energy cost and productivity scores has an energy cost or energy consumption that exceeds a maximum allowable value of energy cost (e.g., exceeds a maximum threshold), then such a set of values of energy cost and productivity scores, and consequently the corresponding sets of values of the control decision variables, may be considered infeasible, according to some embodiments. In some embodiments, the constraints are set based on capabilities of an HVAC system that the process 600 is performed to optimize user inputs, budgetary constraints, etc.
Process 600 includes determining which of the feasible sets of values of energy cost and productivity scores are Pareto optimal solutions (step 608), according to some embodiments. In some embodiments, the step 608 is performed by the building analysis system 304 and/or the BMS system. In some embodiments, the step 608 is performed to determine which of the sets of values of energy cost and productivity scores are Pareto optimal from the feasible sets of values of energy cost and productivity scores. In some embodiments, process 600 includes performing steps 602-608 iteratively to determine sets of decision variables. For example, the decision variables can be iteratively generated based on simulation results (e.g., by generating additional points that are likely to be Pareto optimal based on the results of step 608).
Process 600 includes determining, based on the Pareto optimal solutions, a minimum energy cost solution, a maximum productivity scores solution, and an equal priority energy cost/productivity scores solution, according to some embodiments. In some embodiments, step 610 is performed by the building analysis system 304 and/or the BMS system. In some embodiments, the minimum energy cost solution is the set of values of the energy cost and productivity scores that are Pareto optimal, feasible, and also have a lowest value of the energy cost. In some embodiments, the maximum productivity scores solution is selected from the set of values of the energy cost and productivity scores that are feasible and Pareto optimal, and that has a highest value of the productivity scores. In some embodiments, the equal priority energy cost/productivity scores solution is selected from the set of values of the energy cost and productivity scores that are feasible and Pareto optimal, and that equally prioritizes energy cost and productivity scores. For example, the energy cost/productivity scores solution can be a point that is proximate an inflection of a curve that is fit to the sets of values of energy cost and productivity scores (e.g., including the feasible and infeasible points, only the feasible points, only the Pareto optimal points, etc.).
Process 600 includes providing one or more of the Pareto optimal solutions to a user via a display screen (step 612), according to some embodiments. In some embodiments, step 612 includes operating the user device 312 to display the Pareto optimal solutions to the user as different operational modes or available operating profiles. In some embodiments, step 612 is performed by the user device 312 and the building analysis system 304 and/or the BMS system. In some embodiments, step 612 includes providing the Pareto optimal solutions and historical data (e.g., historical data of actually used control decisions and the resulting energy cost and productivity scores). In some embodiments, step 612 is optional. For example, if the user has already set a mode of operation (e.g., always use maximum productivity scores settings, always use minimum energy cost solution, always use equal priority energy cost/productivity scores solution, etc.), then step 612 can be optional.
Process 600 includes automatically selecting one of the Pareto optimal solutions or receiving a user input of a selected Pareto optimal solution (step 614), according to some embodiments. In some embodiments, a user may select a setting for the building analysis system 304 and/or the BMS system to either automatically select one of the Pareto optimal solutions, or that the Pareto optimal solutions should be provided to the user for selection. In some embodiments, step 614 is performed by the building analysis system 304 and/or the BMS system and the user device 312. For example, step 614 can be performed by a user providing a selection of one of the Pareto optimal solutions (and therefore the corresponding control decisions) to be used by the BMS system for operation, according to some embodiments. In some embodiments, step 614 is performed automatically (e.g., if a user or administrator has selected a predetermined mode of operation for the BMS system) by the building analysis system and/or the BMS system to select one of the Pareto optimal solutions and therefore the corresponding control decisions for operational use of the BMS system.
Process 600 includes operating equipment of the BMS system according to the control decisions of the selected Pareto optimal solution (step 616), according to some embodiments. In some embodiments, step 616 includes operating the BAS system 200. More specifically, step 616 can include operating the HVAC system 100 according to the control decisions of the selected Pareto optimal solution. Advantageously, using the control decisions of the selected Pareto optimal solution can facilitate optimal control of the HVAC system 100 in terms of risk reduction, energy consumption, or an equal priority between productivity scores maximization and energy consumption or energy cost reduction. It should be understood that while
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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.
Claims
1. A building system for a building, the building system including one or more memory devices storing instructions thereon that, when executed by one or more processors, cause the one or more processors to:
- receive, from one or more sensors, air quality measurements associated with one or more spaces of the building;
- generate, using the air quality measurements, a productivity score for at least one of the building or at least one space of the one or more spaces of the building, the productivity score based on a predicted productivity or effect on productivity of one or more occupants of the building in view of the air quality measurements; and
- initiate, based on the productivity score, one or more actions to improve at least one of the productivity score or an air quality of at least one of the building or at least one space of the one or more spaces of the building.
2. The building system of claim 1, wherein the productivity score indicates a predicted work output for at least one first occupant of the one or more occupants of the building, and wherein the instructions further cause the one or more processors to:
- predict a number of absences for the at least one first occupant; and
- predict an impact to the productivity score based on the predicted number of absences for the at least one first occupant.
3. The building system of claim 1, wherein the air quality measurements include one or more air quality metrics, and wherein generating the productivity score includes:
- assigning weight values to the one or more air quality metrics to generate one or more weighted air quality metrics; and
- executing a model to generate the productivity score using the one or more weighted air quality metrics as inputs to the model;
- wherein the model is trained using data correlating different values for the one or more air quality metrics to productivity.
4. The building system of claim 1, wherein the instructions further cause the one or more processors to:
- generate, responsive to generating the productivity score, one or more recommendations for actions to impact the productivity score;
- generate a report including the productivity score and the one or more recommendations, wherein the report indicates at least one of a predicted amount of absences, a predicted amount of tasks completed, or a predicted impact associated with implementing the one or more recommendations;
- cause a user device to display a user interface, the user interface including the report;
- receive, from the user device, a user input indicating a selected recommendation of the one or more recommendations; and
- implement, responsive to receiving the user input, the selected recommendation of the one or more recommendations.
5. The building system of claim 1, wherein the one or more spaces comprise a plurality of spaces and the one or more sensors comprise a plurality of sensors configured to provide the air quality measurements for the plurality of spaces, and wherein the instructions further cause the one or more processors to:
- generate, responsive to receiving the air quality measurements associated with the plurality of spaces of the building, air quality profiles for the plurality of spaces of the building, wherein the air quality profiles are generated based on the air quality measurements associated with the plurality of spaces of the building;
- update, responsive to receiving second air quality measurements associated with the plurality of spaces of the building, the air quality profiles to reflect the second air quality measurements; and
- generate, using the air quality profiles for the plurality of spaces of the building, one or more trends pertaining to air quality of the building.
6. The building system of claim 1, wherein the one or more spaces comprise a plurality of spaces and the one or more sensors comprise a plurality of sensors configured to provide the air quality measurements for the plurality of spaces, and wherein the instructions further cause the one or more processors to:
- generate, using information describing the plurality of spaces of the building, a plurality of space hierarchies, wherein a first space hierarchy of the plurality of space hierarchies associates a first space of the plurality of spaces of the building with a second space of the plurality of spaces of the building;
- generate, using the plurality of space hierarchies and a plurality of air quality profiles, a plurality of productivity scores for the plurality of space hierarchies; and
- determine, using the plurality of productivity scores for the plurality of space hierarchies, whether at least one productivity score of the plurality of productivity scores for the plurality of space hierarchies is outside of a predetermined range.
7. The building system of claim 1, wherein the instructions further cause the one or more processors to:
- determine whether the productivity score is within a predetermined range;
- cause, in response to determining that the productivity score is not within the predetermined range, a building management system to take action to adjust the productivity score; and
- maintain, in response to adjusting the productivity score, the productivity score within the predetermined range.
8. The building system of claim 1, wherein the one or more actions to improve at least one of the productivity score or the air quality of at least one of the building or the at least one space of the one or more spaces include one more control strategies for controlling equipment of the building and wherein the instructions further cause the one or more processors to:
- transmit, to a building management system, control signals causing the building management system to implement at least one control strategy of the one or more control strategies; and
- detect, responsive to transmitting the control signals, an improvement to the productivity score or an improvement to the air quality of at least one of the building or the at least one space of the one or more spaces.
9. The building system of claim 1, wherein the one or more spaces comprise a plurality of spaces, wherein the productivity score includes an aggregated value of a plurality of productivity scores of the plurality of spaces, and wherein the instructions further cause the one or more processors to:
- determine, using the plurality of productivity scores of the plurality of spaces, a difference between a first productivity score of the plurality of productivity scores and a second productivity score of the plurality of productivity scores, wherein the first productivity score of the plurality of productivity scores pertains to a first space of the plurality of spaces and wherein the second productivity score of the plurality of productivity scores pertains to a second space of the plurality of spaces;
- determine, using the difference between the first productivity score of the plurality of productivity scores and the second productivity score of the plurality of productivity scores, whether the difference exceeds a predetermined range; and
- generate, responsive to determining that the difference exceeds the predetermined range, one or more recommendations for actions that impact the productivity score, wherein the one or more recommendations indicate that occupants move from the first space of the plurality of spaces to the second space of the plurality of spaces or that occupants move from the second space of the plurality of spaces to first space of the plurality of spaces;
- wherein the occupants moving causes the difference between the first productivity score of the plurality of productivity scores and the second productivity score of the plurality of productivity scores to be within the predetermined range.
10. The building system of claim 1, wherein the air quality measurements are at least one of:
- total volatile organic compounds (TVOC);
- carbon dioxide (CO2);
- carbon monoxide (CO);
- ozone;
- particulates; or
- formaldehyde.
11. A method for predicting productivity of one or more occupants of a building, the method comprising:
- receiving, by one or more processors from one or more sensors, air quality measurements associated with one or more spaces of the building;
- generating, by the one or more processors using the air quality measurements, a productivity score for at least one of the building or at least one space of the one or more spaces of the building, the productivity score based on a predicted productivity or effect on productivity of the one or more occupants of the building in view of the air quality measurements; and
- initiating, by the one or more processors based on the productivity score, one or more actions to improve at least one of the productivity score or an air quality of at least one of the building or at least one space of the one or more spaces of the building.
12. The method of claim 11, wherein the productivity score indicates a predicted work output for at least one first occupant of the one or more occupants of the building, and further comprising:
- predicting, by the one or more processors, a number of absences for the at least one first occupant; and
- predicting, by the one or more processors, an impact to the productivity score based on the predicted number of absences for the at least one first occupant.
13. The method of claim 11, wherein the air quality measurements include one or more air quality metrics, and wherein generating the productivity score includes:
- assigning, by the one or more processors, weight values to the one or more air quality metrics to generate one or more weighted air quality metrics; and
- executing, by the one or more processors, a model to generate the productivity score using the one or more weighted air quality metrics as inputs to the model;
- wherein the model is trained using data correlating different values for the one or more air quality metrics to productivity.
14. The method of claim 11, further comprising:
- generating, by the one or more processors responsive to generating the productivity score, one or more recommendations for actions to impact the productivity score;
- generating, by the one or more processors, a report including the productivity score and the one or more recommendations, wherein the report indicates at least one of a predicted amount of absences, a predicted amount of tasks completed, or a predicted impact associated with implementing the one or more recommendations;
- causing, by the one or more processors, a user device to display a user interface, the user interface including the report;
- receiving, by the one or more processors from the user device, a user input indicating a selected recommendation of the one or more recommendations; and
- implementing, by the one or more processors responsive to receiving the user input, the selected recommendation of the one or more recommendations.
15. The method of claim 11, wherein the one or more spaces comprise a plurality of spaces and the one or more sensors comprise a plurality of sensors configured to provide the air quality measurements for the plurality of spaces, and further comprising:
- generating, by the one or more processors responsive to receiving the air quality measurements associated with the plurality of spaces of the building, air quality profiles for the plurality of spaces of the building, wherein the air quality profiles are generated based on the air quality measurements associated with the plurality of spaces of the building;
- updating, by the one or more processors responsive to receiving second air quality measurements associated with the plurality of spaces of the building, the air quality profiles to reflect the second air quality measurements; and
- generating, by the one or more processors using the air quality profiles for the plurality of spaces of the building, one or more trends pertaining to air quality of the building.
16. The method of claim 11, wherein the one or more spaces comprise a plurality of spaces and the one or more sensors comprise a plurality of sensors configured to provide the air quality measurements for the plurality of spaces, and further comprising:
- generating, by the one or more processors using information describing the plurality of spaces of the building, a plurality of space hierarchies, wherein a first space hierarchy of the plurality of space hierarchies associates a first space of the plurality of spaces of the building with a second space of the plurality of spaces of the building;
- generating, by the one or more processors using the plurality of space hierarchies and a plurality of air quality profiles, a plurality of productivity scores for the plurality of space hierarchies; and
- determining, by the one or more processors using the plurality of productivity scores for the plurality of space hierarchies, whether at least one productivity score of the plurality of productivity scores for the plurality of space hierarchies is outside of a predetermined range.
17. The method of claim 11, further comprising:
- determining, by the one or more processors, whether the productivity score is within a predetermined range;
- causing, by the one or more processors in response to determining that the productivity score is not within the predetermined range, a building management system to take action to adjust the productivity score; and
- maintaining, by the one or more processors in response to adjusting the productivity score, the productivity score within the predetermined range.
18. One or more non-transitory storage medium storing instructions thereon that, when executed by one or more processors, cause the one or more processors to implement operations comprising:
- receiving, from one or more sensors, air quality measurements associated with one or more spaces of a building;
- generating, using the air quality measurements, a productivity score for at least one of the building or at least one space of the one or more spaces of the building, the productivity score based on a predicted productivity or effect on productivity of one or more occupants of the building in view of the air quality measurements; and
- initiating, based on the productivity score, one or more actions to improve at least one of the productivity score or an air quality of at least one of the building or at least one space of the one or more spaces of the building.
19. The one or more non-transitory storage medium of claim 18, wherein the productivity score indicates a predicted work output for at least one first occupant of the one or more occupants of the building, and the operations further comprising:
- predicting a number of absences for the at least one first occupant; and
- predicting an impact to the productivity score based on the predicted number of absences for the at least one first occupant.
20. The one or more non-transitory storage medium of claim 18, wherein the air quality measurements include one or more air quality metrics, and wherein generating the productivity score includes:
- assigning weight values to the one or more air quality metrics to generate one or more weighted air quality metrics; and
- executing a model to generate the productivity score using the one or more weighted air quality metrics as inputs to the model;
- wherein the model is trained using data correlating different values for the one or more air quality metrics to productivity.
Type: Application
Filed: Apr 7, 2023
Publication Date: Oct 12, 2023
Inventors: Kirk H. Drees (Cedarburg, WI), Tyler A. Smith (Franklin, TN), Jonathan D. Douglas (Mequon, WI), Bernard P. Clement (Mequon, WI)
Application Number: 18/132,200
