Building decarbonization apparatus and method

Abstract

Building energy models (BEMs) are created to simulate building performance across numerous performance parameters. BEMs create time-series metrics for energy consumption and other systems. BEMs are calibrated using data from meters and sensors from the integrated interface to validate building performance simulation. The initial BEM, once calibrated, is an operational model and is a virtual representation of the building. The calibrated operational model is then used to create an optimum model and intermediate step models. The optimal model represents the highest performance level achievable for the building. The step models represent intermediate performance levels for the building. These optional performance levels are whole-building decarbonization scenarios. Once these models are created, the time-series metrics created by the BEMs will be integrated with building meters and sensors real-time, time-series metrics in an integrated interface for measurement and verification, including but not limited to decarbonization planning, monitoring-based commissioning, and interrogation-based commissioning.

Description

BUILDING DECARBONIZATION APPARATUS AND METHODTECHNICAL FIELD

[0001] The subject disclosure is directed to systems, methods, and apparatus for providing building owners and architecture, engineering, and construction teams with the ability to reduce the operational carbon emissions that are associated with buildings.BACKGROUND ART

[0002] Decarbonization of various systems is a fundamental goal of environmental science. Decarbonization can involve reducing carbon dioxide emissions through the use of low carbon power sources and building efficiency to achieve a lower output of greenhouse gasses into the atmosphere. Decarbonization can include reducing carbon emissions by decreasing carbon dioxide output per unit of electricity generated.

[0003] Decarbonization within the construction industry involves reducing or eliminating the net carbon emissions associated with new or existing buildings without sacrificing healthy indoor environmental quality, which requires merging building science with data science. The merging of these tools can produce building performance technology to give owners and developers a clear and affordable path to achieve these goals.

[0004] Every building, existing or in-design, has theoretical optimum performance levels. However, existing tools have not been able to identify those optimum performance levels. Existing tools are used to hypothesize the impact potential of decarbonization measures that incrementally improve performance from a baseline or moment in time. As a result, there is a need for an improved building performance tool that can optimize the decarbonization of buildings through the determination of an optimized decarbonization value.DISCLOSURE OF INVENTION

[0005] In various implementations, A method is performed by one or more processors of a system. A building energy model including a building performance input for a building is created in memory. Calibration data is obtained from a data acquisition system to modify the building performance input and to convert, in memory, the building energy model into an operational model with an operational model building performance input. The optimized value of the operational model building performance input is determined in memory to create an optimized model with an optimized model building performance input. The building performance data is acquired from the data acquisition system. Time series building performance parameter data that includes at least one of the building performance data, the operational model building performance input, and the optimized model buildingDocket No. 15103-002WO performance input is produced for transmission to a display device coupled to the building decarbonization apparatus. The time series building performance parameter data is communicated to a client device.BRIEF DESCRIPTION OF THE DRAWINGS

[0006] FIG. 1 illustrates a schematic diagram of a system for calculating and valuing the decarbonization potential of a building.

[0007] FIG. 2 illustrates a block diagram of a platform for the system shown in FIG. 1.

[0008] FIG. 3 illustrates a operational schematic diagram for the system shown in FIG. 1 .

[0009] FIG. 4 illustrates exemplary output from the system shown in FIG. 1 displayed on a display device.

[0010] FIG. 5 illustrates an exemplary process for the system shown in FIG. 1.

[0011] FIG. 6 illustrates a schematic diagram for a data acquisition device in accordance with the described subject matter.

[0012] FIG. 7 illustrates a schematic diagram for a data acquisition system in accordance with the described subject matter.

[0013] FIG. 8 illustrates an exemplary interface displaying exemplary system output in accordance with the described subject matter.

[0014] FIG. 9 illustrates an exemplary interface displaying a psychometric chart in accordance with the described subject matter.

[0015] FIG. 10 illustrates a schematic diagram of another embodiment of a system for calculating and valuing the decarbonization potential of a building.

[0016] FIG. 11 illustrates the operation of the embodiment shown in FIG. 10.

[0017] FIG. 12 illustrates an embodiment of an exemplary process in accordance with the described subject matter.

[0018] FIG. 13 illustrates an embodiment of another exemplary process in accordance with the described subject matter.

[0019] FIG. 14 illustrates exemplary output in accordance with the exemplary process shown in FIG. 13.

[0020] FIG. 15 illustrates an exemplary computer system in accordance with the described subject matter.MODES FOR CARRYING OUT THE INVENTION

[0021] The subject disclosure is directed to a system for providing building owners with the ability to reduce the operational carbon emissions that are associated with a building and, more particularly, to methods, systems, and apparatus for calculating and valuing the variousDocket No. 15103-002WO carbon emission related metrics of physics-based building emissions models, so that the decarbonization value of the corresponding physical structure can be optimized. The instrumentality uses a simulator to generate the model and trended data to calibrate the model. Then, a decarbonization tool optimizes the decarbonization value and to generate output, so that the physical building can be modified accordingly.

[0022] References to “one embodiment,” “an embodiment,” “an example embodiment,” “one implementation,” “an implementation,” “one example,” “an example” and the like, indicate that the described embodiment, implementation or example can include a particular feature, structure or characteristic, but every embodiment, implementation or example can not necessarily include the particular feature, structure or characteristic. Moreover, such phrases are not necessarily referring to the same embodiment, implementation or example. Further, when a particular feature, structure or characteristic is described in connection with an embodiment, implementation or example, it is to be appreciated that such feature, structure or characteristic can be implemented in connection with other embodiments, implementations or examples whether or not explicitly described.

[0023] Building energy models are created to simulate building performance across numerous performance parameters. Building energy models require basic inputs like physical location, geometry, occupancy and schedules, mechanical, plumbing, and electrical systems, and weather parameters. Building energy models create time-series outputs for energy consumption, indoor air quality, mechanical, plumbing, and electrical systems operational metrics.

[0024] Building energy models are calibrated using data from building meters and sensors from the data acquisition system to validate building performance simulation using actual building performance metrics. The initial building energy model, once calibrated, is called the operational model and is a virtual representation of the building. The calibrated operational model is then used to create the optimum model. The optimal model represents the highest level of performance achieved for the building based on building science.

[0025] Next, the calibrated operational model is used to create additional step models representing optional decarbonization scenarios. These optional decarbonization scenarios represent whole-building performance levels, usually between the operational and optimum models. The step models represent the chosen whole-building performance levels and phased execution plans necessary to transition a building from the operational model to the optimum model to achieve whole-building decarbonization benefits.Docket No. 15103-002WO

[0026] Once these models are created, the building energy models’ time-series metrics will be integrated with building meters and sensors real-time, time-series metrics in an integrated interface for measurement and verification, including but not limited to monitoring-based commissioning, interrogation-based commissioning, and testing of building analytics, wherein the models’ real-time, time-series predictive metrics will be analyzed against the building’s meters and sensors real-time, time-series performance metrics.

[0027] All time-series metrics can be transferred automatically from the integrated interface to other independent data layers, digital twins, and client devices.

[0028] Referring to the drawings and, in particular, to FIGS. 1-5, various features of the subject disclosure are now described in more detail with respect to a building decarbonization system, generally designated as 100. The system 100 addresses the above-described long felt need and allows building owners to assess the optimum decarbonization values of both contemplated buildings and actual physical buildings, such as building 110.

[0029] The system 100 can be implemented within the various embodiments of building performance assessment systems and methods disclosed within U.S. Patent Nos. 10,936,764, 10,956,627, and 11,853,654 and pending U.S. Patent Application No. 17 / 477,618 to Eckenrode, et al., the entire disclosures of which, except for any definitions, disclaimers, disavowals, and inconsistencies, are incorporated herein by reference.

[0030] The system 100 optimizes building emissions and / or emission equivalents against project goals established during planning. The system 100 provides the ability to determine metrics relating to the carbon value of the building 110 and to optimize the carbon value using physics-based models. The system 100 can be utilized to optimize the carbon value of any building, group of buildings, sites and / or communities to achieve decarbonization targets set during pre-planning, planning, conceptual planning, design, operation, etc.

[0031] The system 100 provides building owners and facility managers with the tools and the data necessary to assess, dynamically, carbon metrics. Stakeholders can use the system 100 throughout the building cycle in a manner that is aligned to deliver common decarbonization goals. Specifically, the system 100 can produce metrics for the building 110 relating to the decarbonization potential of the building 110 in the form of a report or a plan that identifies the market value of the optimum decarbonization potential, the avoidance costs associated with the decarbonization potential, and / or the equivalent quantity of carbon dioxide (i.e., CO2e). The CO2e can be measured in metric tons, US tons, and other mathematical conversions. The avoidance costs can include operational expenses, operational utility expenses, environmental costs, and / or social costs of carbon.Docket No. 15103-002WO

[0032] The system 100 can be implemented using a computer, computer system, and / or computing device and can be configured as a special purpose computer or a general purpose computer specifically programmed to generate building models. Further, it is to be appreciated these features can be implemented by various types of operating environments, computer networks, platforms, frameworks, computer architectures, and / or computing devices, including, but not limited, cloud-based computer systems. In this exemplary embodiment, the system 100 utilizes a cloud server 112 connected to a client device 114 over a network 116.

[0033] The client device 114 can be used by a user 118 to operate the system 100. The user 118 can provide input to adjust models or other related information within the system 100.

[0034] The cloud server 112 connects to a physical data acquisition device 120 over the network 116. The physical data acquisition device 120 can connect to the building 110. In this exemplary embodiment, the physical data acquisition device 120 can be a building controller, i.e. Niagara JACE, Distech Apex, or software based products providing similar data aggregation solutions.

[0035] Additionally, the cloud server 112 can connect to a power grid 122 that can supply energy to the building 110 over the network 116. The grid 122 can be any type of suitable power grid, such as a co-gen and district energy systems, community based renewable array, chilled water plant with loops, and / or a steam system. The grid 122 is a source provider that utilizes one or more forms of energy to supply energy to the building 110.

[0036] The cloud server 112 also connects to at least one external government website 124 that includes an operational carbon emissions factor database 126 and at least one over the network 116, as well as at least one third party website 128. The third party website 128 can include industry building performance benchmarks and base lines that can include a database 130 for storing related information.

[0037] In some embodiments, at least one of the websites 124 and 128 can include weather forecast data, which can include a single day or multiday forecast for weather for the location of the building 110. In other embodiments, at least one of the websites 124 and 128 can include empirical or simulated data that can project activity that will occur within the building 110 during a day. The activity can include the number of people that are likely to occupy the building 110 as a function of time. The activity can also include expected commercial, manufacturing, or other industrial activity that is likely to occur during aDocket No. 15103-002WO particular time of day, as well as the environmental impact that that will result from such activity.

[0038] The network 116 can be implemented by any type of network or combination of networks including, without limitation: a wide area network (WAN) such as the Internet, a local area network (LAN), a Peer-to-Peer (P2P) network, a telephone network, a private network, a public network, a packet network, a circuit-switched network, a wired network, and / or a wireless network. Servers and workstations can communicate via networks using various communication protocols (e.g., Internet communication protocols, WAN communication protocols, LAN communications protocols, P2P protocols, telephony protocols, and / or other network communication protocols), various authentication protocols, and / or various data types (web-based data types, audio data types, video data types, image data types, messaging data types, signaling data types, and / or other data types).

[0039] While the system 100, as shown in FIG. 1, includes the cloud server 112 connected to the client device 114 and the data acquisition device 120 over the network 116, it should be understood that the system 100 could be implemented as a single computing device or computing system having a processor and memory.

[0040] As shown in FIG. 2, a platform 132 can reside on the cloud server 112. The platform 132 can include a processor 134 and memory 136. The memory 136 can include computer instructions that can be executed by the processor 134 to utilize a simulator 138.

[0041] Additional instructions can implement an integrated interface 140, a display device 142, and one or more application programming interfaces (APIs) 144. The platform 132 can display reports and / or analytics on the display device 142.

[0042] The simulator 138 can obtain information relating to various building metrics. The information can be obtained from architects, designers, construction managers, modelers, engineers, and sustainability consultants. The information can relate to architectural design, cost estimates, simulated occupancy, subcomponent energy models, weather station output, whole-building energy models and / or other inputs. The information can be obtained manually or through automated data acquisition methods or systems.

[0043] Then, the simulator 138 utilizes the above-described information to generate a physics-based building emissions model 146 that has a plurality of predicted building metrics The model 146 can be a virtual building model and / or a digital twin based the virtual data or metrics for the building 110.

[0044] The virtual building model 146 can include predicted energy consumption metrics, predicted indicators, predicted measurements, and / or predicted sensor outputs. TheDocket No. 15103-002WO model 146 can be a whole-building energy model that establishes building goals during the planning process. The model 146 can be used for monitoring-based commissioning, which is a process that utilizes automated data analytics to continuously monitor the performance of systems over the life of a building. In other embodiments, the model 146 can be used for interrogation based commissioning.

[0045] The virtual building model 146 can include predicted carbon emissions and / or carbon emission equivalents, as well as other metrics relating to biometrics, predicted IAQ / IEQ, and / or predicted utility consumption. The predicted utility consumption can be categorized by sources, including cogeneration systems, district energy chilled- water, district energy steam, electric, gas, potable water, renewable geothermal, renewable photovoltaics, renewable wind, and / or one or more other types of utility. The virtual building model 146 can be a design model or an operational model depending upon when it is generated or modified.

[0046] In some embodiments, the digital twin can be an integrated digital twin. An integrated digital twin represents the real-time integration of physics-based simulation and trended data for a building or groups of buildings.

[0047] The simulator 138 can simulate the grid 122. The simulator 138 can produce data to help identify optimum levels of energy to be supplied by the grid 122. The simulator 138 can ensure that the grid size is proper.

[0048] The simulator 138 can produce metrics by simulating the carbon emissions that are produced through various processes, such as the design, the assembly, and the operation of a building. Then, the platform 134 can acquire actual or trended data from the data acquisition system 120 for the building 110 and / or the grid 122. The trended data and the simulated data can be stored within a data layer 148 within the integrated interface 140.

[0049] As shown in FIGS. 1-2, the data acquisition system 120 can obtain a various actual (i.e., trended) building metrics that can be used to calibrate the model. The data can be sent from the building 110 to the cloud server 112 through the data acquisition system 120 over the network 116.

[0050] Additionally, the simulator 138 can acquire data from the grid 122 through the network 116. The data can include carbon (or carbon dioxide) emission factors or utility grid carbon emission factors. Further, it should be understood that carbon emission factors can be obtained from other similar sources.

[0051] The data acquisition system 120 has the ability to acquire observed or trended data or metrics relating to actual building based upon input from one or more devices and / or sources. The observed or trended data can include observed or trended building metrics,Docket No. 15103-002WO trended indicators, trended measurements, and / or trended sensor outputs. The trended metrics, indicators, measurements and / or sensor outputs can be acquired in real-time or near real-time.

[0052] The data acquisition system 120 can collect physical data. The physical data can relate to sensor output and / or data output relating to ambient lighting, biometrics, carbon dioxide (CO2), carbon monoxide (CO), circadian lighting, formaldehyde, humidity, internally generated noise, lead (Pb), ozone (03), particulate matter (PM2.5 & PM10), radon (Rn), sound pressure level, temperature, volatile organic compounds (VOCs), water quality and / or other data types relating to energy usage or indoor environmental quality.

[0053] The data acquisition system 120 can include automatic temperature controls, building automation system, design and engineering, IAQ / IEQ sensors, manual entry devices, personal data monitors, utility meters, weather stations and / or other inputs, as available.

[0054] As shown in FIGS. 1-2, the platform 134 calibrates the model 146 using data acquired from the data acquisition system 120. Then, a decarbonization tool 150 communicates with the simulator 138 as the simulator 138 generates a series of step models 152-154 to reach an optimum model 156 to optimize the decarbonization of the building 110. The decarbonization tool 150 receives the step models 152-154 and the optimum model 156 from the simulator 138. In some embodiments, the simulator 138 and / or the decarbonization tool 150 optimizes the model 146 using the Passive House Standards.

[0055] The operation of the decarbonization tool 150 is shown in FIGS. 3-5 as process 158. The process 158 is practiced using the system 100 shown in FIG. 1 using the cloud server 112 and the simulator 138 shown in FIGS. 1-2. The process 158 can be utilized to optimize the performance of the building 110 and / or to reduce the carbon emissions associated therewith.

[0056] At Step 160, the decarbonization tool 150 within the server 112 obtains a virtual model or digital twin of the building 110 from the simulator 138. The model can be the operational model 146 shown in FIGS. 1 and 3. The model 146 can be a physics-based model that includes various simulated metrics relating to building emissions and / or emission equivalents, as well as other simulated physical, structural, and / or economic characteristics of the building 110.

[0057] At Step 162, the decarbonization tool 150 receives meter data and / or sensor data from the data acquisition system 120. The data can be stored in memory 136. The data can be received in raw form in native units, such as British thermal units and / or Celsius or Fahrenheit temperature units (i.e., degrees).Docket No. 15103-002WO

[0058] At Step 164, the decarbonization tool 150 sends the meter data and / or the sensor data to the simulator 138, so that the model 146 can be calibrated. The meter data and / or the sensor data is used to calibrate the model 146. The model 146 can be calibrated to mimic the performance of the building 1 lOto comply with industry standard best practices.

[0059] After the model 146 is calibrated, the simulator 138 generates the optimum model 156, as well as one or more of the step models 152-154 that represent intermediate and phased steps between the calibrated version of the operational model 146 and the optimum model 156. Then, the models 146, 152-154, and 156 are transmitted to the cloud server 1 12 for storage in memory 136 at Step 166.

[0060] The decarbonization tool 150 can consider various factors in optimizing the model 156, including whether the building 110 has the proper climate specific thermal barriers, air barriers, mitigation of thermal bridging, balanced fenestration, and balanced mechanical, electrical, and plumbing systems. The decarbonization tool 150 can determine whether the building 110 and the model 146 has properly planned for related building triggers of life cycle, deferred maintenance, planned renovations, and other operational opportunities to transition building performance..

[0061] At Step 168, the decarbonization tool 150 converts meter data, sensor data, and model data into operational carbon emissions values using a carbon consumption factors for carbon accounting purposes. The carbon consumption rate can be an annual blended emissions factor, an hourly emissions factor, and / or any other suitable factors.

[0062] The decarbonization tool 150 can connect to the external website 124 through the APIs 144 over the network 116. The decarbonization tool 150 can access the emissions database 126, which can be the Emissions & Generation Resource Integrated Database (eGRID), which is a comprehensive inventory of environmental attributes of electric power systems. The eGRID database is provided by the U.S. Environmental Protection Agency (EP A).

[0063] The eGRID database is the preeminent source of air emission data for the electric power sector that is based on available plant-specific data for all U.S. electricity generating plants that provide power to the electric grid and report data to the U.S. government. The eGRID database stores data from the Energy Information Administration (EIA) and from Clean Air Markets Program Data that is administered by the EPA.

[0064] The eGRID database includes 8760 (i.e., the number of hours in a year) data concerning power plant emissions and, in particular, the carbon content of such emissions. The decarbonization tool 150 can utilize the emissions data contained within the emissionsDocket No. 15103-002WO database 126 to calculate carbon emissions for the calibrated model during on-peak and off- peak hours to obtain a more accurate carbon value for the calibrated model.

[0065] The decarbonization tool 150 can use the database 126 to obtain a carbon consumption rate to create a metric that calculates carbon consumption. The carbon consumption rate can be an annual blended emissions factor or an hourly emissions factor. The blended factor uses a normalized, uniform rate determined over a predetermined period of time. The hourly factor represents a set of exact hourly measurements of carbon consumption resulting from a particular source of energy during a predetermined period of time.

[0066] The external website 124 and the database 126 functions as a balancing authority to present a more accurate model of carbon consumption for the entire grid. For example, it is known that electricity that is consumed at 3 pm usually has a higher carbon intensity, as compared to electricity that is consumed at 3 am. The optimum model 156 will consider such factors in optimizing the CO2e of the building 110.

[0067] The decarbonization tool 150 can connect to the external website 128 through the APIs 144 over the network 116. Through the website 128 and / or the database 130 can obtain benchmarks and / or baselines to perform comparisons and / or market analysis. The website 128 can implement online software products, such as the ENERGY STAR® Portfolio Manager provided by the U.S. Environmental Protection Agency or other similar products. These software products can provide information, such as the average energy consumption of buildings of a particular size within a climate.

[0068] The decarbonization tool 150 converts all of the data into a CO2e metric. The decarbonization tool 150 provides a pathway to decarbonize the building 110 through the incorporation of building performance data, grid performance data, and 8760 carbon emissions accounting.

[0069] The decarbonization tool 150 can value the decarbonization impact in emissions or in cost. The decarbonization tool 150 can determine an appropriate value for the decarbonization. The decarbonization tool 150 can be used to create a plan, so that building owners can invest in the building 110.

[0070] At Step 170, the system 100 can be used to visualize output relating to the models 146 and 152-156 on the display device 142. The system 100 can visualize and / or export all meter, sensor, and model data (i.e common emission values) to the client device 114. The system 100 can distribute and / or publish the data to URLS, websites, digital twins, etc. toDocket No. 15103-002WO allow for the display of sensor / meter data next to simulated data. Exemplary output is shown in FIG. 4.

[0071] At Step 172, the decarbonization tool 150 can calculate carbon savings per model for each of the model 146 and 152-156. The calculations can be used to suggest further modifications and / or refinement of the models 152-156, as needed. Then, the decarbonization tool 150 can use the carbon savings to determine carbon emission savings using current carbon market values at Step 174.

[0072] At Step 176, the carbon emission savings can be communicated to a carbon market, such as the carbon market 178 shown in FIG. 3, to facilitate transactions thereon. It should be understood that the carbon market values in Step 174 can be obtained from the carbon market 178.

[0073] In some embodiments, the decarbonization tool 150 can connect to one or more APIs residing on the website 124 and 128 to obtain a weather data file that can include historical and / or forecasted weather service data that can be incorporated into one or more of the models 146 and 152-156. In this exemplary embodiment, the weather data file can include a seven-day forecast from the National Weather Service, which is an organization within the National Oceanic and Atmospheric Administration in the U.S. Department of Commerce. In such embodiments, the decarbonization tool 150 can utilize other empirical or forecasted people-related data or industrial / commercial activity data obtained from at least one of the website 124 and / or website 128 shown in FIG. 1.

[0074] The decarbonization tool 150 can perform calculations using annual and hourly grid average emission factors for defined geographic areas based on publicly accessible, reliable data published by the EIA. The decarbonization tool 150 can be used to calculate market-based emissions in instances where the contractual supplier of the electricity is its own balancing authority or has hourly emissions intensity factors available.

[0075] Hourly carbon emissions intensity (El) data is obtained from the EIA Hourly Electric Grid Monitor, which provides hourly electricity generation and hourly carbon dioxide emissions intensity data by balancing authority. This data is updated every twenty- four (24) hours providing timely results. The Carbon Dioxide Emissions Intensity for Consumed Electricity is used for these calculations. The hourly Carbon Dioxide Emissions Intensity factor considers net generation by energy source, estimated carbon dioxide emissions by fuel type, and estimated carbon dioxide emissions for electricity imports and exports.

[0076] Conventional carbon dioxide emissions are calculated, as follows:Docket No. 15103-002WOC02Emissions = EU *EI C02 where CO2 Emissions represents carbon dioxide emissions, EU represents electricity use, and El C02 represents a carbon dioxide emissions intensity factor.

[0077] In this exemplary embodiment, the decarbonization tool 150 can utilize an EIA carbon dioxide emissions intensity factor for consumed electricity that can based on generation data and can consider transmission and distribution (T&D) losses. To account for these losses, EIA emissions intensity can be adjusted using a 5-year average percentage T&D loss factor derived from EIA State Electricity Profile data. In such embodiments, supply and disposition of electricity can be calculated, as follows:EIcO2Adj = EICO2 Cons / (l-T&Dloss%) where EIco2 Adj represents an adjusted emissions intensity factor, EIco2 cons- represents a carbon dioxide hourly emissions intensity factor for consumed electricity, T&Dioss% represents T&D loss percentage calculated from the EIA State Electricity Profile.

[0078] The hourly emissions are then calculated using the adjusted hourly carbon dioxide emissions intensity factors for the respective balancing authority, as follows:CO2ehrly =EUhrly * EIcO2eAdj where CO2ehri represents an adjusted hourly carbon dioxide emissions intensity factor, EUhriy represents hourly electricity use, and EIco2eAdj represents the adjusted emissions intensity factor above.

[0079] Using data from a state electricity profile, the transmission and distribution loss can be calculated as follows:T&Dloss%=EExtl sses / (Etotdisp ~ E drctuse) where T&DiOSs% represents T&D loss percentage, EExtiosses represents estimated losses derived from a state electricity profile table, Etotdisp represents a total disposition quantity determined from the table data, and Edrcmse represents a direct use quantity from the table.

[0080] The decarburization tool 150 can utilize a methodology for carbon dioxide hourly accounting that uses a rolling 5-year average of the transmission and distribution loss factor because a typical state electricity profile has a 2-year lag time for publication. Since the actual transmission and distribution loss factor may not be available in some instances, a rolling 5-year average can be utilized to smooth out abrupt changes from year to year and avoid the need to retroactively change calculation results.

[0081] The carbon dioxide emissions intensity factors for the hourly accounting calculations can be based on EIA data by balancing authority. Balancing authorities areDocket No. 15103-002WO responsible for balancing generation and load within a defined area per North American Electric Reliability Corporation (NERC) reliability standards. There are 66 active balancing authorities in the lower 48 states, eight of which are also divided into sub-regions.

[0082] Some balancing authorities cover multiple states while others cover smaller areas, including many that cover a single state or a single utility. Exemplary balancing authorities include: PJM (PJM Interconnection, LLC); CISO (California Independent System Operator also known as CAISO) - Covering 80% of California and a small part of Nevada; ERCO (Electric Reliability Council of Texas, Inc.); MISO (Midcontinent Independent System Operator); and NYIS (New York Independent System Operator).

[0083] PJM covers all or parts of 13 mid- Atlantic states and the District of Columbia. CISO covers 80% of California and a small part of Nevada. ERCO covers 90% of the state of Texas. MISO covers all or parts of fifteen (15) mid-western states and the Canadian province of Manitoba. NYIS covers all of the State of New York.

[0084] ERCO is unique in that it is a balancing authority, an interconnection, and a regional transmission organization (RTO).

[0085] The emissions intensity factor also changes with time of day and time of season based on its generation mix. The higher standard deviation for CISO emissions intensity reflects the seasonal and daily variation of its higher percentage of solar generation. The variation with time plays a role in calculating carbon dioxide emissions on an hourly emissions intensity basis versus an annual average emissions intensity basis. The variation can be relevant for energy efficiency measures that achieve energy savings during peak emissions intensity hours.

[0086] Additionally, the disclosed system provides a decarbonization tool that starts with a building energy model of a building. The tool obtains grid data and performance information to transform the building energy model into an operational model. Then, the system optimizes the operational model to form an optimum model, so that the building can be modified for optimal decarbonization.

[0087] One aspect of decarbonization depends upon hourly carbon accounting. Hourly carbon accounting has become critical in the built environment due to regulatory emissions reduction and disclosure requirements imposed by many cities, states, and federal governments to decarbonize buildings. In fact, the requirements to decarbonize buildings exist on numerous fronts, including but not limited to corporate voluntary programs, equity investment requirements, sustainability certification programs, step codes, federal, state, and local ordinances, SEC regulations, and other demands.Docket No. 15103-002WO

[0088] Hourly carbon accounting is also critical to making informed decisions on energy management and decarbonization opportunities, especially as the electric grid incorporates increasing amounts of renewable energy, battery storage, solar generation, grid-connected demand management, and other distributed energy resource solutions. Tracking emissions on an hourly basis provides a more accurate method of measuring and verifying carbon emissions impacts.

[0089] Currently, blended annual emissions intensity factors are typically used to calculate certain carbon emissions. The ENERGY STAR® Portfolio Manager provided by the U.S. Environmental Protection Agency (EP A) and many other reporting programs utilize EPA eGRID annual blended factors for calculating location-based carbon emissions. This is not ideal as the carbon intensity of the electric grid fluctuates during the day and is becoming more fluid.

[0090] Calculating carbon dioxide emissions on an hourly basis provides real-time or near real-time tracking of emissions and a transparent, verifiable cumulative emissions accounting method. The methodology for hourly carbon accounting details an improved location-based method of carbon dioxide emissions accounting using U.S. Energy Information Agency (EIA) hourly electric grid monitor data.

[0091] This method unexpectedly improves the accuracy and relevance of location-based emissions as electricity providers transition to renewable energy. Technology-based building systems like operating technologies, independent data layers, advanced data analytics platforms like digital twins and other building analytics technologies can now offer substantiated, trustworthy values for building emissions. Such methods provide credible, verifiable hourly carbon accounting.

[0092] Referring now to FIG. 7 with continuing reference to the foregoing figures, a data acquisition device, generally designated as 200, is illustrated. The data acquisition device 200 can be implemented as a computer, computer system, and / or computing device that can be configured as a special purpose computer or a general purpose computer specifically programmed to acquire data. The data acquisition device 200 can be configured to implement the data acquisition system 120 shown in FIG. 1.

[0093] The data acquisition device 200 includes an operating system 210, a processor 212, memory 214, a screen 216, an input device 218, and an output device 220. The building model generation device 200 can host one or more applications 222 that include data acquisition code 224 and / or model generation code 226.Docket No. 15103-002WQ

[0094] The processor 212 can perform tasks such as signal coding, data processing, input / output processing, power control, and / or other functions. Memory 214 can be used for storing data and / or code for running operating system 210 and / or application(s) 222. Example data can include web pages, text, images, sound files, video data, or other data to be sent to and / or received from one or more network servers or other devices via one or more wired and / or wireless networks.

[0095] The operating system 210, the processor 212, memory 214, and / or application(s) 222 can cooperate to utilize screen 216 and / or to communicate with input device 218 and / or output device 220.

[0096] The application(s) 222 can produce virtual building emission models that predict emissions and / or emission equivalents during the planning phase. The virtual building emission models can be whole-building energy models that can be used to create alternate scenarios for building emissions or emission equivalents.

[0097] Referring now to FIG. 7 with continuing reference to the foregoing figures, a data acquisition system, generally designated as 300, is shown. The data acquisition system 300 can be configured and implemented as the data acquisition system 120 shown in FIG. 1.

[0098] The data acquisition system 300 can include various components and / or sensor assemblies that can acquire physical data from a building. These components and / or sensor assemblies can include automatic temperature controls 310, a building automation system 312, IEQ / IAQ sensors 314, manual entry devices 316, personal data monitors 318, utility meters 320, and weather stations 322. These components and / or sensor assemblies can communicate with a data acquisition device 324. In this exemplary embodiment, the data acquisition device 324 can be the data acquisition device 200 shown in FIG. 6.

[0099] In some embodiments, the automatic temperature controls 310, building automation system 312, IEQ / IAQ sensors 314, manual entry devices 316, personal data monitors 318, utility meters 320, and weather stations 322 can connect to data loggers, such as meters and / or JAVA application control engines, to facilitate communication over the network 420 shown in FIGS. 7A-7B. In this exemplary embodiment, the control engine can include a JACE 8000 - Tridium controller operating with Niagara 4 within the Niagara Framework®. Niagara Framework® is a registered trademark of Tridium, Inc., of Richmond, Virginia.

[0100] Referring to FIG. 8 with continuing reference to the foregoing figures, exemplary output 330 is shown on an interface 332. It is to be appreciated that the output 330 can beDocket No. 15103-002WO displayed on the client device 114 shown in FIG. 1 and / or another other similar screen or display device suitable for showing computer generated output.

[0101] As shown in FIG. 8, the interface 332 can display virtual data output 334, physical data output 336, and / or model output 338. The model output 338 can include carbon emission output 340, carbon emission equivalent output 342, biometrics output 344, IAQ / IEQ output 346 and / or utility consumption output 348.

[0102] Referring to FIG. 9 with continuing reference to the foregoing figures, exemplary output 350 is shown on an interface 352. It is to be appreciated that the output 350 can be displayed on the client device 114 shown in FIG. 1 and / or another other similar screen or display device suitable for showing computer generated output. The output 350 can provide insight into the operation of the system 100 shown in FIG. 1.

[0103] The exemplary output 350 is an interactive graphical thermal comfort chart for displaying trended and simulated data. The output 350 illustrates thermal comfort data for one or more areas and / or buildings in real-time using performance indicators, including dynamic simulation and targeted thermal comfort zones.

[0104] Thermal comfort is one of the most common occupant complaints in buildings. Common causes are drafty windows and envelopes, targeted radiant heat sources, and HVAC failures. Most buildings are designed to create a uniform thermal environment that satisfies the majority of occupants. Further, ASHRAE Standard 55 stipulates only 80% of your occupants need to be comfortable. The output 350 includes a psychrometric chart that can be used during operations to identify thermal comfort complaints and can be used to determine whether the complaints are related to occupant sensitivities or building systems.

[0105] The output 350 can be produced through the plotting of multiple data points representing temperature and humidity at a specific time interval and duration. Then, an area is overlayed the chart to indicate regions or zones of acceptable thermal comfort. In some embodiments, a comfort zone can be defined as the range wherein occupants are satisfied with the surrounding thermal conditions. The plotting the air conditions that are overlayed with a comfort zone can illustrate how passive design strategies can extend the comfort zone by mitigating the variability traditionally associated with indoor temperatures and humidity as a result of envelopes with air leakage.

[0106] Psychrometric charts are complex graphs used to assess the physical and thermodynamic properties of gas-vapor mixtures at a constant pressure. Such charts can be used to assess the properties of moist air, which is useful in the design of heating, ventilation, and air-conditioning systems for buildings. Dynamic psychrometric charts, such as the chartDocket No. 15103-002WO shown as output 350, can add thermal comfort zones to represent the range of conditions people find comfortable under different circumstances (such as summer and winter).

[0107] The output 350 can be used to plot points, dynamically, to represent outdoor air conditions for a better understanding of the treatment that must be performed on air to reach comfortable conditions for the occupants inside a building. When using the output 350 for this purpose, the plot points on the chart can be shown with other data, simultaneous. Exemplary data include indoor sensor data, dynamic simulation data, and historical data for temperature and humidity.

[0108] The output 350 can be used for an unlimited number of simultaneous psychrometric chart analyses and can be used to obtain interactive charts that can be used to view items in greater or lesser detail. The output 350 can include a psychrometric analysis by having a user plot points and connecting the points with process lines. The output 350 can illustrate a customizable psychrometric chart. In some embodiments, data plots can include trended and simulation time-series data, multiple plot data tags on single chart, multiple thermal comfort zones for ASHRAE55 cooling and heating seasons, and time periods and time intervals customized for client use cases. The dynamic psychrometric plots can be exported to image files.

[0109] Dynamic psychrometric charts can be used to illustrate thermal comfort by identifying areas or zones in which temperature and humidity are at comfortable levels. Some psychrometric charts can tie together temperature and humidity levels over time to determine whether the intersections of these three variables fall within certain temperature-humidity comfort zones. Such psychrometric charts can be displayed on a thermal comfort meter.

[0110] Psychrometric charts can include trended data, predicted data, and / or data produced through an analytics platform, which can be based on predicted data and / or trended data. The comfort zone can be determined using any suitable method, such as the graphic comfort zone method, the analytical comfort zone method, or the elevated air speed comfort zone method.

[0111] Referring now to FIGS. 10-11 with continuing reference to the foregoing figures, another embodiment of a building decarbonization system, generally designated as 400, is shown. The system 400 includes an integrated interface 410 that receives data from various sources 412-418. The integrated interface 410 can send data to the source 418 and to other sources 420-422. The data can be sent and / or received through APIs 424.

[0112] The integrated interface 410 can be any software or hardware solution that brings data together that is configured to send and to receive relevant decarbonization data.Docket No. 15103-002WOExemplary solutions include independent data layers, digital twins, data aggregation components, and controllers. The integrated interface 410 has the ability to enable the generation of analytics and / or other data visualization means. The integrated interface 410 has the ability to store historical data and / or to buffer data.

[0113] The integrated interface 410, when it is a controller, can be a JACE ® controller is the hardware platform optimized for Niagara 4*. JACE® is a registered trademark of Tridium, Inc. of Richmond, Virginia.

[0114] The source 412 can be a building. The building can be the building 110 shown in FIG. 1. The source 414 can be a source for carbon emission factors, such as the State Energy Profile Data supplied by the U.S. Energy Information Administration in Washington, DC. The carbon emission factors can be supplied on an 8760 basis.

[0115] The source 414 can be a simulator, such as the simulator 138 shown in FIG. 2. The source 416 can be The WattCarbon Energy Attribute Tracking System (WEATS) by WattCarbon, Inc. of San Francisco, California. The source 420 can be a carbon marketplace. The source 420 can be one or more other independent data layers or digital twins.

[0116] The source 422 can be an air quality monitor, such as RESET® AIR by GIGA of Quebec, Canada. The source 422 can communicate with a building certification software platform 426, such as the LEEDS ARC program by the U.S. Green Building Council LEEDS ARC program.

[0117] The operation of the system 400 is shown in FIG. 11 as process 428. The process 430 can be utilized to optimize the performance of the building 110 and / or to reduce the carbon emissions associated therewith.

[0118] At 430, the integrated interface 410 stores building meter data and sensor data. In some embodiments, the building meter data and sensor data can be trended data that is obtained from a data acquisition system. An exemplary data acquisition system is the data acquisition system 120 shown in FIG. 1.

[0119] At 432, a basic energy model is created. In this exemplary embodiment, the basic energy model can be an 8760 model.

[0120] At 434, the basic energy model is calibrated. The basic energy model can be calibrated with building meter data and sensor data that is stored in the integrated interface 410 in Step 430. The calibration transforms the basic energy model into an operation model.

[0121] At 436, the operation model is used to create an optimum model. The optimum model can be created using a simulator. In this exemplary embodiment, the simulator can be the simulator 138 shown in FIG. 2.Docket No. 15103-002WO

[0122] At 438, one or more step models are created. The step models will have metrics that are intermediate between the metrics for the operational model and the metrics for the optimum model.

[0123] At 440, time series data for the operational model, the optimum model, and the step models is exported to the integrated interface 410.

[0124] At 442, the integrated interface 410 converts the time-series data from the models to key performance indicators. Exemplary key performance indicators include Site EUI, Source EUI, Total Costs, Operational Carbon (GHG) Emissions- Hourly, and Operational Carbon (GHG) Emissions- Blended Annual Rate.

[0125] At 444, the integrated interface 410 converts the time-series data from building meters and sensors into key performance indicators. Exemplary key performance indicators include Site EUI, Source EUI, Total Costs, Operational Carbon (GHG) Emissions- Hourly, and Operational Carbon (GHG) Emissions- Blended Annual Rate.

[0126] At 446, the integrated interface 410 compares the model time-series data from Step 442 to the building meter and sensor data from Step 444 to perform monitoring-based commissioning, interrogation-based commissioning, and testing of building analytics. The conversion of the data in Steps 442-444 into key performance indicators allows the data to be compared using common units.

[0127] At 448, the system 400 uses the integrated interface 410 to create output that displays decarbonization strategies and scenarios, as well as quantitative data relating to decarbonization metrics. In this exemplary embodiment, the output can be displayed on the display device 142 shown in FIG. 2.

[0128] Referring to FIG. 12 with continuing reference to the foregoing figures, another exemplary process, generally designated by the numeral 450, is shown. The exemplary process 450 can be utilized to simulate weather forecast data, such as seven-day forecasts, that can be incorporated into models, such as can be incorporated into one or more of the models 146 and 152-156 shown in FIG. 1. The process 450 can improve physics-based simulation results by incorporating more accurate weather files into models.

[0129] At 451, weather data is acquired. In this exemplary embodiment, daily the 7-day weather forecast time-series data is downloaded for a specific location of a subject building in a simulation from a weather source website to the historian time-series platform. Step 451 can be automated using an API from a weather source website that can be coupled to a historian time-series platform.Docket No. 15103-002WO

[0130] Exemplary weather forecast time-series data can include Weather. API, National Weather Service, or similar weather time-series source data. Exemplary weather forecast time-series data parameters can include Dew Point, Outdoor Humidity, Outdoor Temperature, Pressure, Visibility, Weather State, Weather Summary, Wind Direction, Wind Speed, and other parameters.

[0131] At 452, weather data is loaded into a simulator. In this exemplary embodiment, the simulator can be an IES VE simulator by Integrated Environmental Solutions Limited of Glasgow, Scotland (UK). A daily the 7-day weather forecast time-series data can be uploaded for a specific location of the subject building in the simulation from the historian time-series platform to the physics-based simulation software. Step 452 can be automated with an API from the historian time-series platform coupled to the physics-based simulation software.

[0132] Additionally, Step 452 can be streamlined from the weather source website to the physics-based simulation software using parametric modeling tools. Exemplary parametric modeling tools implemented within a visual programming interface for the 3D modeling program, such as the Grasshopper interface and the Rhino modeling program by Robert McNeel & Associates of Seattle, Washington.

[0133] At 453, the simulator produces a simulation. In this exemplary embodiment, the simulator uses the 7-day weather forecast time-series data that was loaded into physics-based simulation software. The simulator can produce the simulation model daily. Step 453 can be automated with a Windows task scheduler.

[0134] At 454, the simulation results can be downloaded. In this exemplary embodiment, daily time-series simulation results can be downloaded from the physics-based simulation software to the historian time-series platform. The simulation results can include all forms of energy (primary source and submeters), indoor air quality (indoor temperature, relative humidity, and carbon dioxide), and building automation system parameters. The potential sources of data can be expanded to lighting systems and all other operational technologies.

[0135] The process 450 produces more accurate time-series simulations that can used for energy source generation management, operational modeling, and decarbonization tool monitoring. Energy source generation management can be used for on-site generation from thermal energy (hot water, chilled water, steam) plants, renewable energy, and other applications, and for off-site generation from utility providers, district energy, and other applications.

[0136] Operational modeling can include monitoring-based commissioning, interrogation-based commissioning, and / or testing of advanced data analytics.Docket No. 15103-002WODecarbonization tool modeling can be used for whole-building / whole-life performance modeling.

[0137] Referring to FIGS. 13-14 with continuing reference to the foregoing figures, another exemplary process, generally designated by the numeral 460, is shown. The process 460 can be performed by a building decarbonization apparatus, such as the system 100 shown to decarbonize the building 110 shown in FIGS. 1-5 and / or the system 400 shown in FIGS. 10-11.

[0138] The building decarbonization apparatus can utilize a cloud server 1 12 connected to a client device 114 over a network 116, as shown in FIGS. 1-5. Additionally, the building decarbonization apparatus can use and / or interact with the other components shown in FIGS. 1-5, as well as the data acquisition device 200 shown in FIG. 6 and / or the data acquisition system 300 shown in FIG. 7.

[0139] At 461, a building energy model including a building performance input for a building is created in memory. The creation of the building energy model can involve obtaining or identifying metric -based goals and targets for building performance, determining hourly energy consumption, and / or reviewing utility bills. The creation of the building energy model can also involve mechanical, electrical, and plumbing systems inventories, smart building infrastructure assessments, and systems and equipment triggers and sequences.

[0140] For existing buildings, the building energy model can be a physics-based software simulation that dynamically (8760) predicts the hourly energy use for an existing building. The building energy model can take input from a variety of sources, including, but not limited to, building geometry, construction materials, lighting, HVAC, refrigeration, water heating, and renewable generation systems, component efficiencies, control strategies, building use and operation schedules, and local weather. Building energy models can be created and managed using software, such as IES VE by Integrated Environmental Solutions Limited of Glasgow, Scotland, eQuest by James J. Hirsch & Associates of Camarillo, California, and EnergyPlus by the United States Department of Energy.

[0141] The building energy models for existing buildings can be associated with a baseline building energy model, which is a representative building energy model of the existing building using a time period of carbon emissions prior to decarbonization mitigation. The building energy model can be calibrated within the limits of American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) Guidelines 14-2023 using meteorological hourly weather data for the same time period. The calibration path requires minimum acceptable model calibration of a normalized mean bias error (NMBE) ofDocket No. 15103-002WO5% and a coefficient of variation of the root-mean-square error of (CV[RMSE]) of 15% relative to monthly calibration data.

[0142] The building energy models for existing buildings can be associated with a betterment building energy model, which can represent a building energy model of an existing building using a time period of carbon emissions after decarbonization mitigation.

[0143] The source data for existing buildings can represent meteorological hourly weather data used in the building energy model and consumption data for all forms of energy from monthly utility consumption data, metered, hourly consumption data, or another data source, approved by WattCarbon that represents actual building performance for the representative time period.

[0144] For new buildings, the building energy model can be a physics-based software simulation that dynamically (8760) predicts the hourly energy use of the new building. The building energy model can take input from a variety of sources, including, but not limited to, building geometry, construction materials, lighting, HVAC, refrigeration, water heating, and renewable generation systems, component efficiencies, control strategies, building use and operation schedules, and local weather. The building energy models for new building energy models can be created and managed using the same software as used with existing buildings.

[0145] The building energy models for new buildings can be associated with a baseline building energy model, which can represent buildings that would have otherwise been designed and constructed to meet the minimum requirements of local building codes using a time period of carbon emissions prior to building operations. In that instance, the BEM must comply with ASHRAE Standard 90.1-2010 compliance, which provides the minimum requirements for energy-efficient design of most buildings. The model uses 30-year meteorological hourly weather data.

[0146] The building energy models for existing buildings can be associated with a betterment building energy model, which can be a representative building model of the existing building using a time period of carbon emissions after decarbonization mitigation. The building energy model can be calibrated within the limits of ASHRAE Guidelines 14- 2023 using meteorological hourly weather data for the same time period. The calibration can be performed according to ASHRAE guideline 14-2023 Section 4. .2.4 Whole-Building Calibrated Simulation Performance Path requires minimum acceptable model calibration of a normalized mean bias error (NMBE) of 5% and a coefficient of variation of the root-mean- square error of (CV[RMSE]) of 15% relative to monthly calibration data.Docket No. 15103-002WO

[0147] The source data for new buildings represents meteorological hourly weather data used in the BEM and consumption data for all forms of energy from monthly utility consumption data, metered, hourly consumption data, or another data source, approved by WattCarbon, that represents actual building performance for the representative time period.

[0148] The time period for new buildings is the consecutive 12-month period of time ending at the completion of construction for the new building. For existing buildings, the time period is any consecutive 12-month period of time where acceptable utility consumption data is available for all forms of energy used within the building.

[0149] At 462, calibration data is obtained from a data acquisition system to modify the building performance input and to convert, in memory, the building energy model into an operational model with an operational model building performance input. In this exemplary embodiment, the data acquisition system can be the data acquisition system 120 shown in FIG. 1 and / or the data acquisition system 300 shown in FIG. 7.

[0150] The data acquisition system has the ability to acquire observed data (i.e. trended data) or baseline data. The observed data or trended data include inputs or metrics that represent actual building data based upon input from one or more devices and / or sources. The baseline data can be derived from building codes.

[0151] The observed or trended data can include observed or trended building metrics, trended indicators, trended measurements, and / or trended sensor outputs. The trended metrics, indicators, measurements and / or sensor outputs can be acquired in real-time or near real-time.

[0152] The data acquisition system can collect physical data. The physical data can relate to sensor output and / or data output relating to ambient lighting, biometrics, CO2, CO, circadian lighting, formaldehyde, humidity, internally generated noise, Pb, O3, PM2.5 & PM 10, Rn, sound pressure level, temperature, VOCs, water quality and / or other data types relating to energy usage or indoor environmental quality.

[0153] When metered, hourly consumption data is not available for existing buildings, calibrated physics- based dynamics whole-building simulation energy building energy models can be used to generate authenticated hourly consumption data for transactional purposes.

[0154] The building energy models can simulate and can generate authenticated EAC (EACa) hourly consumption data for the WEATS platform for all buildings that do not have metered, hourly consumption data. This verification methodology reliably converts monthly utility data to transactable, authenticated hourly consumption data using proven buildingDocket No. 15103-002WO science technology. In such embodiments, EAC Hourly consumption time series data can be obtained by subtracting betterment building energy model data from baseline building energy model data.

[0155] When metered, hourly consumption data is not available for new buildings, calibrated physics-based dynamics whole-building simulation energy building energy models can be used to generate authenticated hourly consumption data for transactional purposes. In such embodiments, EAC Hourly consumption time series data can be obtained by subtracting betterment building energy model data from baseline building energy model data.

[0156] At 463, the optimized value of the operational model building performance input is determined in memory to create an optimized model with an optimized model building performance input. In this exemplary embodiment, the creation of an optimized model can involve testing various Energy Conservation Measures (ECM) on the operational model to identify the whole-building decarbonization potential, as well as other relevant decarbonization metrics.

[0157] Triggers and sequences can also be identified at this step. Triggers represent life cycle events, deferred maintenance, renovations, or other events that modify the building. Sequences represent the orders in which changes to buildings are made.

[0158] This optimization step can involve a series of sub-steps that include the creation of one or more step models that have inputs that are positioned between the inputs for the operational model and the inputs for the optimized model.

[0159] At 464, the building performance data is acquired from the data acquisition system to compare the building performance data from at least one of the operational model building performance input and the optimized model building performance input to produce time series building performance parameter data.

[0160] At 465, the time series building performance parameter data is communicated to a client device. In this exemplary embodiment, the time series building performance parameter data can be provided in a .CSV file or in any other suitable file format. The output can be used for automated hourly carbon emissions accounting, for visualizing decarbonization scenarios, and / or for connecting time-series data to carbon EAC markets. In some embodiments, the output can be incorporated into reports that provide recommendations in the form of ECMs and recommendations to shift to alternate energy sources, such as renewable energy sources.Docket No. 15103-002WO

[0161] At 466, output based upon the time series building performance parameter data is displayed on the client device. The output can be the output shown in FIG. 4 depicted on display device 142 and / or the output shown in FIG. 14 depicted on display device 470.

[0162] At 467, the time-series data can be used for interrogation-based commissioning to modify the whole-building performance model based upon comparisons of trended data with predicted data. Interrogation-based commissioning uses trended data and / or predicted data to perform physics-based simulations that can be used to identify and to remediate problems, which can improve whole building performance. The simulations can use new building profiles and set new targets to produce operation-based solutions.

[0163] At 468, the time-series data can be used for monitoring -based commissioning to ensure that the energy and indoor environmental goals are maintained over the life of the building.

[0164] As shown in FIG. 14, a visual representation of the production of carbon (in metric tons) for one or more sources of energy is shown over time. The data for the sources of energy can be based upon building meters, building utility invoices, or other similar data sources.

[0165] The horizonal axis can be divided into historical data and simulated data with the historical data representing trended data and the simulated data representing various models, including an operational model, an optimum model and a plurality of step models. The effect of energy conservation measures, triggers, and the use of renewable energy sources on the simulated data can be illustrated.

[0166] While the vertical axis illustrates “Metric Tons of Carbon”, other key performance indicators, such as Site EUI, Source EUI, Total Costs, Greenhouse Gas Emissions - Hourly, Greenhouse Gas Emissions - Annual Blended Rate, or other similar indicators, can be shown in other units.Exemplary Computer System

[0167] Referring now to FIG. 15 with continuing reference to the forgoing figures, an illustrative implementation of a computing device or computer system 500 that can be used in connection with any of the embodiments of the disclosure provided herein is shown.Computing device and / or computer system 500 can be used a component to any apparatus or system for any embodiment shown in FIGS. 1-14.

[0168] Implementations of a computer system are described within the context of a system configured to perform various steps, methods, and / or functionality in accordance withDocket No. 15103-002WO the described subject matter. It is to be appreciated that a computer system can be implemented by one or more computing devices. Implementations of the computer system can be described in the context of “computer-executable instructions” that are executed to perform various steps, methods, and / or functionality in accordance with the described subject matter.

[0169] In general, computers, computer systems, and / or computing devices can include one or more processors and storage devices (e.g., memory and disk drives) as well as various input devices, output devices, communication interfaces, and / or other types of devices. Such systems and devices can include a combination of hardware and software. It can be appreciated that various types of computer-readable storage media can be part of a computer, computer system, and / or computing device. As used herein, the terms “computer-readable storage media” and “computer-readable storage medium” do not mean and unequivocally exclude a propagated signal, a modulated data signal, a carrier wave, or any other type of transitory computer-readable medium. In various implementations, a computer system can include a processor configured to execute computer-executable instructions and a computer- readable storage medium (e.g., memory and / or additional hardware storage) storing computer-executable instructions configured to perform various steps, methods, and / or functionality in accordance with aspects of the described subject matter.

[0170] Computer-executable instructions can be embodied and / or implemented in various ways such as by a computer program (e.g., client program and / or server program), a software application (e.g., client application and / or server application), software code, application code, source code, executable files, executable components, routines, application programming interfaces (APIs), functions, methods, objects, properties, data structures, data types, and / or the like. Computer-executable instructions can be stored on one or more computer-readable storage media and can be executed by one or more processors, computing devices, and / or computer systems to perform particular tasks or implement particular data types in accordance with aspects of the described subject matter.

[0171] A computer, computer system, and / or computing device can implement and utilize one or more program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform particular tasks or implement particular abstract data types.

[0172] A computer, computer system, and / or computing device can be implemented as a distributed computing system or environment in which components are located on different computing devices that are connected to each other through network (e.g., wired and / orDocket No. 15103-002WO wireless) and / or other forms of direct and / or indirect connections. In such distributed computing systems or environments, tasks can be performed by one or more remote processing devices, or within a cloud of one or more devices, that are linked through one or more communications networks. In a distributed computing environment, program modules may be located in both local and remote computer storage media including media storage devices. Still further, the aforementioned instructions may be implemented, in part or in whole, as hardware logic circuits, which may or may not include a processor.

[0173] A computer system can include one or more servers. Servers can be implemented by one or more computing devices such as server computers configured to provide various types of services and / or data stores in accordance with aspects of the described subject matter. Exemplary severs computers can include, without limitation: web servers, front end servers, application servers, database servers, domain controllers, domain name servers, directory servers, and / or other suitable computers.

[0174] Components of computers, computer systems, and / or computing devices can be implemented by software, hardware, firmware or a combination thereof. For example, computer systems can include components implemented by computer-executable instructions that are stored on one or more computer-readable storage media and that are executed to perform various steps, methods, and / or functionality in accordance with aspects of the described subject matter.

[0175] As shown in FIG. 15, the computer system 500 can include one or more processors 510 and one or more articles of manufacture that comprise non-transitory computer-readable storage media (e.g., memory 520 and one or more non-volatile storage media 530). The processor 510 can control writing data to and reading data from the memory 520 and the non-volatile storage device 530 in any suitable manner. To perform any of the functionality described herein, the processor 510 can execute one or more processorexecutable instructions stored in one or more non-transitory computer-readable storage media (e.g., the memory 520), which can serve as non-transitory computer-readable storage media storing processor-executable instructions for execution by the processor 510.

[0176] The terms “program” or “software” are used herein in a generic sense to refer to any type of computer code or set of processor-executable instructions that can be employed to program a computer or other processor to implement various aspects of embodiments as discussed above.

[0177] References to a “module”, “a software module”, and the like, indicate a software component or part of a program, an application, and / or an app that contains one or moreDocket No. 15103-002WO routines. One or more independently modules can comprise a program, an application, and / or an app.

[0178] References to an “app”, an “application”, and a “software application” shall refer to a computer program or group of programs designed for end users. The terms shall encompass standalone applications, thin client applications, thick client applications, webbased applications, such as a browser, and other similar applications.

[0179] Additionally, it should be appreciated that according to one aspect, one or more computer programs that when executed perform methods of the disclosure provided herein need not reside on a single computer or processor, but can be distributed in a modular fashion among different computers or processors to implement various aspects of the disclosure provided herein. Processor-executable instructions can be in many forms, such as program modules, executed by one or more computers or other devices. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform particular tasks or implement particular abstract data types. Typically, the functionality of the program modules can be combined or distributed as desired in various embodiments.

[0180] Also, data structures can be stored in one or more non-transitory computer- readable storage media in any suitable form. For simplicity of illustration, data structures can be shown to have fields that are related through location in the data structure. Such relationships can likewise be achieved by assigning storage for the fields with locations in a non-transitory computer-readable medium that convey relationship between the fields.However, any suitable mechanism can be used to establish relationships among information in fields of a data structure, including through the use of pointers, tags or other mechanisms that establish relationships among data elements.Supported Embodiments

[0181] The detailed description provided above in connection with the appended drawings explicitly describes and supports various features of a building decarbonization apparatus in accordance with the described subject matter. By way of illustration and not limitation, supported embodiments include a building decarbonization apparatus comprising: a data acquisition system for acquiring calibration data and building performance data for a building; one or more processors; and at least one memory coupled to the one or more processors and storing instructions that, when executed by the one or more processors, cause the apparatus to perform operations comprising: creating a building energy model including a building performance input; obtaining calibration data from the data acquisition system toDocket No. 15103-002WO modify the building performance input and to convert the building energy model into an operational model with an operational model building performance input; determining the optimized operational model building performance input to create an optimized model with the optimized model building performance input; acquiring the building performance data from the data acquisition system; and producing time series building performance parameter data that includes at least one of the building performance data, the operational model building performance input, and the optimized model building performance input for transmission to a display device coupled to the building decarbonization apparatus.

[0182] Supported embodiments include the foregoing apparatus, wherein the calibration data is selected from the group consisting of real-time trended data and real-time time-series data.

[0183] Supported embodiments include any of the foregoing apparatus, wherein the output is based upon the time series building performance parameter data.

[0184] Supported embodiments include any of the foregoing apparatus, wherein the output includes the decarbonization potential of the building.

[0185] Supported embodiments include any of the foregoing apparatus, wherein the output includes at least one of a decarbonization strategy and a decarbonization strategy based upon the optimized model.

[0186] Supported embodiments include any of the foregoing apparatus, wherein the instructions include instructions for providing an interrogation-based commissioning module for identifying performance corrections based upon the time series building performance parameter data.

[0187] Supported embodiments include any of the foregoing apparatus, wherein the instructions include instructions for providing a monitoring-based commissioning module for identifying performance corrections based upon the time series building performance parameter data.

[0188] Supported embodiments include any of the foregoing apparatus, wherein the computer readable instructions include instructions for accessing an emissions database residing on a third party website to obtain a grid carbon emission factors to convert the building performance input into a building performance output.

[0189] Supported embodiments include any of the foregoing apparatus, wherein the instructions include instructions for converting calibration data into the same units as the building performance input.Docket No. 15103-002WO

[0190] Supported embodiments include any of the foregoing apparatus, wherein the calibration data and the building performance input are converted into key performance indicators.

[0191] Supported embodiments include any of the foregoing apparatus, wherein the instructions include instructions for producing at least one step model having a building performance input that has a value between the operational model building performance input and the optimized model building performance input.

[0192] Supported embodiments include any of the foregoing apparatus, wherein the computer readable instructions include instructions for: determining the carbon emissions savings for at least one of the operational model, the optimized model, and the at least one step model; calculating the market value of the carbon emissions savings; and communicating the market value of the carbon emissions savings to an external carbon market.

[0193] Supported embodiments include any of the foregoing apparatus, wherein the computer readable instructions include instructions for calculating the avoidance costs for at least one of the operational model, the optimized model, and the at least one step model.

[0194] Supported embodiments include any of the foregoing apparatus, wherein the computer readable instructions include instructions for acquiring weather forecast data from an application programming interface residing on a website and for modifying at least one of the operational model, the optimized model, and the at least one step model using the weather forecast data.

[0195] Supported embodiments include any of the foregoing apparatus, wherein the computer readable instructions include instructions for acquiring building usage data from an application programming interface residing on a website and for modifying at least one of the operational model, the optimized model, and the at least one step model using the building usage data.

[0196] Supported embodiments include any of the foregoing apparatus, wherein the building usage data includes at least one of people occupation data, commercial activity data, industrial activity data, and manufacturing data.

[0197] Supported embodiments include a method performed by one or more processors of a system, the method comprising: creating, in memory, a building energy model including a building performance input for a building; obtaining calibration data from a data acquisition system to modify the building performance input and to convert, in memory, the building energy model into an operational model with an operational model building performance input; determining, in memory, the optimized value of the operational model buildingDocket No. 15103-002WO performance input to create an optimized model with an optimized model building performance input; acquiring the building performance data from the data acquisition system; and producing time series building performance parameter data that includes at least one of the building performance data, the operational model building performance input, and the optimized model building performance input for transmission to a display device coupled to the building decarbonization apparatus; and communicating the time series building performance parameter data to a client device.

[0198] Supported embodiments include the foregoing method, wherein the calibration data is data selected from the group consisting of real-time trended data and real-time timeseries data.

[0199] Supported embodiments include any of the foregoing methods, further comprising: displaying output on the client device based upon the time series building performance parameter data.

[0200] Supported embodiments include any of the foregoing methods, wherein the output includes at least one of a decarbonization strategy and a decarbonization strategy based upon the optimized model.

[0201] Supported embodiments include a device, a system, a computer-readable storage medium, a computer program product and / or means for implementing any of the foregoing apparatus, methods, or portions thereof.

[0202] Supported embodiments can provide various attendant and / or technical advantages in terms of systems, methods, and apparatus that calculate accurate and precise real world carbon emissions metrics for buildings using 8760 emissions data, that calculate carbon emissions metrics using trended data and physics-based building emissions model, or that use models and trended data to make recommendations to reduce carbon emissions for buildings and other similar structures.

[0203] Although the subject matter has been described in language specific to structural features and / or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are presented as example forms of implementing the claims.

Claims

Docket No. 15103-002WOCLAIMSWhat is claimed is:

1. A building decarbonization apparatus comprising: a data acquisition system for acquiring calibration data and building performance data for a building; one or more processors; and at least one memory coupled to the one or more processors and storing instructions that, when executed by the one or more processors, cause the apparatus to perform operations comprising: creating a building energy model including a building performance input; obtaining calibration data from the data acquisition system to modify the building performance input and to convert the building energy model into an operational model with an operational model building performance input; determining the optimized operational model building performance input to create an optimized model with the optimized model building performance input; acquiring the building performance data from the data acquisition system; and producing time series building performance parameter data that includes at least one of the building performance data, the operational model building performance input, and the optimized model building performance input for transmission to a display device coupled to the building decarbonization apparatus.

2. The apparatus of claim 1, wherein the calibration data is selected from the group consisting of real-time trended data and real-time time-series data.

3. The apparatus of claim 1, wherein the output is based upon the time series building performance parameter data.

4. The apparatus of claim 3, wherein the output includes the decarbonization potential of the building.

5. The apparatus of claim 3, wherein the output includes at least one of a decarbonization strategy and a decarbonization strategy based upon the optimized model.Docket No. 15103-002WO6. The apparatus of claim 5, wherein the instructions include instructions for providing an interrogation-based commissioning module for identifying performance corrections based upon the time series building performance parameter data.

7. The apparatus of claim 5, wherein the instructions include instructions for providing a monitoring-based commissioning module for identifying performance corrections based upon the time series building performance parameter data.

8. The apparatus of claim 1, wherein the computer readable instructions include instructions for accessing an emissions database residing on a third party website to obtain a grid carbon emission factors to convert the building performance input into a building performance output.

9. The apparatus of claim 1 , wherein the instructions include instructions for converting calibration data into the same units as the building performance input.

10. The apparatus of claim 9, wherein the calibration data and the building performance input are converted into key performance indicators.

11. The apparatus of claim 1 , wherein the instructions include instructions for producing at least one step model having a building performance input that has a value between the operational model building performance input and the optimized model building performance input.

12. The apparatus of claim 1 1, wherein the computer readable instructions include instructions for: determining the carbon emissions savings for at least one of the operational model, the optimized model, and the at least one step model; calculating the market value of the carbon emissions savings; and communicating the market value of the carbon emissions savings to an external carbon market.Docket No. 15103-002WO13. The apparatus of claim 12, wherein the computer readable instructions include instructions for calculating the avoidance costs for at least one of the operational model, the optimized model, and the at least one step model.

14. The apparatus of claim 11, wherein the computer readable instructions include instructions for acquiring weather forecast data from an application programming interface residing on a website and for modifying at least one of the operational model, the optimized model, and the at least one step model using the weather forecast data.

15. The apparatus of claim 11, wherein the computer readable instructions include instructions for acquiring building usage data from an application programming interface residing on a website and for modifying at least one of the operational model, the optimized model, and the at least one step model using the building usage data.

16. The apparatus of claim 15, wherein the building usage data includes at least one of people occupation data, commercial activity data, industrial activity data, and manufacturing data.

17. A method performed by one or more processors of a system, the method comprising: creating, in memory, a building energy model including a building performance input for a building; obtaining trended data from a data acquisition system to convert, in memory, the building energy model into an operational model with an operational model building performance input; determining, in memory, the optimized value of the operational model building performance input to create an optimized model with an optimized model building performance input; acquiring the building performance data from the data acquisition system; producing time series building performance parameter data that includes at least one of the building performance data, the operational model building performance input, and the optimized model building performance input for transmission to a display device coupled to the building decarbonization apparatus; and communicating the time series building performance parameter data to a client device.Docket No. 15103-002WO18. The method of claim 17, wherein the calibration data is data selected from the group consisting of real-time trended data and real-time time-series data.

19. The method of claim 17, further comprising: displaying output on the client device based upon the time series building performance parameter data.

20. The method of claim 19, wherein the output includes at least one of a decarbonization strategy and a decarbonization strategy based upon the optimized model.

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