Notice of Pre-AIA or AIA Status
The present application is being examined under the pre-AIA first to invent provisions.
This office action is responsive to the applicant’s arguments filed on 01/28/2026.
Claims 1, 3-8, 10-19, and 21-23 are pending. Claims 1, 8, 15, and 21-23 are amended.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 01/28/2026 has been entered.
Response to Arguments
Regarding claim objections:
The objection has been withdrawn in view of the amendment.
Regarding rejections under 35 USC § 112:
The rejection has been withdrawn in view of the amendment.
Regarding rejections under 35 USC § 101:
Applicant’s arguments regarding the 101 rejection are based on newly amended subject matter. Therefore, all arguments are addressed in the 101 rejection of the claims below.
Regarding rejections under 35 USC § 103:
Applicant's arguments filed 12/22/2025 have been fully considered but they are not persuasive.
With respect to the remarks, page 9-11, regarding the overall current draw for the HVAC system and the energy use data for the component received at the controller from the same current sensor, the Examiner respectfully disagrees because Hoeynck teaches this limitation.
To clarify, Hoeynck teaches that both the current and energy use data are measured by the “sensing device” (26) ([0028]: “Within each room 24, there may be one or more energy consumption sensing device 26 and one or more environment sensing device 28. … For example, energy consumption sensing devices 26 may sense voltage, current, power and/or phase of the electricity being consumed, and may monitor and record changes in these parameters with time.”).
The remarks on page 11 also alleges that these sensors are scattered around the building and do not measure the current being drawn by the HVAC system as a whole. Examiner notes that Hoeynck teaches measuring the total power being consumed from these sensor(s) ([0059]: “For example, sensing devices 26 may detect the total electrical power being consumed within building 22 in order to enable processor 30 to estimate the amount of heat that will be generated by such power consumption.”). Examiner also notes that claim 1 does not recite whether the sensors are scattered or not in order to measure the total power being consumed by the building. In fact, the claim recites “sense each of an overall current draw … obtain an energy use data of a component” which appears that the sensor data are gathered from each of the components of the HVAC system in order to obtain the overall current draw of the system. Specification pg. 8 lines 19-24 discloses: “Load disaggregation 48 may be a software module which determines per-load energy consumption from single current measurements of RTU 11. It may employ measurements made by the load baselining manager 47 in combination with a current state of RTU controller's stage outputs to determine each load's contribution to the total current draw.” Pg. 18 lines 3-11 discloses: “The computer may further incorporate a load baselining manager that reduces energy consumption by individual components of the roof top unit by removing detected equipment faults and/or operation inefficiencies in the heating, cooling and mechanical subsystems of the roof top unit as indicated by the fault detection and diagnostic and efficiency monitoring algorithm module, based on signals representing vibration at the roof top unit, total power consumption by the roof top unit indicated by the one or more current sensors, and/or power consumption of each of one or more components indicated by a difference of readings of power consumption at the main meter caused by each of the respective components being turned on and off.” These descriptions from specification also show that the sensor data is gathered for each component in order to determine the total power consumption. Therefore, Drees/Hoeynck teaches the limitation “a current sensor configured to sense each of an overall current draw for the HVAC system comprising a plurality electrical loads and to obtain an energy use data of a component comprising at least one of the plurality of electrical loads of the HVAC system.”
With respect to the remarks, page 12-13, regarding the non-obvious advantage of claim 1, the Examiner respectfully disagrees because as explained previously, Drees/Hoeynck structurally teaches the limitations of claim 1.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 3-8, 10-19, and 21-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract ideas without significantly more.
Step 1: Claims 1, 3-7, and 21 are directed to a device, which is a machine, falling under a statutory category of invention. Claims 8, 10-14, and 22 are directed to a system, which is a machine, falling under a statutory category of invention. Claims 15-19 and 23 are directed to a method, which is a process, falling under a statutory category of invention. Therefore, claims 1, 3-8, 10-19, and 21-23 are directed to patent eligible categories of invention.
Regarding claim 1:
Step 2A Prong 1: The following limitations recite abstract ideas:
The limitation “control setpoints of the component based on past historical measurements” under broadest reasonable interpretation covers mathematical concepts. Specification at page 7 lines 16-20 discloses: “In the present system, a variety of algorithms based on mathematical models of predicted energy consumption may be employed. The algorithms may determine the ideal energy consumption for the current environmental conditions and control setpoints based on past historical measurements and compare the measured energy consumption to this.” Specification at page 15 lines 5-7 also discloses: “The big model for the whole building electricity consumption may be built from particular sub-models that are using the HVAC control signals and measurements (fan speed, cooling stage 1 percentage on, outdoor air temperature, setpoints, and so forth) as explanatory variables.” Specification at page 16 lines 29-31 – page 17 lines 1-6 discloses: “Example models for RTU energy consumption may be indicated by the following formulas. Power=a1·clg1·OAT+a2·clg2·OAT+a3·aoecon+a4·fango+a5·htg1+a5·htg2+a6·(OAT−ZAT)+a7·(OAT−ZAT)2. This formula is just an example model for RTU consumption. OAT may represent outdoor air temperature; clg1/2 may represent a control signal for a cooling stage 1/2; fango may represent a control signal for a blower; htg1/2 may represent a control signal for a heating stage 1/2; aoecon may represent a control signal for an economizer; and ZAT (zone temp) may represent a substituted setpoint.” According to such descriptions, controlling setpoints based on past historical measurements amounts to changing values of corresponding variables in the model equation. Therefore, this amounts to mathematical concepts.
The limitation “create a predictive energy consumption model for the component of the HVAC system based on the energy use data and the overall current draw” under broadest reasonable interpretation covers mathematical concepts. Specification at page 7 lines 16-17 discloses that a variety of algorithms based on mathematical models of predicted energy consumption may be employed. Specification at page 16 lines 29-31 – page 17 line 1 discloses: “Example models for RTU energy consumption may be indicated by the following formulas. Power=a1·clg1·OAT+a2·clg2·OAT+a3·aoecon+a4·fango+a5·htg1+a5·htg2+a6·(OAT−ZAT)+a7·(OAT−ZAT)2.” Therefore, creating a predictive energy consumption model amounts to mathematical concepts involving mathematical formulas.
The limitation “derive a performance degradation for the component based on the environmental condition data, the control setpoints, and the predictive energy consumption model” under broadest reasonable interpretation covers a mental process including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper. For example, specification at Pg. 18, Lines 15-18 discloses: “The fault detection and diagnostic and efficiency monitoring algorithm module may use differences in actual consumption of the equipment versus model-predicted consumption to detect potential issues with the roof top unit or a performance degradation of the roof top unit over a period of time.” Therefore, deriving a performance degradation based on the predictive energy consumption model covers comparing the predicted consumption calculated from the model and the actual consumption and determining a performance degradation based on the difference. This amounts to a mental process because this covers a person mentally observing the difference between the predicted value and the actual value and mentally making a judgment about a performance degradation based on the observation. Examiner notes that specification at page 7 lines 17-20 discloses: “The algorithms may determine the ideal energy consumption for the current environmental conditions and control setpoints based on past historical measurements and compare the measured energy consumption to this.” Therefore, the environmental condition data and the control setpoints are calculated by the model and compared to measured/actual values. Therefore, a performance degradation is derived “based on the environmental condition data, the control setpoints, and the predictive energy consumption model.”
The limitation “detect equipment faults and/or operation inefficiencies in the component based on the performance degradation derived for the component” under broadest reasonable interpretation covers a mental process including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper. For example, a person can mentally observe the derived performance degradation and make a mental judgment about whether there are equipment faults and/or operation inefficiencies.
Step 2A Prong 2: The following limitations recite additional elements:
“a current sensor configured to sense each of an overall current draw for the HVAC system comprising a plurality electrical loads and to obtain an energy use data of a component comprising at least one of the plurality of electrical loads of the HVAC system”
“a controller comprising a computer (CPU) operatively coupled to the current sensor”
“receive environmental condition data”
“receive the energy use data for the component of the HVAC system from the current sensor”
“receive an overall current draw for the HVAC system from the current sensor”
“output an alert based on the detected equipment faults or/or operation inefficiencies”
However, these additional elements do not integrate the judicial exception into a practical application.
The additional elements “a current sensor configured to sense each of an overall current draw for the HVAC system comprising a plurality electrical loads and to obtain an energy use data of a component comprising at least one of the plurality of electrical loads of the HVAC system”; “receive environmental condition data”; “receive the energy use data for the component of the HVAC system from the current sensor”; and “receive an overall current draw for the HVAC system from the current sensor” do not integrate the judicial exception into a practical application because they are data gathering activities. See MPEP 2106.05(g).
The additional element “a controller comprising a computer (CPU) operatively coupled to the current sensor” does not integrate the judicial exception into a practical application because it amounts to no more than mere instructions to apply the judicial exception using a generic computer. See MPEP 2106.05(f).
The additional element “output an alert based on the detected equipment faults or/or operation inefficiencies” does not integrate the judicial exception into a practical application because it amounts to an insignificant extra-solution activity. Specifically, this amounts to a post-solution activity of outputting an alert based on the result. See MPEP 2106.05(g).
Even when viewed in combination, these additional elements do not integrate the judicial exception into a practical application.
Accordingly, the claim does not recite any additional elements that integrate the judicial exception into a practical application.
Step 2B: Furthermore, the additional elements do not amount to significantly more than the judicial exception.
The additional elements “a current sensor configured to sense each of an overall current draw for the HVAC system comprising a plurality electrical loads and to obtain an energy use data of a component comprising at least one of the plurality of electrical loads of the HVAC system”; “receive environmental condition data”; “receive the energy use data for the component of the HVAC system from the current sensor”; and “receive an overall current draw for the HVAC system from the current sensor” are data gathering activities that fall under receiving or transmitting data over a network. Such activities do not amount to significantly more than the judicial exception. See MPEP 2106.05(d)(II).
As previously discussed, the additional element “a controller comprising a computer (CPU) operatively coupled to the current sensor” amounts to no more than mere instructions to apply the exception using a generic computer. Mere instructions to apply an exception using a generic computer do not amount to significantly more than the judicial exception. See MPEP 2106.05(f).
The additional element “output an alert based on the detected equipment faults or/or operation inefficiencies” amounts to an insignificant extra-solution activity which is akin to a well-understood, routine, and conventional activity of presenting offers and gathering statistics. See MPEP 2106.05(d)(II): “iv. Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93”.
Accordingly, the claim does not recite any additional elements that amount to significantly more than the judicial exception.
Therefore, claim 1 is not eligible.
Regarding claim 3: Claim 3 merely further limits the energy use data recited in claim 1. Accordingly, the same analysis used in claim 1 is applicable.
Therefore, claim 3 is not eligible.
Regarding claim 4:
The limitation “partition the predictive energy consumption model into a set of predictive energy consumption submodels for subparts of the component” under broadest reasonable interpretation covers mathematical concepts. Specification at page 14 lines 12-14 discloses that an estimate of submetered power may be obtained by estimating all of the model parameters having main meter data and successive partitioning of a big total model to individual submodels. Therefore, partitioning the predictive energy consumption model covers partitioning a model equation for example by parameters and generating a new set of equations each containing only the parameter relevant to a particular subpart.
The claim does not recite any additional elements that would have provided practical application of or have added significantly more to the cited abstract idea.
Therefore, claim 4 is not eligible.
Regarding claim 5:
The limitation “compare the set of predictive energy consumption submodels to the current drawn by the subparts of the component” under broadest reasonable interpretation covers a mental process including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper. For example, comparing two values covers a person mentally observing the two values and making a judgment based on the observation.
The limitation “derive a performance degradation for the subparts of the component based on the comparison between the set of predictive energy consumption submodels and the current drawn” under broadest reasonable interpretation covers a mental process including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper. For example, this covers a person mentally making a comparison between the two values as explained above and making a judgment about a performance degradation based on the mental observation and comparison.
The claim does not recite any additional elements that would have provided practical application of or have added significantly more to the cited abstract idea.
Therefore, claim 5 is not eligible.
Regarding claim 6:
The limitation “derive the performance degradation for the component based on a comparison of the predictive energy consumption model and the energy use data” under broadest reasonable interpretation covers a mental process including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper for the similar reason as explained in claim 1.
The claim does not recite any additional elements that would have provided practical application of or have added significantly more to the cited abstract idea.
Therefore, claim 6 is not eligible.
Regarding claim 7: Claim 7 merely further limits the current sensor recited in claim 1. Accordingly, the same analysis used in claim 1 is applicable.
Therefore, claim 7 is not eligible.
Regarding claim 8: Claim 8 is substantially similar to claim 1. Therefore, the similar analysis as claim 1 is applicable.
The limitation “a heating, ventilation, and air conditioning (HVAC) system” is an additional element.
Step 2A Prong 2: The additional elements do not integrate the judicial exception into a practical application.
The additional element “a heating, ventilation, and air conditioning (HVAC) system” does not integrate the judicial exception into a practical application because it generally links the use of a judicial exception to a particular technological environment or field of use. Specifically, this amounts to merely indicating a field of use or technological environment in which to apply the recited judicial exception as a HVAC system. See MPEP 2106.05(h).
Even when viewed in combination, these additional elements do not integrate the judicial exception into a practical application.
Accordingly, the claim does not recite any additional elements that integrate the judicial exception into a practical application.
Step 2B: Furthermore, the additional elements do not amount to significantly more than the judicial exception.
As previously explained, the additional element “a heating, ventilation, and air conditioning (HVAC) system” generally links the use of a judicial exception to a particular technological environment. Limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the judicial exception. See MPEP 2106.05(h).
Accordingly, the claim does not recite any additional elements that amount to significantly more than the judicial exception.
Therefore, claim 8 is not eligible.
Regarding claim 12:
The limitation “compare the set of predictive energy consumption submodels to actual energy consumed by the subparts of the component” under broadest reasonable interpretation covers a mental process including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper. For example, this covers someone mentally observing the submodels and the actual energy consumed and making a judgment about their differences.
The limitation “derive a performance degradation for the subparts of the component based on the comparison between the set of predictive energy consumption submodels and the actual energy consumed by the subparts” under broadest reasonable interpretation covers a mental process including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper. For example, this covers a person mentally making a comparison as explained above and making a judgment about a performance degradation based on the mental observation and comparison.
The limitation “wherein the actual energy consumed by the subparts of the component is received in the energy use data for the component” is an additional element.
Step 2A Prong 2: The additional elements do not integrate the judicial exception into a practical application.
The additional element “wherein the actual energy consumed by the subparts of the component is received in the energy use data for the component” does not integrate the judicial exception into a practical application because it is a data gathering activity. See MPEP 2106.05(g).
Even when viewed in combination, these additional elements do not integrate the judicial exception into a practical application.
Accordingly, the claim does not recite any additional elements that integrate the judicial exception into a practical application.
Step 2B: Furthermore, the additional elements do not amount to significantly more than the judicial exception.
The additional element “wherein the actual energy consumed by the subparts of the component is received in the energy use data for the component” is a data gathering activity that falls under receiving or transmitting data over a network. Such activities do not amount to significantly more than the judicial exception. See MPEP 2106.05(d)(II).
Accordingly, the claim does not recite any additional elements that amount to significantly more than the judicial exception.
Therefore, claim 12 is not eligible.
Regarding claim 21:
The limitation “where the controller is further configured to control the HVAC system to remove detected equipment faults and/or operation inefficiencies detected in the component based on the performance degradation derived for the component” does not integrate the judicial exception into a practical application because it amounts to no more than mere instructions to apply the judicial exception. Specifically, this recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished or only generally recites the application of the judicial exception. For example, claim recites that the equipment faults and/or operation inefficiencies are removed by the HVAC system, but it does not recite how the faults or inefficiencies are removed. See MPEP 2106.05(f)(1): “The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it".” Also See MPEP 2106.05(f)(3): “For instance, a claim that generically recites an effect of the judicial exception or claims every mode of accomplishing that effect, amounts to a claim that is merely adding the words "apply it" to the judicial exception. See Internet Patents Corporation v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1418 (Fed. Cir. 2015) (The recitation of maintaining the state of data in an online form without restriction on how the state is maintained and with no description of the mechanism for maintaining the state describes "the effect or result dissociated from any method by which maintaining the state is accomplished" and does not provide a meaningful limitation because it merely states that the abstract idea should be applied to achieve a desired result).”
Claims 9-11, 13-20, and 22-23 are substantially similar to claims 1-4, 6-7, 12, and 21. Therefore, the similar analysis as claim 1 is applicable.
Accordingly, claims 1, 3-8, 10-19, and 21-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. an abstract idea) without anything significantly more.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 6, 8, 13, 15, 19, and 21-23 is/are rejected under 35 U.S.C. 103 as being unpatentable over Drees et al. (US20120022700A1), hereinafter Drees, in view of Hoeynck et al. (US20100025483A1), hereinafter Hoeynck.
Regarding claim 1, Drees discloses
the HVAC system comprising a plurality electrical loads and … a component comprising at least one of the plurality of electrical loads of the HVAC system ([0047]: “Significant electrical loads may be shed by the integrated control layer 116, including, for example, heating and humidification loads, cooling and dehumidification loads, ventilation and fan loads, electric lighting and plug loads (e.g. with secondary thermal loads), electric elevator loads, and the like. The integrated control layer 116 may further be configured to integrate an HVAC subsystem or a lighting subsystem with sunlight shading devices or other “smart window” technologies.”);
a controller comprising a computer (CPU) operatively coupled to the … sensor ([0034]: “For example, HVAC subsystem 140 may include a chiller, a boiler, any number of air handling units, economizers, field controllers, supervisory controllers, actuators, temperature sensors, and other devices for controlling the temperature within a building.”) ([0145]: “Field controller 904 controls one or more components of the BMS and receives or calculates performance values 906 (e.g., sensor inputs, actuator positions, etc.). Controller 904 can store a trend of performance values 906, setpoints and current status in local trend storage 908.”) ([0066]: “For example, FDD layer 114 may receive inputs from building subsystem supervisory controllers or field controllers having FDD features.”) ([0003]: “The controller includes a processing circuit”); and wherein the controller is configured to:
receive environmental condition data ([0071]: “The inputs received from other layers may include environmental or sensor inputs such as temperature, carbon dioxide levels, relative humidity levels, air quality sensor outputs, occupancy sensor outputs, room schedules, and the like.”);
control setpoints of the component based on past historical measurements ([0044]: “For example, the “badge-in” event described above can be used by the integrated control layer 116 (e.g., a control algorithm thereof) to provide new setpoints to an HVAC control algorithm of the HVAC subsystem.”) ([0145]: “Controller 904 can store a trend of performance values 906, setpoints and current status in local trend storage 908.”) ([0060]: “Automatic fault diagnostics module 414 may be configured to use meter data 402, weather data 404, model data 406 (e.g., performance models based on historical building equipment performance), building subsystem data 408, performance indices 410, or other data available at the building subsystem integration layer to complete its fault diagnostics activities.”) ([0064]: “The FDD layer 114 may be configured to maintain detailed historical databases (e.g., relational databases, XML databases, etc.) of relevant data”) ([0074]: “The smart building manager 106 (with input from the user or operating using pre-configured business rules shown in FIG. 3) may be configured to accept time-of-use pricing signals or information from a smart grid (e.g., an energy provider, a smart meter, etc.) and, using its knowledge of historical building system data, control algorithms, calendar information, and/or weather information received from a remote source, may be configured to conduct automatic cost forecasting.”) ([0112]: “Performance value database 524 can store a history of performance values used by training component 522 to generate statistical models, such as model data 406.”) ([0117]: “Process 600 includes receiving a history of performance values (step 602).”) ([0139]: “Process 800 is also shown to include using historical data to create a baseline model that allows energy usage (e.g., kWh) or power consumption (e.g., kW) to be predicted from varying input or predictor variables (e.g., occupancy, space usage, occupancy hours, outdoor air temperature, solar intensity, degree days, etc.).”);
create a predictive energy consumption model for the component of the HVAC system ([0106]: “Once a sufficient history of performance values has been built, the history can be used to generate a statistical model (step 504). Generally speaking, the statistical model is a set of metrics based on, calculated using, or describing the history of performance values. The statistical model is used to predict a behavior of the BMS.”) ([0112]: “Performance value database 524 can store a history of performance values used by training component 522 to generate statistical models, such as model data 406.”) ([0104]: “A statistical process control strategy of varying exemplary embodiments may detect variations in measured data by evaluating the measured data relative to a trained statistical model (e.g., a statistical process control chart). The trained statistical model may be based on measurements taken during a training period (e.g., while the building management system is operating normally, during a steady state operating period, etc.). The trained statistical model is used to predict behavior for the building management system under normal operating conditions.”) ([0247]: “These cost factors may be applied to the energy consumption and demand calculated over a defined period (e.g., a calendar month) using the actual performance data and the predicted performance using the manufacturer's baseline or best baseline models.”) ([0139]: “Process 800 is also shown to include using historical data to create a baseline model that allows energy usage (e.g., kWh) or power consumption (e.g., kW) to be predicted from varying input or predictor variables (e.g., occupancy, space usage, occupancy hours, outdoor air temperature, solar intensity, degree days, etc.).”);
derive a performance degradation for the component based on the environmental condition data, the control setpoints, and the predictive energy consumption model ([0104]: “The trained statistical model is used to predict behavior for the building management system under normal operating conditions.”) ([0106]: “The statistical model is used to predict a behavior of the BMS.”) ([0247]: “These cost factors may be applied to the energy consumption and demand calculated over a defined period (e.g., a calendar month) using the actual performance data and the predicted performance using the manufacturer's baseline or best baseline models.”) ([0062]: “Automated fault diagnostics module 414 may further be configured to compute residuals (differences between measured and expected values) for analysis to determine the fault source.”) ([0235]: “Model-based performance index calculation module 1510 calculates a performance index for the chiller. In an exemplary embodiment, the performance index may be calculated by finding the difference between the actual chiller performance and the model-predicted performance.”); and
detect equipment faults and/or operation inefficiencies in the component based on the performance degradation derived for the component ([0058]: “FDD layer 114 may use some content of data stores 402-410 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.”) ([0060]: “Automatic fault diagnostics module 414 may be configured to use … model data 406, … performance indices 410, or other data available at the building subsystem integration layer to complete its fault diagnostics activities.”) ([0062]: “Automated fault diagnostics module 414 may further be configured to compute residuals (differences between measured and expected values) for analysis to determine the fault source.”) ([0106]: “Once a sufficient history of performance values has been built, the history can be used to generate a statistical model (step 504). Generally speaking, the statistical model is a set of metrics based on, calculated using, or describing the history of performance values. The statistical model is used to predict a behavior of the BMS.”) ([0112]: “Automated fault detection module 412 includes performance value database 524.”) ([0236]: “Faults are detected by detector 1511 when the chiller's actual performance deviates significantly from the predicted performance (e.g., predicted using the “manufacturer baseline” or “best baseline”).”) and
output an alert based on the detected equipment faults or/or operation inefficiencies ([0057]: “The responses to detected or diagnosed faults may include providing an alert message to a user, a maintenance scheduling system, or a control algorithm configured to attempt to repair the fault or to work-around the fault.”) ([0102]: “The controller can then notify a user of the potential fault.”) ([0108]: “FDD layer 114 may then notify a user, a maintenance scheduling system, or a control algorithm configured to attempt to further diagnose the fault, to repair the fault, or to work-around the fault.”).
Drees does not explicitly disclose a current sensor and receiving energy use data for the component of the HVAC system and current draw for the HVAC system from the current sensor.
However, Hoeynck teaches current sensor and receiving energy use data and current draw from the current sensor for predicting energy consumption ([0011]: “The invention may also use pattern recognition and classification techniques to derive a sensor-based behavioral prediction algorithm reaching several hours into the future. This model-based prediction may be used as a baseline for the development of control and optimization techniques.”) ([0013]: “A future value of an energy consumption parameter is predicted based upon the collected current data, the associated time-of-day data, and historic data collected from the environment sensing device and the energy consumption sensing device.”) ([0028]: “For example, energy consumption sensing devices 26 may sense voltage, current, power and/or phase of the electricity being consumed, and may monitor and record changes in these parameters with time.”) ([0033]: “Energy consumption sensing devices 26 may measure characteristics of operating appliances and devices in the building (e.g., AC/DC current, voltage, phase and frequency harmonics).”) ([0034]: “The predictor receives indoor and outdoor environmental cues provided by environment sensing devices 28, including temperature, humidity, acoustics, carbon dioxide, illumination and motion, among others. The predictor also receives device or appliance electrical power consumption signatures including voltage, current, phase and power for each device.”) ([0059]: “For example, sensing devices 26 may detect the total electrical power being consumed within building 22 in order to enable processor 30 to estimate the amount of heat that will be generated by such power consumption.”).
Drees and Hoeynck are analogous to the claimed invention because they are in the same field of predicting energy consumption for a HVAC system using a model based on sensor data.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Hoeynck’s teachings for using a current sensor to include current data in the prediction model to modify Drees to use a current sensor and include current data in the model.
One of ordinary skill in the art would have been motivated to make this modification because current data are important data for measuring energy consumption and therefore incorporating current data would allow predicting energy consumption and a performance degradation more accurately (Hoeynck, [0010]-[0011]: “The present invention provides a method for sensing current human occupancy of a building as well as current energy consumption characteristics in order to predict HVAC operation requirements in the ensuing several hours in view of past occupancy and energy consumption patterns. … Device signatures may include temporal/frequency patterns of voltage, current, and/or phase. Environmental cues may include parameters such as temperature, humidity, carbon dioxide, illumination, and acoustics.”).
Therefore, the combination of Drees and Hoeynck teaches
a current sensor configured to sense each of an overall current draw for the HVAC system comprising a plurality electrical loads and to obtain an energy use data of a component comprising at least one of the plurality of electrical loads of the HVAC system (Drees, [0047]: “Significant electrical loads may be shed by the integrated control layer 116, including, for example, heating and humidification loads, cooling and dehumidification loads, ventilation and fan loads, electric lighting and plug loads (e.g. with secondary thermal loads), electric elevator loads, and the like. The integrated control layer 116 may further be configured to integrate an HVAC subsystem or a lighting subsystem with sunlight shading devices or other “smart window” technologies.”) (Hoeynck, [0028]: “For example, energy consumption sensing devices 26 may sense voltage, current, power and/or phase of the electricity being consumed, and may monitor and record changes in these parameters with time.”) (Hoeynck, [0033]: “Energy consumption sensing devices 26 may measure characteristics of operating appliances and devices in the building (e.g., AC/DC current, voltage, phase and frequency harmonics). … These predictions may be fed into a building automation system that optimally balances the tradeoff of comfort and energy-efficient management of building systems such as HVAC (e.g., residential heating/cooling or commercial ventilation)”) (Hoeynck, [0031]: “For example, processor 30 may receive a schedule of electricity costs at various times of the day, which processor 30 may use in deciding when and/or whether to operate various electrical devices, such as heating ventilating and air conditioning (HVAC)”) (Hoeynck, [0059]: “For example, sensing devices 26 may detect the total electrical power being consumed within building 22 in order to enable processor 30 to estimate the amount of heat that will be generated by such power consumption.”);
a controller comprising a computer (CPU) operatively coupled to the current sensor (Drees, [0034]: “For example, HVAC subsystem 140 may include a chiller, a boiler, any number of air handling units, economizers, field controllers, supervisory controllers, actuators, temperature sensors, and other devices for controlling the temperature within a building.”) (Drees, [0145]: “Field controller 904 controls one or more components of the BMS and receives or calculates performance values 906 (e.g., sensor inputs, actuator positions, etc.). Controller 904 can store a trend of performance values 906, setpoints and current status in local trend storage 908.”) (Drees, [0066]: “For example, FDD layer 114 may receive inputs from building subsystem supervisory controllers or field controllers having FDD features.”) (Drees, [0003]: “The controller includes a processing circuit”) (Hoeynck, [0013]: “A future value of an energy consumption parameter is predicted based upon the collected current data, the associated time-of-day data, and historic data collected from the environment sensing device and the energy consumption sensing device.”) (Hoeynck, [0028]: “For example, energy consumption sensing devices 26 may sense voltage, current, power and/or phase of the electricity being consumed, and may monitor and record changes in these parameters with time.”) (Hoeynck, [0034]: “The predictor receives indoor and outdoor environmental cues provided by environment sensing devices 28, including temperature, humidity, acoustics, carbon dioxide, illumination and motion, among others. The predictor also receives device or appliance electrical power consumption signatures including voltage, current, phase and power for each device.”);
receive the energy use data for the component of the HVAC system from the current sensor (Hoeynck, [0013]: “A future value of an energy consumption parameter is predicted based upon the collected current data, the associated time-of-day data, and historic data collected from the environment sensing device and the energy consumption sensing device.”) (Hoeynck, [0028]: “For example, energy consumption sensing devices 26 may sense voltage, current, power and/or phase of the electricity being consumed, and may monitor and record changes in these parameters with time.”) (Hoeynck, [0033]: “Energy consumption sensing devices 26 may measure characteristics of operating appliances and devices in the building (e.g., AC/DC current, voltage, phase and frequency harmonics).”);
receive an overall current draw for the HVAC system from the current sensor (Hoeynck, [0028]: “For example, energy consumption sensing devices 26 may sense voltage, current, power and/or phase of the electricity being consumed, and may monitor and record changes in these parameters with time.”) (Hoeynck, [0033]: “Energy consumption sensing devices 26 may measure characteristics of operating appliances and devices in the building (e.g., AC/DC current, voltage, phase and frequency harmonics).”) (Hoeynck, [0034]: “The predictor receives indoor and outdoor environmental cues provided by environment sensing devices 28, including temperature, humidity, acoustics, carbon dioxide, illumination and motion, among others. The predictor also receives device or appliance electrical power consumption signatures including voltage, current, phase and power for each device.”); and
create a predictive energy consumption model for the component of the HVAC system based on the energy use data and the overall current draw (Drees, [0104]: “The trained statistical model is used to predict behavior for the building management system under normal operating conditions.”) (Drees, [0106]: “The statistical model is used to predict a behavior of the BMS.”) (Drees, [0247]: “These cost factors may be applied to the energy consumption and demand calculated over a defined period (e.g., a calendar month) using the actual performance data and the predicted performance using the manufacturer's baseline or best baseline models.”) (Drees, [0062]: “Automated fault diagnostics module 414 may further be configured to compute residuals (differences between measured and expected values) for analysis to determine the fault source.”) (Drees, [0235]: “Model-based performance index calculation module 1510 calculates a performance index for the chiller. In an exemplary embodiment, the performance index may be calculated by finding the difference between the actual chiller performance and the model-predicted performance.”) (Hoeynck, [0011]: “The invention may also use pattern recognition and classification techniques to derive a sensor-based behavioral prediction algorithm reaching several hours into the future. This model-based prediction may be used as a baseline for the development of control and optimization techniques.”) (Hoeynck, [0013]: “A future value of an energy consumption parameter is predicted based upon the collected current data, the associated time-of-day data, and historic data collected from the environment sensing device and the energy consumption sensing device.”) (Hoeynck, [0028]: “For example, energy consumption sensing devices 26 may sense voltage, current, power and/or phase of the electricity being consumed, and may monitor and record changes in these parameters with time.”) (Hoeynck, [0033]: “Energy consumption sensing devices 26 may measure characteristics of operating appliances and devices in the building (e.g., AC/DC current, voltage, phase and frequency harmonics).”) (Hoeynck, [0034]: “The predictor receives indoor and outdoor environmental cues provided by environment sensing devices 28, including temperature, humidity, acoustics, carbon dioxide, illumination and motion, among others. The predictor also receives device or appliance electrical power consumption signatures including voltage, current, phase and power for each device.”).
Regarding claim 6, Drees/Hoeynck teaches the device of claim 1 as above. Drees/Hoeynck further teaches the device, wherein the controller is configured to
derive the performance degradation for the component based on a comparison of the predictive energy consumption model and the energy use data (Drees, [0104]: “The trained statistical model is used to predict behavior for the building management system under normal operating conditions.”) (Drees, [0106]: “The statistical model is used to predict a behavior of the BMS.”) (Drees, [0247]: “These cost factors may be applied to the energy consumption and demand calculated over a defined period (e.g., a calendar month) using the actual performance data and the predicted performance using the manufacturer's baseline or best baseline models.”) (Drees, [0062]: “Automated fault diagnostics module 414 may further be configured to compute residuals (differences between measured and expected values) for analysis to determine the fault source.”) (Drees, [0235]: “Model-based performance index calculation module 1510 calculates a performance index for the chiller. In an exemplary embodiment, the performance index may be calculated by finding the difference between the actual chiller performance and the model-predicted performance.”) (Drees, [0139]: “Process 800 is also shown to include using historical data to create a baseline model that allows energy usage (e.g., kWh) or power consumption (e.g., kW) to be predicted from varying input or predictor variables (e.g., occupancy, space usage, occupancy hours, outdoor air temperature, solar intensity, degree days, etc.).”).
Regarding claim 8, Claim 8 is substantially similar to claim 1. Therefore, the similar analysis as claim 1 is applicable.
Furthermore, Drees/Hoeynck teaches
a current sensor configured to sense both an overall current draw for the HVAC system comprising a plurality electrical loads and to obtain an energy use data of a component comprising at least one of the plurality of electrical loads of the HVAC system (Drees, [0047]: “Significant electrical loads may be shed by the integrated control layer 116, including, for example, heating and humidification loads, cooling and dehumidification loads, ventilation and fan loads, electric lighting and plug loads (e.g. with secondary thermal loads), electric elevator loads, and the like. The integrated control layer 116 may further be configured to integrate an HVAC subsystem or a lighting subsystem with sunlight shading devices or other “smart window” technologies.”) (Hoeynck, [0028]: “For example, energy consumption sensing devices 26 may sense voltage, current, power and/or phase of the electricity being consumed, and may monitor and record changes in these parameters with time.”) (Hoeynck, [0033]: “Energy consumption sensing devices 26 may measure characteristics of operating appliances and devices in the building (e.g., AC/DC current, voltage, phase and frequency harmonics). … These predictions may be fed into a building automation system that optimally balances the tradeoff of comfort and energy-efficient management of building systems such as HVAC (e.g., residential heating/cooling or commercial ventilation)”) (Hoeynck, [0031]: “For example, processor 30 may receive a schedule of electricity costs at various times of the day, which processor 30 may use in deciding when and/or whether to operate various electrical devices, such as heating ventilating and air conditioning (HVAC)”) (Hoeynck, [0059]: “For example, sensing devices 26 may detect the total electrical power being consumed within building 22 in order to enable processor 30 to estimate the amount of heat that will be generated by such power consumption.”);
a heating, ventilation, and air conditioning (HVAC) system (Drees, [0032]: “BMS subsystems or devices can include heating, ventilation, and air conditioning (HVAC) subsystems or devices, security subsystems or devices, lighting subsystems or devices, fire alerting subsystems or devices, elevator subsystems or devices, other devices that are capable of managing building functions, or any combination thereof.”) (Drees, [0034]: “For example, HVAC subsystem 140 may include a chiller, a boiler, any number of air handling units, economizers, field controllers, supervisory controllers, actuators, temperature sensors, and other devices for controlling the temperature within a building.”) (Drees, [0044]: “For example, the “badge-in” event described above can be used by the integrated control layer 116 (e.g., a control algorithm thereof) to provide new setpoints to an HVAC control algorithm of the HVAC subsystem.”).
The combination provided for claim 1 is applicable.
Regarding claim 13, Drees/Hoeynck teaches the system of claim 8 as above. Drees/Hoeynck further teaches the system, wherein the controller is configured to
derive the performance degradation for the component based on a comparison of the predictive energy consumption model and the energy use data (Drees, [0104]: “The trained statistical model is used to predict behavior for the building management system under normal operating conditions.”) (Drees, [0106]: “The statistical model is used to predict a behavior of the BMS.”) (Drees, [0247]: “These cost factors may be applied to the energy consumption and demand calculated over a defined period (e.g., a calendar month) using the actual performance data and the predicted performance using the manufacturer's baseline or best baseline models.”) (Drees, [0062]: “Automated fault diagnostics module 414 may further be configured to compute residuals (differences between measured and expected values) for analysis to determine the fault source.”) (Drees, [0235]: “Model-based performance index calculation module 1510 calculates a performance index for the chiller. In an exemplary embodiment, the performance index may be calculated by finding the difference between the actual chiller performance and the model-predicted performance.”) (Drees, [0139]: “Process 800 is also shown to include using historical data to create a baseline model that allows energy usage (e.g., kWh) or power consumption (e.g., kW) to be predicted from varying input or predictor variables (e.g., occupancy, space usage, occupancy hours, outdoor air temperature, solar intensity, degree days, etc.).”).
Regarding claim 15, Drees/Hoeynck teaches
receiving, at a controller, energy use data of a component of the HVAC system from a current sensor operatively coupled to the controller (Drees, [0034]: “For example, HVAC subsystem 140 may include a chiller, a boiler, any number of air handling units, economizers, field controllers, supervisory controllers, actuators, temperature sensors, and other devices for controlling the temperature within a building.”) (Drees, [0145]: “Field controller 904 controls one or more components of the BMS and receives or calculates performance values 906 (e.g., sensor inputs, actuator positions, etc.). Controller 904 can store a trend of performance values 906, setpoints and current status in local trend storage 908.”) (Drees, [0066]: “For example, FDD layer 114 may receive inputs from building subsystem supervisory controllers or field controllers having FDD features.”) (Drees, [0003]: “The controller includes a processing circuit”) (Hoeynck, [0013]: “A future value of an energy consumption parameter is predicted based upon the collected current data, the associated time-of-day data, and historic data collected from the environment sensing device and the energy consumption sensing device.”) (Hoeynck, [0028]: “For example, energy consumption sensing devices 26 may sense voltage, current, power and/or phase of the electricity being consumed, and may monitor and record changes in these parameters with time.”) (Hoeynck, [0033]: “Energy consumption sensing devices 26 may measure characteristics of operating appliances and devices in the building (e.g., AC/DC current, voltage, phase and frequency harmonics).”) (Hoeynck, [0034]: “The predictor receives indoor and outdoor environmental cues provided by environment sensing devices 28, including temperature, humidity, acoustics, carbon dioxide, illumination and motion, among others. The predictor also receives device or appliance electrical power consumption signatures including voltage, current, phase and power for each device.”);
receiving, at the controller, an overall current draw from the current sensor (Drees, [0034]: “For example, HVAC subsystem 140 may include a chiller, a boiler, any number of air handling units, economizers, field controllers, supervisory controllers, actuators, temperature sensors, and other devices for controlling the temperature within a building.”) (Drees, [0145]: “Field controller 904 controls one or more components of the BMS and receives or calculates performance values 906 (e.g., sensor inputs, actuator positions, etc.). Controller 904 can store a trend of performance values 906, setpoints and current status in local trend storage 908.”) (Drees, [0066]: “For example, FDD layer 114 may receive inputs from building subsystem supervisory controllers or field controllers having FDD features.”) (Drees, [0003]: “The controller includes a processing circuit”) (Hoeynck, [0028]: “For example, energy consumption sensing devices 26 may sense voltage, current, power and/or phase of the electricity being consumed, and may monitor and record changes in these parameters with time.”) (Hoeynck, [0033]: “Energy consumption sensing devices 26 may measure characteristics of operating appliances and devices in the building (e.g., AC/DC current, voltage, phase and frequency harmonics).”) (Hoeynck, [0034]: “The predictor receives indoor and outdoor environmental cues provided by environment sensing devices 28, including temperature, humidity, acoustics, carbon dioxide, illumination and motion, among others. The predictor also receives device or appliance electrical power consumption signatures including voltage, current, phase and power for each device.”),
wherein the current sensor provides to the controller both the overall current draw for the HVAC system comprising a plurality electrical loads and the energy use data of a component comprising at least one of the plurality of electrical loads of the HVAC system (Drees, [0047]: “Significant electrical loads may be shed by the integrated control layer 116, including, for example, heating and humidification loads, cooling and dehumidification loads, ventilation and fan loads, electric lighting and plug loads (e.g. with secondary thermal loads), electric elevator loads, and the like. The integrated control layer 116 may further be configured to integrate an HVAC subsystem or a lighting subsystem with sunlight shading devices or other “smart window” technologies.”) (Hoeynck, [0010]: “The present invention provides a method for sensing current human occupancy of a building as well as current energy consumption characteristics in order to predict HVAC operation requirements in the ensuing several hours in view of past occupancy and energy consumption patterns.”) (Hoeynck, [0011]: “The invention may also use pattern recognition and classification techniques to derive a sensor-based behavioral prediction algorithm reaching several hours into the future. This model-based prediction may be used as a baseline for the development of control and optimization techniques.”) (Hoeynck, [0013]: “A future value of an energy consumption parameter is predicted based upon the collected current data, the associated time-of-day data, and historic data collected from the environment sensing device and the energy consumption sensing device.”) (Hoeynck, [0028]: “For example, energy consumption sensing devices 26 may sense voltage, current, power and/or phase of the electricity being consumed, and may monitor and record changes in these parameters with time.”) (Hoeynck, [0033]: “Energy consumption sensing devices 26 may measure characteristics of operating appliances and devices in the building (e.g., AC/DC current, voltage, phase and frequency harmonics). … These predictions may be fed into a building automation system that optimally balances the tradeoff of comfort and energy-efficient management of building systems such as HVAC (e.g., residential heating/cooling or commercial ventilation)”) (Hoeynck, [0034]: “The predictor receives indoor and outdoor environmental cues provided by environment sensing devices 28, including temperature, humidity, acoustics, carbon dioxide, illumination and motion, among others. The predictor also receives device or appliance electrical power consumption signatures including voltage, current, phase and power for each device.”) (Hoeynck, [0031]: “For example, processor 30 may receive a schedule of electricity costs at various times of the day, which processor 30 may use in deciding when and/or whether to operate various electrical devices, such as heating ventilating and air conditioning (HVAC)”) (Hoeynck, [0059]: “For example, sensing devices 26 may detect the total electrical power being consumed within building 22 in order to enable processor 30 to estimate the amount of heat that will be generated by such power consumption.”);
receiving, at the controller, environmental condition data (Drees, [0034]: “For example, HVAC subsystem 140 may include a chiller, a boiler, any number of air handling units, economizers, field controllers, supervisory controllers, actuators, temperature sensors, and other devices for controlling the temperature within a building.”) (Drees, [0145]: “Field controller 904 controls one or more components of the BMS and receives or calculates performance values 906 (e.g., sensor inputs, actuator positions, etc.). Controller 904 can store a trend of performance values 906, setpoints and current status in local trend storage 908.”) (Drees, [0066]: “For example, FDD layer 114 may receive inputs from building subsystem supervisory controllers or field controllers having FDD features.”) (Drees, [0003]: “The controller includes a processing circuit”) (Drees, [0071]: “The inputs received from other layers may include environmental or sensor inputs such as temperature, carbon dioxide levels, relative humidity levels, air quality sensor outputs, occupancy sensor outputs, room schedules, and the like.”);
controlling, with the controller, setpoints of the component based on past historical measurements from the current sensor (Drees, [0044]: “For example, the “badge-in” event described above can be used by the integrated control layer 116 (e.g., a control algorithm thereof) to provide new setpoints to an HVAC control algorithm of the HVAC subsystem.”) (Drees, [0145]: “Controller 904 can store a trend of performance values 906, setpoints and current status in local trend storage 908.”) (Drees, [0060]: “Automatic fault diagnostics module 414 may be configured to use meter data 402, weather data 404, model data 406 (e.g., performance models based on historical building equipment performance), building subsystem data 408, performance indices 410, or other data available at the building subsystem integration layer to complete its fault diagnostics activities.”) (Drees, [0064]: “The FDD layer 114 may be configured to maintain detailed historical databases (e.g., relational databases, XML databases, etc.) of relevant data”) (Drees, [0074]: “The smart building manager 106 (with input from the user or operating using pre-configured business rules shown in FIG. 3) may be configured to accept time-of-use pricing signals or information from a smart grid (e.g., an energy provider, a smart meter, etc.) and, using its knowledge of historical building system data, control algorithms, calendar information, and/or weather information received from a remote source, may be configured to conduct automatic cost forecasting.”) (Drees, [0112]: “Performance value database 524 can store a history of performance values used by training component 522 to generate statistical models, such as model data 406.”) (Drees, [0117]: “Process 600 includes receiving a history of performance values (step 602).”) (Drees, [0139]: “Process 800 is also shown to include using historical data to create a baseline model that allows energy usage (e.g., kWh) or power consumption (e.g., kW) to be predicted from varying input or predictor variables (e.g., occupancy, space usage, occupancy hours, outdoor air temperature, solar intensity, degree days, etc.).”);
creating, with the controller, a predictive energy consumption model for the component of the HVAC system based on the energy use data and the overall current draw (Drees, [0104]: “The trained statistical model is used to predict behavior for the building management system under normal operating conditions.”) (Drees, [0106]: “The statistical model is used to predict a behavior of the BMS.”) (Drees, [0247]: “These cost factors may be applied to the energy consumption and demand calculated over a defined period (e.g., a calendar month) using the actual performance data and the predicted performance using the manufacturer's baseline or best baseline models.”) (Drees, [0062]: “Automated fault diagnostics module 414 may further be configured to compute residuals (differences between measured and expected values) for analysis to determine the fault source.”) (Drees, [0235]: “Model-based performance index calculation module 1510 calculates a performance index for the chiller. In an exemplary embodiment, the performance index may be calculated by finding the difference between the actual chiller performance and the model-predicted performance.”) (Hoeynck, [0011]: “The invention may also use pattern recognition and classification techniques to derive a sensor-based behavioral prediction algorithm reaching several hours into the future. This model-based prediction may be used as a baseline for the development of control and optimization techniques.”) (Hoeynck, [0013]: “A future value of an energy consumption parameter is predicted based upon the collected current data, the associated time-of-day data, and historic data collected from the environment sensing device and the energy consumption sensing device.”) (Hoeynck, [0028]: “For example, energy consumption sensing devices 26 may sense voltage, current, power and/or phase of the electricity being consumed, and may monitor and record changes in these parameters with time.”) (Hoeynck, [0033]: “Energy consumption sensing devices 26 may measure characteristics of operating appliances and devices in the building (e.g., AC/DC current, voltage, phase and frequency harmonics).”) (Hoeynck, [0034]: “The predictor receives indoor and outdoor environmental cues provided by environment sensing devices 28, including temperature, humidity, acoustics, carbon dioxide, illumination and motion, among others. The predictor also receives device or appliance electrical power consumption signatures including voltage, current, phase and power for each device.”); and
deriving, with the controller, a performance degradation for the component based on the environmental condition data, the setpoints, and the predictive energy consumption model (Drees, [0104]: “The trained statistical model is used to predict behavior for the building management system under normal operating conditions.”) (Drees, [0106]: “The statistical model is used to predict a behavior of the BMS.”) (Drees, [0247]: “These cost factors may be applied to the energy consumption and demand calculated over a defined period (e.g., a calendar month) using the actual performance data and the predicted performance using the manufacturer's baseline or best baseline models.”) (Drees, [0062]: “Automated fault diagnostics module 414 may further be configured to compute residuals (differences between measured and expected values) for analysis to determine the fault source.”) (Drees, [0235]: “Model-based performance index calculation module 1510 calculates a performance index for the chiller. In an exemplary embodiment, the performance index may be calculated by finding the difference between the actual chiller performance and the model-predicted performance.”); and
detecting equipment faults and/or operation inefficiencies in the component based on the performance degradation derived for the component (Drees, [0058]: “FDD layer 114 may use some content of data stores 402-410 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.”) (Drees, [0060]: “Automatic fault diagnostics module 414 may be configured to use … model data 406, … performance indices 410, or other data available at the building subsystem integration layer to complete its fault diagnostics activities.”) (Drees, [0062]: “Automated fault diagnostics module 414 may further be configured to compute residuals (differences between measured and expected values) for analysis to determine the fault source.”) (Drees, [0106]: “Once a sufficient history of performance values has been built, the history can be used to generate a statistical model (step 504). Generally speaking, the statistical model is a set of metrics based on, calculated using, or describing the history of performance values. The statistical model is used to predict a behavior of the BMS.”) (Drees, [0112]: “Automated fault detection module 412 includes performance value database 524.”) (Drees, [0236]: “Faults are detected by detector 1511 when the chiller's actual performance deviates significantly from the predicted performance (e.g., predicted using the “manufacturer baseline” or “best baseline”).”) and
outputting an alert based on the detected equipment faults or/or operation inefficiencies (Drees, [0057]: “The responses to detected or diagnosed faults may include providing an alert message to a user, a maintenance scheduling system, or a control algorithm configured to attempt to repair the fault or to work-around the fault.”) (Drees, [0102]: “The controller can then notify a user of the potential fault.”) (Drees, [0108]: “FDD layer 114 may then notify a user, a maintenance scheduling system, or a control algorithm configured to attempt to further diagnose the fault, to repair the fault, or to work-around the fault.”).
The combination provided for claim 1 is applicable.
Regarding claim 19, Drees/Hoeynck teaches the method of claim 15 as above. Drees/Hoeynck further teaches the method, wherein
deriving the performance degradation for the component is based on a comparison of the predictive energy consumption model and the energy use data (Drees, [0104]: “The trained statistical model is used to predict behavior for the building management system under normal operating conditions.”) (Drees, [0106]: “The statistical model is used to predict a behavior of the BMS.”) (Drees, [0247]: “These cost factors may be applied to the energy consumption and demand calculated over a defined period (e.g., a calendar month) using the actual performance data and the predicted performance using the manufacturer's baseline or best baseline models.”) (Drees, [0062]: “Automated fault diagnostics module 414 may further be configured to compute residuals (differences between measured and expected values) for analysis to determine the fault source.”) (Drees, [0235]: “Model-based performance index calculation module 1510 calculates a performance index for the chiller. In an exemplary embodiment, the performance index may be calculated by finding the difference between the actual chiller performance and the model-predicted performance.”) (Drees, [0139]: “Process 800 is also shown to include using historical data to create a baseline model that allows energy usage (e.g., kWh) or power consumption (e.g., kW) to be predicted from varying input or predictor variables (e.g., occupancy, space usage, occupancy hours, outdoor air temperature, solar intensity, degree days, etc.).”).
Regarding claim 21, Drees/Hoeynck teaches the device of claim 1 as above. Drees/Hoeynck further teaches the device, wherein the controller is further configured to
control the HVAC system to remove detected equipment faults and/or operation inefficiencies detected in the component based on the performance degradation derived for the component (Drees, [0108]: “FDD layer 114 may then notify a user, a maintenance scheduling system, or a control algorithm configured to attempt to further diagnose the fault, to repair the fault, or to work-around the fault.”) (Drees, [0254]: “The schedule for resolving faults is provided to a fault resolution and tracking module 1618. Fault resolution and tracking module 1618 is configured to verify if a fault was fixed and if the fix was effective.”).
Regarding claim 22, Drees/Hoeynck teaches the system of claim 8 as above. Drees/Hoeynck further teaches the system, wherein the controller is further configured to
control the HVAC system to remove detected equipment faults and/or operation inefficiencies detected in the component based on the performance degradation derived for the component (Drees, [0108]: “FDD layer 114 may then notify a user, a maintenance scheduling system, or a control algorithm configured to attempt to further diagnose the fault, to repair the fault, or to work-around the fault.”) (Drees, [0254]: “The schedule for resolving faults is provided to a fault resolution and tracking module 1618. Fault resolution and tracking module 1618 is configured to verify if a fault was fixed and if the fix was effective.”).
Regarding claim 23, Drees/Hoeynck teaches the method of claim 15 as above. Drees/Hoeynck further teaches the method, further comprising:
controlling the HVAC system to remove detected equipment faults and/or operation inefficiencies detected in the component based on the performance degradation derived for the component (Drees, [0108]: “FDD layer 114 may then notify a user, a maintenance scheduling system, or a control algorithm configured to attempt to further diagnose the fault, to repair the fault, or to work-around the fault.”) (Drees, [0254]: “The schedule for resolving faults is provided to a fault resolution and tracking module 1618. Fault resolution and tracking module 1618 is configured to verify if a fault was fixed and if the fix was effective.”).
Claim(s) 3-5, 10-12, and 16-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Drees in view of Hoeynck in further view of Federspiel (US20120283881A1).
Regarding claim 3, Drees/Hoeynck teaches the energy use data indicate a current drawn (Hoeynck, [0013]: “A future value of an energy consumption parameter is predicted based upon the collected current data, the associated time-of-day data, and historic data collected from the environment sensing device and the energy consumption sensing device.”) (Hoeynck, [0028]: “For example, energy consumption sensing devices 26 may sense voltage, current, power and/or phase of the electricity being consumed, and may monitor and record changes in these parameters with time.”) (Hoeynck, [0033]: “Energy consumption sensing devices 26 may measure characteristics of operating appliances and devices in the building (e.g., AC/DC current, voltage, phase and frequency harmonics).”).
Drees/Hoeynck does not explicitly teach subparts of the component.
However, Federspiel teaches receiving parameters and calculating a performance for each subpart of a component and summing the performance values of the subparts to obtain an overall performance of the system ([0013]: “For example, environmental measurements (e.g. temperature, humidity, pressure) are received from the sensors. Error or difference values for each sensor are determined in relation to reference values (e.g. temperature settings). To maintain desired environmental settings, changes in an operational parameter (e.g. fan speed) of each module are calculated from contributions of respective difference values. In one aspect, a transfer matrix may be used to weight the difference values and then each contribution may be summed to obtain an overall change in the parameter. This may be done for each parameter of each module.”) ([0034]: “In one embodiment, input 1 of HVAC unit 1 may correspond to the operational parameter of one actuator (e.g. for a cooling valve), and the input 2 of HVAC unit 1 may correspond to a different actuator of the same HVAC unit 1.”) ([0035]: “In another embodiment, one input could be the setpoint for the temperature of an HVAC unit 2. … Other inputs could be the setpoint for the humidity (or the humidifier command), or a command to a variable frequency drive (VFD).”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Federspiel’s teaching for calculating a performance individually by subparts of a component into Drees/Hoeynck to receive energy use data by subparts and calculating a performance by subparts.
One of ordinary skill in the art would have been motivated to make this modification because doing so allows selectively calculating performance values of subparts of interest which would allow eliminating unnecessary computations and allow detecting the source of a fault, if any, more easily by comparing the measurements and predicted/ideal values individually by subcomponent (Federspiel, [0035]: “In another embodiment, one input could be the setpoint for the temperature of an HVAC unit 2. … Other inputs could be the setpoint for the humidity (or the humidifier command), or a command to a variable frequency drive (VFD).”) (Federspiel, [0013]: “For example, environmental measurements (e.g. temperature, humidity, pressure) are received from the sensors. Error or difference values for each sensor are determined in relation to reference values (e.g. temperature settings). To maintain desired environmental settings, changes in an operational parameter (e.g. fan speed) of each module are calculated from contributions of respective difference values. In one aspect, a transfer matrix may be used to weight the difference values and then each contribution may be summed to obtain an overall change in the parameter. This may be done for each parameter of each module.”).
Therefore, the combination of Drees/Hoeynck and Federspiel teaches
wherein the energy use data indicate a current drawn by subparts of the component (Hoeynck, [0013]: “A future value of an energy consumption parameter is predicted based upon the collected current data, the associated time-of-day data, and historic data collected from the environment sensing device and the energy consumption sensing device.”) (Hoeynck, [0028]: “For example, energy consumption sensing devices 26 may sense voltage, current, power and/or phase of the electricity being consumed, and may monitor and record changes in these parameters with time.”) (Hoeynck, [0033]: “Energy consumption sensing devices 26 may measure characteristics of operating appliances and devices in the building (e.g., AC/DC current, voltage, phase and frequency harmonics).”) (Federspiel, [0013]: “For example, environmental measurements (e.g. temperature, humidity, pressure) are received from the sensors. Error or difference values for each sensor are determined in relation to reference values (e.g. temperature settings). To maintain desired environmental settings, changes in an operational parameter (e.g. fan speed) of each module are calculated from contributions of respective difference values. In one aspect, a transfer matrix may be used to weight the difference values and then each contribution may be summed to obtain an overall change in the parameter. This may be done for each parameter of each module.”) (Federspiel, [0034]: “In one embodiment, input 1 of HVAC unit 1 may correspond to the operational parameter of one actuator (e.g. for a cooling valve), and the input 2 of HVAC unit 1 may correspond to a different actuator of the same HVAC unit 1.”) (Federspiel, [0035]: “In another embodiment, one input could be the setpoint for the temperature of an HVAC unit 2. … Other inputs could be the setpoint for the humidity (or the humidifier command), or a command to a variable frequency drive (VFD).”).
Regarding claim 4, Drees/Hoeynck teaches the device of claim 3 as above. Drees/Hoeynck/Federspiel further teaches the device, wherein the controller is configured to
partition the predictive energy consumption model into a set of predictive energy consumption submodels for subparts of the component (Drees, [0104]: “The trained statistical model is used to predict behavior for the building management system under normal operating conditions.”) (Drees, [0106]: “The statistical model is used to predict a behavior of the BMS.”) (Drees, [0247]: “These cost factors may be applied to the energy consumption and demand calculated over a defined period (e.g., a calendar month) using the actual performance data and the predicted performance using the manufacturer's baseline or best baseline models.”) (Drees, [0062]: “Automated fault diagnostics module 414 may further be configured to compute residuals (differences between measured and expected values) for analysis to determine the fault source.”) (Drees, [0235]: “Model-based performance index calculation module 1510 calculates a performance index for the chiller. In an exemplary embodiment, the performance index may be calculated by finding the difference between the actual chiller performance and the model-predicted performance.”) (Federspiel, [0013]: “For example, environmental measurements (e.g. temperature, humidity, pressure) are received from the sensors. Error or difference values for each sensor are determined in relation to reference values (e.g. temperature settings). To maintain desired environmental settings, changes in an operational parameter (e.g. fan speed) of each module are calculated from contributions of respective difference values. In one aspect, a transfer matrix may be used to weight the difference values and then each contribution may be summed to obtain an overall change in the parameter. This may be done for each parameter of each module.”).
The combination provided for claim 3 is applicable.
Regarding claim 5, Drees/Hoeynck teaches the device of claim 4 as above. Drees/Hoeynck/Federspiel further teaches the device, wherein the controller is configured to:
compare the set of predictive energy consumption submodels to the current drawn by the subparts of the component (Drees, [0106]: “The statistical model is used to predict a behavior of the BMS.”) (Drees, [0247]: “These cost factors may be applied to the energy consumption and demand calculated over a defined period (e.g., a calendar month) using the actual performance data and the predicted performance using the manufacturer's baseline or best baseline models.”) (Drees, [0062]: “Automated fault diagnostics module 414 may further be configured to compute residuals (differences between measured and expected values) for analysis to determine the fault source.”) (Drees, [0235]: “Model-based performance index calculation module 1510 calculates a performance index for the chiller. In an exemplary embodiment, the performance index may be calculated by finding the difference between the actual chiller performance and the model-predicted performance.”) (Drees, [0139]: “Process 800 is also shown to include using historical data to create a baseline model that allows energy usage (e.g., kWh) or power consumption (e.g., kW) to be predicted from varying input or predictor variables (e.g., occupancy, space usage, occupancy hours, outdoor air temperature, solar intensity, degree days, etc.).”) (Hoeynck, [0011]: “The invention may also use pattern recognition and classification techniques to derive a sensor-based behavioral prediction algorithm reaching several hours into the future. This model-based prediction may be used as a baseline for the development of control and optimization techniques.”) (Hoeynck, [0013]: “A future value of an energy consumption parameter is predicted based upon the collected current data, the associated time-of-day data, and historic data collected from the environment sensing device and the energy consumption sensing device.”) (Hoeynck, [0028]: “For example, energy consumption sensing devices 26 may sense voltage, current, power and/or phase of the electricity being consumed, and may monitor and record changes in these parameters with time.”) (Hoeynck, [0033]: “Energy consumption sensing devices 26 may measure characteristics of operating appliances and devices in the building (e.g., AC/DC current, voltage, phase and frequency harmonics).”) (Hoeynck, [0034]: “The predictor receives indoor and outdoor environmental cues provided by environment sensing devices 28, including temperature, humidity, acoustics, carbon dioxide, illumination and motion, among others. The predictor also receives device or appliance electrical power consumption signatures including voltage, current, phase and power for each device.”) (Federspiel, [0013]: “For example, environmental measurements (e.g. temperature, humidity, pressure) are received from the sensors. Error or difference values for each sensor are determined in relation to reference values (e.g. temperature settings). To maintain desired environmental settings, changes in an operational parameter (e.g. fan speed) of each module are calculated from contributions of respective difference values. In one aspect, a transfer matrix may be used to weight the difference values and then each contribution may be summed to obtain an overall change in the parameter. This may be done for each parameter of each module.”) (Federspiel, [0034]: “In one embodiment, input 1 of HVAC unit 1 may correspond to the operational parameter of one actuator (e.g. for a cooling valve), and the input 2 of HVAC unit 1 may correspond to a different actuator of the same HVAC unit 1.”) (Federspiel, [0035]: “In another embodiment, one input could be the setpoint for the temperature of an HVAC unit 2. … Other inputs could be the setpoint for the humidity (or the humidifier command), or a command to a variable frequency drive (VFD).”); and
derive a performance degradation for the subparts of the component based on the comparison between the set of predictive energy consumption submodels and the current drawn (Drees, [0104]: “The trained statistical model is used to predict behavior for the building management system under normal operating conditions.”) (Drees, [0106]: “The statistical model is used to predict a behavior of the BMS.”) (Drees, [0247]: “These cost factors may be applied to the energy consumption and demand calculated over a defined period (e.g., a calendar month) using the actual performance data and the predicted performance using the manufacturer's baseline or best baseline models.”) (Drees, [0062]: “Automated fault diagnostics module 414 may further be configured to compute residuals (differences between measured and expected values) for analysis to determine the fault source.”) (Drees, [0235]: “Model-based performance index calculation module 1510 calculates a performance index for the chiller. In an exemplary embodiment, the performance index may be calculated by finding the difference between the actual chiller performance and the model-predicted performance.”) (Drees, [0139]: “Process 800 is also shown to include using historical data to create a baseline model that allows energy usage (e.g., kWh) or power consumption (e.g., kW) to be predicted from varying input or predictor variables (e.g., occupancy, space usage, occupancy hours, outdoor air temperature, solar intensity, degree days, etc.).”) (Federspiel, [0013]: “For example, environmental measurements (e.g. temperature, humidity, pressure) are received from the sensors. Error or difference values for each sensor are determined in relation to reference values (e.g. temperature settings). To maintain desired environmental settings, changes in an operational parameter (e.g. fan speed) of each module are calculated from contributions of respective difference values. In one aspect, a transfer matrix may be used to weight the difference values and then each contribution may be summed to obtain an overall change in the parameter. This may be done for each parameter of each module.”) (Federspiel, [0034]: “In one embodiment, input 1 of HVAC unit 1 may correspond to the operational parameter of one actuator (e.g. for a cooling valve), and the input 2 of HVAC unit 1 may correspond to a different actuator of the same HVAC unit 1.”) (Federspiel, [0035]: “In another embodiment, one input could be the setpoint for the temperature of an HVAC unit 2. … Other inputs could be the setpoint for the humidity (or the humidifier command), or a command to a variable frequency drive (VFD).”).
The combination provided for claim 3 is applicable.
Regarding claim 10, Drees/Hoeynck teaches the system of claim 8 as above. Drees/Hoeynck/Federspiel further teaches the system, wherein
the energy use data indicate a current drawn by subparts of the component (Hoeynck, [0013]: “A future value of an energy consumption parameter is predicted based upon the collected current data, the associated time-of-day data, and historic data collected from the environment sensing device and the energy consumption sensing device.”) (Hoeynck, [0028]: “For example, energy consumption sensing devices 26 may sense voltage, current, power and/or phase of the electricity being consumed, and may monitor and record changes in these parameters with time.”) (Hoeynck, [0033]: “Energy consumption sensing devices 26 may measure characteristics of operating appliances and devices in the building (e.g., AC/DC current, voltage, phase and frequency harmonics).”) (Federspiel, [0013]: “For example, environmental measurements (e.g. temperature, humidity, pressure) are received from the sensors. Error or difference values for each sensor are determined in relation to reference values (e.g. temperature settings). To maintain desired environmental settings, changes in an operational parameter (e.g. fan speed) of each module are calculated from contributions of respective difference values. In one aspect, a transfer matrix may be used to weight the difference values and then each contribution may be summed to obtain an overall change in the parameter. This may be done for each parameter of each module.”) (Federspiel, [0034]: “In one embodiment, input 1 of HVAC unit 1 may correspond to the operational parameter of one actuator (e.g. for a cooling valve), and the input 2 of HVAC unit 1 may correspond to a different actuator of the same HVAC unit 1.”) (Federspiel, [0035]: “In another embodiment, one input could be the setpoint for the temperature of an HVAC unit 2. … Other inputs could be the setpoint for the humidity (or the humidifier command), or a command to a variable frequency drive (VFD).”).
The combination provided for claim 3 is applicable.
33. Regarding claim 11, Drees/Hoeynck teaches the system of claim 8 as above. Drees/Hoeynck/Federspiel further teaches the system, wherein the controller is configured to
partition the predictive energy consumption model into a set of predictive energy consumption submodels for subparts of the component (Drees, [0104]: “The trained statistical model is used to predict behavior for the building management system under normal operating conditions.”) (Drees, [0106]: “The statistical model is used to predict a behavior of the BMS.”) (Drees, [0247]: “These cost factors may be applied to the energy consumption and demand calculated over a defined period (e.g., a calendar month) using the actual performance data and the predicted performance using the manufacturer's baseline or best baseline models.”) (Drees, [0062]: “Automated fault diagnostics module 414 may further be configured to compute residuals (differences between measured and expected values) for analysis to determine the fault source.”) (Drees, [0235]: “Model-based performance index calculation module 1510 calculates a performance index for the chiller. In an exemplary embodiment, the performance index may be calculated by finding the difference between the actual chiller performance and the model-predicted performance.”) (Federspiel, [0013]: “For example, environmental measurements (e.g. temperature, humidity, pressure) are received from the sensors. Error or difference values for each sensor are determined in relation to reference values (e.g. temperature settings). To maintain desired environmental settings, changes in an operational parameter (e.g. fan speed) of each module are calculated from contributions of respective difference values. In one aspect, a transfer matrix may be used to weight the difference values and then each contribution may be summed to obtain an overall change in the parameter. This may be done for each parameter of each module.”).
The combination provided for claim 3 is applicable.
Regarding claim 12, Drees/Hoeynck teaches the system of claim 11 as above. Drees/Hoeynck/Federspiel further teaches the system, wherein the controller is configured to:
compare the set of predictive energy consumption submodels to actual energy consumed by the subparts of the component (Drees, [0106]: “The statistical model is used to predict a behavior of the BMS.”) (Drees, [0247]: “These cost factors may be applied to the energy consumption and demand calculated over a defined period (e.g., a calendar month) using the actual performance data and the predicted performance using the manufacturer's baseline or best baseline models.”) (Drees, [0062]: “Automated fault diagnostics module 414 may further be configured to compute residuals (differences between measured and expected values) for analysis to determine the fault source.”) (Drees, [0235]: “Model-based performance index calculation module 1510 calculates a performance index for the chiller. In an exemplary embodiment, the performance index may be calculated by finding the difference between the actual chiller performance and the model-predicted performance.”) (Federspiel, [0013]: “For example, environmental measurements (e.g. temperature, humidity, pressure) are received from the sensors. Error or difference values for each sensor are determined in relation to reference values (e.g. temperature settings). To maintain desired environmental settings, changes in an operational parameter (e.g. fan speed) of each module are calculated from contributions of respective difference values. In one aspect, a transfer matrix may be used to weight the difference values and then each contribution may be summed to obtain an overall change in the parameter. This may be done for each parameter of each module.”) (Federspiel, [0034]: “In one embodiment, input 1 of HVAC unit 1 may correspond to the operational parameter of one actuator (e.g. for a cooling valve), and the input 2 of HVAC unit 1 may correspond to a different actuator of the same HVAC unit 1.”),
wherein the actual energy consumed by the subparts of the component is received in the energy use data for the component (Drees, [0247]: “These cost factors may be applied to the energy consumption and demand calculated over a defined period (e.g., a calendar month) using the actual performance data and the predicted performance using the manufacturer's baseline or best baseline models.”) (Drees, [0062]: “Automated fault diagnostics module 414 may further be configured to compute residuals (differences between measured and expected values) for analysis to determine the fault source.”) (Federspiel, [0013]: “For example, environmental measurements (e.g. temperature, humidity, pressure) are received from the sensors. Error or difference values for each sensor are determined in relation to reference values (e.g. temperature settings). To maintain desired environmental settings, changes in an operational parameter (e.g. fan speed) of each module are calculated from contributions of respective difference values. In one aspect, a transfer matrix may be used to weight the difference values and then each contribution may be summed to obtain an overall change in the parameter. This may be done for each parameter of each module.”) (Federspiel, [0034]: “In one embodiment, input 1 of HVAC unit 1 may correspond to the operational parameter of one actuator (e.g. for a cooling valve), and the input 2 of HVAC unit 1 may correspond to a different actuator of the same HVAC unit 1.”) (Federspiel, [0035]: “In another embodiment, one input could be the setpoint for the temperature of an HVAC unit 2. … Other inputs could be the setpoint for the humidity (or the humidifier command), or a command to a variable frequency drive (VFD).”); and
derive a performance degradation for the subparts of the component based on the comparison between the set of predictive energy consumption submodels and the actual energy consumed by the subparts (Drees, [0106]: “The statistical model is used to predict a behavior of the BMS.”) (Drees, [0247]: “These cost factors may be applied to the energy consumption and demand calculated over a defined period (e.g., a calendar month) using the actual performance data and the predicted performance using the manufacturer's baseline or best baseline models.”) (Drees, [0062]: “Automated fault diagnostics module 414 may further be configured to compute residuals (differences between measured and expected values) for analysis to determine the fault source.”) (Drees, [0235]: “Model-based performance index calculation module 1510 calculates a performance index for the chiller. In an exemplary embodiment, the performance index may be calculated by finding the difference between the actual chiller performance and the model-predicted performance.”) (Federspiel, [0013]: “For example, environmental measurements (e.g. temperature, humidity, pressure) are received from the sensors. Error or difference values for each sensor are determined in relation to reference values (e.g. temperature settings). To maintain desired environmental settings, changes in an operational parameter (e.g. fan speed) of each module are calculated from contributions of respective difference values. In one aspect, a transfer matrix may be used to weight the difference values and then each contribution may be summed to obtain an overall change in the parameter. This may be done for each parameter of each module.”) (Federspiel, [0034]: “In one embodiment, input 1 of HVAC unit 1 may correspond to the operational parameter of one actuator (e.g. for a cooling valve), and the input 2 of HVAC unit 1 may correspond to a different actuator of the same HVAC unit 1.”) (Federspiel, [0035]: “In another embodiment, one input could be the setpoint for the temperature of an HVAC unit 2. … Other inputs could be the setpoint for the humidity (or the humidifier command), or a command to a variable frequency drive (VFD).”).
The combination provided for claim 3 is applicable.
Regarding claim 16, Drees/Hoeynck teaches the method of claim 15 as above. Drees/Hoeynck/Federspiel further teaches the method, wherein
the energy use data indicate a current drawn by subparts of the component (Hoeynck, [0013]: “A future value of an energy consumption parameter is predicted based upon the collected current data, the associated time-of-day data, and historic data collected from the environment sensing device and the energy consumption sensing device.”) (Hoeynck, [0028]: “For example, energy consumption sensing devices 26 may sense voltage, current, power and/or phase of the electricity being consumed, and may monitor and record changes in these parameters with time.”) (Hoeynck, [0033]: “Energy consumption sensing devices 26 may measure characteristics of operating appliances and devices in the building (e.g., AC/DC current, voltage, phase and frequency harmonics).”) (Federspiel, [0013]: “For example, environmental measurements (e.g. temperature, humidity, pressure) are received from the sensors. Error or difference values for each sensor are determined in relation to reference values (e.g. temperature settings). To maintain desired environmental settings, changes in an operational parameter (e.g. fan speed) of each module are calculated from contributions of respective difference values. In one aspect, a transfer matrix may be used to weight the difference values and then each contribution may be summed to obtain an overall change in the parameter. This may be done for each parameter of each module.”) (Federspiel, [0034]: “In one embodiment, input 1 of HVAC unit 1 may correspond to the operational parameter of one actuator (e.g. for a cooling valve), and the input 2 of HVAC unit 1 may correspond to a different actuator of the same HVAC unit 1.”) (Federspiel, [0035]: “In another embodiment, one input could be the setpoint for the temperature of an HVAC unit 2. … Other inputs could be the setpoint for the humidity (or the humidifier command), or a command to a variable frequency drive (VFD).”).
The combination provided for claim 3 is applicable.
Regarding claim 17, Drees/Hoeynck teaches the method of claim 15 as above. Drees/Hoeynck/Federspiel further teaches the method, further comprising
partitioning, with the controller, the predictive energy consumption model into a set of predictive energy consumption submodels for subparts of the component (Drees, [0104]: “The trained statistical model is used to predict behavior for the building management system under normal operating conditions.”) (Drees, [0106]: “The statistical model is used to predict a behavior of the BMS.”) (Drees, [0247]: “These cost factors may be applied to the energy consumption and demand calculated over a defined period (e.g., a calendar month) using the actual performance data and the predicted performance using the manufacturer's baseline or best baseline models.”) (Drees, [0062]: “Automated fault diagnostics module 414 may further be configured to compute residuals (differences between measured and expected values) for analysis to determine the fault source.”) (Drees, [0235]: “Model-based performance index calculation module 1510 calculates a performance index for the chiller. In an exemplary embodiment, the performance index may be calculated by finding the difference between the actual chiller performance and the model-predicted performance.”) (Federspiel, [0013]: “For example, environmental measurements (e.g. temperature, humidity, pressure) are received from the sensors. Error or difference values for each sensor are determined in relation to reference values (e.g. temperature settings). To maintain desired environmental settings, changes in an operational parameter (e.g. fan speed) of each module are calculated from contributions of respective difference values. In one aspect, a transfer matrix may be used to weight the difference values and then each contribution may be summed to obtain an overall change in the parameter. This may be done for each parameter of each module.”).
The combination provided for claim 3 is applicable.
Regarding claim 18, Drees/Hoeynck teaches the method of claim 17 as above. Drees/Hoeynck/Federspiel further teaches the method, further comprising:
comparing, with the controller, the set of predictive energy consumption submodels to actual energy consumed by the subparts of the component (Drees, [0106]: “The statistical model is used to predict a behavior of the BMS.”) (Drees, [0247]: “These cost factors may be applied to the energy consumption and demand calculated over a defined period (e.g., a calendar month) using the actual performance data and the predicted performance using the manufacturer's baseline or best baseline models.”) (Drees, [0062]: “Automated fault diagnostics module 414 may further be configured to compute residuals (differences between measured and expected values) for analysis to determine the fault source.”) (Drees, [0235]: “Model-based performance index calculation module 1510 calculates a performance index for the chiller. In an exemplary embodiment, the performance index may be calculated by finding the difference between the actual chiller performance and the model-predicted performance.”) (Federspiel, [0013]: “For example, environmental measurements (e.g. temperature, humidity, pressure) are received from the sensors. Error or difference values for each sensor are determined in relation to reference values (e.g. temperature settings). To maintain desired environmental settings, changes in an operational parameter (e.g. fan speed) of each module are calculated from contributions of respective difference values. In one aspect, a transfer matrix may be used to weight the difference values and then each contribution may be summed to obtain an overall change in the parameter. This may be done for each parameter of each module.”) (Federspiel, [0034]: “In one embodiment, input 1 of HVAC unit 1 may correspond to the operational parameter of one actuator (e.g. for a cooling valve), and the input 2 of HVAC unit 1 may correspond to a different actuator of the same HVAC unit 1.”),
the actual energy consumed by the subparts of the component is received in the energy use data for the component (Drees, [0247]: “These cost factors may be applied to the energy consumption and demand calculated over a defined period (e.g., a calendar month) using the actual performance data and the predicted performance using the manufacturer's baseline or best baseline models.”) (Drees, [0062]: “Automated fault diagnostics module 414 may further be configured to compute residuals (differences between measured and expected values) for analysis to determine the fault source.”) (Federspiel, [0013]: “For example, environmental measurements (e.g. temperature, humidity, pressure) are received from the sensors. Error or difference values for each sensor are determined in relation to reference values (e.g. temperature settings). To maintain desired environmental settings, changes in an operational parameter (e.g. fan speed) of each module are calculated from contributions of respective difference values. In one aspect, a transfer matrix may be used to weight the difference values and then each contribution may be summed to obtain an overall change in the parameter. This may be done for each parameter of each module.”) (Federspiel, [0034]: “In one embodiment, input 1 of HVAC unit 1 may correspond to the operational parameter of one actuator (e.g. for a cooling valve), and the input 2 of HVAC unit 1 may correspond to a different actuator of the same HVAC unit 1.”) (Federspiel, [0035]: “In another embodiment, one input could be the setpoint for the temperature of an HVAC unit 2. … Other inputs could be the setpoint for the humidity (or the humidifier command), or a command to a variable frequency drive (VFD).”); and
wherein deriving a performance degradation for the component comprises deriving a performance degradation for the subparts of the component based on the comparison between the set of predictive energy consumption submodels and the actual energy consumed by the subparts (Drees, [0106]: “The statistical model is used to predict a behavior of the BMS.”) (Drees, [0247]: “These cost factors may be applied to the energy consumption and demand calculated over a defined period (e.g., a calendar month) using the actual performance data and the predicted performance using the manufacturer's baseline or best baseline models.”) (Drees, [0062]: “Automated fault diagnostics module 414 may further be configured to compute residuals (differences between measured and expected values) for analysis to determine the fault source.”) (Drees, [0235]: “Model-based performance index calculation module 1510 calculates a performance index for the chiller. In an exemplary embodiment, the performance index may be calculated by finding the difference between the actual chiller performance and the model-predicted performance.”) (Federspiel, [0013]: “For example, environmental measurements (e.g. temperature, humidity, pressure) are received from the sensors. Error or difference values for each sensor are determined in relation to reference values (e.g. temperature settings). To maintain desired environmental settings, changes in an operational parameter (e.g. fan speed) of each module are calculated from contributions of respective difference values. In one aspect, a transfer matrix may be used to weight the difference values and then each contribution may be summed to obtain an overall change in the parameter. This may be done for each parameter of each module.”) (Federspiel, [0034]: “In one embodiment, input 1 of HVAC unit 1 may correspond to the operational parameter of one actuator (e.g. for a cooling valve), and the input 2 of HVAC unit 1 may correspond to a different actuator of the same HVAC unit 1.”) (Federspiel, [0035]: “In another embodiment, one input could be the setpoint for the temperature of an HVAC unit 2. … Other inputs could be the setpoint for the humidity (or the humidifier command), or a command to a variable frequency drive (VFD).”).
The combination provided for claim 3 is applicable.
Claim(s) 7 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Drees in view of Hoeynck in further view of Cumming et al. (US20050065743A1), hereinafter Cumming.
Regarding claim 7, Drees/Hoeynck does not explicitly teach
wherein the current sensor comprises a clamp-on sensor.
However, Cumming teaches
wherein the current sensor comprises a clamp-on sensor ([0064]: “However, installation of the current connections can often be accomplished with the circuit energized when using non-contact sensors, such as “clamp-on” CTs which rely on induced current flow, as the risk of injury or damage is lessened.”).
Drees/Hoeynck and Cumming are analogous to the claimed invention because they are in the same field of monitoring energy consumption for a HVAC system based on sensor data.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Cumming’s teaching for using clamp-on sensors for measuring current to modify the sensors of Drees/Hoeynck to provide clamp-on sensors for measuring current.
One of ordinary skill in the art would have been motivated to make this modification because clamp-on sensors allow measuring current in a non-invasive manner, reducing risk of injury or damage (Cumming, [0064]: “However, installation of the current connections can often be accomplished with the circuit energized when using non-contact sensors, such as “clamp-on” CTs which rely on induced current flow, as the risk of injury or damage is lessened.”).
Regarding claim 14, Drees/Hoeynck teaches the system of claim 8 as above. Drees/Hoeynck/Cumming further teaches the system, wherein
the current sensor comprises a clamp-on sensor (Cumming, [0064]: “However, installation of the current connections can often be accomplished with the circuit energized when using non-contact sensors, such as “clamp-on” CTs which rely on induced current flow, as the risk of injury or damage is lessened.”).
The combination provided for claim 7 is applicable.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Sanderford, Jr. et al. (US20120041696A1) and Patel (US20110074382A1) disclose using an electricity meter which senses the current drawn and energy consumption
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/HEIN JEONG/Examiner, Art Unit 2186
/RENEE D CHAVEZ/Supervisory Patent Examiner, Art Unit 2186