Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1-20 are pending, of which claims 1, 11, and 20 are independent claims.
Priority
Applicant’s claim for the priority benefit of US provisional application No. 63/541,632 filed on September 29, 2023 is acknowledged.
Information Disclosure Statement
The references cited in the information disclosure statement (IDS) submitted on October 8, 2024 has been considered by the examiner.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter, which the inventor or a joint inventor regards as the invention.
Claims 5 and 15 are rejected under 35 U.S.C. 112(b), as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Claims 5 and 15 recite “determining whether the parameters of the calibration model are practical in representing physics of building thermal dynamics; and adjusting the values of the parameters of the calibration model in response to determining that the values of the parameters of the calibration model are not practical in representing the physics of the building thermal dynamics.” The Office is unable to appreciate the intended meaning of “…practical” in representing physics of building thermal dynamics and “…not practical” in representing the physics of the building thermal dynamics. The meaning of “practical” cannot be determined based on the recitations of the claims. The Specification does not offer a definition or meaning of the term “practical”. Also, what are “physics” properties that the claim is referring to? The intended scope of this term is unclear. Paragraph [0117] of the published Specification provides:
In some embodiments, step 708 includes checking the feasibility of the calibration parameters β0-β3 and making adjustments to the calibration parameters β0-β3 if any of them are determined to be infeasible or unrealistic (e.g., not representative of a real physical system, not practical in representing physics of building thermal dynamics). For example, a first option may include checking whether the scale factor β0 is less than zero. If β0<0, step 708 may include setting β0=1 and rerunning the regression in step 708 to determine the values of the remaining calibration parameters β1-β3. As a second option, step 708 may include setting β2=0 and β3=0, constraining β0≥0, and rerunning the regression in step 708 to determine the values of β0 and β1. As a third option, step 708 may include performing the regression to determine values of all the calibration parameters β0-β3 and modifying β0 to be at least 0.1. In some embodiments, step 708 includes performing all of the first option, the second option, and the third option, each of which results in a different set of values for β0-β3, and then checking which set of values of β0-β3 results in the lowest error (e.g., mean square error (MSE) of the regression process. The set of values of β0-β3 with the lowest error may then be selected for use in subsequent steps of process 700.
However, there is not clear meaning of the term “practical”. If the intended meaning is to be feasible or realistic, such term are subjective terms that a person of ordinary skill in the relevant art would need to ascertain. There is no objective meaning of the term that would clearly define the intended scope of “practical”. The Federal Circuit has affirmed a judgment invalidating patent claims for indefiniteness where the claims used “optimal” and “best” language without providing objective boundaries for those terms of degree. Akamai Technologies, Inc. v. MediaPointe, Inc., No. 2024-1571 (Fed. Cir. Nov. 25, 2025). The Office respectfully submits that “practical” does not provide objective boundaries, either in the claimed recitation or the Specification. Furthermore, the Specification does not provide an adequate meaning to the term “physics” recited in the claim. The Specification simply repeats this term in the description provided therein.
As the intended scope of the claim is unclear, the Office is unable to perform a proper prior art search of claims 5 and 15.
Claim Objections
The following claims are objected to for lack of antecedent support or for redundancies. The Examiner recommends the following changes:
Claim 20, line 5, insert “and” after “load;”.
Appropriate correction is respectfully requested.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
Claim 20 is rejected under 35 U.S.C. 102(a)(1) as being anticipated by Sun et al. (US 2019/0178522 A1) (“Sun”).
Regarding independent claim 20, Sun teaches:
A controller for heating, ventilating, or air conditioning (HVAC) equipment using a predicted heating or cooling load of the HVAC equipment, the controller comprising: Sun: FIGS. 1 and 2 and Paragraph [0044] (“The system includes a basic database, a load predicting software (a load predicting unit), and a controller. The basic database includes historical information (historical data). The historical information is transmitted to the load predicting software, thus the load predicting software can calculate a predicted load value based on the historical information and the measured information (measured data), and output the predicted load value. The controller issues an instruction based on the predicted load value, to perform a startup action. The load predicting software revises the predicted load value according to the measured building load (the subway load) which is fed back, and operates sequentially and circularly. Herein, the measured information may also be referred to be foreseen information.”)
a calibration model configured to relate the predicted heating or cooling load to an actual heating or cooling load using values of the predicted heating or cooling load and values of the actual heating or cooling load; Sun: Paragraph [0031] (“In an embodiment, the load-predicting and control method further includes steps of:”) Sun: Paragraph [0032] (“determining an actual load amount based on the measured data of the sensing system by a load amount determining unit of the subway heating, ventilation and air conditioning system after the first load prediction ends;”) Sun: Paragraph [0033] (“revising, by the load predicting unit, the horizontal factor and the normalized periodic factor based on a difference between the first predicted load value and the actual load amount fed back from the load amount determining unit, to obtain an updated horizontal factor and an updated normalized periodic factor;”) Sun: Paragraph [0047] (“In some embodiments, the actuator feeds the measured building load back to the load predicting software. In other embodiments, the subway heating, ventilation and air conditioning system may further include a load amount determining unit, which is, for example, integrated in the controller or in the load predicting unit. The load amount determining unit determines the actual load amount based on measured data of a sensing system which is composed by various sensors and a part of which may be connected in a corresponding actuator. Thus, the load predicting unit revises the next predicted load of the next load prediction based on the difference between the predicted load value and the actual load amount fed back from the load amount determining unit.”) Sun: Paragraph [0055] (“In some embodiments, after the load predicting software is started, it automatically starts the load predicting calculation according to a preset air conditioning time period, obtains the corresponding historical data from the basic database, inputs the real-time person number, the dry bulb temperature, the relative humidity and the measured cold load, which can be directly measured or obtained through calculation, to calculate the predicted load value of the next instant, and transmits the predicted load value to the controller via the communication module. According to the predicted load value, the controller calculates the number of the cooling machines, the chilled water pumps, the cooling water pumps, the cooling towers and the electric butterfly valves to be started; and according to the preset time, the controller issues a start command, to perform a startup action. Through issuing a control command, the controller can also control the capacity rate of each of the cold station devices (for example, for a variable frequency water pump, its output capacity can be adjusted by controlling the frequency of the variable frequency water pump)…After obtaining the next predicted value output by the load predicting software, according to the difference between the next predicted value and the measured value, the controller further regulates the output amount of the cooling machine, the flow rate of the cooling water and the flow rate of the chilled water, to realize a feedforward control.”) [The first predicted load value reads on “the predicted heating or cooling load” and the actual load amount reads on “actual heating or cooling load”. The step of updating the factors using the difference between the first predicted load value and the actual load amount to revise or update the next periodic load of the next load prediction reads on “a calibration model…”.]
a processor configured to use the calibration model to calculate calibrated values of the predicted heating or cooling load of the HVAC equipment and Sun: Paragraphs [0031]-[0033] and [0055] [As described above.] Sun: Paragraph [0034] (“transmitting, by the sensing system, a second measured real-time person number in the subway station and a second environment temperature to the load predicting unit, and calculating, by the load predicting unit, a second person number correction coefficient and a second temperature correction coefficient of a second load prediction;”) Sun: Paragraph [0035] (“calculating a second predicted load value, according to the updated horizontal factor, the updated normalized periodic factor, and the second person number correction coefficient and the second temperature correction coefficient of the second load prediction.”) [The second load prediction values using the updated factors or the correction coefficients for the load prediction model reads on “calculate calibrated values of the predicted heating or cooling load”.]
to operate the HVAC equipment using the calibrated values of the predicted heating or cooling load. Sun: Paragraphs [0031]-[0035] and [0055] [As described above.] Sun: Paragraph [0036] (“The load-predicting and control system and method for the subway heating, ventilation and air conditioning system provided by the present invention, on the basis of the historical information in combination with the measured information of future instants, calculate the predicted load value by adopting the improved seasonal exponential smoothing method, and output the predicted load value to control the cooling machine, the chilled water pump, the cooling water pump, the cooling tower, and so on. Moreover, the present invention continues revising the output capacity of the cooling machine according to the difference between the measured load amount of the building (the subway station) and the predicted load value, thereby improving the overall control effect of the subway cold station.”) [Using the second or next load prediction value to regulate the output of the cooling machine or feedforward control reads on “to operate the HVAC equipment using the calibrated values of the predicted heating or cooling load.”)
It is noted that any citations to specific paragraphs or figures in the prior art references and any interpretation of the reference should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. See MPEP 2123.
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.
Claims 1-3, 9-13, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Sun in view of Horesh et al. (US Patent Publication No. 2016/0091904 A1) (“Horesh”).
Regarding independent claim 1, Sun teaches:
A controller for heating, ventilating, or air conditioning (HVAC) equipment using a plurality of values of a predicted heating or cooling load of the HVAC equipment …, cause the one or more processors to perform operations; Sun: FIGS. 1 and 2 and Paragraph [0044] (“The system includes a basic database, a load predicting software (a load predicting unit), and a controller. The basic database includes historical information (historical data). The historical information is transmitted to the load predicting software, thus the load predicting software can calculate a predicted load value based on the historical information and the measured information (measured data), and output the predicted load value. The controller issues an instruction based on the predicted load value, to perform a startup action. The load predicting software revises the predicted load value according to the measured building load (the subway load) which is fed back, and operates sequentially and circularly. Herein, the measured information may also be referred to be foreseen information.”)
generating a calibration model that relates the predicted heating or cooling load to actual heating or cooling load using the plurality of values of the predicted heating or cooling load and the plurality of values of the actual heating or cooling load; Sun: Paragraph [0031] (“In an embodiment, the load-predicting and control method further includes steps of:”) Sun: Paragraph [0032] (“determining an actual load amount based on the measured data of the sensing system by a load amount determining unit of the subway heating, ventilation and air conditioning system after the first load prediction ends;”) Sun: Paragraph [0033] (“revising, by the load predicting unit, the horizontal factor and the normalized periodic factor based on a difference between the first predicted load value and the actual load amount fed back from the load amount determining unit, to obtain an updated horizontal factor and an updated normalized periodic factor;”) Sun: Paragraph [0047] (“In some embodiments, the actuator feeds the measured building load back to the load predicting software. In other embodiments, the subway heating, ventilation and air conditioning system may further include a load amount determining unit, which is, for example, integrated in the controller or in the load predicting unit. The load amount determining unit determines the actual load amount based on measured data of a sensing system which is composed by various sensors and a part of which may be connected in a corresponding actuator. Thus, the load predicting unit revises the next predicted load of the next load prediction based on the difference between the predicted load value and the actual load amount fed back from the load amount determining unit.”) Sun: Paragraph [0055] (“In some embodiments, after the load predicting software is started, it automatically starts the load predicting calculation according to a preset air conditioning time period, obtains the corresponding historical data from the basic database, inputs the real-time person number, the dry bulb temperature, the relative humidity and the measured cold load, which can be directly measured or obtained through calculation, to calculate the predicted load value of the next instant, and transmits the predicted load value to the controller via the communication module. According to the predicted load value, the controller calculates the number of the cooling machines, the chilled water pumps, the cooling water pumps, the cooling towers and the electric butterfly valves to be started; and according to the preset time, the controller issues a start command, to perform a startup action. Through issuing a control command, the controller can also control the capacity rate of each of the cold station devices (for example, for a variable frequency water pump, its output capacity can be adjusted by controlling the frequency of the variable frequency water pump)…After obtaining the next predicted value output by the load predicting software, according to the difference between the next predicted value and the measured value, the controller further regulates the output amount of the cooling machine, the flow rate of the cooling water and the flow rate of the chilled water, to realize a feedforward control.”) [The first predicted load value reads on “the predicted heating or cooling load” and the actual load amount reads on “actual heating or cooling load”. The step of updating the factors using the difference between the first predicted load value and the actual load amount to revise or update the next periodic load of the next load prediction reads on “generating a calibration model…”.]
using the calibration model to calculate calibrated values of the predicted heating or cooling load of the HVAC equipment; and Sun: Paragraphs [0031]-[0033] and [0055] [As described above.] Sun: Paragraph [0034] (“transmitting, by the sensing system, a second measured real-time person number in the subway station and a second environment temperature to the load predicting unit, and calculating, by the load predicting unit, a second person number correction coefficient and a second temperature correction coefficient of a second load prediction;”) Sun: Paragraph [0035] (“calculating a second predicted load value, according to the updated horizontal factor, the updated normalized periodic factor, and the second person number correction coefficient and the second temperature correction coefficient of the second load prediction.”) [The second load prediction values using the updated factors or the correction coefficients for the load prediction model reads on “calculate calibrated values of the predicted heating or cooling load”.]
operating the HVAC equipment using the calibrated values of the predicted heating or cooling load. Sun: Paragraphs [0031]-[0035] and [0055] [As described above.] Sun: Paragraph [0036] (“The load-predicting and control system and method for the subway heating, ventilation and air conditioning system provided by the present invention, on the basis of the historical information in combination with the measured information of future instants, calculate the predicted load value by adopting the improved seasonal exponential smoothing method, and output the predicted load value to control the cooling machine, the chilled water pump, the cooling water pump, the cooling tower, and so on. Moreover, the present invention continues revising the output capacity of the cooling machine according to the difference between the measured load amount of the building (the subway station) and the predicted load value, thereby improving the overall control effect of the subway cold station.”) [Using the second or next load prediction value to regulate the output of the cooling machine or feedforward control reads on “operating the HVAC equipment using the calibrated values of the predicted heating or cooling load.”)
Sun does not expressly teach “a plurality of time steps within a time period, the controller comprising one or more processors and one or more non-transitory computer-readable media storing instructions that, when executed by the one or more processors,…”. However, Horesh describes a thermal behavior model. Horesh teaches:
A controller for heating, ventilating, or air conditioning (HVAC) equipment using a plurality of values of a predicted heating or cooling load of the HVAC equipment at a plurality of time steps within a time period, the controller comprising one or more processors and one or more non-transitory computer-readable media storing instructions that, when executed by the one or more processors, … Horesh: Paragraph [0059] (“At 506, the calibrated thermal models of 502 are utilized to generate predicted or forecasted values for zone temperature and power usage over time (e.g., over a control time period) given the values for zone temperature, weather data, and building operations data. The given values for zone temperature may include zone temperature data from previous time periods. The given values for weather data may include ambient temperature from previous time periods and/or the current time period. The given values for building operations data may include calendar data such a day of the week, time of the day, holiday that provide information as to whether the building is operating or not. As an example, at 506, for every interval of time (e.g., every 10 minutes) for the control time period (e.g., the next 24 hours), a zone temperature and power usage may be predicted.”) Horesh: Paragraph [0088] (“The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer readable storage medium includes the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire.”)
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Sun and Horesh before them, to provide a plurality of time steps within a time period, the controller comprising one or more processors and one or more non-transitory computer-readable media storing instructions that, when executed by the one or more processors because the references are in the same field of endeavor as the claimed invention and they are focused on controlling HVAC systems.
One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to do this modification because it would provide a controller that minimizes the total energy costs considering demand response signal at different intervals during a time period. Horesh Paragraph [0059]
Regarding claim 2, Sun and Horesh teach all the claimed features of claim 1, from which claim 2 depends. Sun further teaches:
The controller of Claim 1, wherein using the calibration model to calculate the calibrated values of the predicted heating or cooling load comprises performing an equation-based calibration process comprising: providing the plurality of values of the predicted heating or cooling load as inputs to the calibration model; and calculating adjusted values of the predicted heating or cooling load as outputs of the calibration model. Sun: Paragraphs [0031]-[0035] and [0055] [As described in claim 1.] [The predicted load value and the actual load amount fed back reads on “providing the plurality of values of the predicted heating or cooling load as inputs”. The calculated second load prediction reads on “calculating adjusted values of the predicted heating or cooling load”.]
Regarding claim 3, Sun and Horesh teach all the claimed features of claim 1, from which claim 3 depends. Sun further teaches:
The controller of Claim 1, wherein using the calibration model to calculate the calibrated values of the predicted heating or cooling load comprises performing a based calibration process comprising: obtaining adjusted model parameters by modifying parameters of a predictive model based on parameters of the calibration model; replacing the parameters of the predictive model with the adjusted model parameters to generate a calibrated predictive model; and using the calibrated predictive model to directly output the calibrated values of the predicted heating or cooling load. Sun: Paragraphs [0031]-[0035] and [0055] [As described in claim 1.] [The factors or the coefficients of the first predicted load value reads on “parameters of the calibration model” and the revising or updating of the factors and the coefficients to determine the second load prediction values reads on “replacing the parameters of the predictive model with the adjusted model parameters… to directly output the calibrated values of the predicted heating or cooling load.”]
Regarding claim 9, Sun and Horesh teach all the claimed features of claim 1, from which claim 9 depends. Sun further teaches:
The controller of Claim 1, wherein operating the HVAC equipment using the calibrated values of the predicted heating or cooling load comprises performing an optimization-based control process using the calibrated values of the predicted heating or cooling load to generate control actions for the HVAC equipment over a future time period. Sun: Paragraph [0055] (“According to the predicted load value, the controller calculates the number of the cooling machines, the chilled water pumps, the cooling water pumps, the cooling towers and the electric butterfly valves to be started; and according to the preset time, the controller issues a start command, to perform a startup action. Through issuing a control command, the controller can also control the capacity rate of each of the cold station devices (for example, for a variable frequency water pump, its output capacity can be adjusted by controlling the frequency of the variable frequency water pump). When the next predicted value is not obtained, according to the difference between the previous predicted value and the measured value, the controller adjusts the cold supply amount of the cold station through regulating, for example, the flow rate of the cooling water and the flow rate of the chilled water, and so on. After obtaining the next predicted value output by the load predicting software, according to the difference between the next predicted value and the measured value, the controller further regulates the output amount of the cooling machine, the flow rate of the cooling water and the flow rate of the chilled water, to realize a feedforward control.”)
Regarding claim 10, Sun and Horesh teach all the claimed features of claim 1, from which claim 10 depends. Horesh further teaches:
The controller of Claim 1, wherein operating the HVAC equipment using the calibrated values of the predicted heating or cooling load comprises: estimating a cost savings resulting from operating the HVAC equipment in accordance with the calibrated values of the predicted heating or cooling load; and using the cost savings to perform a measurement and verification process for the HVAC equipment. Horesh: Paragraph [0004] (“The method may also comprise constructing a thermal behavior model of the building zone based on the time series data. The method may further comprise predicting based on the constructed thermal behavior model at least forecasted zone temperature values and energy usage values for a next control time period. The method may also comprise constructing an objective function based on at least a dynamically priced grid energy cost, occupant comfort matrix, and one or more of: energy storage system cost and associated operational cost, and energy generation system cost and associated green house emission cost and associated operational cost. The method may further comprise constructing a plurality of constraints based on at least the forecasted zone temperature values and energy usage values for the next control time period. The method may also comprise determining a control profile of the HVAC system and sourcing decision of energy load of the HVAC system simultaneously based on the objective function and the plurality of constraints. The method may further comprise transmitting the control profile to the building automation controller for controlling the HVAC system in accordance with the control profile.”) Horesh: Paragraph [0017] (“The control profile is then communicated to the controller (e.g., a Building Automation System) to control the HVAC system. In one embodiment, the method includes a Model Predictive Control (MPC) framework for a building HVAC control that reduces energy costs of HVAC operations proactively using an optimal control method and considering the dynamic price of grid purchased electricity, on-site stored electricity, and/or on-site generated electricity. The method and/or computer system can serve as an energy reduction and demand response tool that not only optimize the energy costs in buildings but also reduce energy production, stabilize energy supply (grid), and promote smart grid.”)
The motivation to combine Sun and Horesh as provided in claim 1 is incorporated herein.
Regarding independent claim 11, Sun teaches:
A method for operating heating, ventilating, or air conditioning (HVAC) equipment using a predictive model for the HVAC equipment to calculate a plurality of values of a predicted heating or cooling load of the HVAC equipment …, the method comprising: Sun: FIGS. 1 and 2 and Paragraph [0044] (“The system includes a basic database, a load predicting software (a load predicting unit), and a controller. The basic database includes historical information (historical data). The historical information is transmitted to the load predicting software, thus the load predicting software can calculate a predicted load value based on the historical information and the measured information (measured data), and output the predicted load value. The controller issues an instruction based on the predicted load value, to perform a startup action. The load predicting software revises the predicted load value according to the measured building load (the subway load) which is fed back, and operates sequentially and circularly. Herein, the measured information may also be referred to be foreseen information.”)
obtaining a plurality of values of an actual heating or cooling load of the HVAC equipment at the plurality of time steps within the time period; generating a calibration model that relates the predicted heating or cooling load to the actual heating or cooling load using the plurality of values of the predicted heating or cooling load and the plurality of values of the actual heating or cooling load; Sun: Paragraph [0031] (“In an embodiment, the load-predicting and control method further includes steps of:”) Sun: Paragraph [0032] (“determining an actual load amount based on the measured data of the sensing system by a load amount determining unit of the subway heating, ventilation and air conditioning system after the first load prediction ends;”) Sun: Paragraph [0033] (“revising, by the load predicting unit, the horizontal factor and the normalized periodic factor based on a difference between the first predicted load value and the actual load amount fed back from the load amount determining unit, to obtain an updated horizontal factor and an updated normalized periodic factor;”) Sun: Paragraph [0047] (“In some embodiments, the actuator feeds the measured building load back to the load predicting software. In other embodiments, the subway heating, ventilation and air conditioning system may further include a load amount determining unit, which is, for example, integrated in the controller or in the load predicting unit. The load amount determining unit determines the actual load amount based on measured data of a sensing system which is composed by various sensors and a part of which may be connected in a corresponding actuator. Thus, the load predicting unit revises the next predicted load of the next load prediction based on the difference between the predicted load value and the actual load amount fed back from the load amount determining unit.”) Sun: Paragraph [0055] (“In some embodiments, after the load predicting software is started, it automatically starts the load predicting calculation according to a preset air conditioning time period, obtains the corresponding historical data from the basic database, inputs the real-time person number, the dry bulb temperature, the relative humidity and the measured cold load, which can be directly measured or obtained through calculation, to calculate the predicted load value of the next instant, and transmits the predicted load value to the controller via the communication module. According to the predicted load value, the controller calculates the number of the cooling machines, the chilled water pumps, the cooling water pumps, the cooling towers and the electric butterfly valves to be started; and according to the preset time, the controller issues a start command, to perform a startup action. Through issuing a control command, the controller can also control the capacity rate of each of the cold station devices (for example, for a variable frequency water pump, its output capacity can be adjusted by controlling the frequency of the variable frequency water pump)…After obtaining the next predicted value output by the load predicting software, according to the difference between the next predicted value and the measured value, the controller further regulates the output amount of the cooling machine, the flow rate of the cooling water and the flow rate of the chilled water, to realize a feedforward control.”) [The first predicted load value reads on “the predicted heating or cooling load” and the actual load amount reads on “actual heating or cooling load”. The step of updating the factors using the difference between the first predicted load value and the actual load amount to revise or update the next periodic load of the next load prediction reads on “generating a calibration model…”.]
using the calibration model to calculate calibrated values of the predicted heating or cooling load of the HVAC equipment; and Sun: Paragraphs [0031]-[0033] and [0055] [As described above.] Sun: Paragraph [0034] (“transmitting, by the sensing system, a second measured real-time person number in the subway station and a second environment temperature to the load predicting unit, and calculating, by the load predicting unit, a second person number correction coefficient and a second temperature correction coefficient of a second load prediction;”) Sun: Paragraph [0035] (“calculating a second predicted load value, according to the updated horizontal factor, the updated normalized periodic factor, and the second person number correction coefficient and the second temperature correction coefficient of the second load prediction.”) [The second load prediction values using the updated factors or the correction coefficients for the load prediction model reads on “calculate calibrated values of the predicted heating or cooling load”.]
operating the HVAC equipment using the calibrated values of the predicted heating or cooling load. Sun: Paragraphs [0031]-[0035] and [0055] [As described above.] Sun: Paragraph [0036] (“The load-predicting and control system and method for the subway heating, ventilation and air conditioning system provided by the present invention, on the basis of the historical information in combination with the measured information of future instants, calculate the predicted load value by adopting the improved seasonal exponential smoothing method, and output the predicted load value to control the cooling machine, the chilled water pump, the cooling water pump, the cooling tower, and so on. Moreover, the present invention continues revising the output capacity of the cooling machine according to the difference between the measured load amount of the building (the subway station) and the predicted load value, thereby improving the overall control effect of the subway cold station.”) [Using the second or next load prediction value to regulate the output of the cooling machine or feedforward control reads on “operating the HVAC equipment using the calibrated values of the predicted heating or cooling load.”)
Sun does not expressly teach “a plurality of time steps within a time period, the controller comprising one or more processors and one or more non-transitory computer-readable media storing instructions that, when executed by the one or more processors,…”. However, Horesh describes a thermal behavior model. Horesh teaches:
calculate a plurality of values of a predicted heating or cooling load of the HVAC equipment at a plurality of time steps within a time period … obtaining a plurality of values of an actual heating or cooling load of the HVAC equipment at the plurality of time steps within the time period;… Horesh: Paragraph [0059] (“At 506, the calibrated thermal models of 502 are utilized to generate predicted or forecasted values for zone temperature and power usage over time (e.g., over a control time period) given the values for zone temperature, weather data, and building operations data. The given values for zone temperature may include zone temperature data from previous time periods. The given values for weather data may include ambient temperature from previous time periods and/or the current time period. The given values for building operations data may include calendar data such a day of the week, time of the day, holiday that provide information as to whether the building is operating or not. As an example, at 506, for every interval of time (e.g., every 10 minutes) for the control time period (e.g., the next 24 hours), a zone temperature and power usage may be predicted.”)
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Sun and Horesh before them, to calculate a plurality of values of a predicted heating or cooling load of the HVAC equipment at a plurality of time steps within a time period and to obtain a plurality of values of an actual heating or cooling load of the HVAC equipment at the plurality of time steps within the time period because the references are in the same field of endeavor as the claimed invention and they are focused on controlling HVAC systems.
One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to do this modification because it would provide a controller that minimizes the total energy costs considering demand response signal at different intervals during a time period. Horesh Paragraph [0059]
Regarding claim 12, the claim recites similar limitations as corresponding claim 2 and is rejected using the same teachings and rationale.
Regarding claim 13, the claim recites similar limitations as corresponding claim 3 and is rejected using the same teachings and rationale.
Regarding claim 19, the claim recites similar limitations as corresponding claim 9 and is rejected using the same teachings and rationale.
Claims 4, 6, 14, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Sun, Horesh, and in view of Fan, C. and Ding, Y., 2019. Cooling load prediction and optimal operation of HVAC systems using a multiple nonlinear regression model. Energy and Buildings, 197, pp.7-17. (“Fan”).
Regarding claim 4, Sun and Horesh teach all the claimed features of claim 1, from which claim 4 depends. Sun and Horesh do not expressly teach the features of claim 4. Fan describes cooling load prediction and optimal operation of HVAC systems. Fan teaches:
The controller of Claim 1, wherein generating the calibration model comprises performing a regression process using the plurality of values of the predicted heating or cooling load at the plurality of time steps and the plurality of values of the actual heating or cooling load at the plurality of time steps to generate values of parameters of the calibration model. Fan: Abstract (“The key variables in the MNR model are selected based on sensitivity analysis, and calibration methods can be used to enhance the prediction accuracy. The prediction process can be divided into three steps. The first step requires determining the weight coefficients on the input variables based on historical load and weather data. Second, the initially predicted load is obtained using the weight coefficients and predicted input variables. Finally, the initially predicted results were calibrated using relative calibration methods. The initial load prediction and final calibration are based on data from the same day, which is taken as a reference. The performance of the established MNR model is validated against a real case in Guangzhou using measured cooling load data and weather data. A case study shows that the accuracy of the final calibrated load is higher than the initially predicted load. The MNR model also produces more accurate results compared with three traditional regression models. The accurate cooling load prediction method can be used as an operation control strategy in real applications.”) Fan: Page 11, Section 3.2, first column (“A multiple nonlinear regression (MNR) model was developed for predicting the cooling load. … The prediction process with MNR is shown in Fig. 2. The prediction method can be divided into three steps. The method is first used to determine the weight coefficient of the input variables based on historical load and weather data. Second, the initially predicted load is obtained from the weight coefficient and data corresponding to the predicted input variables. Finally, the initially predicted load is calibrated using the historical load and the errors predicted over the previous 2 h. In fact, the measured load over the previous 2 h is easy to obtain and has a great impact on the predicted load at a later time. Such as previous 3 h, 4 h or more longer hours historical loads have low influence levels. Some people [16] used the previous 2 h load for calibration and achieved good enhancements. The HVAC system was maintained and monitored with the building's automatic control system (BACs). The input variables at the target time t+τ, such as the outdoor dry-bulb temperature (T) and relative humidity (H), are usually available from a local weather forecast. The hourly predicted solar radiation (R) is shown in Appendix (A1 and A2). The historical occupancy changes (O), lighting power (L), and cooling load data (CL) can be collected using the BACs, and weather data can be collected with monitoring instruments.”) [The weather data and measured load over several hours reads on “the actual heating or cooling load at the plurality of time steps”.]
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Sun, Horesh, and Fan before them, to perform a regression process using the plurality of values of the predicted heating or cooling load at the plurality of time steps and the plurality of values of the actual heating or cooling load at the plurality of time steps to generate values of parameters of the calibration model because the references are in the same field of endeavor as the claimed invention and they are focused on controlling HVAC systems.
One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to do this modification because it would provide a higher accuracy of the final calibrated load than the initially predicted load. The MNR model also produces more accurate results compared with three traditional regression models. The accurate cooling load prediction method can be used as an operation control strategy in real applications. Fan Abstract
Regarding claim 6, Sun and Horesh teach all the claimed features of claim 1, from which claim 6 depends. Sun and Horesh do not expressly teach the features of claim 4. Fan describes cooling load prediction and optimal operation of HVAC systems. Fan teaches:
The controller of Claim 1, the operations further comprising obtaining initial states of a predictive model by running the predictive model for a previous time period prior to the time period comprising the plurality of time steps. Fan: Abstract (“The key variables in the MNR model are selected based on sensitivity analysis, and calibration methods can be used to enhance the prediction accuracy. The prediction process can be divided into three steps. The first step requires determining the weight coefficients on the input variables based on historical load and weather data. Second, the initially predicted load is obtained using the weight coefficients and predicted input variables. Finally, the initially predicted results were calibrated using relative calibration methods. The initial load prediction and final calibration are based on data from the same day, which is taken as a reference. The performance of the established MNR model is validated against a real case in Guangzhou using measured cooling load data and weather data. A case study shows that the accuracy of the final calibrated load is higher than the initially predicted load. The MNR model also produces more accurate results compared with three traditional regression models. The accurate cooling load prediction method can be used as an operation control strategy in real applications.”) Fan: Page 11, Section 3.3, first to second columns and Table 5 (“The prediction process with MNR is shown in Fig. 2. The prediction method can be divided into three steps. The method is first used to determine the weight coefficient of the input variables based on historical load and weather data. Second, the initially predicted load is obtained from the weight coefficient and data corresponding to the predicted input variables. Finally, the initially predicted load is calibrated using the historical load and the errors predicted over the previous 2 h. In fact, the measured load over the previous 2 h is easy to obtain and has a great impact on the predicted load at a later time. Such as previous 3 h, 4 h or more longer hours historical loads have low influence levels. Some people [16] used the previous 2 h load for calibration and achieved good enhancements. The HVAC system was maintained and monitored with the building's automatic control system (BACs). The input variables at the target time t+τ, such as the outdoor dry-bulb temperature (T) and relative humidity (H), are usually available from a local weather forecast. The hourly predicted solar radiation (R) is shown in Appendix (A1 and A2). The historical occupancy changes (O), lighting power (L), and cooling load data (CL) can be collected using the BACs, and weather data can be collected with monitoring instruments.”) Fan: Table 6 and Page 12, Section 4.1, first column (“The test instruments used to gather historical weather and load data are listed in Table 6. The measured time interval is 1 h.”)
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Sun, Horesh, and Fan before them, to obtain initial states of a predictive model by running the predictive model for a previous time period prior to the time period comprising the plurality of time steps because the references are in the same field of endeavor as the claimed invention and they are focused on controlling HVAC systems.
One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to do this modification because it would provide a higher accuracy of the final calibrated load than the initially predicted load as the measured load over the previous 2 h is easy to obtain and has a great impact on the predicted load at a later time. Fan: Page 11, Section 3.3, first to second columns.
Regarding claim 14, the claim recites similar limitations as corresponding claim 4 and is rejected using the same teachings and rationale.
Regarding claim 16, the claim recites similar limitations as corresponding claim 6 and is rejected using the same teachings and rationale.
Claims 8 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Sun, Horesh, and in view of Sala-Cardoso, E., Delgado-Prieto, M., Kampouropoulos, K. and Romeral, L., 2020. Predictive chiller operation: A data-driven loading and scheduling approach. Energy and Buildings, 208, p.109639. (“Sala-Cardoso”).
Regarding claim 8, Sun and Horesh teach all the claimed features of claim 1, from which claim 8 depends. Although Fan describes that the output the predicted load value to control the cooling machine, the chilled water pump, the cooling water pump, the cooling tower, and so on and continues revising the output capacity of the cooling machine according to the difference between the measured load amount of the building (the subway station) and the predicted load value, thereby improving the overall control effect of the subway cold station, Fan and Horesh do not expressly teach a sequence of control actions. Sala-Cardoso describes proper sequencing and optimal loading of chillers using load forecasting scheme. Sala-Cardoso teaches:
The controller of Claim 1, wherein operating the HVAC equipment using the calibrated values of the predicted heating or cooling load comprises: using the calibrated values of the predicted heating or cooling load to plan a sequence of control actions for the HVAC equipment over a future time period; and operating the HVAC equipment over the future time period using the sequence of control actions. Sala-Cardoso: Page 4, Section 2.2.1, first and second columns (“In this study, a suitable thermal power load forecasting methodology is required for supporting the model-predictive control, improving the controller response by anticipating power demand changes… The main steps of the forecasting methodology are displayed in Fig. 2. As shown, the required signals are extracted from the building's historical database a), which are the signals corresponding to occupancy sensors b) and the power monitoring c). After loading the necessary signals, the modeling of the occupancy d) takes place, alongside the estimation of the thermal demand e) from the load profile of the distribution bus. Finally, the modelling of the power demand f) is performed, combining the occupancy model and the estimated thermal demand. In order to improve the accuracy of the power demand forecasting, the methodology incorporates the level of activity in the building by implementing an artificial activity indicator. This indicator is built by aggregating presence detectors installed in each of the individual spaces and common areas in the building. Then, an activity model is implemented using recurrent neural networks to enhance the consideration of dynamic temporal patterns, while the power demand characterization is carried out by means of an adaptive neuro-fuzzy inference system structure.”) Sala-Cardoso: Page 5, Section 3.2.2, second column, to Page 6, first column (“This loop is proposed to operate on top of the previous one, iterating over N steps in the prediction horizon to determine the optimal future sequence of control actions that will satisfy the forecasted load demand… Thus, knowing the future state of bus temperatures is required to be able to apply the operating mode control loop during future iterations in order to determine the future control sequence. To solve this problem, a simulation model of the distribution bus is employed, implemented in this case as a first order energy storage model with a single capacity coefficient, estimated using the historical data available. Using the bus model, a control sequence is determined by the described depth-first algorithm that iterates from each of the setpoint candidates, simulating the application of the control action on the distribution bus d) while keeping track of the COP achieved by each sequence over the optimization horizon. Each of the obtained control sequences took locally optimal decisions at each iteration according to the objective function, thus finally the entire control sequences are evaluated using the objective function, which allows the final selection of the predicted setpoints h), i.e. the optimal control sequence over the optimization horizon for matching the predicted cooling load profile.”)
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Sun, Horesh, and Sala-Cardoso before them, to provide using the calibrated values of the predicted heating or cooling load to plan a sequence of control actions for the HVAC equipment over a future time period; and operating the HVAC equipment over the future time period using the sequence of control actions because the references are in the same field of endeavor as the claimed invention and they are focused on controlling HVAC systems.
One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to do this modification because it would provide a framework for implementing a control strategy aimed at solving the optimal loading and scheduling problem in HVAC by reducing power demand, or improving the efficiency of the involved energy systems. Sala-Cardoso Abstract and Section 6, Conclusions
Regarding claim 18, the claim recites similar limitations as corresponding claim 8 and is rejected using the same teachings and rationale.
It is noted that any citations to specific paragraphs or figures in the prior art references and any interpretation of the reference should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. See MPEP 2123.
Allowable Subject Matter
The subject matter of claims 7 and 17 is found to be allowable over the prior art of record and would be considered allowable.
While the prior art shows a load-predicting and control system (see Sun et al. (US Patent Publication No. 2019/0178522 A1); Horesh et al. (US Patent Publication No. 2016/0091904 A1); Fan, C. and Ding, Y., 2019. Cooling load prediction and optimal operation of HVAC systems using a multiple nonlinear regression model. Energy and Buildings, 197, pp.7-17.; Sala-Cardoso, E., Delgado-Prieto, M., Kampouropoulos, K. and Romeral, L., 2020. Predictive chiller operation: A data-driven loading and scheduling approach. Energy and Buildings, 208, p.109639.; US Patent Publication No. 2018/0323994 A1 to Jones et al.) the prior art, individually or combined, does not teach or suggest “using a predictive model to calculate the plurality of values of the predicted heating or cooling load comprises: determining whether the HVAC equipment are on or off at each time step of the time period; setting a value of the predicted heating or cooling load to zero for each time step during which the HVAC equipment are off during the time period; and using the predictive model to calculate a value of the predicted heating or cooling load for each time step during which the HVAC equipment are on during the time period” as recited in claim 7 and similarly recited in claim 17. It is this concept that defines the present application over the prior art of record.
As allowable subject matter has been indicated, applicant's reply must either comply with all formal requirements or specifically traverse each requirement not complied with. See 37 CFR 1.111(b) and MPEP § 707.07(a).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US Patent Publication No. 2018/0323994 A1 to Jones et al. describes systems and methods for sequencing HVAC equipment of an HVAC system using data recorded in situ to build a model capable of making predictions about equipment efficiency and using that information, in combination with predictions about building load, to produce an operational sequence for the HVAC equipment that promotes an improved or optimized overall energy efficiency for the HVAC system. In one embodiment, the process is automated and utilizes Bayesian computational models or algorithms to generate an initial sequence. The process reduces engineering hours and may advantageously provide a means to predict potential sequencing problems for similar types of HVAC equipment.
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/ALICIA M. CHOI/Primary Patent Examiner, Art Unit 2117
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