DETAILED ACTION
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 12/07/2025 has been entered.
Status of Claims
The following is a Non-Final Office Action in response to applicant’s Request for Continued Examination filed on 12/07/2025.
Claims 1, 3, 4, 6, 9, 11, 13, 14, and 17-20 are amended. Claims 2, 5, 8, 12, and 16 are cancelled. Claims 1, 3, 4, 6, 7, 9- 11, 13-15, and 17-20 are considered in this Office Action. Claims 1, 3, 4, 6, 7, 9-11, 13-15, and 17-20 are currently pending.
Response to Amendments
Applicant’s amendment necessitated the new ground(s) of rejection set forth in this Office Action.
Applicant’s arguments and amendments with respect to the 35 U.S.C. §101 rejection to claims have been considered, applicant arguments and amendments overcome the 35 USC 101 rejection. The rejection is therefore withdrawn.
Applicant’s amendments to claim 6 is considered, claim objection is withdrawn.
Applicant’s amendments with respect to the 35 U.S.C. §112(b) rejection to claims have been considered, applicant’s amendments overcome the 35 USC 112(b) rejection. The rejection is therefore withdrawn.
Applicant’s arguments and amendments with respect to the 35 U.S.C. §103 rejection to claims have been considered, however they are primarily raised in light of applicant’s amendments. An updated the 35 U.S.C. §103 rejection will address applicant’s amendment.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
Claims 1, 3, 4, 6, 7, 9, 10, 11, 13, 14, 15, and 17-20 are rejected under 35 U.S.C. 112(a) as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor at the time the application was filed, had possession of the claimed invention.
With respect to claims 1, 11, and 19, the claims recite “identify two or more previously unknown dual consumers…” however, the examiner is unable to find any generic or specific description or steps in the instant specification that show the applicant is in possession of a technique that distinguishes between known dual consumers and unknown dual consumers, or performing any step of determining unknowingness. Applicant’s specification in “[00042] PGMS 130 is further configured to comprise a power consumption database 205 operatively connected to PMC 201.Power consumption database 205 is configured to accommodate data informative of individual grid power consumption (GPC) of consumers 104. Accommodated data can include data as provided by power meters 105, such data being timestamped and associated with respective consumers and are referred to hereinafter as consumer power meter (CPM) data.” While the specification discloses identifying of “dual consumer” (i.e., [0063]- [0066]) that is not the same as the distinct claim requirement that the dual consumers are “previously unknown.”
Thus, there is no evidence of a complete specific application or embodiment to satisfy the requirement that the description is set forth "in such full, clear, concise, and exact terms" to show possession of the claimed invention. Fields V. Conover, 443 F.2d 1386, 1392, 170 USPQ 276, 280 (CCPA 1971).
Claims 3, 4, 6, 7, 9, 10, 13, 14, 15, 17, 18, and 20 depend from one of claims 1, 11, and 19 and fail to cure the deficiency noted above, and are therefore rejected based on dependency.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 2, 9, 11, 13, 17, 19, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over in view of Rahul Mohan (US 2015/0142347 A1, hereinafter “Mohan”) in view of Fabio Mantovani (US 2018/0216961 A1, hereinafter “Mantovani”) in view of David Potter (US 201/0163754 A1, hereinafter “Potter”) in view of Takayuki Eda (US 2018/0248375 A1, hereinafter “Eda”).
Claim 1
Mohan teaches:
A method of managing an electrical power grid operatively connected to a plurality of consumers in a geographical area, the method comprising, by a computer: processing timestamped data informative of one or more weather conditions and of individual grid power consumption (GPC) by the plurality of consumers (Mohan teaches [0025] Data may be obtained and/or accessed in various manners. energy usage data may be obtained from Green Button (an industry effort to provide transparent energy usage data, which is generally provided in hourly intervals))to identify two or more previously unknown dual consumers each connected to one or more alternative power sources with power generating dependable on the one or more weather conditions([0049] With reference to FIG. 6, an exemplary process 60 for disaggregating solar contribution from a whole house profile based on high frequency data, (i) identifying correlations between weather and spikes in the data; (ii) establishing spikes caused by weather; [0021] solar output may be predicted for unseen homes (and therefore, homes where solar contribution is not directly measured), by using training data. [0035] When using low frequency whole-house energy consumption data, the energy contribution of solar panels must be determined and disaggregated. disaggregating the solar energy produced from the low frequency whole-house energy consumption data),
wherein a GPC of a given consumer from the plurality of consumers is measured by a power consumption meter associated therewith ([0025] Data may be obtained and/or accessed in various manners. Alternatively, energy usage data may be obtained from Smart Meters--for example using a Smart Meter Home Area Network channel);
separately for each of the identified previously unknown two or more dual consumers, using a trained Forecasting Machine Learning (FML) Model to provide individual forecast of alternative power production by connected alternative power source([0006] disaggregating energy produced by solar panels from low frequency whole-house energy consumption data for a specific house, comprising: predicting solar energy generation for the specific house by applying a machine learning model to generate a non-linear model of solar intensity. [0007] disaggregating energy produced by solar panels from low frequency whole-house energy consumption data for a specific house received from a Smart Meter, comprising: a prediction module configured to predict solar energy generation for the specific house. [0021] solar output may be predicted for unseen homes (and therefore, homes where solar contribution is not directly measured), by using training data).
While Mohan teaches [0006] disaggregating energy produced by solar panels from low frequency whole-house energy consumption data for a specific house, comprising: predicting solar energy generation for the specific house by applying a machine learning model to generate a non-linear model of solar intensity. [0007] disaggregating energy produced by solar panels from low frequency whole-house energy consumption data for a specific house received from a Smart Meter, comprising: a prediction module configured to predict solar energy generation for the specific house. [0021] solar output may be predicted for unseen homes (and therefore, homes where solar contribution is not directly measured), by using training data. Mohan does not explicitly teach the following; however analogues reference in the field of energy predication model, Mantovani teaches:
and wherein the two or more previously unknown dual consumers are identified with no measurements of individual alternative power production ([0016] determination of unauthorized interconnection of the customer includes pulling a record of list of customers identified to have solar but not net energy metering (NEM), and matching the record with estimated system size. [0022] methods for identifying unconnected solar customers).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system for power distribution grid management of Mohan with Mantovani to include the two or more previously unknown dual consumers are identified with no measurements of individual alternative power production. Doing so would provide more up to date and accurate source of distributed solar photovoltaic system location data to capture locational value of distributed solar photovoltaic and plan for hosting capacity while understanding system impacts and minimizing costs [0007].
While Mohan teaches [0006] disaggregating energy produced by solar panels from low frequency whole-house energy consumption data for a specific house, comprising: predicting solar energy generation for the specific house by applying a machine learning model to generate a non-linear model of solar intensity. [0007] disaggregating energy produced by solar panels from low frequency whole-house energy consumption data for a specific house received from a Smart Meter, comprising: a prediction module configured to predict solar energy generation for the specific house. [0021] solar output may be predicted for unseen homes (and therefore, homes where solar contribution is not directly measured), by using training data. Mohan does not explicitly teach the following; however analogues reference in the field of energy predication model, Potter teaches:
wherein the FML model is trained on historical GPC and weather conditions data to forecast the alternative power production in accordance with a forecast of the one or more weather conditions in the geographical area([0064] a decision engine 302 (e.g., a software application running on the computer system 102) first obtains an energy storage device status, load (or demand) forecast, and power production forecast, one or more power consumption entities 306, and an alternative energy source 308 (e.g., a renewable energy source). [0057] a power load (demand) 218, which represents an estimated or predicted amount of energy, e.g., electricity, one or more power consumption entities are to consume, during a prospective time period (e.g., the next week or three hours from the current time); [0058] a first power supply 220, which represents an estimated or predicted amount of energy an alternative energy source (e.g., a wind farm) is to provide, during a different or the same prospective time period (e.g., how many megawatt-hours electricity a wind turbine at downtown "Windy City"--Chicago--will produce, during the next week or during the next 6 hours); [0089] the computer system determines (512) a first power supply from the alternative energy source in accordance with (i) a historical power supply from the alternative energy source over a first historical period (e.g., yesterday), and (ii) a historical power supply from the alternative energy source over a second historical period (e.g., the day before yesterday). For example, power production data from two weeks ago as well as those from three weeks ago by a wind farm are used (e.g., averaged or calculated using a differential equation) to determine (e.g., predict or estimate) the wind farm's power production tomorrow);
and using the provided total forecast to trigger one or more management actions with regard to power production in the electrical power grid, wherein the one or more management actions comprise providing commands for automatically power distributing between the power grid and a power storage facility([0038] the computer system 102 includes a load determination module 150, a supply determination module 112, a decision engine 114, a power storage module 116, and a controller 118. In some implementations, the computer system 102 measures or predicts power load from one or more power consumption entities 104, as well as potential power supply from the alternative energy source 108. In some implementations, the computer system 102, based on the predicted supply and load, stores excess power supply from the alternative energy source 108 to the energy storage device 110, as well as adjusts power product by the alternative energy source 108. ), wherein the one or more management actions comprise issuing by the computer at least one command related to at least one of: charging/discharging one or more batteries connected to the grid and controlling thermostat set-point change in a set of points connected to the grid(Potter [0065] when power supply by the renewable energy source (e.g., a photovoltaic farm, sometimes also called a PV farm) exceeds load (or demand) by the power consumption entities (as indicated by "High Renewable Output" in FIG. 3), the decision engine charges (310) the rechargeable energy storage device to store excess power supply. ).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system for power distribution grid management of Mohan and Mantovani with Potter to include the FML model is trained on historical GPC and weather conditions data to forecast the alternative power production in accordance with a forecast of the one or more weather conditions in the geographical area and using the provided total forecast to trigger one or more management actions with regard to power production in the electrical power grid, wherein the one or more management actions comprise providing commands for automatically power distributing between the power grid and a power storage facility, wherein the one or more management actions comprise issuing by the computer at least one command related to at least one of: charging/discharging one or more batteries connected to the grid and controlling thermostat set-point change in a set of points connected to the grid. Doing so would aid in regulating an alternative energy source that is decoupled from a power grid. [0005].
While Mohan teaches [0006] disaggregating energy produced by solar panels from low frequency whole-house energy consumption data for a specific house, comprising: predicting solar energy generation for the specific house by applying a machine learning model to generate a non-linear model of solar intensity. [0007] disaggregating energy produced by solar panels from low frequency whole-house energy consumption data for a specific house received from a Smart Meter, comprising: a prediction module configured to predict solar energy generation for the specific house. [0021] solar output may be predicted for unseen homes (and therefore, homes where solar contribution is not directly measured), by using training data. Mohan does not explicitly teach the following; however analogues reference in the field of energy predication model, Eda teaches:
and using the individual forecasts for the two or more previously unknown dual consumers to provide a total forecast of total alternative power production for a group of previously unknown dual consumers (Eda teaches [0097] Next, in step S13, the demand prediction unit 12 predicts a demand a [kW] for each utility customer in the group G.sub.1 at a predetermined time, and an accumulated demand as [kWh] from a present time to a predetermined time. [0101] Next, in step S16, the utility customer group demand prediction unit 17 adds a demand a of each of the utility customers A, B, C . . . included in the group G.sub.1, thereby calculating an occasionally predicted total demand Ta [kW] of the group G. Next, in step S17, the utility customer group power generation prediction unit 18 adds an assumed amount of generated power b [kW] of each solar panel 21 belonging to the group G.sub.1, thereby calculating an occasionally predicted total amount of generated power Tb [kW] of the solar panels 21 belonging to the group G).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system for power distribution grid management of Mohan, Mantovani, and Potter with Eda to include using the individual forecasts for the two or more previously unknown dual consumers to provide a total forecast of total alternative power production for a group of previously unknown dual consumers. Doing so would aid in accurately predict supply and demand of power of a utility customer group that includes multiple utility customers. [0008].
Claim 3
While Mohan teaches [0006] disaggregating energy produced by solar panels from low frequency whole-house energy consumption data for a specific house, comprising: predicting solar energy generation for the specific house by applying a machine learning model to generate a non-linear model of solar intensity. [0007] disaggregating energy produced by solar panels from low frequency whole-house energy consumption data for a specific house received from a Smart Meter, comprising: a prediction module configured to predict solar energy generation for the specific house. [0021] solar output may be predicted for unseen homes (and therefore, homes where solar contribution is not directly measured), by using training data. Mohan does not explicitly teach the following; however analogues reference in the field of energy predication model, Mantovani teaches:
The method of Claim 1, wherein a previously unknown dual consumer is identified as having inverse relationship between the data informative of the consumer's individual GPC and the data informative of one or more weather conditions comparing to other consumers in a group of similar consumers ([0022] methods for identifying unconnected solar customers. [0071] customers can be required to have a negative correlation between hourly kW and irradiance (GHI). Table 7 and [0095] the unauthorized access detection system and method can construct a ‘neighborhood’ of fifty closest neighbors to a customer suspected of failing. Further, in some embodiments, an average of their normalized average total channel 1 output can be calculated and used to compare the 3-day moving average of the difference between the customer and their neighborhood. Examiner notes a negative correlation is an inverse relationship, where hourly KW is consumption (GPC type) and irradiance is weather/solar condition).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system for power distribution grid management of Mohan with Mantovani to include a previously unknown dual consumer is identified as having inverse relationship between the data informative of the consumer's individual GPC and the data informative of one or more weather conditions comparing to other consumers in a group of similar consumers. Doing so would provide more up to date and accurate source of distributed solar photovoltaic system location data to capture locational value of distributed solar photovoltaic and plan for hosting capacity while understanding system impacts and minimizing costs [0007].
Claim 9
While Mohan teaches [0006] disaggregating energy produced by solar panels from low frequency whole-house energy consumption data for a specific house, comprising: predicting solar energy generation for the specific house by applying a machine learning model to generate a non-linear model of solar intensity. [0007] disaggregating energy produced by solar panels from low frequency whole-house energy consumption data for a specific house received from a Smart Meter, comprising: a prediction module configured to predict solar energy generation for the specific house. [0021] solar output may be predicted for unseen homes (and therefore, homes where solar contribution is not directly measured), by using training data. [0025] Data may be obtained and/or accessed in various manners. Energy usage data may be obtained from Green Button (an industry effort to provide transparent energy usage data, which is generally provided in hourly intervals. Mohan does not explicitly teach the following; however analogues reference in the field of energy predication model, Mantovani teaches:
The method of Claim 1, wherein the group of identified previously unknown dual consumers is constituted by at least one of: all identified previously unknown dual consumers, identified previously unknown dual consumers having the same type of the alternative energy source, identified previously unknown dual consumers having similar GPC patterns, identified previously unknown dual consumers having similar GPC requirements([0016] determination of unauthorized interconnection of the customer includes pulling a record of list of customers identified to have solar but not net energy metering (NEM), and matching the record with estimated system size).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system for power distribution grid management of Mohan with Mantovani to include the group of identified previously unknown dual consumers is constituted by at least one of: all identified previously unknown dual consumers, identified previously unknown dual consumers having the same type of the alternative energy source, identified previously unknown dual consumers having similar GPC patterns, identified previously unknown dual consumers having similar GPC requirements. Doing so would provide more up to date and accurate source of distributed solar photovoltaic system location data to capture locational value of distributed solar photovoltaic and plan for hosting capacity while understanding system impacts and minimizing costs [0007].
Claim 11/19
Mohan teaches:
A system capable of managing an electrical power grid operatively connected to a plurality of consumers in a geographical area, the system comprising a computer configured to: process timestamped data informative of one or more weather conditions and of individual grid power consumption (GPC) by the plurality of consumers (Mohan teaches [0028] processor [0025] Data may be obtained and/or accessed in various manners. energy usage data may be obtained from Green Button (an industry effort to provide transparent energy usage data, which is generally provided in hourly intervals))to identify two or more previously unknown dual consumers each connected to one or more alternative power sources with power generating dependable on the one or more weather conditions([0049] With reference to FIG. 6, an exemplary process 60 for disaggregating solar contribution from a whole house profile based on high frequency data, (i) identifying correlations between weather and spikes in the data; (ii) establishing spikes caused by weather; [0021] solar output may be predicted for unseen homes (and therefore, homes where solar contribution is not directly measured), by using training data. [0035] When using low frequency whole-house energy consumption data, the energy contribution of solar panels must be determined and disaggregated. disaggregating the solar energy produced from the low frequency whole-house energy consumption data),
wherein a GPC of a given consumer from the plurality of consumers is measured by a power consumption meter associated therewith ([0025] Data may be obtained and/or accessed in various manners. Alternatively, energy usage data may be obtained from Smart Meters--for example using a Smart Meter Home Area Network channel);
separately for each of the identified previously unknown two or more dual consumers, using a trained Forecasting Machine Learning (FML) Model to provide individual forecast of alternative power production by connected alternative power source([0006] disaggregating energy produced by solar panels from low frequency whole-house energy consumption data for a specific house, comprising: predicting solar energy generation for the specific house by applying a machine learning model to generate a non-linear model of solar intensity. [0007] disaggregating energy produced by solar panels from low frequency whole-house energy consumption data for a specific house received from a Smart Meter, comprising: a prediction module configured to predict solar energy generation for the specific house. [0021] solar output may be predicted for unseen homes (and therefore, homes where solar contribution is not directly measured), by using training data).
While Mohan teaches [0006] disaggregating energy produced by solar panels from low frequency whole-house energy consumption data for a specific house, comprising: predicting solar energy generation for the specific house by applying a machine learning model to generate a non-linear model of solar intensity. [0007] disaggregating energy produced by solar panels from low frequency whole-house energy consumption data for a specific house received from a Smart Meter, comprising: a prediction module configured to predict solar energy generation for the specific house. [0021] solar output may be predicted for unseen homes (and therefore, homes where solar contribution is not directly measured), by using training data. Mohan does not explicitly teach the following; however analogues reference in the field of energy predication model, Mantovani teaches:
wherein each previously unknown dual consumer is identified in accordance with a relationship between the consumer's individual GPC and the one or more weather conditions during a certain time period and with no measurements of individual alternative power production ([0008] monitoring unauthorized electrical grid access comprising operating a hardware system with at least one processor executing instructions from a non-transitory computer-readable storage medium of an electrical grid access detection system. [0016] determination of unauthorized interconnection of the customer includes pulling a record of list of customers identified to have solar but not net energy metering (NEM), and matching the record with estimated system size. [0022] methods for identifying unconnected solar customers. [0051] predicting or determining whether or not a residential customer has a solar photovoltaic system at their premises, and the size of the solar photovoltaic system. source data can comprise data correlated with solar photovoltaic production including various weather variables. pulled data can contain date/time stamp and customer identifier for each interval reading).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system for power distribution grid management of Mohan with Mantovani to include each previously unknown dual consumer is identified in accordance with a relationship between the consumer's individual GPC and the one or more weather conditions during a certain time period and with no measurements of individual alternative power production. Doing so would provide more up to date and accurate source of distributed solar photovoltaic system location data to capture locational value of distributed solar photovoltaic and plan for hosting capacity while understanding system impacts and minimizing costs [0007].
While Mohan teaches [0006] disaggregating energy produced by solar panels from low frequency whole-house energy consumption data for a specific house, comprising: predicting solar energy generation for the specific house by applying a machine learning model to generate a non-linear model of solar intensity. [0007] disaggregating energy produced by solar panels from low frequency whole-house energy consumption data for a specific house received from a Smart Meter, comprising: a prediction module configured to predict solar energy generation for the specific house. [0021] solar output may be predicted for unseen homes (and therefore, homes where solar contribution is not directly measured), by using training data. Mohan does not explicitly teach the following; however analogues reference in the field of energy predication model, Potter teaches:
wherein the FML model is trained on historical GPC and weather conditions data to forecast the alternative power production in accordance with a forecast of the one or more weather conditions in the geographical area([0064] a decision engine 302 (e.g., a software application running on the computer system 102) first obtains an energy storage device status, load (or demand) forecast, and power production forecast, one or more power consumption entities 306, and an alternative energy source 308 (e.g., a renewable energy source). [0057] a power load (demand) 218, which represents an estimated or predicted amount of energy, e.g., electricity, one or more power consumption entities are to consume, during a prospective time period (e.g., the next week or three hours from the current time); [0058] a first power supply 220, which represents an estimated or predicted amount of energy an alternative energy source (e.g., a wind farm) is to provide, during a different or the same prospective time period (e.g., how many megawatt-hours electricity a wind turbine at downtown "Windy City"--Chicago--will produce, during the next week or during the next 6 hours); [0089] the computer system determines (512) a first power supply from the alternative energy source in accordance with (i) a historical power supply from the alternative energy source over a first historical period (e.g., yesterday), and (ii) a historical power supply from the alternative energy source over a second historical period (e.g., the day before yesterday). For example, power production data from two weeks ago as well as those from three weeks ago by a wind farm are used (e.g., averaged or calculated using a differential equation) to determine (e.g., predict or estimate) the wind farm's power production tomorrow);
and use the provided total forecast to trigger one or more management actions with regard to power production in the electrical power grid, wherein the one or more management actions comprise providing commands for automatically power distributing between the power grid and a power storage facility([0038] the computer system 102 includes a load determination module 150, a supply determination module 112, a decision engine 114, a power storage module 116, and a controller 118. In some implementations, the computer system 102 measures or predicts power load from one or more power consumption entities 104, as well as potential power supply from the alternative energy source 108. In some implementations, the computer system 102, based on the predicted supply and load, stores excess power supply from the alternative energy source 108 to the energy storage device 110, as well as adjusts power product by the alternative energy source 108. ), wherein the one or more management actions comprise issuing by the computer at least one command related to at least one of: charging/discharging one or more batteries connected to the grid and controlling thermostat set-point change in a set of points connected to the grid(Potter [0065] when power supply by the renewable energy source (e.g., a photovoltaic farm, sometimes also called a PV farm) exceeds load (or demand) by the power consumption entities (as indicated by "High Renewable Output" in FIG. 3), the decision engine charges (310) the rechargeable energy storage device to store excess power supply. ).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system for power distribution grid management of Mohan and Mantovani with Potter to include the FML model is trained on historical GPC and weather conditions data to forecast the alternative power production in accordance with a forecast of the one or more weather conditions in the geographical area and using the provided total forecast to trigger one or more management actions with regard to power production in the electrical power grid, wherein the one or more management actions comprise providing commands for automatically power distributing between the power grid and a power storage facility, wherein the one or more management actions comprise issuing by the computer at least one command related to at least one of: charging/discharging one or more batteries connected to the grid and controlling thermostat set-point change in a set of points connected to the grid. Doing so would aid in regulating an alternative energy source that is decoupled from a power grid. [0005].
While Mohan teaches [0006] disaggregating energy produced by solar panels from low frequency whole-house energy consumption data for a specific house, comprising: predicting solar energy generation for the specific house by applying a machine learning model to generate a non-linear model of solar intensity. [0007] disaggregating energy produced by solar panels from low frequency whole-house energy consumption data for a specific house received from a Smart Meter, comprising: a prediction module configured to predict solar energy generation for the specific house. [0021] solar output may be predicted for unseen homes (and therefore, homes where solar contribution is not directly measured), by using training data. Mohan does not explicitly teach the following; however analogues reference in the field of energy predication model, Eda teaches:
use the individual forecasts for the two or more previously unknown dual consumers to provide a total forecast of total alternative power production for a group of previously unknown dual consumers(Eda teaches [0097] Next, in step S13, the demand prediction unit 12 predicts a demand a [kW] for each utility customer in the group G.sub.1 at a predetermined time, and an accumulated demand as [kWh] from a present time to a predetermined time. [0101] Next, in step S16, the utility customer group demand prediction unit 17 adds a demand a of each of the utility customers A, B, C . . . included in the group G.sub.1, thereby calculating an occasionally predicted total demand Ta [kW] of the group G. Next, in step S17, the utility customer group power generation prediction unit 18 adds an assumed amount of generated power b [kW] of each solar panel 21 belonging to the group G.sub.1, thereby calculating an occasionally predicted total amount of generated power Tb [kW] of the solar panels 21 belonging to the group G).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system for power distribution grid management of Mohan, Mantovani, and Potter with Eda to include using the individual forecasts for the two or more previously unknown dual consumers to provide a total forecast of total alternative power production for a group of previously unknown dual consumers. Doing so would aid in accurately predict supply and demand of power of a utility customer group that includes multiple utility customers. [0008].
Claim 13/20
While Mohan teaches [0006] disaggregating energy produced by solar panels from low frequency whole-house energy consumption data for a specific house, comprising: predicting solar energy generation for the specific house by applying a machine learning model to generate a non-linear model of solar intensity. [0007] disaggregating energy produced by solar panels from low frequency whole-house energy consumption data for a specific house received from a Smart Meter, comprising: a prediction module configured to predict solar energy generation for the specific house. [0021] solar output may be predicted for unseen homes (and therefore, homes where solar contribution is not directly measured), by using training data. Mohan does not explicitly teach the following; however analogues reference in the field of energy predication model, Mantovani teaches:
The system of Claim 11, wherein a previously unknown dual consumer is identified as having inverse relationship between the data informative of the consumer's individual GPC and the data informative of one or more weather conditions by one of the following: i) comparing to other consumers in a group of similar consumers; ii) with the help of a machine learning model trained to identify patterns of GPC depending on weather conditions in the geographical area ([0022] methods for identifying unconnected solar customers. [0071] customers can be required to have a negative correlation between hourly kW and irradiance (GHI). Table 7 and [0095] the unauthorized access detection system and method can construct a ‘neighborhood’ of fifty closest neighbors to a customer suspected of failing. Further, in some embodiments, an average of their normalized average total channel 1 output can be calculated and used to compare the 3-day moving average of the difference between the customer and their neighborhood. Examiner notes a negative correlation is an inverse relationship, where hourly KW is consumption (GPC type) and irradiance is weather/solar condition).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system for power distribution grid management of Mohan with Mantovani to include a previously unknown dual consumer is identified as having inverse relationship between the data informative of the consumer's individual GPC and the data informative of one or more weather conditions by one of the following: i) comparing to other consumers in a group of similar consumers; ii) with the help of a machine learning model trained to identify patterns of GPC depending on weather conditions in the geographical area. Doing so would provide more up to date and accurate source of distributed solar photovoltaic system location data to capture locational value of distributed solar photovoltaic and plan for hosting capacity while understanding system impacts and minimizing costs [0007].
Claim 17
While Mohan teaches [0006] disaggregating energy produced by solar panels from low frequency whole-house energy consumption data for a specific house, comprising: predicting solar energy generation for the specific house by applying a machine learning model to generate a non-linear model of solar intensity. [0007] disaggregating energy produced by solar panels from low frequency whole-house energy consumption data for a specific house received from a Smart Meter, comprising: a prediction module configured to predict solar energy generation for the specific house. [0021] solar output may be predicted for unseen homes (and therefore, homes where solar contribution is not directly measured), by using training data. [0025] Data may be obtained and/or accessed in various manners. Energy usage data may be obtained from Green Button (an industry effort to provide transparent energy usage data, which is generally provided in hourly intervals. Mohan does not explicitly teach the following; however analogues reference in the field of energy predication model, Mantovani teaches:
The system of Claim 14, wherein the group of identified previously unknown dual consumers is constituted by at least one of: all identified previously unknown dual consumers, identified previously unknown dual consumers having the same type of the alternative energy source, identified previously unknown dual consumers having similar GPC patterns, identified previously unknown dual consumers having similar GPC requirements([0016] determination of unauthorized interconnection of the customer includes pulling a record of list of customers identified to have solar but not net energy metering (NEM), and matching the record with estimated system size).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system for power distribution grid management of Mohan with Mantovani to include the group of identified previously unknown dual consumers is constituted by at least one of: all identified previously unknown dual consumers, identified previously unknown dual consumers having the same type of the alternative energy source, identified previously unknown dual consumers having similar GPC patterns, identified previously unknown dual consumers having similar GPC requirements. Doing so would provide more up to date and accurate source of distributed solar photovoltaic system location data to capture locational value of distributed solar photovoltaic and plan for hosting capacity while understanding system impacts and minimizing costs [0007].
Claims 4, 6, 7, 14, and 15 are rejected under 35 U.S.C. 103 as being unpatentable over in view of Mohan in view of Mantovani in view of Potter in view of Eda, as applied in claims 1 and 11, and further in view of Charles McBrearty (US 2016/0306906 A1, hereinafter “McBrearty”).
Claim 4
While Mohan teaches [0006] disaggregating energy produced by solar panels from low frequency whole-house energy consumption data for a specific house, comprising: predicting solar energy generation for the specific house by applying a machine learning model to generate a non-linear model of solar intensity. [0007] disaggregating energy produced by solar panels from low frequency whole-house energy consumption data for a specific house received from a Smart Meter, comprising: a prediction module configured to predict solar energy generation for the specific house. [0021] solar output may be predicted for unseen homes (and therefore, homes where solar contribution is not directly measured), by using training data. [0025] Data may be obtained and/or accessed in various manners. Energy usage data may be obtained from Green Button (an industry effort to provide transparent energy usage data, which is generally provided in hourly intervals. Mantovani teaches [0007] By combining load data, weather patterns, customer locations, and interconnection data, it is possible to identify patterns which are consistent with interconnected solar photovoltaic systems. This pattern matching can be used to predict whether a particular location has an interconnected solar photovoltaic system. Mohan and Mantovani do not explicitly teach the following; however, analogues reference in the field of energy predication model, McBrearty teaches:
The method of Claim 1, wherein a previously unknown dual consumer is identified, with the help of a machine learning model trained to identify patterns of GPC depending on weather conditions in the geographical area, as having inverse relationship between the data informative of the consumer's individual GPC and the data informative of one or more weather conditions([0036] describes he Geographic Average may be calculated by an algorithm which produces an average of the Normalized Performances for each area covered. [0054] construct a large data set of example patterns for certain types of systems, define certain features that tend to be characteristic of the systems in the data set, and use either statistical correlation techniques, or machine learning optimization (e.g. neural networks) to define classification thresholds in order to automatically identify a system type and use the developed thresholds feature sets and data history thresholds to automatically classify data streams according to different system types, wherein the characteristics of the monitored data are influenced by geographic-specific characteristics, like sunrise/sunset times or weather characteristics. As long as the monitored data stream has associated timestamps, it is possible to determine the location by finding a geographic location that would best match the observed energy consumption or generation characteristics of the monitored data stream. For example, if the type of generation source (or consumption) is known, one can model the expected behavior of this generation or consumption data stream under actual recorded weather around the world, and the best fitting match statistically is likely to be the actual physical location).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system for power distribution grid management of Mohan, Mantovani, Potter, and Eda with McBrearty to include a previously unknown dual consumer is identified, with the help of a machine learning model trained to identify patterns of GPC depending on weather conditions in the geographical area, as having inverse relationship between the data informative of the consumer's individual GPC and the data informative of one or more weather conditions. Doing so would aid in understanding output of solar resource assessments are databases that catalog the regional intensity of the solar resource, on a given number of minutes increment or an hourly basis. [0002].
Claim 6
While Mohan teaches [0006] disaggregating energy produced by solar panels from low frequency whole-house energy consumption data for a specific house, comprising: predicting solar energy generation for the specific house by applying a machine learning model to generate a non-linear model of solar intensity. [0007] disaggregating energy produced by solar panels from low frequency whole-house energy consumption data for a specific house received from a Smart Meter, comprising: a prediction module configured to predict solar energy generation for the specific house. [0021] solar output may be predicted for unseen homes (and therefore, homes where solar contribution is not directly measured), by using training data. [0025] Data may be obtained and/or accessed in various manners. Energy usage data may be obtained from Green Button (an industry effort to provide transparent energy usage data, which is generally provided in hourly intervals. Mantovani teaches [0007] By combining load data, weather patterns, customer locations, and interconnection data, it is possible to identify patterns which are consistent with interconnected solar photovoltaic systems. This pattern matching can be used to predict whether a particular location has an interconnected solar photovoltaic system. Mohan and Mantovani do not explicitly teach the following; however analogues reference in the field of energy predication model, McBrearty teaches:
The method of Claim 1, further comprising processing the timestamped GPC and weather condition data to identify types of alternative power sources respectively connected to the identified previously unknown two or more dual consumers, wherein, for a given previously unknown dual consumer, the FML Model corresponds to an identified type of a connected alternative power source([0052] The method may next comprise the step of defining at least one characteristic feature for each at least one system type to provide at least one system type and correlated characteristic feature and saving the at least one system type and correlated characteristic feature in the at least one data server (516). A characteristic feature may be, for example, time of sunrise, time of sunset, associated timestamps, energy consumption, wind system output, weather, configuration, time of year, user habits, system size, tracker versus fixed, energy profile shape, east west orientation, north-south orientation, homeowner type, heating type, temperature sensitivity, consumption data, utilized energy, utilized generation, system derate factors and air conditioning status. [0054] construct a large data set of example patterns for certain types of systems, define certain features that tend to be characteristic of the systems in the data set, and use either statistical correlation techniques, or machine learning optimization (e.g. neural networks) to define classification thresholds in order to automatically identify a system type [0054] s long as the monitored data stream has associated timestamps, it is possible to determine the location by finding a geographic location that would best match the observed energy consumption or generation characteristics of the monitored data stream. For example, if the type of generation source (or consumption) is known, one can model the expected behavior of this generation or consumption data stream under actual recorded weather around the world (including sunrise sunset times), By maintaining a time history of weather conditions across a large area (e.g., North America and Europe), and modeling PV system outputs across all of these geographies, one can match the time history of a PV system's output to the geography of data it most closely matches. This matching process can be improved by narrowing the range of geographic areas (e.g. using the preceding longitudinal technique), or by improving the estimated model output of a PV system via leveraging known system characteristics (either known beforehand or identified as described in the section below about “Identification of system information for a solar PV system”). Output from a wind system is highly dependent on wind conditions. By maintaining a time history of weather conditions across a large area, and modeling the wind system outputs across all the locations, one can match the time history of a wind system's output to the geographic location it most closely matches).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system for power distribution grid management of Mohan, Mantovani, Potter, and Eda with McBrearty to include processing the timestamped GPC and weather condition data to identify types of alternative power sources respectively connected to the identified previously unknown two or more dual consumers, wherein, for a given previously unknown dual consumer, the FML Model corresponds to an identified type of a connected alternative power source. Doing so would aid in understanding output of solar resource assessments are databases that catalog the regional intensity of the solar resource, on a given number of minutes increment or an hourly basis. [0002].
Claim 7
While Mohan teaches [0006] disaggregating energy produced by solar panels from low frequency whole-house energy consumption data for a specific house, comprising: predicting solar energy generation for the specific house by applying a machine learning model to generate a non-linear model of solar intensity. [0007] disaggregating energy produced by solar panels from low frequency whole-house energy consumption data for a specific house received from a Smart Meter, comprising: a prediction module configured to predict solar energy generation for the specific house. [0021] solar output may be predicted for unseen homes (and therefore, homes where solar contribution is not directly measured), by using training data. [0025] Data may be obtained and/or accessed in various manners. Energy usage data may be obtained from Green Button (an industry effort to provide transparent energy usage data, which is generally provided in hourly intervals. Mantovani teaches [0007] By combining load data, weather patterns, customer locations, and interconnection data, it is possible to identify patterns which are consistent with interconnected solar photovoltaic systems. This pattern matching can be used to predict whether a particular location has an interconnected solar photovoltaic system. Mohan does not explicitly teach the following; however analogues reference in the field of energy predication model, Mantovani teaches:
The method of Claim 6, wherein the type of the connected alternative energy source is identified with the help of a machine learning model trained to identify patterns of alternative power production being a function of the one or more weather conditions in one or more geographical areas([0049] and Fig.4 describe a load plot for a customer connected to an electric grid where the load goes negative, meaning the customer is using less power than they are generating; see also Table 2 items 15-16 and [0068]-[0069] describe obtaining the load prediction for a customer by correlating the weather with the power use of the building. [0007] By combining load data, weather patterns, customer locations, and interconnection data, it is possible to identify patterns which are consistent with interconnected solar photovoltaic systems).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system for power distribution grid management of Mohan, Potter, Eda, and McBrearty with Mantovani to include the type of the connected alternative energy source is identified with the help of a machine learning model trained to identify patterns of alternative power production being a function of the one or more weather conditions in one or more geographical areas. Doing so would help efficiently capture locational value of distributed power grid and its consumer and plan for hosting capacity by adjusting and making changes while understanding system impacts and minimizing costs.
Claim 14
While Mohan teaches [0006] disaggregating energy produced by solar panels from low frequency whole-house energy consumption data for a specific house, comprising: predicting solar energy generation for the specific house by applying a machine learning model to generate a non-linear model of solar intensity. [0007] disaggregating energy produced by solar panels from low frequency whole-house energy consumption data for a specific house received from a Smart Meter, comprising: a prediction module configured to predict solar energy generation for the specific house. [0021] solar output may be predicted for unseen homes (and therefore, homes where solar contribution is not directly measured), by using training data. [0025] Data may be obtained and/or accessed in various manners. Energy usage data may be obtained from Green Button (an industry effort to provide transparent energy usage data, which is generally provided in hourly intervals. Mantovani teaches [0007] By combining load data, weather patterns, customer locations, and interconnection data, it is possible to identify patterns which are consistent with interconnected solar photovoltaic systems. This pattern matching can be used to predict whether a particular location has an interconnected solar photovoltaic system. Mohan and Mantovani do not explicitly teach the following; however analogues reference in the field of energy predication model, McBrearty teaches:
The system of Claim 11, wherein the computer is further configured to process the timestamped GPC and weather condition data to identify types of alternative power sources respectively connected to the identified previously unknown two or more dual consumers, wherein, for a given previously unknown dual consumer, the FML Model corresponds to an identified type of a connected alternative power source ([0052] The method may next comprise the step of defining at least one characteristic feature for each at least one system type to provide at least one system type and correlated characteristic feature and saving the at least one system type and correlated characteristic feature in the at least one data server (516). A characteristic feature may be, for example, time of sunrise, time of sunset, associated timestamps, energy consumption, wind system output, weather, configuration, time of year, user habits, system size, tracker versus fixed, energy profile shape, east west orientation, north-south orientation, homeowner type, heating type, temperature sensitivity, consumption data, utilized energy, utilized generation, system derate factors and air conditioning status. [0054] construct a large data set of example patterns for certain types of systems, define certain features that tend to be characteristic of the systems in the data set, and use either statistical correlation techniques, or machine learning optimization (e.g. neural networks) to define classification thresholds in order to automatically identify a system type [0054] s long as the monitored data stream has associated timestamps, it is possible to determine the location by finding a geographic location that would best match the observed energy consumption or generation characteristics of the monitored data stream. For example, if the type of generation source (or consumption) is known, one can model the expected behavior of this generation or consumption data stream under actual recorded weather around the world (including sunrise sunset times), By maintaining a time history of weather conditions across a large area (e.g., North America and Europe), and modeling PV system outputs across all of these geographies, one can match the time history of a PV system's output to the geography of data it most closely matches. This matching process can be improved by narrowing the range of geographic areas (e.g. using the preceding longitudinal technique), or by improving the estimated model output of a PV system via leveraging known system characteristics (either known beforehand or identified as described in the section below about “Identification of system information for a solar PV system”). Output from a wind system is highly dependent on wind conditions. By maintaining a time history of weather conditions across a large area, and modeling the wind system outputs across all the locations, one can match the time history of a wind system's output to the geographic location it most closely matches).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system for power distribution grid management of Mohan, Mantovani, Potter, and Eda with McBrearty to include processing the timestamped GPC and weather condition data to identify types of alternative power sources respectively connected to the identified previously unknown two or more dual consumers, wherein, for a given previously unknown dual consumer, the FML Model corresponds to an identified type of a connected alternative power source. Doing so would aid in understanding output of solar resource assessments are databases that catalog the regional intensity of the solar resource, on a given number of minutes increment or an hourly basis. [0002].
Claim 15
While Mohan teaches [0006] disaggregating energy produced by solar panels from low frequency whole-house energy consumption data for a specific house, comprising: predicting solar energy generation for the specific house by applying a machine learning model to generate a non-linear model of solar intensity. [0007] disaggregating energy produced by solar panels from low frequency whole-house energy consumption data for a specific house received from a Smart Meter, comprising: a prediction module configured to predict solar energy generation for the specific house. [0021] solar output may be predicted for unseen homes (and therefore, homes where solar contribution is not directly measured), by using training data. [0025] Data may be obtained and/or accessed in various manners. Energy usage data may be obtained from Green Button (an industry effort to provide transparent energy usage data, which is generally provided in hourly intervals. This pattern matching can be used to predict whether a particular location has an interconnected solar photovoltaic system. Mohan does not explicitly teach the following; however analogues reference in the field of energy predication model, Mantovani teaches:
The system of Claim 14, wherein the type of the connected alternative energy source is identified with the help of a machine learning model trained to identify patterns of alternative power production being a function of the one or more weather conditions in one or more geographical areas ([0049] and Fig.4 describe a load plot for a customer connected to an electric grid where the load goes negative, meaning the customer is using less power than they are generating; see also Table 2 items 15-16 and [0068]-[0069] describe obtaining the load prediction for a customer by correlating the weather with the power use of the building. [0007] By combining load data, weather patterns, customer locations, and interconnection data, it is possible to identify patterns which are consistent with interconnected solar photovoltaic systems).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system for power distribution grid management of Mohan, Potter, Eda, and McBrearty with Mantovani to include the type of the connected alternative energy source is identified with the help of a machine learning model trained to identify patterns of alternative power production being a function of the one or more weather conditions in one or more geographical areas. Doing so would help efficiently capture locational value of distributed power grid and its consumer and plan for hosting capacity by adjusting and making changes while understanding system impacts and minimizing costs.
Claims 10 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over in view of Mohan in view of Mantovani in view of Potter in view of Eda, as applied in claim 1 and 11, and further Nagata Satoshi (WO 2008117392 A1, hereinafter “Satoshi”) and in view of Harish Bharti (US 2017/0102683 A1, hereinafter “Bharti”).
Claim 10/18
While Mohan teaches [0006] disaggregating energy produced by solar panels from low frequency whole-house energy consumption data for a specific house, comprising: predicting solar energy generation for the specific house by applying a machine learning model to generate a non-linear model of solar intensity. [0007] disaggregating energy produced by solar panels from low frequency whole-house energy consumption data for a specific house received from a Smart Meter, comprising: a prediction module configured to predict solar energy generation for the specific house. [0021] solar output may be predicted for unseen homes (and therefore, homes where solar contribution is not directly measured), by using training data. [0025] Data may be obtained and/or accessed in various manners. Energy usage data may be obtained from Green Button (an industry effort to provide transparent energy usage data, which is generally provided in hourly intervals. This pattern matching can be used to predict whether a particular location has an interconnected solar photovoltaic system. Mohan does not explicitly teach the following; however analogues reference in the field of energy predication model, Satoshi teaches:
The method of Claim 1, wherein training the FML model comprises: using historical data informative of the one or more weather conditions and of individual GPC by different consumers to obtain a plurality of FML models having different parameters and initially trained to forecast GPC in accordance with a forecast of the one or more weather conditions; using the plurality of initially trained FML models to forecast GPC in accordance with the one or more weather conditions forecasted for a testing period and thereby obtaining a set of net load data time series forecasted by the plurality of FML models([0028]In the present invention, firstly, the "total photovoltaic power generation amount", "day maximum power demand amount" and "total power demand amount" of each electric power supplier and demander of the next day are estimated (predicted). For the estimation, weather forecasts and past weather information of the next day of each region of the supply and demander and the adjacent region, and "total solar cell power generation amount", "day maximum power demand amount", "day total power demand amount" Calendar information (day of the week, public holiday), theoretical solar radiation amount data is input to the hierarchical neural network. The neural network learns the data combination of the climate patterns of each electric power supplier and demander's area and surrounding areas and the actual results of the total electricity generation amount and the electricity demand of the area as a pattern and collates the weather forecast pattern of the next day with the past pattern And performs nonlinear interpolation estimation with. In this pattern learning, the model is updated using observation data everyday, so estimation accuracy also continues to improve day by day. In addition, it responds to environmental changes within each region of each electric power supplier and demander (solar battery total capacity, customer's change, long-term climate change, meteorological abnormal weather, etc.) by autonomous model update. It is unnecessary to construct each supplier / demander database within the area of each power customer. [0029]The above prediction is executed in the following procedure. (I) Prepare a neural network model that predicts the amount of electricity generation and electricity demand (if it does not exist, create a temporary model with dummy data). The past actual data of this model, the weather forecast of the next day, calendar information, and the amount of solar radiation (theoretical value) at the sunny day of the day are input. (At this time, it is desirable to add not only the area but also the weather information of the neighboring area in order to improve the prediction accuracy.) (Ii) Estimate total power generation amount, maximum power demand amount, total power demand amount. (Nonlinear interpolation estimation by pattern matching) (iii) Collect actual data for neural network relearning. Collect actual data and prepare for neural network re-learning. The actual data is various actual data (power generation amount, maximum power, total power, weather, calendar information, theoretical solar radiation amount) in a certain past period including the current day. (Iv) Re-learn to the neural network using back propagation (error back propagation method). (V) In the updated neural network, predict the total power generation amount, maximum power demand amount, total power demand amount of the next day. Hereinafter, the accuracy of the prediction data is increased by repeating (i) to (v)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system for power distribution grid management of Mohan, Mantovani, Potter, and Eda with Satoshi to include using historical data informative of the one or more weather conditions and of individual GPC by different consumers to obtain a plurality of FML models having different parameters and initially trained to forecast GPC in accordance with a forecast of the one or more weather conditions; using the plurality of initially trained FML models to forecast GPC in accordance with the one or more weather conditions forecasted for a testing period and thereby obtaining a set of net load data time series forecasted by the plurality of FML models. Doing so would help efficiently capture locational value of distributed power grid and its consumer and plan for hosting capacity while understanding system impacts and minimizing costs.
While Mohan teaches [0006] disaggregating energy produced by solar panels from low frequency whole-house energy consumption data for a specific house, comprising: predicting solar energy generation for the specific house by applying a machine learning model to generate a non-linear model of solar intensity. [0007] disaggregating energy produced by solar panels from low frequency whole-house energy consumption data for a specific house received from a Smart Meter, comprising: a prediction module configured to predict solar energy generation for the specific house. [0021] solar output may be predicted for unseen homes (and therefore, homes where solar contribution is not directly measured), by using training data. [0025] Data may be obtained and/or accessed in various manners. Energy usage data may be obtained from Green Button (an industry effort to provide transparent energy usage data, which is generally provided in hourly intervals. This pattern matching can be used to predict whether a particular location has an interconnected solar photovoltaic system. Mohan does not explicitly teach the following; however analogues reference in the field of energy predication model, Bharti teaches:
and comparing the forecasted net load data time series in the set of net load data time series with net load data time series measured during the testing period and selecting a FML model providing the best net load forecast for the testing period, thereby giving rise to the trained FML ([0070] Referring again to FIG. 4, at 108 each of the models identified at 102 is run to generate respective sets of energy load forecasts for one (or more) of the combinations of the time scale periods and grid hierarchy elements as a function of its/their respective set(s) of currently-prioritized contextual influencing factors. [0071] At 110 the generated energy load forecasts are compared to actual, historic energy loads for the time scale period/grid hierarchy element combinations to determine differences between the historic forecast energy loads and the energy loads generated as a function of the current set of prioritized contextual influencing factors. Aspects perform prioritization and weight assignment of the contextual variables with respect to historical time period as the load forecast output is compared to the actual energy load for that time period).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system for power distribution grid management of Mohan, Mantovani, Potter, and Eda with Bharti to include comparing the forecasted net load data time series in the set of net load data time series with net load data time series measured during the testing period and selecting a FML model providing the best net load forecast for the testing period, thereby giving rise to the trained FML, as taught by Bharti. Doing so would help efficiently capture locational value of distributed power grid and its consumer and plan for hosting capacity while understanding system impacts and minimizing costs.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
US 20160371289 A1
Method And System for Recommending Potential Changes in Energy Consumption in A Built Environment
Kopp; Phillip et al.
US 20180262010 A1
Device for Controlling Load Frequency and Method for Controlling Load Frequency
Kato; Daichi et al.
US 20150161233 A1
Customer energy consumption segmentation using time-series data
Flora; June et al.
US 20190213693 A1
POWER GENERATION AMOUNT ESTIMATION APPARATUS, DISTRIBUTION GRID SYSTEM, AND POWER GENERATION AMOUNT ESTIMATION METHOD
Itaya; Nobuhiko
US 20140375126 A1
ELECTRIC POWER RETAIL MANAGEMENT APPARATUS AND ELECTRIC POWER RETAIL MANAGEMENT METHOD
KITAGISHI; Ikuo
Any inquiry concerning this communication or earlier communications from the examiner should be directed to REHAM K ABOUZAHRA whose telephone number is (571)272-0419. The examiner can normally be reached M-F 7:00 AM to 5:00 PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Brian Epstein can be reached at (571)-270-5389. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/REHAM K ABOUZAHRA/Examiner, Art Unit 3625