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 .
Allowable Subject Matter
Claims 9, 10, 12 and 14 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Claim Rejections - 35 USC § 102
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 (i.e., changing from AIA to pre-AIA ) 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 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(s) 1-8, 11, 13, and 15-17 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Jenkins et al (US PAT. 11,416,936, herein Jenkins).
Regarding claim 1, Jenkins teaches the cited prior art teach A method for predicting electric energy consumption in an electric grid , said method comprising:
acquiring first detection data including detection values related to an actual electric energy consumption in said electric grid (7:15-20 “r may generate the forecasts using, for example, prediction techniques such as regression that includes information from a variety of sources including one or more of historical weather and weather forecasts, historical power supply, historical power demand, historical nodal transmission characteristics, and well as indicators of market sentiment”);
acquiring additional detection data including detection values related to the energy consumption in said electric grid during at least a time window preceding a reference instant (7:15-20 “r may generate the forecasts using, for example, prediction techniques such as regression that includes information from a variety of sources including one or more of historical weather and weather forecasts, historical power supply, historical power demand, historical nodal transmission characteristics, and well as indicators of market sentiment”);
acquiring calendar data including chronological information associated to the operation of said electric grid (13:30-35 “the processor may obtain timing information such as on peak, off peak, or another demand level; demand timing according to date of week, weekday, weekend, and the like. The processor may obtain 320 calendar information, such as power demand timing based on date, month, season, and the like; and other suitable timing information.”);
calculating training data based on the acquired detection data and calendar data (24:20-30 “processor may evaluate optimal DA and RT strategy against a back-test of real prices to refine the optimal operation further, for example by training a neural network to minimize the error between the optimal DA and RT strategy based on DA and RT price forecasts and the optimal DA and RT strategy based on DA and RT realized prices and applying the network itself to correct for future optimal DA and RT strategy”);
based on said training data, setting a linear auto-regressive mathematical model describing the trend of the electric energy consumption in said electric grid, said linear auto-regressive mathematical model being configured to process at least a set of exogenous input values indicative of at least a periodic function approximating the profile of the electric energy consumption in said electric grid over said at least a time window preceding said reference instant (7:40-55 “processor may use all or a subset of the data to generate a mathematical model (such as an autoregressive-moving-average model with exogenous inputs or “ARMAX”, a feedforward neural network, a recurring neural network, or similar advanced regression models known to those skilled in the art) which predicts the DA and/or RT pricing for multiple prediction horizons. By employing a mathematical model, the processor may extrapolate into the future historical trends of sensitivity to different variables to enable the generation of multiple future scenarios with corresponding probabilities. The processor may employ one or more statistical descriptions of historical behavior not captured in the mathematical model, also known as residuals, to add stochastic information back on the model output, to accurately simulate data with properties which resemble a true signal”)
and based on said linear auto-regressive model, calculating prediction data including prediction values related to the electric energy consumption in said electric grid during a time window following said reference instant (12:35-55 “processor may use all or a subset of the data and generate a mathematical model (such as an autoregressive-moving-average model with exogenous inputs, ARMAX, a feed-forward neural network, a recurring neural network, or similar advanced regression models known to those skilled in the art) to predict the DA and/or RT pricing for multiple prediction horizons. Historical trends of sensitivity to different processes can be extrapolated into the future to generate multiple future scenarios with corresponding probabilistic weighing. The processor may augment the DA and RT pricing information with a fundamental model based on the supply stack in the region for additional fidelity. For example, DA price forecasts can be calculated emulating the methodology by which generation assets are committed to supply electricity, including the solution of linear and mixed-integer linear problems with a cost minimization target”).
Regarding claim 2, the cited prior art teach The method, according to claim 1.
Jenkins teaches the method further comprising acquiring second detection data including detection values related to the energy consumption in said electric grid during a first time window preceding said reference instant, wherein said linear auto-regressive mathematical model is configured to process first exogenous input values indicative of a first periodic function approximating the profile of the electric energy consumption in said electric grid over said first time window (7:40-55 “processor may use all or a subset of the data to generate a mathematical model (such as an autoregressive-moving-average model with exogenous inputs or “ARMAX”, a feedforward neural network, a recurring neural network, or similar advanced regression models known to those skilled in the art) which predicts the DA and/or RT pricing for multiple prediction horizons. By employing a mathematical model, the processor may extrapolate into the future historical trends of sensitivity to different variables to enable the generation of multiple future scenarios with corresponding probabilities. The processor may employ one or more statistical descriptions of historical behavior not captured in the mathematical model, also known as residuals, to add stochastic information back on the model output, to accurately simulate data with properties which resemble a true signal”).
Regarding claim 3, the cited prior art teach The method, according to claim 1.
Jenkins teaches the method further comprising acquiring third detection data including detection values related to the energy consumption in said electric grid during a second time window preceding said reference instant , wherein said linear auto-regressive mathematical model is configured to process second exogenous input values indicative of a second periodic function approximating the profile of the electric energy consumption in said electric grid over said second time window (7:40-55 “processor may use all or a subset of the data to generate a mathematical model (such as an autoregressive-moving-average model with exogenous inputs or “ARMAX”, a feedforward neural network, a recurring neural network, or similar advanced regression models known to those skilled in the art) which predicts the DA and/or RT pricing for multiple prediction horizons. By employing a mathematical model, the processor may extrapolate into the future historical trends of sensitivity to different variables to enable the generation of multiple future scenarios with corresponding probabilities. The processor may employ one or more statistical descriptions of historical behavior not captured in the mathematical model, also known as residuals, to add stochastic information back on the model output, to accurately simulate data with properties which resemble a true signal”).
Regarding claim 4, the cited prior art teach The method, according to claim 1.
Jenkins teaches wherein calculating said training data includes processing the acquired first detection data to check the correctness of said data (24:20-30 “For example, the processor may evaluate optimal DA and RT strategy against a back-test of real prices to refine the optimal operation further, for example by training a neural network to minimize the error between the optimal DA and RT strategy based on DA and RT price forecasts and the optimal DA and RT strategy based on DA and RT realized prices and applying the network itself to correct for future optimal DA and RT strategy. With the strategy refined based on RT pricing, the processor may participate in a Real Time energy market and deliver volumes of energy based on the RT price as determined by the RT strategy. The processor may control the renewable power asset and any associated energy storage to deliver 342 committed power based on any existing DA commitments and the refined RT delivery strategy”).
Regarding claim 5, the cited prior art teach The method, according to claim 2.
Jenkins teaches wherein calculating said training data includes processing the acquired second detection data to identify the trend of the electric energy consumption in the electric grid during the first time window (7:40-55 “processor may use all or a subset of the data to generate a mathematical model (such as an autoregressive-moving-average model with exogenous inputs or “ARMAX”, a feedforward neural network, a recurring neural network, or similar advanced regression models known to those skilled in the art) which predicts the DA and/or RT pricing for multiple prediction horizons. By employing a mathematical model, the processor may extrapolate into the future historical trends of sensitivity to different variables to enable the generation of multiple future scenarios with corresponding probabilities. The processor may employ one or more statistical descriptions of historical behavior not captured in the mathematical model, also known as residuals, to add stochastic information back on the model output, to accurately simulate data with properties which resemble a true signal”)..
Regarding claim 6, the cited prior art teach The method, according to claim 3.
Jenkins teaches wherein calculating said training data includes processing the acquired third detection data to identify the trend of the electric energy consumption in the electric grid during the second time window (7:40-55 “processor may use all or a subset of the data to generate a mathematical model (such as an autoregressive-moving-average model with exogenous inputs or “ARMAX”, a feedforward neural network, a recurring neural network, or similar advanced regression models known to those skilled in the art) which predicts the DA and/or RT pricing for multiple prediction horizons. By employing a mathematical model, the processor may extrapolate into the future historical trends of sensitivity to different variables to enable the generation of multiple future scenarios with corresponding probabilities. The processor may employ one or more statistical descriptions of historical behavior not captured in the mathematical model, also known as residuals, to add stochastic information back on the model output, to accurately simulate data with properties which resemble a true signal”).
Regarding claim 7, the cited prior art teach The method according to claim 1.
Jenkins teaches wherein said prediction data are cyclically calculated with a predefined time granularity and with a predefined time horizon (20:40-55 “optimal DA commitments identified, the processor may estimate 330 an optimal RT schedule for the renewable power asset. For example, in a second pass, with the optimal day ahead commitments as fixed and the DA prices known, the processor may use the RT forecasts (e.g., with a rolling horizon) to estimate an optimal RT schedule across a range of scenarios that may be weighted by their probability. The optimal RT schedule identifies RT bids and volumes for generation. Equation 35 is a value maximizing mathematical formulation for the determination of optimal RT power curtailment of a renewable power asset without associated energy storage”).
Regarding claim 8, the cited prior art teach The method, according to claim 1.
Jenkin teaches wherein said linear auto-regressive mathematical model is a linear ARX mathematical model with one or more exogenous inputs (7:40-55 “processor may use all or a subset of the data to generate a mathematical model (such as an autoregressive-moving-average model with exogenous inputs or “ARMAX”, a feedforward neural network, a recurring neural network, or similar advanced regression models known to those skilled in the art) which predicts the DA and/or RT pricing for multiple prediction horizons. By employing a mathematical model, the processor may extrapolate into the future historical trends of sensitivity to different variables to enable the generation of multiple future scenarios with corresponding probabilities. The processor may employ one or more statistical descriptions of historical behavior not captured in the mathematical model, also known as residuals, to add stochastic information back on the model output, to accurately simulate data with properties which resemble a true signal”).
Regarding claim 11, the cited prior art teach The method, according to claim 1.
Jenkins teaches comprising carrying out a first check procedure to check the computational performances of said mathematical model (24 :20-35 “the processor may evaluate optimal DA and RT strategy against a back-test of real prices to refine the optimal operation further, for example by training a neural network to minimize the error between the optimal DA and RT strategy based on DA and RT price forecasts and the optimal DA and RT strategy based on DA and RT realized prices and applying the network itself to correct for future optimal DA and RT strategy. With the strategy refined based on RT pricing, the processor may participate in a Real Time energy market and deliver volumes of energy based on the RT price as determined by the RT strategy. The processor may control the renewable power asset and any associated energy storage to deliver 342 committed power based on any existing DA commitments and the refined RT delivery strategy”).
Regarding claim 13, the cited prior art teach The method, according to claim 1.
Jenkins teaches comprising carrying out a second check procedure to check the electric energy consumption predicted by said mathematical model (20:40-55).
Regarding claim 15, the cited prior art teach A computer program, which is stored or storable in a non-transitory computer-readable storage medium, wherein it comprises software instructions to implement the method , according to claim 1 (see the rejection of claim 1).
Regarding claim 16, the cited prior art teach A computerized device comprising data processing resources configured to execute software instructions to implement the method, according to claim 1 (see the rejection of claim 1)..
Regarding claim 17, the cited prior art teach The computerized device, according to claim 16, wherein it is an intelligent electronic device configured to manage operations of an electric power distribution grid (9-10:55-20).
Conclusion
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/TAMEEM D SIDDIQUEE/
Primary Examiner
Art Unit 2116
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