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US 2014/0278107 A1Published application
Methods and systems for real-time solar forecasting incorporating a ground network
AssigneeLocus Energy, LLC
InventorsShawn Kerrigan, Matthew Williams, Sergey Koltakov, Alexander Thornton
Life of this patent
Estimating the term…
Abstract
This application relates generally to systems and methods for validating solar irradiance nowcasts, solar power nowcasts and forecasts in real-time using a network of solar power systems and solar irradiance sensors. This application also relates to systems and methods for augmenting solar irradiance forecasts and solar power forecasts in real-time using a network of solar power systems and solar irradiance sensors.
Claims17
A computer processor implemented method of validating solar irradiance forecasts, said method comprising the steps of;
- providing a set of renewable energy systems having at least two renewable energy systems each having a measured solar irradiance from a solar irradiance weather sensor at a location n and time t and an estimated solar irradiance from a solar irradiance forecast feed at a location n and time t in a computer processor;
- determining by the computer processor a set of matched pairs of location and time from the measured solar irradiance from a from weather sensor at a location n and time t and the estimated solar irradiance from a solar irradiance forecast feed at a location n and time t;
- calculating by the computer processor a validation metric of the set of matched pairs by the computer processor for at least one of:
- all locations at time t;
- location n at all times;
- all locations at all times;
- a subset of locations at time t;
- location n at a subset of times;
- and
- a subset of locations at a subset of times.
7 more elements
A computer processor implemented method of validating solar power production forecasts, said method comprising the steps of;
- providing a set of renewable energy systems having at least two renewable energy systems each having a measured power production from a from power meter at a location n and time t and an estimated solar power production from a solar power production forecast feed at a location n and time t in a computer processor;
- determining by the computer processor a set of matched pairs of location n and time t from the measured power production from a from power meter at a location n and time t and an estimated solar power production from a solar power production forecast feed at a location n and time t in a computer processor;
- calculating by the computer processor a validation metric of the set of matched pairs by the computer processor for at least one of:
- all locations at time t;
- location n at all times;
- all locations at all times;
- a subset of locations at time t;
- location n at a subset of times;
- and
- a subset of locations at a subset of times.
7 more elements
A computer processor implemented method of augmenting solar irradiance forecasts, said method comprising the steps of;
- providing in a computer processor a set of solar irradiance sensors having at least two solar irradiance sensors each having a measurement of solar irradiance at location n and at time t and forecasted solar irradiance data at location n and at time t;
- determining in the computer processor at least one set of solar irradiance data variables at a location n and time t;
- matching by the computer processor all data variables at location n and time t, including all locations within distance d of location n and all time periods with s time periods of time t at those locations to provide a matched set of solar irradiance data variables at a location n and time t;
- training a machine learning algorithm in a computer processor to minimize to provide a trained machine learning algorithm according to:
- Σi=1jMeasured Solar Irradianceni−{circumflex over (f)}(•ni)
- where j is the number of time points for which data is available to train the algorithm, •ni is the matched set of solar irradiance data variables at a location n and time t and {circumflex over (f)}(•ni) is a function for predicting solar irradiance at location n at time i using data •ni;
- augmenting the forecasted solar irradiance data using the trained machine learning algorithm:
- Augmented Forecasted Solar Irradiancent={circumflex over (f)}(•nt)
- where •nt is the matched set of solar irradiance data variables at a location n and time t and {circumflex over (f)}(•nt) is a function for predicting solar irradiance at location n at time t using data •nt.
6 more elements
A computer processor implemented method of augmenting solar power production forecasts, said method comprising the steps of;
- providing in a computer processor a set of solar power meters having at least two solar power meters each having a measurement of power production at location n and at time t and forecasted power production at location n and at time t;
- determining in the computer processor at least one set of solar power production data variables at a location n and time t;
- matching by the computer processor all solar power production data variables at location n and time t, including all locations within distance d of location n and all time periods with s time periods of time t at those locations to provide a matched set of solar power production data variables at a location n and time t;
- training a machine learning algorithm in a computer processor to minimize to provide a trained machine learning algorithm according to:
- Σi=1jMeasured Solar Irradianceni−{circumflex over (f)}(•ni)
- where j is the number of time points for which data is available to train the algorithm, •ni is the matched set of solar power production data variables at a location n and time t and {circumflex over (f)}(•ni) is a function for predicting solar power production at location n at time i using data •ni;
- augmenting the forecasted solar power production data using the trained machine learning algorithm:
- Augmented Forecasted Solar Irradiancent={circumflex over (f)}(•nt)
- where •nt is the matched set of solar power production data variables at a location n and time t and {circumflex over (f)}(•nt) is a function for predicting solar power production at location n at time t using data •nt.
6 more elements
A computer processor implemented method of augmenting solar irradiance forecasts, said method comprising the steps of;
- providing in a computer processor a set of solar irradiance sensors having at least two solar irradiance sensors each having a measurement of solar irradiance at location n and at time t and forecasted solar irradiance data at location n and at time t;
- determining in the computer processor at least one set of solar irradiance data variables at a location n and time t;
- matching by the computer processor all data variables at location n and time t, including all locations within distance d of location n and all time periods with s time periods of time t at those locations to provide a matched set of solar irradiance data variables at a location n and time t;
- providing in a computer processor a set of solar power meters having at least two solar power meters each having a measurement of power production at location n and at time t and forecasted power production at location n and at time t;
- determining in the computer processor at least one set of solar power production data variables at a location n and time t;
- matching by the computer processor all solar power production data variables at location n and time t, including all locations within distance d of location n and all time periods with s time periods of time t at those locations to provide a matched set of solar power production data variables at a location n and time t;
- training a machine learning algorithm in a computer processor to minimize to provide a trained machine learning algorithm according to:
- Σi=1jMeasured Solar Irradianceni−{circumflex over (f)}(•ni)
- where j is the number of time points for which data is available to train the algorithm, •ni is the matched set of solar irradiance data variables at a location n and time t and {circumflex over (f)}(•ni) is a function for predicting solar irradiance at location n at time i using data •ni;
- augmenting the forecasted solar irradiance data using the trained machine learning algorithm:
- Augmented Forecasted Solar Irradiancent={circumflex over (f)}(•nt)
- where •nt is the matched set of solar irradiance data variables at a location n and time t and {circumflex over (f)}(•nt) is a function for predicting solar irradiance at location n at time t using data •nt,
- further training a machine learning algorithm in a computer processor to minimize to provide a trained machine learning algorithm according to:
- Σi=1jMeasured Solar Irradianceni−{circumflex over (f)}(•ni)
- where j is the number of time points for which data is available to train the algorithm, •ni is the matched set of solar power production data variables at a location n and time t and {circumflex over (f)}(•ni) is a function for predicting solar power production at location n at time i using data •ni;
- augmenting the forecasted solar power production data using the trained machine learning algorithm:
- Augmented Forecasted Solar Irradiancent={circumflex over (f)}(•nt)
- where •nt is the matched set of solar power production data variables at a location n and time t and {circumflex over (f)}(•nt) is a function for predicting solar power production at location n at time t using data •nt.
15 more elements
A computer processor implemented method of augmenting solar power production forecasts, said method comprising the steps of;
- providing in a computer processor a set of solar power meters having at least two solar power meters each having a measurement of power production at location n and at time t and forecasted power production at location n and at time t;
- determining in the computer processor at least one set of solar power production data variables at a location n and time t;
- matching by the computer processor all solar power production data variables at location n and time t, including all locations within distance d of location n and all time periods with s time periods of time t at those locations to provide a matched set of solar power production data variables at a location n and time t;
- providing in a computer processor a set of solar irradiance sensors having at least two solar irradiance sensors each having a measurement of solar irradiance at location n and at time t and forecasted solar irradiance data at location n and at time t;
- determining in the computer processor at least one set of solar irradiance data variables at a location n and time t;
- matching by the computer processor all data variables at location n and time t, including all locations within distance d of location n and all time periods with s time periods of time t at those locations to provide a matched set of solar irradiance data variables at a location n and time t;
- training a machine learning algorithm to minimize to provide a trained machine learning algorithm according to:
- Σi=1jMeasured Solar Irradianceni−{circumflex over (f)}(•ni)
- where j is the number of time points for which data is available to train the algorithm, •ni is the matched set of solar power production data variables at a location n and time t and {circumflex over (f)}(•ni) is a function for predicting solar power production at location n at time i using data •ni;
- augmenting the forecasted solar power production data using the trained machine learning algorithm:
- Augmented Forecasted Solar Irradiancent={circumflex over (f)}(•nt)
- where •nt is the matched set of solar power production data variables at a location n and time t and {circumflex over (f)}(•nt) is a function for predicting solar power production at location n at time t using data •nt,
- further training a machine learning algorithm to minimize to provide a trained machine learning algorithm according to:
- Σi=1jMeasured Solar Irradianceni−{circumflex over (f)}(•ni)
- where j is the number of time points for which data is available to train the algorithm, •ni is the matched set of solar irradiance data variables at a location n and time t and {circumflex over (f)}(•ni) is a function for predicting solar irradiance at location n at time i using data •ni;
- augmenting the forecasted solar irradiance data using the trained machine learning algorithm:
- Augmented Forecasted Solar Irradiancent={circumflex over (f)}(•nt)
- where •nt is the matched set of solar irradiance data variables at a location n and time t and {circumflex over (f)}(•nt) is a function for predicting solar irradiance at location n at time t using data •nt.
15 more elements
Dependencies are inferred from the claim text.