IP Library › Patent Application 13796367
Patent Application
App. No. 13/796,367

METHODS AND SYSTEMS FOR REAL-TIME SOLAR FORECASTING INCORPORATING A GROUND NETWORK

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Patent No.
US None
App. No.
13/796,367
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.

Claims (215)

1 . 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.

2 . A method as in claim 1 , wherein said renewable energy system is a photovoltaic system.

3 . A method as in claim 1 , wherein the validation metric is selected from the group consisting of model error, mean absolute error and root mean square error.

4 . A method as in claim 1 , wherein said solar irradiance weather sensor is selected from the group consisting of pyranometer, pyrheliometer and photovoltaic reference cell sensor.

5 . 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.

6 . A method as in claim 5 , wherein said renewable energy system is a photovoltaic system.

7 . A method as in claim 5 , wherein the validation metric is selected from the group consisting of model error, mean absolute error and root mean square error.

8 . 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:

Σ i=1 j Measured Solar Irradiance ni −{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 Irradiance nt ={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 .

9 . A method as in claim 8 , wherein said set of solar irradiance data variables are selected from the group consisting of:

estimated solar irradiance data using measured solar power from a meter at location n and time t;

forecasted solar irradiance data from a solar irradiance forecast feed at location n and time t;

infrared brightness temperatures at location n and time t;

ambient temperature at location n and time t;

humidity at location n and time t;

dew point at location n and time t;

wind speed at location n and time t;

air pressure at location n and time t;

extraterrestrial solar irradiance at location n and time t;

sun earth distance at location n and time t;

declination at location n and time t;

hour angle at location n and time t;

zenith angle at location n and time t;

air mass at location n and time t;

turbidity at location n and time t;

cloudiness index at location n and time t;

clear sky irradiance at location n and time t;

altitude at location n and time t;

hour of day at location n and time t; and

month of year at location n and time t.

10 . 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:

Σ i=1 j Measured Solar Irradiance ni −{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 Irradiance nt ={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 .

11 . A method as in claim 10 , wherein said set of solar power production data variables are selected from the group consisting of:

measured solar irradiance data from a meter at location n and time t;

estimated solar irradiance data using measured solar power from a meter at location n and time t;

forecasted solar irradiance data from a solar irradiance forecast feed at location n and time t;

infrared brightness temperatures at location n and time t;

snow cover status at location n and time t;

ambient temperature at location n and time t;

humidity at location n and time t;

dew point at location n and time t;

wind speed at location n and time t;

air pressure at location n and time t;

extraterrestrial solar irradiance at location n and time t;

sun earth distance at location n and time t;

declination at location n and time t;

hour angle at location n and time t;

zenith angle at location n and time t;

air mass at location n and time t;

turbidity at location n and time t;

cloudiness index at location n and time t;

clear sky irradiance at location n and time t;

altitude at location n and time t;

hour of day at location n and time t; and

month of year at location n and time t.

12 . 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:

Σ i=1 j Measured Solar Irradiance ni −{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 Irradiance nt ={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:

Σ i=1 j Measured Solar Irradiance ni −{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 Irradiance nt ={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 .

13 . A method as in claim 12 , wherein said set of solar irradiance data variables are selected from the group consisting of:

estimated solar irradiance data using measured solar power from a meter at location n and time t;

forecasted solar irradiance data from a solar irradiance forecast feed at location n and time t;

infrared brightness temperatures at location n and time t;

ambient temperature at location n and time t;

humidity at location n and time t;

dew point at location n and time t;

wind speed at location n and time t;

air pressure at location n and time t;

extraterrestrial solar irradiance at location n and time t;

sun earth distance at location n and time t;

declination at location n and time t;

hour angle at location n and time t;

zenith angle at location n and time t;

air mass at location n and time t;

turbidity at location n and time t;

cloudiness index at location n and time t;

clear sky irradiance at location n and time t;

altitude at location n and time t;

hour of day at location n and time t; and

month of year at location n and time t.

14 . A method as in claim 12 , wherein said set of solar power production data variables are selected from the group consisting of:

measured solar irradiance data from a meter at location n and time t;

estimated solar irradiance data using measured solar power from a meter at location n and time t;

forecasted solar irradiance data from a solar irradiance forecast feed at location n and time t;

infrared brightness temperatures at location n and time t;

snow cover status at location n and time t;

ambient temperature at location n and time t;

humidity at location n and time t;

dew point at location n and time t;

wind speed at location n and time t;

air pressure at location n and time t;

extraterrestrial solar irradiance at location n and time t;

sun earth distance at location n and time t;

declination at location n and time t;

hour angle at location n and time t;

zenith angle at location n and time t;

air mass at location n and time t;

turbidity at location n and time t;

cloudiness index at location n and time t;

clear sky irradiance at location n and time t;

altitude at location n and time t;

hour of day at location n and time t; and

month of year at location n and time t.

15 . 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:

Σ i=1 j Measured Solar Irradiance ni −{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 Irradiance nt ={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:

Σ i=1 j Measured Solar Irradiance ni −{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 Irradiance nt ={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 .

16 . A method as in claim 15 , wherein said set of solar irradiance data variables are selected from the group consisting of:

estimated solar irradiance data using measured solar power from a meter at location n and time t;

forecasted solar irradiance data from a solar irradiance forecast feed at location n and time t;

infrared brightness temperatures at location n and time t;

ambient temperature at location n and time t;

humidity at location n and time t;

dew point at location n and time t;

wind speed at location n and time t;

air pressure at location n and time t;

extraterrestrial solar irradiance at location n and time t;

sun earth distance at location n and time t;

declination at location n and time t;

hour angle at location n and time t;

zenith angle at location n and time t;

air mass at location n and time t;

turbidity at location n and time t;

cloudiness index at location n and time t;

clear sky irradiance at location n and time t;

altitude at location n and time t;

hour of day at location n and time t; and

month of year at location n and time t.

17 . A method as in claim 15 , wherein said set of solar power production data variables are selected from the group consisting of:

measured solar irradiance data from a meter at location n and time t;

estimated solar irradiance data using measured solar power from a meter at location n and time t;

forecasted solar irradiance data from a solar irradiance forecast feed at location n and time t;

infrared brightness temperatures at location n and time t;

snow cover status at location n and time t;

ambient temperature at location n and time t;

humidity at location n and time t;

dew point at location n and time t;

wind speed at location n and time t;

air pressure at location n and time t;

extraterrestrial solar irradiance at location n and time t;

sun earth distance at location n and time t;

declination at location n and time t;

hour angle at location n and time t;

zenith angle at location n and time t;

air mass at location n and time t;

turbidity at location n and time t;

cloudiness index at location n and time t;

clear sky irradiance at location n and time t;

altitude at location n and time t;

hour of day at location n and time t; and

month of year at location n and time t.

Assignments (2)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Sep 15, 2018
From: KERRIGAN, SHAWN; WILLIAMS, MATTHEW; KOLTAKOV, SERGEY; THORNTON, ALEXANDER
To: LOCUS ENERGY, LLC
Reel/Frame 046884/0847 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jun 15, 2015
From: LOCUS ENERGY, LLC
To: LOCUS ENERGY, INC.
Reel/Frame 035903/0306 →