Systems and methods for ramp predictions for forecasting power using neighboring sites

1 . A non-transitory computer-readable medium comprising executable instructions, the executable instructions being executable by one or more processors to perform a method, the method comprising:

receiving, from sensors at a target renewable energy site and a first nearby site, real-time power measurements from variable power generation assets;

at a weather forecast time, determining, by the one or more processors, a lag between the target renewable energy site and the first nearby site for which respective power measurements are correlated;

selecting, by the one or more processors, a first forecast look-ahead time;

determining, by the one or more processors, if the first forecast look-ahead time is less than or equal to the lag between the target renewable energy site and the first nearby site for which the respective power measurements are correlated;

determining, by the one or more processors, a series of lagged power measurements at a time of forecast which constitute a series of correlation-based forecasts for power generation at the target renewable energy site, the series of lagged power measurements being based at least in part on the lag between the target renewable energy site and the first nearby site;

generating, by the one or more processors, a set of ramp predictors incorporating correlation-based forecasts from the first nearby site for the time of forecast and the first forecast look-ahead time based on the series of lagged power measurements, the series of lagged power measurements being based at least in part on the lag between the target renewable energy site and the first nearby site, each ramp predictor of the set of ramp predictors including a difference between measured power at a time index and a measured power at a current timestamp;

receiving power forecast errors for one or more variable power generation assets of the target renewable energy site;

applying, by the one or more processors, sets of decision trees to one or more of the ramp predictors of the set of ramp predictors and the power forecast errors to obtain predicted forecast errors;

generating, by one or more processors, second power forecasts for a set of look-ahead times for one or more variable power generation assets of the target renewable energy site based on first power forecasts and the predicted forecast errors;

transmitting the second power forecasts to a power system; and

balancing load and generation in an electrical network based on the transmitted second power forecasts, wherein the electrical network comprises the target renewable energy site.

2 . The non-transitory computer-readable medium of claim 1 , wherein, at the weather forecast time, determining the lag between the target renewable energy site and the first nearby site for which the respective power measurements are correlated, comprises at the weather forecast time, determining the lag between the target renewable energy site and the first nearby site for which the respective power measurements are maximally correlated with a first correlation coefficient of the target renewable energy site and the first nearby site that is dependent on time.

3 . The non-transitory computer-readable medium of claim 2 , wherein the first correlation coefficient of the target renewable energy site and the first nearby site changes over time based on weather conditions.

4 . The non-transitory computer-readable medium of claim 1 , wherein the first nearby site is a renewable energy site.

5 . The non-transitory computer-readable medium of claim 1 , wherein the series of lagged power measurements are determined if a first correlation coefficient of the target renewable energy site and the first nearby site is greater than a threshold value, the first correlation coefficient indicating a strength of the correlation between the first nearby site and the target renewable energy site.

6 . The non-transitory computer-readable medium of claim 1 , wherein the measured power is correlation-based forecast power.

7 . The non-transitory computer-readable medium of claim 1 , wherein the series of lagged power measurements are determined if the first forecast look-ahead time is less than or equal to the lag between the target renewable energy site and the first nearby site for which the respective power measurements are correlated.

8 . The non-transitory computer-readable medium of claim 1 , the method further comprising:

at the weather forecast time, determining a lag between the target renewable energy site and a second nearby site for which respective power measurements are correlated;

determining if the first forecast look-ahead time is less than or equal to the lag between the target renewable energy site and the second nearby site for which the respective power measurements are correlated;

generating a series of lagged power measurements at the time of forecast which constitute a series of correlation-based forecasts for power generation at the target renewable energy site, the series of lagged power measurements being based at least in part on the lag between the target renewable energy site and the second nearby site; and

determining a set of ramp predictors incorporating correlation-based forecasts from the first nearby site for the time of forecast and the first forecast look-ahead time based on the series of lagged power measurements, the series of lagged power measurements being based at least in part on the lag between the target renewable energy site and the second nearby site, each ramp predictor of the set of ramp predictors including a difference between measured power at a time index and a measured power at a current timestamp.

9 . The non-transitory computer-readable medium of claim 8 , wherein applying the sets of decision trees to one or more of the ramp predictors of the set of ramp predictors and the power forecast errors to obtain the predicted forecast errors comprises:

selecting at least one of the one of more of the ramp predictors of the set of ramp predictors with a most positive correlation coefficient relative to others of the set of ramp predictors; and

applying the sets of decision trees to the at least one of the one or more of the ramp predictors of the set of ramp predictors.

10 . A system comprising at least one processor and memory containing instructions, the instructions being executable by the at least one processor to:

receive, from sensors at a target renewable energy site and a first nearby site, real-time power measurements from variable power generation assets;

at a weather forecast time, determine, by one or more processors, a lag between the target renewable energy site and the first nearby site for which respective power measurements are correlated;

select, by one or more processors, a first forecast look-ahead time;

determine, by one or more processors, if the first forecast look-ahead time is less than or equal to the lag between the target renewable energy site and the first nearby site for which the respective power measurements are correlated;

determine, by one or more processors, a series of lagged power measurements at a time of forecast which constitute a series of correlation-based forecasts for power generation at the target renewable energy site, the series of lagged power measurements being based at least in part on the lag between the target renewable energy site and the first nearby site;

generate, by one or more processors, a set of ramp predictors incorporating correlation-based forecasts from the first nearby site for the time of forecast and the first forecast look-ahead time based on the series of lagged power measurements, the series of lagged power measurements being based at least in part on the lag between the target renewable energy site and the first nearby site, each ramp predictor of the set of ramp predictors including a difference between measured power at a time index and a measured power at a current timestamp;

receive power forecast errors for one or more variable power generation assets of the target renewable energy site;

apply, by one or more processors, sets of decision trees to one or more of the ramp predictors of the set of ramp predictors and the power forecast errors to obtain predicted forecast errors;

generate, by one or more processors, second power forecasts for a set of look-ahead times for one or more variable power generation assets of the target renewable energy site based on first power forecasts and the predicted forecast errors;

transmit the second power forecasts to a power system; and

balancing load and generation in an electrical network based on the transmitted second power forecasts, wherein the electrical network comprises the target renewable energy site.

11 . The system of claim 10 , wherein, at the weather forecast time, determine the lag between the target renewable energy site and the first nearby site for which the respective power measurements are correlated, comprises at the weather forecast time, determine the lag between the target renewable energy site and the first nearby site for which the respective power measurements are maximally correlated with a first correlation coefficient of the target renewable energy site and the first nearby site that is dependent on time.

12 . The system of claim 11 , wherein the first correlation coefficient of the target renewable energy site and the first nearby site changes over time based on weather conditions.

13 . The system of claim 10 , wherein the first nearby site is a renewable energy site.

14 . The system of claim 10 , wherein the series of lagged power measurements are determined if a first correlation coefficient of the target renewable energy site and the first nearby site is greater than a threshold value, the first correlation coefficient indicating a strength of the correlation between the first nearby site and the target renewable energy site.

15 . The system of claim 10 , wherein the measured power is correlation-based forecast power.

16 . The system of claim 10 , wherein the series of lagged power measurements are determined if the first forecast look-ahead time is less than or equal to the lag between the target renewable energy site and the first nearby site for which the respective power measurements are correlated.

17 . The system of claim 10 , further comprising:

at the weather forecast time, determine a lag between the target renewable energy site and a second nearby site for which respective power measurements are correlated;

determine if the first forecast look-ahead time is less than or equal to the lag between the target renewable energy site and the second nearby site for which the respective power measurements are correlated;

determine a series of lagged power measurements at the time of forecast which constitute a series of correlation-based forecasts for power generation at the target renewable energy site, the series of lagged power measurements being based at least in part on the lag between the target renewable energy site and the second nearby site; and

generate a set of ramp predictors incorporating correlation-based forecasts from the first nearby site for the time of forecast and the first forecast look-ahead time based on the series of lagged power measurements, the series of lagged power measurements being based at least in part on the lag between the target renewable energy site and the second nearby site, each ramp predictor of the set of ramp predictors including a difference between measured power at a time index and a measured power at a current timestamp.

18 . The system of claim 17 , wherein apply the sets of decision trees to one or more of the ramp predictors of the set of ramp predictors and the power forecast errors to obtain the predicted forecast errors comprises:

select at least one of the one of more of the ramp predictors of the set of ramp predictors with a most positive correlation coefficient relative to others of the set of ramp predictors; and

apply the sets of decision trees to the at least one of the one or more of the ramp predictors of the set of ramp predictors.

19 . A method comprising:

receiving, from sensors at a target renewable energy site and a first nearby site, real-time power measurements from variable power generation assets;

at a weather forecast time, determining a lag between the target renewable energy site and the first nearby site for which respective power measurements are correlated;

selecting, by one or more processors, a first forecast look-ahead time;

determining, by the one or more processors, if the first forecast look-ahead time is less than or equal to the lag between the target renewable energy site and the first nearby site for which the respective power measurements are correlated;

determining, by the one or more processors, a series of lagged power measurements at a time of forecast which constitute a series of correlation-based forecasts for power generation at the target renewable energy site, the series of lagged power measurements being based at least in part on the lag between the target renewable energy site and the first nearby site;

generating, by the one or more processors, a set of ramp predictors incorporating correlation-based forecasts from the first site for the time of forecast and the first forecast look-ahead time based on the series of lagged power measurements, the series of lagged power measurements being based at least in part on the lag between the target renewable energy site and the first nearby site, each of the set of ramp predictors including a difference between measured power at a time index and a measured power at a current timestamp;

receiving power forecast errors for one or more variable power generation assets of the target renewable energy site;

applying, by the one or more processors, sets of decision trees to one or more of the ramp predictors of the set of ramp predictors and the power forecast errors to obtain predicted forecast errors;

generating, by the one or more processors, second power forecasts for a set of look-ahead times for one or more variable power generation assets of the target renewable energy site based on first power forecasts and the predicted forecast errors;

transmitting the second power forecasts to a power system; and

balancing load and generation in an electrical network based on the transmitted second power forecasts, wherein the electrical network comprises the target renewable energy site.