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

Patent metadata

Field	Value
Publication number	US-2023299580-A1
Application number	US-202318186029-A
Country	US
Kind code	A1
Filing date	Mar 17, 2023
Priority date	Mar 17, 2022
Publication date	Sep 21, 2023
Grant date	—

Abstract

An example method includes, at a weather forecast time, determining a lag between a target renewable energy site and a first nearby site for which respective power measurements are correlated, selecting a first forecast look-ahead time, determining if the first forecast look-ahead time is less than or equal to the lag, determining 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 site based on the lag, generating a set of ramp predictors incorporating correlation-based forecasts from the first site and the first forecast look-ahead time, receiving power forecast errors, applying sets of decision trees to the predictors and the power forecast errors to obtain predicted forecast errors, and generating second power forecasts for the set of look-ahead times of the target site based on the first power forecasts and the predicted forecast errors.

First claim

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:
at a weather forecast time, determining a lag between a target renewable energy site and a first nearby site for which respective power measurements are correlated; selecting a first forecast look-ahead time; determining 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 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 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 first target renewable energy site; applying sets of decision trees to one or more of the predictors of the set of ramp predictors and the power forecast errors to obtain predicted forecast errors; and generating second power forecasts for the set of look-ahead times for one or more variable power generation assets of the target renewable energy site based on the first power forecasts and the predicted forecast errors.

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, 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.

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 determine 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 second nearby site, each of the set 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 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 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 the one of the one or more 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:
at a weather forecast time, determine a lag between a target renewable energy site and a first nearby site for which respective power measurements are correlated; select a first forecast look-ahead time; determine 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 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 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; receive power forecast errors for one or more variable power generation assets of the first renewable energy site; apply sets of decision trees to one or more of the predictors of the set of ramp predictors and the power forecast errors to obtain predicted forecast errors; and generate second power forecasts for the set of look-ahead times for one or more variable power generation assets of the target renewable energy site based on the first power forecasts and the predicted forecast errors.

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

Assignees

• Utopus Insights Inc

Inventors

• Keerthisinghe Chanaka
• Reis Machado Da Silva Ana Rita
• Calvo Paulino Tardaguila
• Theis Thomas N
• Horvath Gabor

Classifications

• H02J2101/28

  Wind energy · CPC title

• H02J2101/22

  Solar energy · CPC title

• G05B23/0221 Primary

  Preprocessing measurements, e.g. data collection rate adjustment; Standardization of measurements; Time series or signal analysis, e.g. frequency analysis or wavelets; Trustworthiness of measurements; Indexes therefor; Measurements using easily measured parameters to estimate parameters difficult to measure; Virtual sensor creation; De-noising; Sensor fusion; Unconventional preprocessing inherently present in specific fault detection methods like PCA-based methods · CPC title

• G05B2223/06

  Remote monitoring · CPC title

• G05B15/02

  electric · CPC title