Systems and methods for forecasting power generated by variable power generation assets

Patent metadata

Field	Value
Publication number	US-2023297093-A1
Application number	US-202318185682-A
Country	US
Kind code	A1
Filing date	Mar 17, 2023
Priority date	Mar 17, 2022
Publication date	Sep 21, 2023
Grant date	—

Abstract

A variable power generation asset may be subject to ramp events, which are large variations in power generated by the variable power generation asset within a short period of time. A variable power generation forecast system may receive or generate benchmark power forecasts and generated power measurements. Ramp predictors are based on the benchmark power forecasts and the generated power measurements. The variable power generation forecast system utilizes a feedback error correction model that predicts forecast errors for the benchmark power forecasts at various look-ahead times. The variable power generation forecast system applies sets of decision trees to the ramp predictors and last known forecast errors of the benchmark power forecasts to obtain the predicted forecast errors. The variable power generation forecast systems uses the predicted forecast errors and the benchmark power forecasts to generate more accurate power forecasts at the various look-ahead times.

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:
receiving first power forecasts for a set of look-ahead times for one or more variable power generation assets, a variable power generation asset subject to ramp events, a ramp event being a large variation in power generated by the variable power generation asset within a short period of time; receiving generated power measurements for the one or more variable power generation assets, the generated power measurements including a reference generated power measurement and one or more generated power measurements prior to the reference generated power measurement; generating ramp predictors for the set of look-ahead times, a ramp predictor including a set of values, a value obtained by subtracting the reference generated power measurement from one of the one or more generated power measurements prior to the reference generated power measurement or by subtracting the reference generated power measurement from a first power forecast for a look-ahead time of the set of look-ahead times; receiving power forecast errors for the one or more variable power generation assets; applying sets of decision trees to the 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 the one or more variable power generation assets based on the first power forecasts and the predicted forecast errors.

2 . The non-transitory computer-readable medium of claim 1 wherein generating second power forecasts for the set of look-ahead times for the one or more variable power generation assets based on the first power forecasts and the predicted forecast errors includes adding the first power forecasts and the predicted forecast errors to obtain the second power forecasts.

3 . The non-transitory computer-readable medium of claim 1 wherein a set of values for a ramp predictor for a look-ahead time of the set of look-ahead times that is prior to or at a threshold look-ahead time includes values obtained by subtracting the reference generated power measurement from the one or more generated power measurements prior to the reference generated power measurement and values obtained by subtracting the reference generated power measurement from a subset of the first power forecasts for a subset of the set of look-ahead times.

4 . The non-transitory computer-readable medium of claim 1 wherein a set of values for a ramp predictor for a look-ahead time of the set of look-ahead times that is after a threshold look-ahead time includes values obtained by subtracting the reference generated power measurement from a subset of the first power forecasts for a subset of the set of look-ahead times.

5 . The non-transitory computer-readable medium of claim 1 , the method further comprising:
receiving weather forecast data for a geographic area that includes the one or more variable power generation assets; and applying a machine learning forecast model to the generated power measurements and the weather forecast data to obtain a first subset of the first power forecasts for a first subset of the set of look-ahead times that are prior to or at a threshold look-ahead time.

6 . The non-transitory computer-readable medium of claim 5 , the method further comprising applying the machine learning forecast model to the weather forecast data to obtain a second subset of the first power forecasts for a second subset of the set of look-ahead times that are after a threshold look-ahead time.

7 . The non-transitory computer-readable medium of claim 1 , the method further comprising:
receiving weather forecast data for a geographic area that includes the one or more variable power generation assets; and applying at a first time a machine learning forecast model to the generated power measurements and the weather forecast data to obtain the first power forecasts for the set of look-ahead times, wherein the reference generated power measurement is a power measurement for the one or more variable power generation assets measured at a time generally at or just prior to the first time.

8 . The non-transitory computer-readable medium of claim 1 wherein a size of the set of values for the ramp predictor for a particular look-ahead time of the set of look-ahead times is based on the particular look-ahead time.

9 . The non-transitory computer-readable medium of claim 1 , the method further comprising:
receiving a power forecast data set; generating a ramp predictor data set based on the power forecast data set; generating a forecast error data set based on the power forecast data set; and training the sets of decision trees on the power forecast data set, the ramp predictor data set, and the forecast error data set.

10 . The non-transitory computer-readable medium of claim 1 wherein the second power forecasts have an average normalized mean-absolute error (nMAE) for the set of look-ahead times that is lower than an average nMAE for the set of look-ahead times that the first power forecasts have.

11 . A method comprising:
receiving first power forecasts for a set of look-ahead times for one or more variable power generation assets, a variable power generation asset subject to ramp events, a ramp event being a large variation in power generated by the variable power generation asset within a short period of time; receiving generated power measurements for the one or more variable power generation assets, the generated power measurements including a reference generated power measurement and one or more generated power measurements prior to the reference generated power measurement; generating ramp predictors for the set of look-ahead times, a ramp predictor including a set of values, a value obtained by subtracting the reference generated power measurement from one of the one or more generated power measurements prior to the reference generated power measurement or by subtracting the reference generated power measurement from a first power forecast for a look-ahead time of the set of look-ahead times; receiving power forecast errors for the one or more variable power generation assets; applying sets of decision trees to the 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 the one or more variable power generation assets based on the first power forecasts and the predicted forecast errors.

12 . The method of claim 11 wherein generating second power forecasts for the set of look-ahead times for the one or more variable power generation assets based on the first power forecasts and the predicted forecast errors includes adding the first power forecasts and the predicted forecast errors to obtain the second power forecasts.

13 . The method of claim 11 wherein a set of values for a ramp predictor for a look-ahead time of the set of look-ahead times that is prior to or at a threshold look-ahead time includes values obtained by subtracting the reference generated power measurement from the one or more generated power measurements prior to the reference generated power measurement and values obtained by subtracting the reference generated power measurement from a subset of the first power forecasts for a subset of the set of look-ahead times.

14 . The method of claim 11 wherein a set of values for a ramp predictor for a look-ahead time of the set of look-ahead times that is after a threshold loo

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