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

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.

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 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. 
     
     
         15 . The method of  claim 11 , 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.   
     
     
         16 . The method of  claim 15 , 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. 
     
     
         17 . The method of  claim 11 , 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.   
     
     
         18 . The method of  claim 11  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. 
     
     
         19 . The method of  claim 11 , 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.   
     
     
         20 . A system comprising at least one processor and memory containing instructions, the instructions being executable by the at least one processor to:
 receive 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;   receive 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;   generate 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;   receive power forecast errors for the one or more variable power generation assets;   apply sets of decision trees to the 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 the one or more variable power generation assets based on the first power forecasts and the predicted forecast errors.   
     
     
         21 . 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 weather forecast data for a geographic area, the geographic area including one or more variable power generation assets, a variable power generation asset subject to ramp events, a ramp event being a large variation in the power generated by the one or more variable power generation assets within a short period of time;   receiving first generated power measurements for the one or more variable power generation assets, the first generated power measurements including a first reference generated power measurement and one or more first generated power measurements prior to the first reference generated power measurement;   applying a machine learning forecast model to the first weather forecast data and the first generated power measurements to obtain first power forecasts for a first set of look-ahead times for the one or more variable power generation assets;   receiving second weather forecast data for the geographic area;   receiving second generated power measurements for the one or more variable power generation assets, the second generated power measurements including a second reference generated power measurement and one or more second generated power measurements prior to the second reference generated power measurement;   generating first ramp predictors for a second set of look-ahead times, a first ramp predictor including a set of values, a value obtained by subtracting the second reference generated power measurement from one of the one or more second generated power measurements prior to the second reference generated power measurement or by subtracting the second reference generated power measurement from a first power forecast for a look-ahead time of the first set of look-ahead times; and   applying the machine learning forecast model to the second weather forecast data, the second generated power measurements, and the first ramp predictors to obtain second power forecasts for a second set of look-ahead times for the one or more variable power generation assets.   
     
     
         22 . The non-transitory computer-readable medium of  claim 21 , the method further comprising:
 receiving third weather forecast data for the geographic area;   receiving third generated power measurements for the one or more variable power generation assets, the third generated power measurements including a third reference generated power measurement and one or more third generated power measurements prior to the third reference generated power measurement;   generating second ramp predictors for a third set of look-ahead times, a second ramp predictor including a set of values, a value obtained by subtracting the third reference generated power measurement from one of the one or more third generated power measurements prior to the third reference generated power measurement or by subtracting the third reference generated power measurement from a second power forecast for a look-ahead time of the second set of look-ahead times; and   applying the machine learning forecast model to the third weather forecast data, the third generated power measurements, and the second ramp predictors to obtain third power forecasts for a third set of look-ahead times for the one or more variable power generation assets.   
     
     
         23 . The non-transitory computer-readable medium of  claim 21  wherein a set of values for a first ramp predictor for a look-ahead time of the first 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 first set of look-ahead times. 
     
     
         24 . The non-transitory computer-readable medium of  claim 21  wherein a set of values for a first ramp predictor for a look-ahead time of the first 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 first set of look-ahead times. 
     
     
         25 . The non-transitory computer-readable medium of  claim 21  wherein applying the machine learning forecast model to the first weather forecast data and the first generated power measurements to obtain first power forecasts includes applying the machine learning forecast model to the first weather forecast data and the first generated power measurements to obtain a first subset of the first power forecasts for a first subset of the first set of look-ahead times that are prior to or at a threshold look-ahead time. 
     
     
         26 . The non-transitory computer-readable medium of  claim 25  wherein applying the machine learning forecast model to the first weather forecast data and the first generated power measurements to obtain first power forecasts includes applying the machine learning forecast model to the first weather forecast data to obtain a second subset of the first power forecasts for a second subset of the first set of look-ahead times that are after a threshold look-ahead time. 
     
     
         27 . The non-transitory computer-readable medium of  claim 21  wherein applying the machine learning forecast model to the first weather forecast data and the first generated power measurements to obtain the first power forecasts for the first set of look-ahead times for the one or more variable power generation assets includes applying at a first time the machine learning forecast model to the first weather forecast data and the first generated power measurements to obtain the first power forecasts for the first set of look-ahead times for the one or more variable power generation assets, and where 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. 
     
     
         28 . The non-transitory computer-readable medium of  claim 21  wherein a size of the set of values for the first ramp predictor for a particular look-ahead time of the first set of look-ahead times is based on the particular look-ahead time. 
     
     
         29 . The non-transitory computer-readable medium of  claim 21 , the method further comprising:
 receiving a training data set, the training data set including historical data from the one or more variable power generation assets;   training a precursor machine learning forecast model on the training data set;   receiving a power forecast data set from the precursor machine learning forecast model;   generating a ramp predictor data set based on the power forecast data set; and   training the machine learning forecast model on the training data set and the ramp predictor data set.   
     
     
         30 . A method comprising:
 receiving first weather forecast data for a geographic area, the geographic area including one or more variable power generation assets, a variable power generation asset subject to ramp events, a ramp event being a large variation in the power generated by the one or more variable power generation assets within a short period of time;   receiving first generated power measurements for the one or more variable power generation assets, the first generated power measurements including a first reference generated power measurement and one or more first generated power measurements prior to the first reference generated power measurement;   applying a machine learning forecast model to the first weather forecast data and the first generated power measurements to obtain first power forecasts for a first set of look-ahead times for the one or more variable power generation assets;   receiving second weather forecast data for the geographic area;   receiving second generated power measurements for the one or more variable power generation assets, the second generated power measurements including a second reference generated power measurement and one or more second generated power measurements prior to the second reference generated power measurement;   generating first ramp predictors for a second set of look-ahead times, a first ramp predictor including a set of values, a value obtained by subtracting the second reference generated power measurement from one of the one or more second generated power measurements prior to the second reference generated power measurement or by subtracting the second reference generated power measurement from a first power forecast for a look-ahead time of the first set of look-ahead times; and   applying the machine learning forecast model to the second weather forecast data, the second generated power measurements, and the first ramp predictors to obtain second power forecasts for a second set of look-ahead times for the one or more variable power generation assets.   
     
     
         31 . The method of  claim 30 , further comprising:
 receiving third weather forecast data for the geographic area;   receiving third generated power measurements for the one or more variable power generation assets, the third generated power measurements including a third reference generated power measurement and one or more third generated power measurements prior to the third reference generated power measurement;   generating second ramp predictors for a third set of look-ahead times, a second ramp predictor including a set of values, a value obtained by subtracting the third reference generated power measurement from one of the one or more third generated power measurements prior to the third reference generated power measurement or by subtracting the third reference generated power measurement from a second power forecast for a look-ahead time of the second set of look-ahead times; and   applying the machine learning forecast model to the third weather forecast data, the third generated power measurements, and the second ramp predictors to obtain third power forecasts for a third set of look-ahead times for the one or more variable power generation assets.   
     
     
         32 . The method of  claim 30  wherein a set of values for a first ramp predictor for a look-ahead time of the first 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 first set of look-ahead times. 
     
     
         33 . The method of  claim 30  wherein a set of values for a first ramp predictor for a look-ahead time of the first 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 first set of look-ahead times. 
     
     
         34 . The method of  claim 30  wherein applying the machine learning forecast model to the first weather forecast data and the first generated power measurements to obtain first power forecasts includes applying the machine learning forecast model to the first weather forecast data and the first generated power measurements to obtain a first subset of the first power forecasts for a first subset of the first set of look-ahead times that are prior to or at a threshold look-ahead time. 
     
     
         35 . The method of  claim 30  wherein applying the machine learning forecast model to the first weather forecast data and the first generated power measurements to obtain first power forecasts includes applying the machine learning forecast model to the first weather forecast data to obtain a second subset of the first power forecasts for a second subset of the first set of look-ahead times that are after a threshold look-ahead time. 
     
     
         36 . The method of  claim 30  wherein applying the machine learning forecast model to the first weather forecast data and the first generated power measurements to obtain the first power forecasts for the first set of look-ahead times for the one or more variable power generation assets includes applying at a first time the machine learning forecast model to the first weather forecast data and the first generated power measurements to obtain the first power forecasts for the first set of look-ahead times for the one or more variable power generation assets, and where 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. 
     
     
         37 . The method of  claim 30  wherein a size of the set of values for the first ramp predictor for a particular look-ahead time of the first set of look-ahead times is based on the particular look-ahead time. 
     
     
         38 . The method of  claim 30 , further comprising:
 receiving a training data set, the training data set including historical data from the one or more variable power generation assets;   training a precursor machine learning forecast model on the training data set;   receiving a power forecast data set from the precursor machine learning forecast model;   generating a ramp predictor data set based on the power forecast data set; and   training the machine learning forecast model on the training data set and the ramp predictor data set.   
     
     
         39 . A system comprising at least one processor and memory containing instructions, the instructions being executable by the at least one processor to:
 receive first weather forecast data for a geographic area, the geographic area including one or more variable power generation assets, a variable power generation asset subject to ramp events, a ramp event being a large variation in the power generated by the one or more variable power generation assets within a short period of time;   receive first generated power measurements for the one or more variable power generation assets, the first generated power measurements including a first reference generated power measurement and one or more first generated power measurements prior to the first reference generated power measurement;   apply a machine learning forecast model to the first weather forecast data and the first generated power measurements to obtain first power forecasts for a first set of look-ahead times for the one or more variable power generation assets;   receive second weather forecast data for the geographic area;   receive second generated power measurements for the one or more variable power generation assets, the second generated power measurements including a second reference generated power measurement and one or more second generated power measurements prior to the second reference generated power measurement;   generate first ramp predictors for a second set of look-ahead times, a first ramp predictor including a set of values, a value obtained by subtracting the second reference generated power measurement from one of the one or more second generated power measurements prior to the second reference generated power measurement or by subtracting the second reference generated power measurement from a first power forecast for a look-ahead time of the first set of look-ahead times; and   apply the machine learning forecast model to the second weather forecast data, the second generated power measurements, and the first ramp predictors to obtain second power forecasts for a second set of look-ahead times for the one or more variable power generation assets.   
     
     
         40 . The system of  claim 39 , the instructions being further executable by the at least one processor to:
 receive third weather forecast data for the geographic area;   receive third generated power measurements for the one or more variable power generation assets, the third generated power measurements including a third reference generated power measurement and one or more third generated power measurements prior to the third reference generated power measurement;   generate second ramp predictors for a third set of look-ahead times, a second ramp predictor including a set of values, a value obtained by subtracting the third reference generated power measurement from one of the one or more third generated power measurements prior to the third reference generated power measurement or by subtracting the third reference generated power measurement from a second power forecast for a look-ahead time of the second set of look-ahead times; and   apply the machine learning forecast model to the third weather forecast data, the third generated power measurements, and the second ramp predictors to obtain third power forecasts for a third set of look-ahead times for the one or more variable power generation assets.