CN112686416A - Wind power probability prediction method and system - Google Patents

Wind power probability prediction method and system Download PDF

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CN112686416A
CN112686416A CN201910986356.XA CN201910986356A CN112686416A CN 112686416 A CN112686416 A CN 112686416A CN 201910986356 A CN201910986356 A CN 201910986356A CN 112686416 A CN112686416 A CN 112686416A
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probability
wind power
distribution function
preset
cumulative distribution
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王钊
王伟胜
刘纯
冯双磊
王勃
车建峰
靳双龙
汪步惟
张菲
韩振永
宋宗朋
滑申冰
王铮
姜文玲
赵艳青
裴岩
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State Grid Corp of China SGCC
China Electric Power Research Institute Co Ltd CEPRI
Electric Power Research Institute of State Grid Shandong Electric Power Co Ltd
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State Grid Corp of China SGCC
China Electric Power Research Institute Co Ltd CEPRI
Electric Power Research Institute of State Grid Shandong Electric Power Co Ltd
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Abstract

The invention relates to a wind power probability prediction method and a system, comprising the following steps: determining a regression coefficient matrix corresponding to the preset probability of the wind power probability predicted accumulative distribution function according to the quantile corresponding to the preset probability of the wind power probability predicted accumulative distribution function at each moment in the historical time period; and correcting quantiles corresponding to the preset probability of the wind power probability predicted accumulative distribution function at the predicted time by using a regression coefficient matrix corresponding to the preset probability of the wind power probability predicted accumulative distribution function. According to the technical scheme provided by the invention, the quantile result of the wind power probability prediction can be more accurately obtained, so that the wind power can be more accurately predicted, meanwhile, the probability prediction algorithm has small calculation amount and high calculation efficiency, and the method has wide popularization and application scenes.

Description

Wind power probability prediction method and system
Technical Field
The invention relates to the field of wind power probability prediction, in particular to a wind power probability prediction method and a wind power probability prediction system.
Background
The traditional wind power prediction is mainly based on deterministic prediction, and the predicted power given at each moment is a point (namely a predicted mean value), however, the prediction cannot be guaranteed to be completely accurate, so that a prediction error exists, namely the prediction has uncertainty. To describe this uncertainty, one gives the conditional probability distribution of the predicted power at each instant by probabilistic prediction.
The result form of Probability Prediction mainly includes Probability Density Function (PDF), Cumulative Distribution Function (CDF), Quantile (Quantile), and Prediction Interval (PI). The quantile is a discrete probability prediction result form common in wind power probability prediction, can be directly extracted from CDF, and the prediction interval forms an upper boundary and a lower boundary of the interval through a pair of quantiles.
After the wind power probability prediction model is deployed, with the continuous increase of new data, the model trained according to the old data is gradually not suitable for the previous prediction, the prediction quality is reduced, and modeling training needs to be performed again by combining the new data so as to adapt to the new data characteristics.
But since probabilistic predictive models are often computationally complex and time consuming, re-modeling is costly.
Disclosure of Invention
Aiming at the defects of the prior art, the invention aims to provide a wind power probability prediction method and a wind power probability prediction system.
The purpose of the invention is realized by adopting the following technical scheme:
the invention provides a wind power probability prediction method, which is improved in that the method comprises the following steps:
determining a regression coefficient matrix corresponding to the preset probability of the wind power probability predicted accumulative distribution function according to the quantile corresponding to the preset probability of the wind power probability predicted accumulative distribution function at each moment in the historical time period;
and correcting quantiles corresponding to the preset probability of the wind power probability predicted accumulative distribution function at the predicted time by using a regression coefficient matrix corresponding to the preset probability of the wind power probability predicted accumulative distribution function.
Preferably, the determining a regression coefficient matrix corresponding to the preset probability of the wind power probability predicted cumulative distribution function according to the quantile corresponding to the preset probability of the wind power probability predicted cumulative distribution function at each moment in the historical period includes:
step 1: determining a regression coefficient matrix corresponding to the preset probability of the wind power probability predicted accumulative distribution function by utilizing the quantile corresponding to the preset probability of the wind power probability predicted accumulative distribution function at the previous n moments in the historical time period;
step 2: correcting quantiles corresponding to the preset probability of the wind power probability predicted accumulative distribution function from the N +1 th moment to the Nth moment in the historical period by using a regression coefficient matrix corresponding to the preset probability of the wind power probability predicted accumulative distribution function;
and step 3: updating quantiles corresponding to the preset probability of the wind power probability prediction accumulative distribution function from the (N + 1) th moment to the Nth moment in the historical time period;
and 4, step 4: if the quantiles corresponding to the preset probabilities of the wind power probability prediction cumulative distribution functions from the N +1 th moment to the Nth moment in the updated historical time period simultaneously meet a first preset condition and a second preset condition, outputting regression coefficient matrixes corresponding to the preset probabilities of the wind power probability prediction cumulative distribution functions, otherwise, outputting the regression coefficient matrixes corresponding to the preset probabilities of the wind power probability prediction cumulative distribution functions to be [0 … 0 … 1, 0 ];
wherein N belongs to (1-N), N is the total time of the historical time period, and the matrix [0 … 0 … 1, 0] is a vector of 1 Xm order.
Further, the step 1 includes:
the preset probability of solving the cumulative distribution function of the wind power probability prediction shown in the following formula by using a simple search method is tauiTime-corresponding regression coefficient matrix
Figure BDA0002236818320000026
Figure BDA0002236818320000021
In the formula (I), the compound is shown in the specification,
Figure BDA0002236818320000022
the preset probability of the cumulative distribution function for predicting the wind power probability is tauiThe coefficients of the k-th order terms of the corresponding quantiles,
Figure BDA0002236818320000023
the preset probability of the cumulative distribution function for predicting the wind power probability is tauiChecking function of time correspondence, ptIs the actual wind power at the historical time t, tauiThe method comprises the steps that the ith preset probability of a cumulative distribution function predicted by the wind power probability is set, i belongs to (1-p), and p is the preset probability total number of the cumulative distribution function predicted by the wind power probability;
determining the preset probability of the cumulative distribution function of the wind power probability prediction to be tau according to the following formulaiChecking function of time correspondence
Figure BDA0002236818320000024
Figure BDA0002236818320000025
In the formula (I), the compound is shown in the specification,
Figure BDA0002236818320000031
the preset probability of the cumulative distribution function for the wind power probability prediction at the historical moment t is tauiA time-corresponding quantile polynomial matrix;
determining the preset probability of the cumulative distribution function of the wind power probability prediction at the historical moment t as tau according to the following formulaiTime-corresponding quantile polynomial matrix
Figure BDA0002236818320000032
Figure BDA0002236818320000033
In the formula (I), the compound is shown in the specification,
Figure BDA0002236818320000034
the preset probability of the cumulative distribution function for the wind power probability prediction at the historical moment t is tauiThe time is the k power of the corresponding quantile, k belongs to (1-m), m is a preset polynomial order, t belongs to (1-N), N belongs to (1-N), and N is the total time of the historical time period.
Further, the step 2 includes:
correcting the preset probability of the wind power probability prediction accumulative distribution function of the historical moment j according to the following formula to be tauiQuantile of time correspondence
Figure BDA0002236818320000035
Figure BDA0002236818320000036
In the formula (I), the compound is shown in the specification,
Figure BDA0002236818320000037
the preset probability of the cumulative distribution function for predicting the wind power probability is tauiA matrix of regression coefficients corresponding to the time,
Figure BDA0002236818320000038
the preset probability of the cumulative distribution function for the wind power probability prediction of the historical moment j is tauiTime-corresponding quantile polynomial matrices, tauiThe ith preset probability of the cumulative distribution function for wind power probability prediction is represented by i ∈ (1-p), and p is the cumulative probability of the wind power probability predictionThe preset probability total number of the distribution function;
determining the preset probability of the cumulative distribution function of the wind power probability prediction at the historical moment j as tau according to the following formulaiTime-corresponding quantile polynomial matrix
Figure BDA0002236818320000039
Figure BDA00022368183200000310
In the formula (I), the compound is shown in the specification,
Figure BDA00022368183200000311
the preset probability of the cumulative distribution function for the wind power probability prediction at the historical moment t is tauiThe time is the k power of the corresponding quantile, k belongs to (1-m), m is a preset polynomial order, t belongs to (1-N), N belongs to (1-N), and N is the total time of the historical time period.
Further, the step 3 includes:
and arranging the quantiles corresponding to the preset probabilities of the wind power probability prediction accumulative distribution function at the historical moment j after correction in an ascending order, and updating the ith number in the ascending order to the quantile corresponding to the ith preset probability of the wind power probability prediction accumulative distribution function at the historical moment j.
Further, the first preset condition is determined according to the following formula:
Figure BDA0002236818320000041
in the formula, R2i) The preset probability of the cumulative distribution function for predicting the wind power probability from the N +1 th moment to the Nth moment in the updated historical period is tauiMean reliability deviation value of time-corresponding quantiles, R1i) Forecasting a cumulative distribution function for wind power probability forecasting from the (N + 1) th moment to the Nth moment in a historical period before correctionLet the probability be τiAverage reliability deviation values of corresponding quantiles;
determining the preset probability of the cumulative distribution function of the wind power probability prediction from the N +1 th moment to the Nth moment in the updated historical period as tau according to the following formulaiMean reliability deviation value R of time-corresponding quantiles2i):
Figure BDA0002236818320000042
In the formula, xi'ji) The preset probability of the cumulative distribution function for predicting the probability of the wind power at the jth moment in the updated historical time period is tauiAn indicative function of the corresponding quantiles in time, tauiThe method comprises the steps that the ith preset probability of a cumulative distribution function predicted by the wind power probability is set, i belongs to (1-p), and p is the preset probability total number of the cumulative distribution function predicted by the wind power probability;
determining the preset probability of the cumulative distribution function of the wind power probability prediction from the (N + 1) th moment to the Nth moment in the historical period before correction as tau according to the following formulaiMean reliability deviation value R of time-corresponding quantiles1i):
Figure BDA0002236818320000043
In the formula, xiji) The preset probability of the cumulative distribution function for predicting the wind power probability at the jth moment in the historical time period before correction is tauiAn indicative function of the corresponding quantiles;
determining the preset probability of the cumulative distribution function of the wind power probability prediction at the jth moment in the updated historical time period as tau according to the following formulaiIndication function xi 'of corresponding quantiles'ji):
Figure BDA0002236818320000051
In the formula (I), the compound is shown in the specification,
Figure BDA0002236818320000052
the preset probability of the cumulative distribution function of the wind power probability prediction at the jth moment in the updated historical time period is tauiA quantile corresponding to the hour;
determining the preset probability of the cumulative distribution function of the wind power probability prediction at the jth moment in the historical period before correction as tau according to the following formulaiIndicative function xi of quantiles corresponding to timeji):
Figure BDA0002236818320000053
In the formula (I), the compound is shown in the specification,
Figure BDA0002236818320000054
the preset probability of the cumulative distribution function for predicting the wind power probability at the jth moment in the historical time period before correction is tauiA quantile corresponding to the hour;
determining the second preset condition according to the following formula:
Figure BDA0002236818320000055
in the formula, S1jQuantile loss value of cumulative distribution function for wind power probability prediction at jth moment in historical period before correction, S2jThe quantile loss value of the cumulative distribution function for wind power probability prediction at the jth moment in the updated historical time period is obtained;
determining a quantile loss value S of an accumulative distribution function of wind power probability prediction at the jth moment in a historical period before optimization according to the following formula1j
Figure BDA0002236818320000056
Determining a quantile loss value S of an accumulative distribution function of wind power probability prediction at the jth moment in a historical period before optimization according to the following formula2j
Figure BDA0002236818320000061
In the formula, j belongs to (N +1, N), N belongs to (1-N), and N is the total time in the history period.
Preferably, the correcting the quantile corresponding to the preset probability of the wind power probability predicted cumulative distribution function at the predicted time by using the regression coefficient matrix corresponding to the preset probability of the wind power probability predicted cumulative distribution function includes:
the preset probability of the wind power probability prediction accumulative distribution function of the prediction time r is corrected to be tau according to the following formulaiQuantile of time correspondence
Figure BDA0002236818320000062
Figure BDA0002236818320000063
In the formula (I), the compound is shown in the specification,
Figure BDA0002236818320000064
the preset probability of the cumulative distribution function for predicting the wind power probability is tauiA matrix of regression coefficients corresponding to the time,
Figure BDA0002236818320000065
the preset probability of the cumulative distribution function for predicting the wind power probability at the predicted time r is tauiTime-corresponding quantile polynomial matrices, tauiThe method comprises the steps that the ith preset probability of a cumulative distribution function predicted by the wind power probability is set, i belongs to (1-p), and p is the preset probability total number of the cumulative distribution function predicted by the wind power probability;
wherein the prediction of the cumulative distribution function of the wind power probability prediction of the prediction time r is determined according to the following formulaLet the probability be τiTime-corresponding quantile polynomial matrix
Figure BDA0002236818320000066
Figure BDA0002236818320000067
In the formula (I), the compound is shown in the specification,
Figure BDA0002236818320000068
the preset probability of the cumulative distribution function for the wind power probability prediction at the historical moment t is tauiThe time is the k power of the corresponding quantile, k belongs to (1-m), m is a preset polynomial order, t belongs to (1-N), N belongs to (1-N), and N is the total time of the historical time period.
The invention provides a wind power probability prediction system, and the improvement is that the system comprises:
the determining module is used for determining a regression coefficient matrix corresponding to the preset probability of the wind power probability predicted accumulative distribution function according to the quantile corresponding to the preset probability of the wind power probability predicted accumulative distribution function at each moment in the historical time period;
and the correction module is used for correcting the quantile corresponding to the preset probability of the wind power probability predicted accumulative distribution function at the predicted time by using the regression coefficient matrix corresponding to the preset probability of the wind power probability predicted accumulative distribution function.
Preferably, the determining module includes:
the first determining unit is used for determining a regression coefficient matrix corresponding to the preset probability of the wind power probability predicted accumulative distribution function by utilizing the quantile corresponding to the preset probability of the wind power probability predicted accumulative distribution function at the previous n moments in the historical time period;
the correction unit is used for correcting quantiles corresponding to the preset probability of the wind power probability prediction accumulative distribution function from the N +1 th moment to the Nth moment in the historical period by using a regression coefficient matrix corresponding to the preset probability of the wind power probability prediction accumulative distribution function;
the updating unit is used for updating quantiles corresponding to the preset probability of the wind power probability prediction accumulative distribution function from the (N + 1) th moment to the Nth moment in the historical time period;
the output unit is used for outputting a regression coefficient matrix corresponding to the preset probability of the wind power probability prediction cumulative distribution function if the quantile corresponding to the preset probability of the wind power probability prediction cumulative distribution function from the N +1 th moment to the Nth moment in the updated historical period meets a first preset condition and a second preset condition at the same time, otherwise, outputting the regression coefficient matrix corresponding to the preset probability of the wind power probability prediction cumulative distribution function [0 … 0 … 1, 0 ];
wherein N belongs to (1-N), N is the total time of the historical time period, and the matrix [0 … 0 … 1, 0] is a vector of 1 Xm order.
Further, the first determining unit is configured to:
the preset probability of solving the cumulative distribution function of the wind power probability prediction shown in the following formula by using a simple search method is tauiTime-corresponding regression coefficient matrix
Figure BDA0002236818320000071
Figure BDA0002236818320000072
In the formula (I), the compound is shown in the specification,
Figure BDA0002236818320000073
the preset probability of the cumulative distribution function for predicting the wind power probability is tauiThe coefficients of the k-th order terms of the corresponding quantiles,
Figure BDA0002236818320000074
the preset probability of the cumulative distribution function for predicting the wind power probability is tauiChecking function of time correspondence, ptIs the actual wind power at the historical time t, tauiThe method comprises the steps that the ith preset probability of a cumulative distribution function predicted by the wind power probability is set, i belongs to (1-p), and p is the preset probability total number of the cumulative distribution function predicted by the wind power probability;
determining the preset probability of the cumulative distribution function of the wind power probability prediction to be tau according to the following formulaiChecking function of time correspondence
Figure BDA0002236818320000075
Figure BDA0002236818320000081
In the formula (I), the compound is shown in the specification,
Figure BDA0002236818320000082
the preset probability of the cumulative distribution function for the wind power probability prediction at the historical moment t is tauiA time-corresponding quantile polynomial matrix;
determining the preset probability of the cumulative distribution function of the wind power probability prediction at the historical moment t as tau according to the following formulaiTime-corresponding quantile polynomial matrix
Figure BDA0002236818320000083
Figure BDA0002236818320000084
In the formula (I), the compound is shown in the specification,
Figure BDA0002236818320000085
the preset probability of the cumulative distribution function for the wind power probability prediction at the historical moment t is tauiThe time is the k power of the corresponding quantile, k belongs to (1-m), m is a preset polynomial order, t belongs to (1-N), N belongs to (1-N), and N is the total time of the historical time period.
Further, the correction unit is configured to:
wind power probability prediction of historical moment j is corrected according to the following formulaIs τ toiQuantile of time correspondence
Figure BDA0002236818320000086
Figure BDA0002236818320000087
In the formula (I), the compound is shown in the specification,
Figure BDA0002236818320000088
the preset probability of the cumulative distribution function for predicting the wind power probability is tauiA matrix of regression coefficients corresponding to the time,
Figure BDA0002236818320000089
the preset probability of the cumulative distribution function for the wind power probability prediction of the historical moment j is tauiTime-corresponding quantile polynomial matrices, tauiThe method comprises the steps that the ith preset probability of a cumulative distribution function predicted by the wind power probability is set, i belongs to (1-p), and p is the preset probability total number of the cumulative distribution function predicted by the wind power probability;
determining the preset probability of the cumulative distribution function of the wind power probability prediction at the historical moment j as tau according to the following formulaiTime-corresponding quantile polynomial matrix
Figure BDA00022368183200000810
Figure BDA00022368183200000811
In the formula (I), the compound is shown in the specification,
Figure BDA00022368183200000812
the preset probability of the cumulative distribution function for the wind power probability prediction at the historical moment t is tauiThe time corresponds to the k power of quantile, k belongs to (1-m), m is a preset polynomial order, t belongs to (1-N), N belongs to (1-N), and N is in the historical time periodTotal number of time instants.
Further, the update unit includes:
and arranging the quantiles corresponding to the preset probabilities of the wind power probability prediction accumulative distribution function at the historical moment j after correction in an ascending order, and updating the ith number in the ascending order to the quantile corresponding to the ith preset probability of the wind power probability prediction accumulative distribution function at the historical moment j.
Further, the first preset condition is determined according to the following formula:
Figure BDA0002236818320000091
in the formula, R2i) The preset probability of the cumulative distribution function for predicting the wind power probability from the N +1 th moment to the Nth moment in the updated historical period is tauiMean reliability deviation value of time-corresponding quantiles, R1i) The preset probability of the cumulative distribution function for predicting the wind power probability from the (N + 1) th moment to the Nth moment in the historical period before correction is tauiAverage reliability deviation values of corresponding quantiles;
determining the preset probability of the cumulative distribution function of the wind power probability prediction from the N +1 th moment to the Nth moment in the updated historical period as tau according to the following formulaiMean reliability deviation value R of time-corresponding quantiles2i):
Figure BDA0002236818320000092
In the formula, xi'ji) The preset probability of the cumulative distribution function for predicting the probability of the wind power at the jth moment in the updated historical time period is tauiAn indicative function of the corresponding quantiles in time, tauiThe method comprises the steps that the ith preset probability of a cumulative distribution function predicted by the wind power probability is set, i belongs to (1-p), and p is the preset probability total number of the cumulative distribution function predicted by the wind power probability;
determining the preset probability of the cumulative distribution function of the wind power probability prediction from the (N + 1) th moment to the Nth moment in the historical period before correction as tau according to the following formulaiMean reliability deviation value R of time-corresponding quantiles1i):
Figure BDA0002236818320000093
In the formula, xiji) The preset probability of the cumulative distribution function for predicting the wind power probability at the jth moment in the historical time period before correction is tauiAn indicative function of the corresponding quantiles;
determining the preset probability of the cumulative distribution function of the wind power probability prediction at the jth moment in the updated historical time period as tau according to the following formulaiIndication function xi 'of corresponding quantiles'ji):
Figure BDA0002236818320000101
In the formula (I), the compound is shown in the specification,
Figure BDA0002236818320000106
the preset probability of the cumulative distribution function of the wind power probability prediction at the jth moment in the updated historical time period is tauiA quantile corresponding to the hour;
determining the preset probability of the cumulative distribution function of the wind power probability prediction at the jth moment in the historical period before correction as tau according to the following formulaiIndicative function xi of quantiles corresponding to timeji):
Figure BDA0002236818320000102
In the formula (I), the compound is shown in the specification,
Figure BDA0002236818320000103
to correct forThe preset probability of the cumulative distribution function of the wind power probability prediction at the jth moment in the previous historical period is tauiA quantile corresponding to the hour;
determining the second preset condition according to the following formula:
Figure BDA0002236818320000104
in the formula, S1jQuantile loss value of cumulative distribution function for wind power probability prediction at jth moment in historical period before correction, S2jThe quantile loss value of the cumulative distribution function for wind power probability prediction at the jth moment in the updated historical time period is obtained;
determining a quantile loss value S of an accumulative distribution function of wind power probability prediction at the jth moment in a historical period before optimization according to the following formula1j
Figure BDA0002236818320000105
Determining a quantile loss value S of an accumulative distribution function of wind power probability prediction at the jth moment in a historical period before optimization according to the following formula2j
Figure BDA0002236818320000111
In the formula, j belongs to (N +1, N), N belongs to (1-N), and N is the total time in the history period.
Preferably, the modification module is configured to:
the preset probability of the wind power probability prediction accumulative distribution function of the prediction time r is corrected to be tau according to the following formulaiQuantile of time correspondence
Figure BDA0002236818320000112
Figure BDA0002236818320000113
In the formula (I), the compound is shown in the specification,
Figure BDA0002236818320000114
the preset probability of the cumulative distribution function for predicting the wind power probability is tauiA matrix of regression coefficients corresponding to the time,
Figure BDA0002236818320000115
the preset probability of the cumulative distribution function for predicting the wind power probability at the predicted time r is tauiTime-corresponding quantile polynomial matrices, tauiThe method comprises the steps that the ith preset probability of a cumulative distribution function predicted by the wind power probability is set, i belongs to (1-p), and p is the preset probability total number of the cumulative distribution function predicted by the wind power probability;
determining the preset probability of the wind power probability prediction accumulative distribution function at the prediction time r as tau according to the following formulaiTime-corresponding quantile polynomial matrix
Figure BDA0002236818320000116
Figure BDA0002236818320000117
In the formula (I), the compound is shown in the specification,
Figure BDA0002236818320000118
the preset probability of the cumulative distribution function for the wind power probability prediction at the historical moment t is tauiThe time is the k power of the corresponding quantile, k belongs to (1-m), m is a preset polynomial order, t belongs to (1-N), N belongs to (1-N), and N is the total time of the historical time period.
Compared with the closest prior art, the invention has the following beneficial effects:
according to the technical scheme provided by the invention, a regression coefficient matrix corresponding to the preset probability of the wind power probability predicted accumulative distribution function is determined according to the quantile corresponding to the preset probability of the wind power probability predicted accumulative distribution function at each moment in the historical time period; correcting quantiles corresponding to the preset probability of the wind power probability predicted accumulative distribution function at the predicted time by using a regression coefficient matrix corresponding to the preset probability of the wind power probability predicted accumulative distribution function; the quantile result of the wind power probability prediction can be obtained more accurately, the wind power can be predicted more accurately, meanwhile, the probability prediction algorithm is small in calculation amount and high in calculation efficiency, and the method has wide popularization and application scenes.
Drawings
FIG. 1 is a flow chart of a wind power probability prediction method;
FIG. 2 is a graph comparing an original probability prediction curve and an optimized probability prediction curve;
FIG. 3 is a diagram of a wind power probability prediction system.
Detailed Description
The following describes embodiments of the present invention in further detail with reference to the accompanying drawings.
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention provides a wind power probability prediction method, as shown in fig. 1, the method comprises the following steps:
step 101, determining a regression coefficient matrix corresponding to a preset probability of the wind power probability predicted accumulative distribution function according to a quantile corresponding to the preset probability of the wind power probability predicted accumulative distribution function at each moment in a historical time period;
and 102, correcting quantiles corresponding to the preset probability of the wind power probability predicted accumulative distribution function at the predicted time by using a regression coefficient matrix corresponding to the preset probability of the wind power probability predicted accumulative distribution function.
In the best embodiment of the invention, the quantile form wind power probability prediction value of the prediction time output by the existing wind power probability prediction model is optimized, and a more accurate wind power probability prediction result is obtained.
Specifically, the step 101 includes:
step 1: determining a regression coefficient matrix corresponding to the preset probability of the wind power probability predicted accumulative distribution function by utilizing the quantile corresponding to the preset probability of the wind power probability predicted accumulative distribution function at the previous n moments in the historical time period;
step 2: correcting quantiles corresponding to the preset probability of the wind power probability predicted accumulative distribution function from the N +1 th moment to the Nth moment in the historical period by using a regression coefficient matrix corresponding to the preset probability of the wind power probability predicted accumulative distribution function;
and step 3: updating quantiles corresponding to the preset probability of the wind power probability prediction accumulative distribution function from the (N + 1) th moment to the Nth moment in the historical time period;
and 4, step 4: if the quantiles corresponding to the preset probabilities of the wind power probability prediction cumulative distribution functions from the N +1 th moment to the Nth moment in the updated historical time period simultaneously meet a first preset condition and a second preset condition, outputting regression coefficient matrixes corresponding to the preset probabilities of the wind power probability prediction cumulative distribution functions, otherwise, outputting the regression coefficient matrixes corresponding to the preset probabilities of the wind power probability prediction cumulative distribution functions to be [0 … 0 … 1, 0 ];
wherein N belongs to (1-N), N is the total time of the historical time period, and the matrix [0 … 0 … 1, 0] is a vector of 1 Xm order.
Further, the step 1 includes:
the preset probability of solving the cumulative distribution function of the wind power probability prediction shown in the following formula by using a simple search method is tauiTime-corresponding regression coefficient matrix
Figure BDA0002236818320000131
Figure BDA0002236818320000132
In the formula (I), the compound is shown in the specification,
Figure BDA0002236818320000133
the preset probability of the cumulative distribution function for predicting the wind power probability is tauiThe coefficients of the k-th order terms of the corresponding quantiles,
Figure BDA0002236818320000134
the preset probability of the cumulative distribution function for predicting the wind power probability is tauiChecking function of time correspondence, ptIs the actual wind power at the historical time t, tauiThe method comprises the steps that the ith preset probability of a cumulative distribution function predicted by the wind power probability is set, i belongs to (1-p), and p is the preset probability total number of the cumulative distribution function predicted by the wind power probability;
in the preferred embodiment of the present invention, there is a wide variety of software that can implement simplex search algorithms, such as the fminsearch function of MATLAB.
Determining the preset probability of the cumulative distribution function of the wind power probability prediction to be tau according to the following formulaiChecking function of time correspondence
Figure BDA0002236818320000135
Figure BDA0002236818320000136
In the formula (I), the compound is shown in the specification,
Figure BDA0002236818320000137
the preset probability of the cumulative distribution function for the wind power probability prediction at the historical moment t is tauiA time-corresponding quantile polynomial matrix;
determining the preset probability of the cumulative distribution function of the wind power probability prediction at the historical moment t according to the following formulaτiTime-corresponding quantile polynomial matrix
Figure BDA0002236818320000138
Figure BDA0002236818320000139
In the formula (I), the compound is shown in the specification,
Figure BDA0002236818320000141
the preset probability of the cumulative distribution function for the wind power probability prediction at the historical moment t is tauiThe time is the k power of the corresponding quantile, k belongs to (1-m), m is a preset polynomial order, t belongs to (1-N), N belongs to (1-N), and N is the total time of the historical time period.
In the preferred embodiment of the present invention, m can be different integer values, and the optimal value is selected according to the actual effect, and when m is 1, the optimal value is a unary linear regression matrix.
Further, the step 2 includes:
correcting the preset probability of the wind power probability prediction accumulative distribution function of the historical moment j according to the following formula to be tauiQuantile of time correspondence
Figure BDA0002236818320000142
Figure BDA0002236818320000143
In the formula (I), the compound is shown in the specification,
Figure BDA0002236818320000144
the preset probability of the cumulative distribution function for predicting the wind power probability is tauiA matrix of regression coefficients corresponding to the time,
Figure BDA0002236818320000145
the preset probability of the cumulative distribution function for the wind power probability prediction of the historical moment j is tauiTime-corresponding quantile polynomial matrices, tauiThe method comprises the steps that the ith preset probability of a cumulative distribution function predicted by the wind power probability is set, i belongs to (1-p), and p is the preset probability total number of the cumulative distribution function predicted by the wind power probability;
determining the preset probability of the cumulative distribution function of the wind power probability prediction at the historical moment j as tau according to the following formulaiTime-corresponding quantile polynomial matrix
Figure BDA0002236818320000146
Figure BDA0002236818320000147
In the formula (I), the compound is shown in the specification,
Figure BDA0002236818320000148
the preset probability of the cumulative distribution function for the wind power probability prediction at the historical moment t is tauiThe time is the k power of the corresponding quantile, k belongs to (1-m), m is a preset polynomial order, t belongs to (1-N), N belongs to (1-N), and N is the total time of the historical time period.
Further, the step 3 includes:
and arranging the quantiles corresponding to the preset probabilities of the wind power probability prediction accumulative distribution function at the historical moment j after correction in an ascending order, and updating the ith number in the ascending order to the quantile corresponding to the ith preset probability of the wind power probability prediction accumulative distribution function at the historical moment j.
Further, the first preset condition is determined according to the following formula:
Figure BDA0002236818320000151
in the formula, R2i) The preset probability of the cumulative distribution function for predicting the wind power probability from the N +1 th moment to the Nth moment in the updated historical period is tauiTime pairMean reliability deviation of corresponding quantiles, R1i) The preset probability of the cumulative distribution function for predicting the wind power probability from the (N + 1) th moment to the Nth moment in the historical period before correction is tauiAverage reliability deviation values of corresponding quantiles;
determining the preset probability of the cumulative distribution function of the wind power probability prediction from the N +1 th moment to the Nth moment in the updated historical period as tau according to the following formulaiMean reliability deviation value R of time-corresponding quantiles2i):
Figure BDA0002236818320000152
In the formula, xi'ji) The preset probability of the cumulative distribution function for predicting the probability of the wind power at the jth moment in the updated historical time period is tauiAn indicative function of the corresponding quantiles in time, tauiThe method comprises the steps that the ith preset probability of a cumulative distribution function predicted by the wind power probability is set, i belongs to (1-p), and p is the preset probability total number of the cumulative distribution function predicted by the wind power probability;
determining the preset probability of the cumulative distribution function of the wind power probability prediction from the (N + 1) th moment to the Nth moment in the historical period before correction as tau according to the following formulaiMean reliability deviation value R of time-corresponding quantiles1i):
Figure BDA0002236818320000153
In the formula, xiji) The preset probability of the cumulative distribution function for predicting the wind power probability at the jth moment in the historical time period before correction is tauiAn indicative function of the corresponding quantiles;
determining the preset probability of the cumulative distribution function of the wind power probability prediction at the jth moment in the updated historical time period as tau according to the following formulaiIndication function xi 'of corresponding quantiles'ji):
Figure BDA0002236818320000161
In the formula (I), the compound is shown in the specification,
Figure BDA0002236818320000162
the preset probability of the cumulative distribution function of the wind power probability prediction at the jth moment in the updated historical time period is tauiA quantile corresponding to the hour;
determining the preset probability of the cumulative distribution function of the wind power probability prediction at the jth moment in the historical period before correction as tau according to the following formulaiIndicative function xi of quantiles corresponding to timeji):
Figure BDA0002236818320000163
In the formula (I), the compound is shown in the specification,
Figure BDA0002236818320000164
the preset probability of the cumulative distribution function for predicting the wind power probability at the jth moment in the historical time period before correction is tauiA quantile corresponding to the hour;
determining the second preset condition according to the following formula:
Figure BDA0002236818320000165
in the formula, S1jQuantile loss value of cumulative distribution function for wind power probability prediction at jth moment in historical period before correction, S2jThe quantile loss value of the cumulative distribution function for wind power probability prediction at the jth moment in the updated historical time period is obtained;
determining a quantile loss value S of an accumulative distribution function of wind power probability prediction at the jth moment in a historical period before optimization according to the following formula1j
Figure BDA0002236818320000166
Determining a quantile loss value S of an accumulative distribution function of wind power probability prediction at the jth moment in a historical period before optimization according to the following formula2j
Figure BDA0002236818320000167
In the formula, j belongs to (N +1, N), N belongs to (1-N), and N is the total time in the history period.
Specifically, the step 102 includes:
the preset probability of the wind power probability prediction accumulative distribution function of the prediction time r is corrected to be tau according to the following formulaiQuantile of time correspondence
Figure BDA0002236818320000171
Figure BDA0002236818320000172
In the formula (I), the compound is shown in the specification,
Figure BDA0002236818320000173
the preset probability of the cumulative distribution function for predicting the wind power probability is tauiA matrix of regression coefficients corresponding to the time,
Figure BDA0002236818320000174
the preset probability of the cumulative distribution function for predicting the wind power probability at the predicted time r is tauiTime-corresponding quantile polynomial matrices, tauiThe method comprises the steps that the ith preset probability of a cumulative distribution function predicted by the wind power probability is set, i belongs to (1-p), and p is the preset probability total number of the cumulative distribution function predicted by the wind power probability;
wherein, the preset summary of the wind power probability prediction accumulative distribution function of the prediction time r is determined according to the following formulaRate of tauiTime-corresponding quantile polynomial matrix
Figure BDA0002236818320000175
Figure BDA0002236818320000176
In the formula (I), the compound is shown in the specification,
Figure BDA0002236818320000177
the preset probability of the cumulative distribution function for the wind power probability prediction at the historical moment t is tauiThe time is the k power of the corresponding quantile, k belongs to (1-m), m is a preset polynomial order, t belongs to (1-N), N belongs to (1-N), and N is the total time of the historical time period.
In the best embodiment of the invention, the wind power probability prediction is mainly used for measuring the probability condition of the wind power at the prediction moment, namely providing data basis for the prediction of the wind power at the prediction moment.
When the technical scheme provided by the invention is used, online optimization is recommended to be carried out when newly-added data are accumulated for more than 20 days, on one hand, the probability prediction index calculated by too little data is unstable, and on the other hand, the problem of local overfitting easily occurs to the model.
An example of an algorithm is given below:
1) a computing platform: intel (R) core (TM) i7-6500U CPU @2.50GHz and RAM 8.00GB
2) Training data: data dimension 2253 × 21
3) The preset probabilities of the 21 wind power probability prediction accumulative distribution functions are respectively as follows:
2.5%,5%,7.5%,10%,12.5%,15%,17.5%,20%,22.5%,25%,50%,75%,77.5%,80%,82.5%,85%,87.5%,90%,92.5%,95%,97.5%
4) the head quantiles and the tail quantiles sequentially form a prediction interval of the following confidence degrees:
95%,90%,85%,80%,75%,70%,65%,60%,55%,50%
for example: the quantiles 97.5% and 2.5% make up the prediction interval of 97.5% -2.5% ═ 95%.
5) Calculation of parameter estimation 17.34s
6) The number of samples in the detection set is 2252
7)RelImp=45.1087%,S1t=0.027,S1t0.026, therefore, the optimized results were used.
Fig. 2 shows a prediction interval graph, prediction intervals with different confidence degrees are represented by colors with different shades, a blue star-shaped dotted line is actual power, and a red solid line is a 50% quantile.
The invention provides a wind power probability prediction system, as shown in fig. 3, the system comprises:
the determining module is used for determining a regression coefficient matrix corresponding to the preset probability of the wind power probability predicted accumulative distribution function according to the quantile corresponding to the preset probability of the wind power probability predicted accumulative distribution function at each moment in the historical time period;
and the correction module is used for correcting the quantile corresponding to the preset probability of the wind power probability predicted accumulative distribution function at the predicted time by using the regression coefficient matrix corresponding to the preset probability of the wind power probability predicted accumulative distribution function.
Specifically, the determining module includes:
the first determining unit is used for determining a regression coefficient matrix corresponding to the preset probability of the wind power probability predicted accumulative distribution function by utilizing the quantile corresponding to the preset probability of the wind power probability predicted accumulative distribution function at the previous n moments in the historical time period;
the correction unit is used for correcting quantiles corresponding to the preset probability of the wind power probability prediction accumulative distribution function from the N +1 th moment to the Nth moment in the historical period by using a regression coefficient matrix corresponding to the preset probability of the wind power probability prediction accumulative distribution function;
the updating unit is used for updating quantiles corresponding to the preset probability of the wind power probability prediction accumulative distribution function from the (N + 1) th moment to the Nth moment in the historical time period;
the output unit is used for outputting a regression coefficient matrix corresponding to the preset probability of the wind power probability prediction cumulative distribution function if the quantile corresponding to the preset probability of the wind power probability prediction cumulative distribution function from the N +1 th moment to the Nth moment in the updated historical period meets a first preset condition and a second preset condition at the same time, otherwise, outputting the regression coefficient matrix corresponding to the preset probability of the wind power probability prediction cumulative distribution function [0 … 0 … 1, 0 ];
wherein N belongs to (1-N), N is the total time of the historical time period, and the matrix [0 … 0 … 1, 0] is a vector of 1 Xm order.
Specifically, the first determining unit is configured to:
the preset probability of solving the cumulative distribution function of the wind power probability prediction shown in the following formula by using a simple search method is tauiTime-corresponding regression coefficient matrix
Figure BDA0002236818320000191
Figure BDA0002236818320000192
In the formula (I), the compound is shown in the specification,
Figure BDA0002236818320000193
the preset probability of the cumulative distribution function for predicting the wind power probability is tauiThe coefficients of the k-th order terms of the corresponding quantiles,
Figure BDA0002236818320000194
the preset probability of the cumulative distribution function for predicting the wind power probability is tauiChecking function of time correspondence, ptIs the actual wind power at the historical time t, tauiThe method comprises the steps that the ith preset probability of a cumulative distribution function predicted by the wind power probability is set, i belongs to (1-p), and p is the preset probability total number of the cumulative distribution function predicted by the wind power probability;
determining the preset probability of the cumulative distribution function of the wind power probability prediction to be tau according to the following formulaiChecking function of time correspondence
Figure BDA0002236818320000195
Figure BDA0002236818320000196
In the formula (I), the compound is shown in the specification,
Figure BDA0002236818320000197
the preset probability of the cumulative distribution function for the wind power probability prediction at the historical moment t is tauiA time-corresponding quantile polynomial matrix;
determining the preset probability of the cumulative distribution function of the wind power probability prediction at the historical moment t as tau according to the following formulaiTime-corresponding quantile polynomial matrix
Figure BDA0002236818320000198
Figure BDA0002236818320000199
In the formula (I), the compound is shown in the specification,
Figure BDA00022368183200001910
the preset probability of the cumulative distribution function for the wind power probability prediction at the historical moment t is tauiThe time is the k power of the corresponding quantile, k belongs to (1-m), m is a preset polynomial order, t belongs to (1-N), N belongs to (1-N), and N is the total time of the historical time period.
Specifically, the correction unit is configured to:
correcting the preset probability of the wind power probability prediction accumulative distribution function of the historical moment j according to the following formula to be tauiQuantile of time correspondence
Figure BDA00022368183200001911
Figure BDA00022368183200001912
In the formula (I), the compound is shown in the specification,
Figure BDA0002236818320000201
the preset probability of the cumulative distribution function for predicting the wind power probability is tauiA matrix of regression coefficients corresponding to the time,
Figure BDA0002236818320000202
the preset probability of the cumulative distribution function for the wind power probability prediction of the historical moment j is tauiTime-corresponding quantile polynomial matrices, tauiThe method comprises the steps that the ith preset probability of a cumulative distribution function predicted by the wind power probability is set, i belongs to (1-p), and p is the preset probability total number of the cumulative distribution function predicted by the wind power probability;
determining the preset probability of the cumulative distribution function of the wind power probability prediction at the historical moment j as tau according to the following formulaiTime-corresponding quantile polynomial matrix
Figure BDA0002236818320000203
Figure BDA0002236818320000204
In the formula (I), the compound is shown in the specification,
Figure BDA0002236818320000205
the preset probability of the cumulative distribution function for the wind power probability prediction at the historical moment t is tauiThe time is the k power of the corresponding quantile, k belongs to (1-m), m is a preset polynomial order, t belongs to (1-N), N belongs to (1-N), and N is the total time of the historical time period.
Specifically, the update unit includes:
and arranging the quantiles corresponding to the preset probabilities of the wind power probability prediction accumulative distribution function at the historical moment j after correction in an ascending order, and updating the ith number in the ascending order to the quantile corresponding to the ith preset probability of the wind power probability prediction accumulative distribution function at the historical moment j.
Specifically, the first preset condition is determined according to the following formula:
Figure BDA0002236818320000206
in the formula, R2i) The preset probability of the cumulative distribution function for predicting the wind power probability from the N +1 th moment to the Nth moment in the updated historical period is tauiMean reliability deviation value of time-corresponding quantiles, R1i) The preset probability of the cumulative distribution function for predicting the wind power probability from the (N + 1) th moment to the Nth moment in the historical period before correction is tauiAverage reliability deviation values of corresponding quantiles;
determining the preset probability of the cumulative distribution function of the wind power probability prediction from the N +1 th moment to the Nth moment in the updated historical period as tau according to the following formulaiMean reliability deviation value R of time-corresponding quantiles2i):
Figure BDA0002236818320000211
In the formula, xi'ji) The preset probability of the cumulative distribution function for predicting the probability of the wind power at the jth moment in the updated historical time period is tauiAn indicative function of the corresponding quantiles in time, tauiThe method comprises the steps that the ith preset probability of a cumulative distribution function predicted by the wind power probability is set, i belongs to (1-p), and p is the preset probability total number of the cumulative distribution function predicted by the wind power probability;
determining the preset probability of the cumulative distribution function of the wind power probability prediction from the (N + 1) th moment to the Nth moment in the historical period before correction as tau according to the following formulaiTime of day correspondingMean reliability deviation value R of quantile1i):
Figure BDA0002236818320000212
In the formula, xiji) The preset probability of the cumulative distribution function for predicting the wind power probability at the jth moment in the historical time period before correction is tauiAn indicative function of the corresponding quantiles;
determining the preset probability of the cumulative distribution function of the wind power probability prediction at the jth moment in the updated historical time period as tau according to the following formulaiIndication function xi 'of corresponding quantiles'ji):
Figure BDA0002236818320000213
In the formula (I), the compound is shown in the specification,
Figure BDA0002236818320000214
the preset probability of the cumulative distribution function of the wind power probability prediction at the jth moment in the updated historical time period is tauiA quantile corresponding to the hour;
determining the preset probability of the cumulative distribution function of the wind power probability prediction at the jth moment in the historical period before correction as tau according to the following formulaiIndicative function xi of quantiles corresponding to timeji):
Figure BDA0002236818320000221
In the formula (I), the compound is shown in the specification,
Figure BDA0002236818320000222
the preset probability of the cumulative distribution function for predicting the wind power probability at the jth moment in the historical time period before correction is tauiA quantile corresponding to the hour;
determining the second preset condition according to the following formula:
Figure BDA0002236818320000223
in the formula, S1jQuantile loss value of cumulative distribution function for wind power probability prediction at jth moment in historical period before correction, S2jThe quantile loss value of the cumulative distribution function for wind power probability prediction at the jth moment in the updated historical time period is obtained;
determining a quantile loss value S of an accumulative distribution function of wind power probability prediction at the jth moment in a historical period before optimization according to the following formula1j
Figure BDA0002236818320000224
Determining a quantile loss value S of an accumulative distribution function of wind power probability prediction at the jth moment in a historical period before optimization according to the following formula2j
Figure BDA0002236818320000225
In the formula, j belongs to (N +1, N), N belongs to (1-N), and N is the total time in the history period.
Specifically, the correction module is configured to:
the preset probability of the wind power probability prediction accumulative distribution function of the prediction time r is corrected to be tau according to the following formulaiQuantile of time correspondence
Figure BDA0002236818320000226
Figure BDA0002236818320000227
In the formula (I), the compound is shown in the specification,
Figure BDA0002236818320000228
the preset probability of the cumulative distribution function for predicting the wind power probability is tauiA matrix of regression coefficients corresponding to the time,
Figure BDA0002236818320000229
the preset probability of the cumulative distribution function for predicting the wind power probability at the predicted time r is tauiTime-corresponding quantile polynomial matrices, tauiThe method comprises the steps that the ith preset probability of a cumulative distribution function predicted by the wind power probability is set, i belongs to (1-p), and p is the preset probability total number of the cumulative distribution function predicted by the wind power probability;
determining the preset probability of the wind power probability prediction accumulative distribution function at the prediction time r as tau according to the following formulaiTime-corresponding quantile polynomial matrix
Figure BDA0002236818320000231
Figure BDA0002236818320000232
In the formula (I), the compound is shown in the specification,
Figure BDA0002236818320000233
the preset probability of the cumulative distribution function for the wind power probability prediction at the historical moment t is tauiThe time is the k power of the corresponding quantile, k belongs to (1-m), m is a preset polynomial order, t belongs to (1-N), N belongs to (1-N), and N is the total time of the historical time period.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.

Claims (14)

1. A wind power probability prediction method is characterized by comprising the following steps:
determining a regression coefficient matrix corresponding to the preset probability of the wind power probability predicted accumulative distribution function according to the quantile corresponding to the preset probability of the wind power probability predicted accumulative distribution function at each moment in the historical time period;
and correcting quantiles corresponding to the preset probability of the wind power probability predicted accumulative distribution function at the predicted time by using a regression coefficient matrix corresponding to the preset probability of the wind power probability predicted accumulative distribution function.
2. The method of claim 1, wherein the determining a regression coefficient matrix corresponding to the preset probability of the cumulative distribution function of the wind power probability prediction according to the quantile corresponding to the preset probability of the cumulative distribution function of the wind power probability prediction at each time in the historical period comprises:
step 1: determining a regression coefficient matrix corresponding to the preset probability of the wind power probability predicted accumulative distribution function by utilizing the quantile corresponding to the preset probability of the wind power probability predicted accumulative distribution function at the previous n moments in the historical time period;
step 2: correcting quantiles corresponding to the preset probability of the wind power probability predicted accumulative distribution function from the N +1 th moment to the Nth moment in the historical period by using a regression coefficient matrix corresponding to the preset probability of the wind power probability predicted accumulative distribution function;
and step 3: updating quantiles corresponding to the preset probability of the wind power probability prediction accumulative distribution function from the (N + 1) th moment to the Nth moment in the historical time period;
and 4, step 4: if the quantiles corresponding to the preset probabilities of the wind power probability prediction cumulative distribution functions from the N +1 th moment to the Nth moment in the updated historical time period simultaneously meet a first preset condition and a second preset condition, outputting regression coefficient matrixes corresponding to the preset probabilities of the wind power probability prediction cumulative distribution functions, otherwise, outputting the regression coefficient matrixes corresponding to the preset probabilities of the wind power probability prediction cumulative distribution functions to be [0 … 0 … 1, 0 ];
wherein N belongs to (1-N), N is the total time of the historical time period, and the matrix [0 … 0 … 1, 0] is a vector of 1 Xm order.
3. The method of claim 2, wherein step 1 comprises:
the preset probability of solving the cumulative distribution function of the wind power probability prediction shown in the following formula by using a simple search method is tauiTime-corresponding regression coefficient matrix
Figure FDA0002236818310000013
Figure FDA0002236818310000011
In the formula (I), the compound is shown in the specification,
Figure FDA0002236818310000014
the preset probability of the cumulative distribution function for predicting the wind power probability is tauiThe coefficients of the k-th order terms of the corresponding quantiles,
Figure FDA0002236818310000012
the preset probability of the cumulative distribution function for predicting the wind power probability is tauiChecking function of time correspondence, ptIs the actual wind power at the historical time t, tauiThe method comprises the steps that the ith preset probability of a cumulative distribution function predicted by the wind power probability is set, i belongs to (1-p), and p is the preset probability total number of the cumulative distribution function predicted by the wind power probability;
determining the preset probability of the cumulative distribution function of the wind power probability prediction to be tau according to the following formulaiChecking function of time correspondence
Figure FDA0002236818310000021
Figure FDA0002236818310000022
In the formula (I), the compound is shown in the specification,
Figure FDA0002236818310000023
the preset probability of the cumulative distribution function for the wind power probability prediction at the historical moment t is tauiA time-corresponding quantile polynomial matrix;
determining the preset probability of the cumulative distribution function of the wind power probability prediction at the historical moment t as tau according to the following formulaiTime-corresponding quantile polynomial matrix
Figure FDA0002236818310000024
Figure FDA0002236818310000025
In the formula (I), the compound is shown in the specification,
Figure FDA0002236818310000026
the preset probability of the cumulative distribution function for the wind power probability prediction at the historical moment t is tauiThe time is the k power of the corresponding quantile, k belongs to (1-m), m is a preset polynomial order, t belongs to (1-N), N belongs to (1-N), and N is the total time of the historical time period.
4. The method of claim 2, wherein step 2, comprises:
correcting the preset probability of the wind power probability prediction accumulative distribution function of the historical moment j according to the following formula to be tauiQuantile of time correspondence
Figure FDA0002236818310000027
Figure FDA0002236818310000028
In the formula (I), the compound is shown in the specification,
Figure FDA0002236818310000029
the preset probability of the cumulative distribution function for predicting the wind power probability is tauiA matrix of regression coefficients corresponding to the time,
Figure FDA00022368183100000210
the preset probability of the cumulative distribution function for the wind power probability prediction of the historical moment j is tauiTime-corresponding quantile polynomial matrices, tauiThe method comprises the steps that the ith preset probability of a cumulative distribution function predicted by the wind power probability is set, i belongs to (1-p), and p is the preset probability total number of the cumulative distribution function predicted by the wind power probability;
determining the preset probability of the cumulative distribution function of the wind power probability prediction at the historical moment j as tau according to the following formulaiTime-corresponding quantile polynomial matrix
Figure FDA00022368183100000211
Figure FDA0002236818310000031
In the formula (I), the compound is shown in the specification,
Figure FDA0002236818310000032
the preset probability of the cumulative distribution function for the wind power probability prediction at the historical moment t is tauiThe time is the k power of the corresponding quantile, k belongs to (1-m), m is a preset polynomial order, t belongs to (1-N), N belongs to (1-N), and N is the total time of the historical time period.
5. The method of claim 2, wherein step 3, comprises:
and arranging the quantiles corresponding to the preset probabilities of the wind power probability prediction accumulative distribution function at the historical moment j after correction in an ascending order, and updating the ith number in the ascending order to the quantile corresponding to the ith preset probability of the wind power probability prediction accumulative distribution function at the historical moment j.
6. The method of claim 2, wherein the first predetermined condition is determined as follows:
Figure FDA0002236818310000033
in the formula, R2i) The preset probability of the cumulative distribution function for predicting the wind power probability from the N +1 th moment to the Nth moment in the updated historical period is tauiMean reliability deviation value of time-corresponding quantiles, R1i) The preset probability of the cumulative distribution function for predicting the wind power probability from the (N + 1) th moment to the Nth moment in the historical period before correction is tauiAverage reliability deviation values of corresponding quantiles;
determining the preset probability of the cumulative distribution function of the wind power probability prediction from the N +1 th moment to the Nth moment in the updated historical period as tau according to the following formulaiMean reliability deviation value R of time-corresponding quantiles2i):
Figure FDA0002236818310000034
In the formula, xij'(τi) The preset probability of the cumulative distribution function for predicting the probability of the wind power at the jth moment in the updated historical time period is tauiAn indicative function of the corresponding quantiles in time, tauiThe method comprises the steps that the ith preset probability of a cumulative distribution function predicted by the wind power probability is set, i belongs to (1-p), and p is the preset probability total number of the cumulative distribution function predicted by the wind power probability;
determining the preset probability of the cumulative distribution function of the wind power probability prediction from the (N + 1) th moment to the Nth moment in the historical period before correction as tau according to the following formulaiMean reliability deviation value R of time-corresponding quantiles1i):
Figure FDA0002236818310000041
In the formula, xiji) The preset probability of the cumulative distribution function for predicting the wind power probability at the jth moment in the historical time period before correction is tauiAn indicative function of the corresponding quantiles;
determining the preset probability of the cumulative distribution function of the wind power probability prediction at the jth moment in the updated historical time period as tau according to the following formulaiIndication function xi 'of corresponding quantiles'ji):
Figure FDA0002236818310000042
In the formula (I), the compound is shown in the specification,
Figure FDA0002236818310000043
the preset probability of the cumulative distribution function of the wind power probability prediction at the jth moment in the updated historical time period is tauiA quantile corresponding to the hour;
determining the preset probability of the cumulative distribution function of the wind power probability prediction at the jth moment in the historical period before correction as tau according to the following formulaiIndicative function xi of quantiles corresponding to timeji):
Figure FDA0002236818310000044
In the formula (I), the compound is shown in the specification,
Figure FDA0002236818310000045
the preset probability of the cumulative distribution function for predicting the wind power probability at the jth moment in the historical time period before correction is tauiA quantile corresponding to the hour;
determining the second preset condition according to the following formula:
Figure FDA0002236818310000046
in the formula, S1jQuantile loss value of cumulative distribution function for wind power probability prediction at jth moment in historical period before correction, S2jThe quantile loss value of the cumulative distribution function for wind power probability prediction at the jth moment in the updated historical time period is obtained;
determining a quantile loss value S of an accumulative distribution function of wind power probability prediction at the jth moment in a historical period before optimization according to the following formula1j
Figure FDA0002236818310000051
Determining a quantile loss value S of an accumulative distribution function of wind power probability prediction at the jth moment in a historical period before optimization according to the following formula2j
Figure FDA0002236818310000052
In the formula, j belongs to (N +1, N), N belongs to (1-N), and N is the total time in the history period.
7. The method according to claim 1, wherein the correcting the quantile corresponding to the preset probability of the wind power probability predicted cumulative distribution function at the predicted time by using the regression coefficient matrix corresponding to the preset probability of the wind power probability predicted cumulative distribution function comprises:
wind power probability of predicted time r is corrected according to the following formulaThe predetermined probability of the cumulative distribution function of the rate prediction is tauiQuantile of time correspondence
Figure FDA0002236818310000053
Figure FDA0002236818310000054
In the formula (I), the compound is shown in the specification,
Figure FDA0002236818310000055
the preset probability of the cumulative distribution function for predicting the wind power probability is tauiA matrix of regression coefficients corresponding to the time,
Figure FDA0002236818310000056
the preset probability of the cumulative distribution function for predicting the wind power probability at the predicted time r is tauiTime-corresponding quantile polynomial matrices, tauiThe method comprises the steps that the ith preset probability of a cumulative distribution function predicted by the wind power probability is set, i belongs to (1-p), and p is the preset probability total number of the cumulative distribution function predicted by the wind power probability;
determining the preset probability of the wind power probability prediction accumulative distribution function at the prediction time r as tau according to the following formulaiTime-corresponding quantile polynomial matrix
Figure FDA0002236818310000057
Figure FDA0002236818310000061
In the formula (I), the compound is shown in the specification,
Figure FDA0002236818310000062
the preset probability of the cumulative distribution function for predicting the wind power probability at the predicted time r is tauiThe time is the k power of the corresponding quantile,k belongs to (1-m), and m is a preset polynomial order.
8. A wind power probability prediction system, the system comprising:
the determining module is used for determining a regression coefficient matrix corresponding to the preset probability of the wind power probability predicted accumulative distribution function according to the quantile corresponding to the preset probability of the wind power probability predicted accumulative distribution function at each moment in the historical time period;
and the correction module is used for correcting the quantile corresponding to the preset probability of the wind power probability predicted accumulative distribution function at the predicted time by using the regression coefficient matrix corresponding to the preset probability of the wind power probability predicted accumulative distribution function.
9. The system of claim 8, wherein the determination module comprises:
the first determining unit is used for determining a regression coefficient matrix corresponding to the preset probability of the wind power probability predicted accumulative distribution function by utilizing the quantile corresponding to the preset probability of the wind power probability predicted accumulative distribution function at the previous n moments in the historical time period;
the correction unit is used for correcting quantiles corresponding to the preset probability of the wind power probability prediction accumulative distribution function from the N +1 th moment to the Nth moment in the historical period by using a regression coefficient matrix corresponding to the preset probability of the wind power probability prediction accumulative distribution function;
the updating unit is used for updating quantiles corresponding to the preset probability of the wind power probability prediction accumulative distribution function from the (N + 1) th moment to the Nth moment in the historical time period;
the output unit is used for outputting a regression coefficient matrix corresponding to the preset probability of the wind power probability prediction cumulative distribution function if the quantile corresponding to the preset probability of the wind power probability prediction cumulative distribution function from the N +1 th moment to the Nth moment in the updated historical period meets a first preset condition and a second preset condition at the same time, otherwise, outputting the regression coefficient matrix corresponding to the preset probability of the wind power probability prediction cumulative distribution function [0 … 0 … 1, 0 ];
wherein N belongs to (1-N), N is the total time of the historical time period, and the matrix [0 … 0 … 1, 0] is a vector of 1 Xm order.
10. The system of claim 9, wherein the first determination unit is to:
the preset probability of solving the cumulative distribution function of the wind power probability prediction shown in the following formula by using a simple search method is tauiTime-corresponding regression coefficient matrix
Figure FDA0002236818310000063
Figure FDA0002236818310000071
In the formula (I), the compound is shown in the specification,
Figure FDA0002236818310000072
the preset probability of the cumulative distribution function for predicting the wind power probability is tauiThe coefficients of the k-th order terms of the corresponding quantiles,
Figure FDA0002236818310000073
the preset probability of the cumulative distribution function for predicting the wind power probability is tauiChecking function of time correspondence, ptIs the actual wind power at the historical time t, tauiThe method comprises the steps that the ith preset probability of a cumulative distribution function predicted by the wind power probability is set, i belongs to (1-p), and p is the preset probability total number of the cumulative distribution function predicted by the wind power probability;
determining the preset probability of the cumulative distribution function of the wind power probability prediction to be tau according to the following formulaiChecking function of time correspondence
Figure FDA0002236818310000074
Figure FDA0002236818310000075
In the formula (I), the compound is shown in the specification,
Figure FDA0002236818310000076
the preset probability of the cumulative distribution function for the wind power probability prediction at the historical moment t is tauiA time-corresponding quantile polynomial matrix;
determining the preset probability of the cumulative distribution function of the wind power probability prediction at the historical moment t as tau according to the following formulaiTime-corresponding quantile polynomial matrix
Figure FDA0002236818310000077
Figure FDA0002236818310000078
In the formula (I), the compound is shown in the specification,
Figure FDA0002236818310000079
the preset probability of the cumulative distribution function for the wind power probability prediction at the historical moment t is tauiThe time is the k power of the corresponding quantile, k belongs to (1-m), m is a preset polynomial order, t belongs to (1-N), N belongs to (1-N), and N is the total time of the historical time period.
11. The system of claim 9, wherein the modification unit is to:
correcting the preset probability of the wind power probability prediction accumulative distribution function of the historical moment j according to the following formula to be tauiQuantile of time correspondence
Figure FDA00022368183100000710
Figure FDA00022368183100000711
In the formula (I), the compound is shown in the specification,
Figure FDA00022368183100000712
the preset probability of the cumulative distribution function for predicting the wind power probability is tauiA matrix of regression coefficients corresponding to the time,
Figure FDA00022368183100000713
the preset probability of the cumulative distribution function for the wind power probability prediction of the historical moment j is tauiTime-corresponding quantile polynomial matrices, tauiThe method comprises the steps that the ith preset probability of a cumulative distribution function predicted by the wind power probability is set, i belongs to (1-p), and p is the preset probability total number of the cumulative distribution function predicted by the wind power probability;
determining the preset probability of the cumulative distribution function of the wind power probability prediction at the historical moment j as tau according to the following formulaiTime-corresponding quantile polynomial matrix
Figure FDA0002236818310000081
Figure FDA0002236818310000082
In the formula (I), the compound is shown in the specification,
Figure FDA0002236818310000083
the preset probability of the cumulative distribution function for the wind power probability prediction at the historical moment t is tauiThe time is the k power of the corresponding quantile, k belongs to (1-m), m is a preset polynomial order, t belongs to (1-N), N belongs to (1-N), and N is the total time of the historical time period.
12. The system of claim 9, wherein the update unit comprises:
and arranging the quantiles corresponding to the preset probabilities of the wind power probability prediction accumulative distribution function at the historical moment j after correction in an ascending order, and updating the ith number in the ascending order to the quantile corresponding to the ith preset probability of the wind power probability prediction accumulative distribution function at the historical moment j.
13. The system of claim 9, wherein the first predetermined condition is determined as follows:
Figure FDA0002236818310000084
in the formula, R2i) The preset probability of the cumulative distribution function for predicting the wind power probability from the N +1 th moment to the Nth moment in the updated historical period is tauiMean reliability deviation value of time-corresponding quantiles, R1i) The preset probability of the cumulative distribution function for predicting the wind power probability from the (N + 1) th moment to the Nth moment in the historical period before correction is tauiAverage reliability deviation values of corresponding quantiles;
determining the preset probability of the cumulative distribution function of the wind power probability prediction from the N +1 th moment to the Nth moment in the updated historical period as tau according to the following formulaiMean reliability deviation value R of time-corresponding quantiles2i):
Figure FDA0002236818310000091
In the formula, xi'ji) The preset probability of the cumulative distribution function for predicting the probability of the wind power at the jth moment in the updated historical time period is tauiAn indicative function of the corresponding quantiles in time, tauiThe method comprises the steps that the ith preset probability of a cumulative distribution function predicted by the wind power probability is set, i belongs to (1-p), and p is the preset probability total number of the cumulative distribution function predicted by the wind power probability;
determining the preset probability of the cumulative distribution function of the wind power probability prediction from the (N + 1) th moment to the Nth moment in the historical period before correction as tau according to the following formulaiMean reliability deviation value R of time-corresponding quantiles1i):
Figure FDA0002236818310000092
In the formula, xiji) The preset probability of the cumulative distribution function for predicting the wind power probability at the jth moment in the historical time period before correction is tauiAn indicative function of the corresponding quantiles;
determining the preset probability of the cumulative distribution function of the wind power probability prediction at the jth moment in the updated historical time period as tau according to the following formulaiIndication function xi 'of corresponding quantiles'ji):
Figure FDA0002236818310000093
In the formula (I), the compound is shown in the specification,
Figure FDA0002236818310000094
the preset probability of the cumulative distribution function of the wind power probability prediction at the jth moment in the updated historical time period is tauiA quantile corresponding to the hour;
determining the preset probability of the cumulative distribution function of the wind power probability prediction at the jth moment in the historical period before correction as tau according to the following formulaiIndicative function xi of quantiles corresponding to timeji):
Figure FDA0002236818310000095
In the formula (I), the compound is shown in the specification,
Figure FDA0002236818310000101
the preset probability of the cumulative distribution function for predicting the wind power probability at the jth moment in the historical time period before correction is tauiA quantile corresponding to the hour;
determining the second preset condition according to the following formula:
Figure FDA0002236818310000102
in the formula, S1jQuantile loss value of cumulative distribution function for wind power probability prediction at jth moment in historical period before correction, S2jThe quantile loss value of the cumulative distribution function for wind power probability prediction at the jth moment in the updated historical time period is obtained;
determining a quantile loss value S of an accumulative distribution function of wind power probability prediction at the jth moment in a historical period before optimization according to the following formula1j
Figure FDA0002236818310000103
Determining a quantile loss value S of an accumulative distribution function of wind power probability prediction at the jth moment in a historical period before optimization according to the following formula2j
Figure FDA0002236818310000104
In the formula, j belongs to (N +1, N), N belongs to (1-N), and N is the total time in the history period.
14. The system of claim 8, wherein the correction module is to:
the preset probability of the wind power probability prediction accumulative distribution function of the prediction time r is corrected to be tau according to the following formulaiQuantile of time correspondence
Figure FDA0002236818310000105
Figure FDA0002236818310000106
In the formula (I), the compound is shown in the specification,
Figure FDA0002236818310000107
the preset probability of the cumulative distribution function for predicting the wind power probability is tauiA matrix of regression coefficients corresponding to the time,
Figure FDA0002236818310000108
the preset probability of the cumulative distribution function for predicting the wind power probability at the predicted time r is tauiTime-corresponding quantile polynomial matrices, tauiThe method comprises the steps that the ith preset probability of a cumulative distribution function predicted by the wind power probability is set, i belongs to (1-p), and p is the preset probability total number of the cumulative distribution function predicted by the wind power probability;
determining the preset probability of the wind power probability prediction accumulative distribution function at the prediction time r as tau according to the following formulaiTime-corresponding quantile polynomial matrix
Figure FDA0002236818310000111
Figure FDA0002236818310000112
In the formula (I), the compound is shown in the specification,
Figure FDA0002236818310000113
the preset probability of the cumulative distribution function for the wind power probability prediction at the historical moment t is tauiThe time is the k power of the corresponding quantile, k belongs to (1-m), m is a preset polynomial order, t belongs to (1-N), N belongs to (1-N), and N is the total time of the historical time period.
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CN117150446A (en) * 2023-10-30 2023-12-01 武汉华信数据系统有限公司 Blower operation state identification method and device, electronic equipment and storage medium

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117150446A (en) * 2023-10-30 2023-12-01 武汉华信数据系统有限公司 Blower operation state identification method and device, electronic equipment and storage medium
CN117150446B (en) * 2023-10-30 2024-02-09 武汉华信数据系统有限公司 Blower operation state identification method and device, electronic equipment and storage medium

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