CN112270439A - Ultra-short-term wind power prediction method and device, electronic equipment and storage medium - Google Patents

Ultra-short-term wind power prediction method and device, electronic equipment and storage medium Download PDF

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CN112270439A
CN112270439A CN202011172181.8A CN202011172181A CN112270439A CN 112270439 A CN112270439 A CN 112270439A CN 202011172181 A CN202011172181 A CN 202011172181A CN 112270439 A CN112270439 A CN 112270439A
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向婕
雍正
杨弃
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Sprixin Technology Co ltd
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Abstract

The embodiment of the invention provides a method and a device for predicting ultra-short-term wind power, electronic equipment and a storage medium, wherein the method comprises the following steps: determining an ultra-short-term prediction sequence P of t moments after the current moment k according to the real-time power, the real-time wind speed and the short-term prediction power of the current moment k of the wind power planto(ii) a Determining a climbing matrix corresponding to short-term preset power at t moments after the current moment k, inputting climbing characteristics corresponding to the climbing matrix and the climbing matrix into a climbing prediction probability model to obtain a climbing state category, and determining a future climbing state matrix corresponding to the climbing state category; ultra-short-term prediction sequence P for t moments after current moment k is corrected based on future climbing state matrixoAnd determining the ultra-short-term wind power prediction result. The embodiment of the invention passes the climbing at the current momentThe climbing type is determined according to the state type, and the ultra-short-term power of the wind power plant is corrected according to the climbing type, so that the time delay phenomenon of ultra-short-term prediction can be reduced.

Description

Ultra-short-term wind power prediction method and device, electronic equipment and storage medium
Technical Field
The invention relates to the technical field of electric power, in particular to an ultra-short-term wind power prediction method and device, electronic equipment and a storage medium.
Background
Wind energy is a clean and pollution-free renewable energy source, but wind has randomness and uncontrollable property, and unstable wind energy can generate huge impact on a power grid after being connected to the power grid, so that safe and stable operation of a power grid system is influenced. Wind power prediction is an important means for helping to realize stable operation of wind power integration. The wind power prediction method can be divided into two types according to the difference of input data: a power prediction method based on numerical weather forecast and a power prediction method based on historical data. The current ultra-short-term wind power prediction generally predicts the future short-time power by taking the current instantaneous power as a basis, and the method has an obvious time delay phenomenon during prediction.
Disclosure of Invention
Aiming at the problems in the prior art, the embodiment of the invention provides an ultra-short-term wind power prediction method and device.
In a first aspect, an embodiment of the present invention provides an ultra-short-term wind power prediction method, including:
determining an ultra-short-term prediction sequence P of t moments after the current moment k according to the real-time power, the real-time wind speed and the short-term prediction power of the current moment k of the wind power planto；
Determining a climbing matrix corresponding to the short-term preset power at t moments after the current moment k, inputting the climbing matrix and climbing characteristics corresponding to the climbing matrix into a climbing prediction probability model to obtain a climbing state category, and determining a future climbing state matrix corresponding to the climbing state category;
correcting the ultra-short-term prediction sequence P of t moments after the current moment k based on the future climbing state matrixoDetermining the ultra-short-period wind power prediction result;
the climbing prediction probability model is obtained by training based on a machine learning algorithm by adopting climbing matrix sample data of historical predicted power of a wind power plant and climbing characteristics corresponding to a climbing matrix of the historical predicted power as input data and adopting a climbing state category corresponding to the climbing matrix of the historical predicted power as output data.
Further, still include:
acquiring historical data of a wind power plant; the historical data of the wind power plant comprise historical actual power of the wind power plant and historical actual wind speed of the wind power plant;
based on the historical data of the wind power plant and the length n of a preset time window, calculating and determining a corresponding actual climbing matrix window by window according to a time sequence;
dividing the actual climbing matrix into j types according to the corresponding characteristics of the actual climbing matrix to obtain the central point of the j types of actual climbing matrices;
acquiring historical short-term predicted power, adopting the length of the preset time window, and calculating window by window according to the time sequence to determine a corresponding predicted climbing matrix and climbing characteristics of the predicted climbing matrix;
and taking the central point of the j-type actual climbing matrix as an output value, taking the predicted climbing matrix and the climbing characteristics of the predicted climbing matrix as input values, and establishing the climbing prediction probability model.
Further, determining a climbing matrix specifically includes:
according to the first relation model, making time difference on the power of the wind power station; wherein the first relational model is as follows:
Pdelta＝P-Psheft(1)
wherein, PdeltaIs the time series difference of power, P is the grid-connected power, Psheft(1)Is a grid-connected power offset value;
calculating the climbing time according to the second relation model; wherein the second relational model is as follows:
tclam＝tmax+tmin
wherein, tclamFor the length of time of climbing a slope, tmaxFor the corresponding time point of the maximum value of the actual power, tminThe time point corresponding to the minimum value of the actual power is taken as the time point;
determining a climbing matrix according to the third relation model; wherein the third relation model is as follows:
C(k)＝F(k)·E(k)·Pdelta·F(k)T
wherein C (k) is a climbing matrix, k represents the current time, F (k) is n x t of the kth pointclamFilter matrix, E (k) is identity matrix, PdeltaTime series difference of power, F (k)TN x t representing the k-th pointclamAnd (3) transposing the filter matrix, wherein T represents transposing, and n is the length of a preset time window.
Further, the dividing the actual climbing matrix into j types according to the corresponding features of the actual climbing matrix specifically includes:
adopting a k-means clustering method to divide the actual climbing matrix into j types according to the corresponding characteristics of the actual climbing matrix; wherein, the actual climbing matrix correspondingly includes the characteristics: time of climbing tclamAnd the climbing peak PmaxAnd a climbing starting point Pmin。
Further, the ultra-short-term prediction sequence P of t moments after the current moment k is determined according to the real-time power, the real-time wind speed and the short-term prediction power of the current moment k of the wind power plantoThe method specifically comprises the following steps:
inputting historical actual power of the wind power plant, historical actual wind speed of the wind power plant and historical short-term predicted power of the wind power plant by adopting a regression model, outputting real-time power at the moment to be predicted, and establishing an ultra-short-term power regression model;
substituting the real-time power, the real-time wind speed and the short-term prediction power of the current moment k of the wind power plant into the ultra-short term power regression model, and determining the ultra-short term prediction sequence P of t moments after the current moment ko。
Further, the ultra-short-term prediction sequence P of t moments after the current moment k is corrected based on the future climbing state matrixoDetermining the ultra-short-term wind power prediction result, which specifically comprises the following steps:
calculating a prediction confidence coefficient according to a fourth relation model based on the future climbing state matrix; wherein the fourth relational model is:
λ＝E|C(k-1)R·C(k-1)′P|-E|C(k-1)R|·E|C(k-1)′P|
wherein λ is the confidence of prediction, E is the function of expectation, C (k-1)RIs an actual climbing matrix at the moment of k-1, C (k-1)'PA future climbing state matrix corresponding to the climbing state of the target class corresponding to the actual climbing matrix at the moment k-1 is obtained, wherein k represents the current moment;
according to a fifth relation model, performing ultra-short-term prediction sequence P on t moments after the current moment k based on the prediction confidenceoCorrecting; wherein the fifth relational model is:
Pf＝Po+λ·F(k)-1·C(k)′P·E(k)-1F(k)-T
wherein, PfRepresents the ultra-short-term wind power prediction result P of t moments after the current moment koDenotes an ultra-short term prediction sequence at t times k after the current time, λ is a prediction confidence, F (k) denotes a filter matrix of k points, C (k)'PExpressed as a future climbing state matrix corresponding to the climbing state class at time k, E (k) is an identity matrix, E (k)-1Denotes the inversion of E (k), F (k)-TDenotes the transposed inversion of F (k).
Further, after acquiring the historical data of the wind farm, the method further includes:
carrying out data cleaning and feature construction on the historical data of the wind power plant;
wherein the data cleaning comprises removing a dead value and/or removing an abnormal value and/or judging a power limit and/or restoring the power limit;
wherein, the characteristic construction comprises data expansion, and data which contains effective characteristics and has data volume less than a preset value are copied according to a time sequence and filled into an original data sequence.
In a second aspect, an embodiment of the present invention provides an ultra-short-term wind power prediction apparatus, including:
a first determining module, configured to determine an ultra-short-term prediction sequence P at t moments after a current moment k according to a real-time power, a real-time wind speed, and a short-term prediction power of the wind farm at the current moment ko；
A second determining module, configured to determine a climbing matrix corresponding to short-term preset power at t times after the current time k, input the climbing matrix and climbing characteristics corresponding to the climbing matrix into a climbing prediction probability model, obtain a climbing state category, and determine a future climbing state matrix corresponding to the climbing state category;
a correction module for correcting the ultra-short-term predicted sequence P of t moments after the current moment k based on the future climbing state matrixoDetermining the ultra-short-period wind power prediction result;
the climbing prediction probability model is obtained by training based on a machine learning algorithm by adopting climbing matrix sample data of historical predicted power of a wind power plant and climbing characteristics corresponding to a climbing matrix of the historical predicted power as input data and adopting a climbing state category corresponding to the climbing matrix of the historical predicted power as output data.
In a third aspect, an embodiment of the present invention further provides an electronic device, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor implements the steps of the ultra-short-term wind power prediction method according to the first aspect when executing the program.
In a fourth aspect, an embodiment of the present invention further provides a non-transitory computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps of the ultra-short-term wind power prediction method according to the first aspect.
As can be seen from the foregoing technical solutions, the ultra-short-term wind power prediction method, the apparatus, the electronic device, and the storage medium provided in the embodiments of the present invention determine, for the time delay phenomenon of the current ultra-short-term wind power prediction result, the ultra-short-term prediction sequence P at t times after the current time k according to the real-time power, the real-time wind speed, and the short-term prediction power at the current time k of the wind farmoThen, corresponding to the short-term preset power at t moments after the current moment k, a climbing matrix is obtained, and the climbing matrix is obtainedInputting the climbing characteristics corresponding to the climbing matrix into a climbing prediction probability model to obtain a climbing state category, and determining a future climbing state matrix corresponding to the climbing state category; the climbing prediction probability model is obtained by training based on a machine learning algorithm by adopting climbing matrix sample data of historical predicted power of a wind power plant and climbing characteristics corresponding to a climbing matrix of the historical predicted power as input data and adopting a climbing state category corresponding to the climbing matrix of the historical predicted power as output data; correcting the ultra-short-term prediction sequence P of t moments after the current moment k through a future climbing state matrixoDetermining the prediction result of the ultra-short-period wind power to obtain the final ultra-short-period prediction value at 0-t moment, and determining the grade of the climbing state at the current momentoAnd correcting to determine the ultra-short-term wind power prediction result, so that the time delay phenomenon of ultra-short-term prediction can be effectively reduced, the ultra-short-term prediction power prediction precision of the wind power plant is improved, and the prediction accuracy is improved.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a schematic flow chart of a method for predicting ultra-short-term wind power according to an embodiment of the present invention;
fig. 2 is a schematic diagram illustrating comparison of application effects of the ultra-short-term wind power prediction method according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an ultra-short-term wind power prediction apparatus according to an embodiment of the present invention;
fig. 4 is a schematic physical structure diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
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 described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some embodiments, 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 ultra-short-term wind power prediction method provided by the invention is explained and illustrated in detail through specific embodiments.
Fig. 1 is a schematic flow chart of a method for predicting ultra-short-term wind power according to an embodiment of the present invention; as shown in fig. 1, the method includes:
step 101: determining an ultra-short-term prediction sequence P of t moments after the current moment k according to the real-time power, the real-time wind speed and the short-term prediction power of the current moment k of the wind power planto。
In this step, it should be noted that the real-time power, the real-time wind speed and the short-term predicted power of the wind farm at the present time are obtained; the short-term predicted power is directly downloaded data, can be obtained through purchased meteorological data, can also be obtained through observation of cloud layer movement through a satellite, the future movement locus is calculated through a model, the future predicted wind speed is obtained, and then the predicted wind speed is obtained through a mathematical model.
In the step, a prediction algorithm is substituted by real-time power, real-time wind speed and short-term prediction power of the current moment k of the wind power plant to obtain an ultra-short-term prediction sequence of t moments after the current moment (namely, the ultra-short-term prediction sequence P of t moments after the current moment ko) (ii) a The prediction algorithm can be realized through any regression model, historical actual power, wind speed and predicted power of the wind power plant are input, actual power at a time point needing to be predicted is output, and an ultra-short-term power regression model is obtained; substituting the real-time power, the wind speed and the short-term prediction power into a regression model to obtain an original ultra-short-term prediction sequence P of n points in the futureo。
Step 102: determining a climbing matrix corresponding to the short-term preset power at t moments after the current moment k, inputting the climbing matrix and climbing characteristics corresponding to the climbing matrix into a climbing prediction probability model to obtain a climbing state category, and determining a future climbing state matrix corresponding to the climbing state category.
In this step, it should be noted that the climbing prediction probability model is obtained by training based on a machine learning algorithm, using, as input data, climbing matrix sample data of the historical predicted power of the wind farm and climbing characteristics corresponding to the climbing matrix of the historical predicted power, and using, as output data, a climbing state category corresponding to the climbing matrix of the historical predicted power.
In this step, for example, a climbing matrix C (k-1) corresponding to the short-term preset power at the current time k and the time k-1 is calculated respectivelyPAnd C (k)PActual power ramp matrix C (k-1)RAnd C (k)RGeneral formula C (k-1)PAnd C (k)PThe grade of the climbing state is obtained by bringing the climbing prediction probability model, and the corresponding future climbing matrix C (k-1) 'is obtained according to the grade of the climbing state'PAnd C (k)'P。
Step 103: correcting the ultra-short-term prediction sequence P of t moments after the current moment k based on the future climbing state matrixoAnd determining the ultra-short-term wind power prediction result.
In this step, it should be noted that the super-short-term prediction sequence P at t times after the current time k is corrected by the future climbing state matrixoAnd obtaining the final ultra-short-term predicted value at the time from 0 to t, and determining the ultra-short-term wind power prediction result.
For better understanding of this step, for example, the following steps may be taken:
calculating the confidence coefficient of the climbing matrix obtained by predicting at the last moment, wherein the calculation method comprises the following steps:
λ＝E|C(k-1)R·C(k-1)′P|-E|C(k-1)R|·E|C(k-1)′P|
ultra-short term prediction sequence P for original future n pointsoAnd correcting by using the predicted climbing matrix, wherein the calculation method comprises the following steps:
Pf＝Po+λ·F(k)-1·C(k)′P·E(k)-1F(k)-T
p obtained finallyfNamely the ultra-short-term wind power prediction result of n points in the future.
FIG. 2 is a power comparison graph of a certain wind power plant in the northwest region, and the ultra-short term prediction in the graph is a predicted value of 2 hours, so that it can be seen that the original ultra-short term prediction has obvious time delay, and the time delay phenomenon is obviously reduced after the hill climbing prediction is added.
According to the technical scheme, the ultra-short-term wind power prediction method provided by the embodiment of the invention determines the ultra-short-term prediction sequence P at t moments after the current moment k according to the real-time power, the real-time wind speed and the short-term prediction power of the current moment k of the wind power plant aiming at the time delay phenomenon of the current ultra-short-term wind power prediction resultoThen, inputting a climbing matrix corresponding to the short-term preset power at t moments after the current moment k, inputting the climbing matrix and climbing characteristics corresponding to the climbing matrix into a climbing prediction probability model to obtain a climbing state category, and determining a future climbing state matrix corresponding to the climbing state category; the climbing prediction probability model is obtained by training based on a machine learning algorithm by adopting climbing matrix sample data of historical predicted power of a wind power plant and climbing characteristics corresponding to a climbing matrix of the historical predicted power as input data and adopting a climbing state category corresponding to the climbing matrix of the historical predicted power as output data; correcting the ultra-short-term prediction sequence P of t moments after the current moment k through a future climbing state matrixoDetermining the prediction result of the ultra-short-period wind power to obtain the final ultra-short-period prediction value at 0-t moment, and determining the grade of the climbing state at the current momentoMaking correction to determine the prediction result of ultra-short-term wind power, and making it possible to haveThe time delay phenomenon of ultra-short term prediction is effectively reduced, the ultra-short term prediction power prediction precision of the wind power plant is improved, and the prediction accuracy is improved.
On the basis of the above embodiment, in this embodiment, the method further includes:
acquiring historical data of a wind power plant; the historical data of the wind power plant comprise historical actual power of the wind power plant and historical actual wind speed of the wind power plant;
based on the historical data of the wind power plant and the length n of a preset time window, calculating and determining a corresponding actual climbing matrix window by window according to a time sequence;
dividing the actual climbing matrix into j types according to the corresponding characteristics of the actual climbing matrix to obtain the central point of the j types of actual climbing matrices;
acquiring historical short-term predicted power, adopting the length of the preset time window, and calculating window by window according to the time sequence to determine a corresponding predicted climbing matrix and climbing characteristics of the predicted climbing matrix;
and taking the central point of the j-type actual climbing matrix as an output value, taking the predicted climbing matrix and the climbing characteristics of the predicted climbing matrix as input values, and establishing the climbing prediction probability model.
In this embodiment, it should be noted that, historical actual power of the wind farm and historical actual wind speed of the wind farm are obtained, then a time window with a length of n is taken for the historical data, the interval length can be determined according to the total amount of the historical data and the length of the time window, and a corresponding actual climbing matrix is obtained by window-by-window calculation according to the time sequence. E.g. by taking a time window for the actual data and calculating the ramp time, tclam＝tmax+tminWherein t ismaxAt a time corresponding to the maximum value of the actual power, tminThe time point corresponding to the minimum value of the actual power is obtained; constructing a climbing characteristic matrix according to the climbing duration: c (k) ═ f (k) · e (k) · Pdelta·F(k)TWherein F (k) is n x t at the k-th pointclamThe order filtering matrix, E (k), is the identity matrix. The actual climbing matrix can be divided into j classes by a K-means clustering method, and the clustering method can also be realized by adopting other traditional clustering methods. Poly(s) are polymerizedThe input of the class method comprises the following steps: time of climbing tclamAnd the climbing peak PmaxStarting point P for climbingminAnd a climbing matrix C (k), wherein the clustering output is the central point of the j-type climbing matrix; the main purpose of clustering is to exclude a few extreme cases and abnormal cases, the number j of categories can be determined according to requirements, the larger the value j is, the higher the probability of regarding the extreme cases as a category is, and the smaller the value j is, the less the types of climbing are reflected.
In this embodiment, it should be noted that historical short-term predicted power is obtained, a corresponding predicted climbing matrix and climbing characteristics of the predicted climbing matrix are obtained through calculation in the same time window, and the calculation mode of the predicted climbing matrix is the same as that of an actual climbing matrix. Wherein, climbing characteristic includes: the climbing time, the climbing peak and the climbing starting point.
In this embodiment, the central point of the j-class actual climbing matrix is taken as a target value, the predicted climbing matrix and the characteristics thereof are taken as characteristic values, a climbing prediction probability model is constructed, namely the predicted climbing matrix and the characteristics thereof are used as input, the class m (m is 1 … j) corresponding to the actual climbing matrix at the moment corresponding to the predicted climbing matrix is used as output, a probability model for predicting the climbing state through the predicted climbing matrix is manufactured by using a random forest method, and the probability model can also be realized by adopting other traditional probability models.
According to the technical scheme, the ultra-short-period wind power prediction method provided by the embodiment of the invention is characterized in that the central point of the j-class actual climbing matrix is taken as a target value, the predicted climbing matrix and the characteristics of the predicted climbing matrix are taken as characteristic values, a climbing prediction probability model is constructed, the climbing state has N classes, the probability of each class in the N classes is calculated, the climbing prediction probability model can determine the climbing type according to the class corresponding to the maximum probability, and therefore, the ultra-short-period wind power prediction result is determined subsequently according to the predicted climbing type, and a more accurate prediction result is obtained.
On the basis of the foregoing embodiment, in this embodiment, the determining the climbing matrix specifically includes:
according to the first relation model, making time difference on the power of the wind power station; wherein the first relational model is as follows:
Pdelta＝P-Psheft(1)
wherein, PdeltaIs the time series difference of power, P is the grid-connected power, Psheft(1)Is a grid-connected power offset value;
calculating the climbing time according to the second relation model; wherein the second relational model is as follows:
tclam＝tmax+tmin
wherein, tclamFor the length of time of climbing a slope, tmaxFor the corresponding time point of the maximum value of the actual power, tminThe time point corresponding to the minimum value of the actual power is taken as the time point;
determining a climbing matrix according to the third relation model; wherein the third relation model is as follows:
C(k)＝F(k)·E(k)·Pdelta·F(k)T
wherein C (k) is a climbing matrix, k represents the current time, F (k) is n x t of the kth pointclamFilter matrix, E (k) is identity matrix, PdeltaTime series difference of power, F (k)TN x t representing the k-th pointclamAnd (3) transposing the filter matrix, wherein T represents transposing, and n is the length of a preset time window.
In this embodiment, for example, the method for calculating the climbing matrix includes:
firstly, making a time difference on the actual grid-connected power of a power station:
Pdelta＝P-Psheft(1)
wherein, PdeltaIs the time series difference of power, P is the grid-connected power, Psheft(1)Is the grid-tied power offset value.
Then, a time window is taken for actual data and the climbing duration is calculated:
tclam＝tmax+tmin
wherein, tclamFor the length of time of climbing a slope, tmaxFor the corresponding time point of the maximum value of the actual power, tminThe actual power minimum value corresponds to the time point.
Constructing a climbing matrix according to the climbing duration:
C(k)＝F(k)·E(k)·Pdelta·F(k)T
where c (k) is a climbing matrix, k denotes a current time, k is 1, …, L-n, L is a total data length, and f (k) is n × t at a kth pointclamFilter matrix, E (k) is identity matrix, PdeltaTime series difference of power, F (k)TN x t representing the k-th pointclamAnd (3) transposing the filter matrix, wherein T represents transposing, and n is the length of a preset time window.
On the basis of the foregoing embodiment, in this embodiment, the dividing the actual climbing matrix into j types according to the corresponding features of the actual climbing matrix specifically includes:
adopting a k-means clustering method to divide the actual climbing matrix into j types according to the corresponding characteristics of the actual climbing matrix; wherein, the actual climbing matrix correspondingly includes the characteristics: time of climbing tclamAnd the climbing peak PmaxAnd a climbing starting point Pmin。
In this embodiment, it should be noted that the actual climbing matrix is divided into j classes by a k-means clustering method, and the k-means clustering method is an unsupervised clustering algorithm, and is relatively simple to implement, excellent in clustering effect, and fast in convergence speed. The algorithm principle is that for a given sample set, the sample set is divided into j clusters according to the size between sample distances.
According to the technical scheme, the ultrashort-term wind power prediction method provided by the embodiment of the invention adopts a k-means clustering method, and is relatively simple to realize, excellent in clustering effect and high in convergence rate.
On the basis of the above embodiment, in this embodiment, the ultra-short-term prediction sequence P at t moments after the current moment k is determined according to the real-time power, the real-time wind speed and the short-term prediction power of the current moment k of the wind farmoThe method specifically comprises the following steps:
inputting historical actual power of the wind power plant, historical actual wind speed of the wind power plant and historical short-term predicted power of the wind power plant by adopting a regression model, outputting real-time power at the moment to be predicted, and establishing an ultra-short-term power regression model;
substituting the real-time power, the real-time wind speed and the short-term prediction power of the current moment k of the wind power plant into the ultra-short term power regression model, and determining the ultra-short term prediction sequence P of t moments after the current moment ko。
In this embodiment, the ultra-short term prediction sequence P at t times after the current time k is determined based on the ultra-short term power regression modelo. It should be noted that the regression model can clearly indicate the significant relationship between the independent variable and the dependent variable, and indicate the influence degree of a plurality of independent variables on one dependent variable, thereby being beneficial for a data analyst to eliminate and estimate an optimal set of variables for constructing the prediction model.
On the basis of the foregoing embodiment, in this embodiment, the ultra-short-term prediction sequence P at t times after the current time k is corrected based on the future climbing state matrixoDetermining the ultra-short-term wind power prediction result, which specifically comprises the following steps:
calculating a prediction confidence coefficient according to a fourth relation model based on the future climbing state matrix; wherein the fourth relational model is:
λ＝E|C(k-1)R·C(k-1)′P|-EC(k-1)R|·E|C(k-1)′P|
wherein λ is the confidence of prediction, E is the function of expectation, C (k-1)RIs an actual climbing matrix at the moment of k-1, C (k-1)'PA future climbing state matrix corresponding to the climbing state of the target class corresponding to the actual climbing matrix at the moment k-1 is obtained, wherein k represents the current moment;
according to a fifth relation model, performing ultra-short-term prediction sequence P on t moments after the current moment k based on the prediction confidenceoCorrecting; wherein the fifth relational model is:
Pf＝Po+λ·F(k)-1·C(k)′P·E(k)-1F(k)-T
wherein, PfRepresents the ultra-short-term wind power prediction result P of t moments after the current moment koUltra-short period representing t times after current time kPrediction number column, λ is prediction confidence, F (k) represents a filter matrix of k points, C (k)'PExpressed as a future climbing state matrix corresponding to the climbing state class at time k, E (k) is an identity matrix, E (k)-1Denotes the inversion of E (k), F (k)-TThe transpose inversions of F (k) are arithmetic symbols.
According to the technical scheme, the ultra-short-term wind power prediction method provided by the embodiment of the invention is based on the future climbing state matrix and calculates the prediction confidence coefficient according to the fourth relation model, namely the probability is large, and the confidence level is high when the confidence interval is large; on the basis of the sequence P, the original ultra-short-term prediction sequence of n future points is carried outo(i.e., the ultra-short-term predicted sequence P of t times after the current time ko) And correcting to determine the ultra-short-term wind power prediction result, so that the time delay phenomenon of ultra-short-term prediction can be effectively reduced, the ultra-short-term prediction power prediction precision of the wind power plant is improved, and the prediction accuracy is improved.
On the basis of the foregoing embodiment, in this embodiment, after obtaining the historical data of the wind farm, the method further includes:
carrying out data cleaning and feature construction on the historical data of the wind power plant;
wherein the data cleaning comprises removing a dead value and/or removing an abnormal value and/or judging a power limit and/or restoring the power limit;
wherein, the characteristic construction comprises data expansion, and data which contains effective characteristics and has data volume less than a preset value are copied according to a time sequence and filled into an original data sequence.
According to the technical scheme, the ultra-short-term wind power prediction method provided by the embodiment of the invention obtains objective and accurate historical data by performing data cleaning and feature construction on the historical data of the wind power plant, and the historical data contains more effective features, so that the follow-up prediction work is facilitated.
Fig. 3 is a schematic structural diagram of an ultra-short-term wind power prediction apparatus according to an embodiment of the present invention, as shown in fig. 3, the apparatus includes: a first determination module 201, a second determination module 202, and a correction module 203, wherein:
the first determining module 201 is configured to determine an ultra-short-term prediction sequence P at t moments after a current moment k according to a real-time power, a real-time wind speed, and a short-term prediction power of the wind farm at the current moment ko；
A second determining module 202, configured to determine a climbing matrix corresponding to short-term preset power at t times after the current time k, input a climbing characteristic of the climbing matrix and the climbing matrix into a climbing prediction probability model to obtain a climbing state category, and determine a future climbing state matrix corresponding to the climbing state category;
a correcting module 203 for correcting the ultra-short term predicted sequence P of t moments after the current moment k based on the future climbing state matrixoDetermining the ultra-short-period wind power prediction result;
the climbing prediction probability model is obtained by training based on a machine learning algorithm by adopting climbing matrix sample data of historical predicted power of a wind power plant and climbing characteristics corresponding to a climbing matrix of the historical predicted power as input data and adopting a climbing state category corresponding to the climbing matrix of the historical predicted power as output data.
The ultra-short-term wind power prediction device provided by the embodiment of the present invention may be specifically used to execute the ultra-short-term wind power prediction method of the above embodiment, and the technical principle and the beneficial effects thereof are similar, and reference may be specifically made to the above embodiment, which is not described herein again.
Based on the same inventive concept, an embodiment of the present invention provides an electronic device, which specifically includes the following components, with reference to fig. 4: a processor 301, a communication interface 303, a memory 302, and a communication bus 304;
the processor 301, the communication interface 303 and the memory 302 complete mutual communication through the communication bus 304; the communication interface 303 is used for realizing information transmission between related devices such as modeling software, an intelligent manufacturing equipment module library and the like; the processor 301 is used for calling the computer program in the memory 302, and the processor executes the computer programWhen the method provided by the above method embodiments is implemented, for example, the following steps are implemented when the processor executes the computer program: determining an ultra-short-term prediction sequence P of t moments after the current moment k according to the real-time power, the real-time wind speed and the short-term prediction power of the current moment k of the wind power planto(ii) a Determining a climbing matrix corresponding to the short-term preset power at t moments after the current moment k, inputting the climbing matrix and climbing characteristics corresponding to the climbing matrix into a climbing prediction probability model to obtain a climbing state category, and determining a future climbing state matrix corresponding to the climbing state category; correcting the ultra-short-term prediction sequence P of t moments after the current moment k based on the future climbing state matrixoDetermining the ultra-short-period wind power prediction result; the climbing prediction probability model is obtained by training based on a machine learning algorithm by adopting climbing matrix sample data of historical predicted power of a wind power plant and climbing characteristics corresponding to a climbing matrix of the historical predicted power as input data and adopting a climbing state category corresponding to the climbing matrix of the historical predicted power as output data.
Based on the same inventive concept, a non-transitory computer-readable storage medium is further provided, on which a computer program is stored, which when executed by a processor is implemented to perform the methods provided by the above method embodiments, for example, determining an ultra-short-term prediction sequence P at t moments after a current moment k of a wind farm according to a real-time power, a real-time wind speed and a short-term prediction power of the wind farm at the current moment ko(ii) a Determining a climbing matrix corresponding to the short-term preset power at t moments after the current moment k, inputting the climbing matrix and climbing characteristics corresponding to the climbing matrix into a climbing prediction probability model to obtain a climbing state category, and determining a future climbing state matrix corresponding to the climbing state category; correcting the ultra-short-term prediction sequence P of t moments after the current moment k based on the future climbing state matrixoDetermining the ultra-short-period wind power prediction result; wherein the climbing prediction probability model is climbing matrix sample data obtained by adopting historical predicted power of the wind power plantAnd the climbing characteristics corresponding to the climbing matrix of the historical prediction power are used as input data, and the climbing state category corresponding to the climbing matrix of the historical prediction power is used as output data and is obtained by training based on a machine learning algorithm.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and the parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium, such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods of the various embodiments or some parts of the embodiments.
In addition, in the present invention, terms such as "first" and "second" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "a plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
Moreover, in the present invention, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
Furthermore, in the description herein, reference to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.

Claims (10)

1. An ultra-short-term wind power prediction method is characterized by comprising the following steps:
determining an ultra-short-term prediction sequence P of t moments after the current moment k according to the real-time power, the real-time wind speed and the short-term prediction power of the current moment k of the wind power planto；
Determining a climbing matrix corresponding to the short-term preset power at t moments after the current moment k, inputting the climbing matrix and climbing characteristics corresponding to the climbing matrix into a climbing prediction probability model to obtain a climbing state category, and determining a future climbing state matrix corresponding to the climbing state category;
correcting the ultra-short-term prediction sequence P of t moments after the current moment k based on the future climbing state matrixoDetermining the ultra-short-period wind power prediction result;
the climbing prediction probability model is obtained by training based on a machine learning algorithm by adopting climbing matrix sample data of historical predicted power of a wind power plant and climbing characteristics corresponding to a climbing matrix of the historical predicted power as input data and adopting a climbing state category corresponding to the climbing matrix of the historical predicted power as output data.
2. The ultra-short-term wind power prediction method of claim 1, further comprising:
acquiring historical data of a wind power plant; the historical data of the wind power plant comprise historical actual power of the wind power plant and historical actual wind speed of the wind power plant;
based on the historical data of the wind power plant and the length n of a preset time window, calculating and determining a corresponding actual climbing matrix window by window according to a time sequence;
dividing the actual climbing matrix into j types according to the corresponding characteristics of the actual climbing matrix to obtain the central point of the j types of actual climbing matrices;
acquiring historical short-term predicted power, adopting the length of the preset time window, and calculating window by window according to the time sequence to determine a corresponding predicted climbing matrix and climbing characteristics of the predicted climbing matrix;
and taking the central point of the j-type actual climbing matrix as an output value, taking the predicted climbing matrix and the climbing characteristics of the predicted climbing matrix as input values, and establishing the climbing prediction probability model.
3. The ultra-short-term wind power prediction method according to claim 2, wherein determining a climbing matrix specifically comprises:
according to the first relation model, making time difference on the power of the wind power station; wherein the first relational model is as follows:
Pdelta＝P-Psheft(1)
wherein, PdeltaIs the time series difference of power, P is the grid-connected power, Psheft(1)Is a grid-connected power offset value;
calculating the climbing time according to the second relation model; wherein the second relational model is as follows:
tclam＝tmax+tmin
wherein, tclamFor the length of time of climbing a slope, tmaxFor the corresponding time point of the maximum value of the actual power, tminThe time point corresponding to the minimum value of the actual power is taken as the time point;
determining a climbing matrix according to the third relation model; wherein the third relation model is as follows:
C(k)＝F(k)·E(k)·Pdelta·F(k)T
wherein C (k) is a climbing matrix, k represents the current time, F (k) is n x t of the kth pointclamFilter matrix, E (k) is identity matrix, PdeltaTime series difference of power, F (k)TN x t representing the k-th pointclamAnd (3) transposing the filter matrix, wherein T represents transposing, and n is the length of a preset time window.
4. The ultra-short-term wind power prediction method according to claim 3, wherein the classifying the actual climbing matrix into j categories according to the corresponding features of the actual climbing matrix specifically comprises:
using k-means clustering method to cluster the saidThe actual climbing matrix is divided into j types according to the corresponding ground characteristics of the actual climbing matrix; wherein, the actual climbing matrix correspondingly includes the characteristics: time of climbing tclamAnd the climbing peak PmaxAnd a climbing starting point Pmin。
5. The ultra-short-term wind power prediction method according to claim 1, wherein the ultra-short-term prediction sequence P at t moments after the current moment k is determined according to the real-time power, the real-time wind speed and the short-term prediction power of the current moment k of the wind farmoThe method specifically comprises the following steps:
inputting historical actual power of the wind power plant, historical actual wind speed of the wind power plant and historical short-term predicted power of the wind power plant by adopting a regression model, outputting real-time power at the moment to be predicted, and establishing an ultra-short-term power regression model;
substituting the real-time power, the real-time wind speed and the short-term prediction power of the current moment k of the wind power plant into the ultra-short term power regression model, and determining the ultra-short term prediction sequence P of t moments after the current moment ko。
6. The ultra-short-term wind power prediction method according to claim 1, wherein the ultra-short-term prediction sequence P at t moments after the current moment k is corrected based on the future climbing state matrixoDetermining the ultra-short-term wind power prediction result, which specifically comprises the following steps:
calculating a prediction confidence coefficient according to a fourth relation model based on the future climbing state matrix; wherein the fourth relational model is:
λ＝E|C(k-1)R·C(k-1)′P|-E|C(k-1)R|·E|C(k-1)′P|
wherein λ is the confidence of prediction, E is the function of expectation, C (k-1)RIs an actual climbing matrix at the moment of k-1, C (k-1)'PA future climbing state matrix corresponding to the climbing state of the target class corresponding to the actual climbing matrix at the moment k-1 is obtained, wherein k represents the current moment;
according to a fifth relational model, based on predictionsMeasuring confidence degree to the ultra-short-term prediction sequence P of t moments after the current moment koCorrecting; wherein the fifth relational model is:
Pf＝Po+λ·F(k)-1·C(k)′P·E(k)-1F(k)-T
wherein, PfRepresents the ultra-short-term wind power prediction result P of t moments after the current moment koDenotes an ultra-short term prediction sequence at t times k after the current time, λ is a prediction confidence, F (k) denotes a filter matrix of k points, C (k)'PExpressed as a future climbing state matrix corresponding to the climbing state class at time k, E (k) is an identity matrix, E (k)-1Denotes the inversion of E (k), F (k)-TDenotes the transposed inversion of F (k).
7. The ultra-short-term wind power prediction method according to claim 2, wherein after obtaining wind farm historical data, the method further comprises:
carrying out data cleaning and feature construction on the historical data of the wind power plant;
wherein the data cleaning comprises removing a dead value and/or removing an abnormal value and/or judging a power limit and/or restoring the power limit;
wherein, the characteristic construction comprises data expansion, and data which contains effective characteristics and has data volume less than a preset value are copied according to a time sequence and filled into an original data sequence.
8. An ultra-short-term wind power prediction device, comprising:
a first determining module, configured to determine an ultra-short-term prediction sequence P at t moments after a current moment k according to a real-time power, a real-time wind speed, and a short-term prediction power of the wind farm at the current moment ko；
A second determining module, configured to determine a climbing matrix corresponding to short-term preset power at t times after the current time k, input the climbing matrix and climbing characteristics corresponding to the climbing matrix into a climbing prediction probability model, obtain a climbing state category, and determine a future climbing state matrix corresponding to the climbing state category;
a correction module for correcting the ultra-short-term predicted sequence P of t moments after the current moment k based on the future climbing state matrixoDetermining the ultra-short-period wind power prediction result;
the climbing prediction probability model is obtained by training based on a machine learning algorithm by adopting climbing matrix sample data of historical predicted power of a wind power plant and climbing characteristics corresponding to a climbing matrix of the historical predicted power as input data and adopting a climbing state category corresponding to the climbing matrix of the historical predicted power as output data.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the ultra-short-term wind power prediction method as claimed in any one of claims 1 to 7 when executing the program.
10. A non-transitory computer readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the ultra-short-term wind power prediction method according to any one of claims 1 to 7.
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