CN110110339B - Japanese hydrologic forecast error correction method and system - Google Patents
Japanese hydrologic forecast error correction method and system Download PDFInfo
- Publication number
- CN110110339B CN110110339B CN201810085197.1A CN201810085197A CN110110339B CN 110110339 B CN110110339 B CN 110110339B CN 201810085197 A CN201810085197 A CN 201810085197A CN 110110339 B CN110110339 B CN 110110339B
- Authority
- CN
- China
- Prior art keywords
- hydrologic
- forecast
- prediction
- value
- error
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/23—Clustering techniques
- G06F18/232—Non-hierarchical techniques
- G06F18/2321—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions
- G06F18/23213—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions with fixed number of clusters, e.g. K-means clustering
-
- G06F19/00—
Abstract
The invention relates to a Japanese hydrologic forecast error correction method and system, including calculating the forecast value and forecast error matrix percentile interval of the hydrologic forecast process; carrying out K-means clustering on the prediction value and the prediction error matrix percentile interval; establishing a typical category regression model of the prediction value after K-means clustering and the prediction error matrix percentile interval; and establishing a prediction value correction model of prediction error estimation of the typical category regression model. The invention adopts a day-ahead error characteristic identification method which takes error time series modal decomposition and error characteristic classification into account to carry out decomposition, identification and positioning on an error dynamic process, and then carries out characteristic matching in practical application, thereby improving the day-ahead hydrological prediction level.
Description
Technical Field
The invention relates to a correction method and a correction system for a renewable energy power generation technology, in particular to a Japanese hydrological forecast error correction method and a Japanese hydrological forecast error correction system.
Background
Errors of hydrologic prediction have certain uncertainty, and especially in the Japanese hydrologic prediction, the adopted process-driven model method or data-driven model method cannot effectively avoid the problem of deviation uncertainty of the Japanese flow prediction. And the flood peak information obtained by the real-time hydrological survey station of the drainage basin is utilized, and the warehouse entry flow and the flood peak flow are corrected on the basis of the flood peak information, so that the method depends on the upstream occurrence time of the flood peak and the flood peak flow, and has the limitations of short forecast period, dependence on drainage basin runoff characteristics and the like. For hydrologic prediction with a long forecast period, relatively few actual measurement data can be used, and more correction schemes can be found in sequence characteristics and historical prediction rules.
For application, extending the forecast period will bring risks in terms of error stability and information reliability, and how to improve the error suppression capability of the hydrological forecast in the future determines the practical degree of the forecast model to a certain extent. In order to solve the problem, different types of error real-time correction technologies are usually adopted to intervene in an original prediction result output by a model, wherein a common mode is analysis based on a time sequence of an error, for example, the error correction is realized by means of an autoregressive model or a BP neural network, and the like.
Secondly, in the prior art, different error characteristics are generally not regarded as each configuration of the whole error dynamic process, so that the same processing strategy with different characteristics such as systematic errors and nonlinear errors is easy to occur in the aspect of error correction, the error suppression capability is reduced, and the overall performance of the hydrologic prediction model is weakened. In the case of a pre-day hydrologic forecast, there may be large estimation errors in precipitation due to the forecast period.
Disclosure of Invention
The invention aims to provide a Japanese hydrologic prediction error correction method and a Japanese hydrologic prediction error correction system, which are suitable for hydrologic prediction error correction which is longer than the conventional hydrologic prediction and keeps a higher prediction level in a longer prediction period, so as to solve the problems that different error characteristics are not generally regarded as each configuration of the whole error dynamic process, so that the same processing strategy with different characteristics such as systematic errors, nonlinear errors and the like is easy to appear in the aspect of error correction, the error suppression capability is reduced, and the overall performance of a hydrologic prediction model is weakened.
The purpose of the invention is realized by adopting the following technical scheme:
the invention provides a Japanese hydrologic prediction error correction method, which is improved in that:
acquiring hydrologic forecast data;
correcting the acquired hydrologic forecast data based on a pre-established correction model; wherein the content of the first and second substances,
the pre-established correction model comprises: and clustering and generating the K-means value of the percentile interval based on the historical hydrologic prediction value and the prediction error matrix.
Further, the method comprises the following steps: the acquiring of the hydrologic forecast data comprises acquiring historical hydrologic forecast data and historical hydrologic actual measurement data.
Further, the method comprises the following steps: the pre-established correction model comprises:
calculating a prediction value and a prediction error matrix percentile interval of the hydrologic prediction process according to the historical hydrologic prediction data and the historical hydrologic actual measurement data;
performing K-means clustering on the prediction value of the hydrologic prediction process and the prediction error matrix percentile interval;
establishing a typical category regression model of the prediction value after K-means clustering and the prediction error matrix percentile interval;
and establishing a correction model according to the typical category regression model.
Further: before calculating the hydrologic prediction value and prediction error matrix percentile interval according to the acquired historical hydrologic prediction data and the historical hydrologic actual measurement data, constructing a prediction value and a prediction error matrix;
the constructing of the forecasting value and the forecasting error matrix in the hydrologic forecasting process comprises the following steps:
determining a forecast value and a forecast error in a hydrologic forecast process according to historical hydrologic forecast data and historical hydrologic actual measurement data;
constructing a forecast value and a forecast error matrix according to the forecast value and the forecast error in the hydrologic forecast process;
forecasting value P 'in hydrologic forecasting process according to forecasting value and forecasting error matrix of percentile method' ij And (5) grouping the components in different regions.
Further: the forecasting value and the forecasting error in the hydrologic forecasting process are as follows:
E ij ＝P ij -P' ij ；
wherein, E ij To predict errors, P ij Is actually measured value, P' ij Predicting a value for the hydrologic prediction process, wherein i is more than or equal to 1 and less than or equal to a set threshold, the set threshold =24 hours/time resolution between day-by-day samples, and j is more than or equal to 1 and less than or equal to n; n is the number of days predicted for which analysis is to be accounted.
Further, the method comprises the following steps: the hydrologic prediction process prediction value and prediction error matrix are expressed as:
A＝(P′ ij ,E ij )
wherein: x is a prediction value and prediction error matrix, A i1 、A i2 ...A ij Both represent the hydrologic prediction process prediction values and the i-th row, column 1, and the i-th row, column 2.
Further: the step of grouping the forecast values in the hydrologic forecast process according to the percentile method forecast values and the forecast error matrix comprises the following steps:
forecasting value P 'through hydrologic forecasting process' ij P 'is carried out according to a percentile method on the premise of ascending order' ij Percentile inter-partition collection, P' ij The percentile inter-partition collection is expressed as: x (T) t ，E ij ) Represents when P' ij Forecasting value with size between randomly selected hydrologic forecasting processCorresponding error samples in the interval Tt, and forecasting value interval P 'in the hydrologic prediction process' ij The error samples are classified under the condition of each percentile interval,
t＝[1,100,1]。
further, the method comprises the following steps: and the K-means clustering of the prediction value and the prediction error matrix percentile interval is represented as follows:
wherein: f (P) k ) K-means clustering representing prediction values of the hydrologic prediction process and the prediction error matrix percentile interval; p is k To make f (P) k ) One division at a minimum, P k ＝{C 1 ，…，C k }；
Indicates the center position of the nth cluster, n =1, \8230;, k, d (x) y ,m n ) Represents x y To m n A distance; x is a radical of a fluorine atom y Is X (T) t ，E ij ) The element (1) in (1); m is a unit of n Represents the center position of the nth cluster; t is a unit of t And the forecast value interval of the randomly selected hydrological forecasting process is represented.
Further: the typical class regression model is represented as:
E ij ＝a·P' ij +b
wherein: a and b are preset regression coefficients, P' ij Predicting values for a hydrologic prediction process; eij represents the prediction error.
Further: correcting the acquired hydrologic forecast based on a pre-established correction model, wherein the correction is carried out according to the following formula:
P' t ＝f(P' t )
wherein: p 'on the left' t Representing a specific forecast value in a forecast value interval of the hydrologic forecast process; f (P' t ) Representing coincidence with K-means aggregationClass gives the correction values for a typical subset of the forecasted timing traces.
The invention also provides a Japanese hydrologic prediction error correction system, which is improved in that: the method comprises the following steps:
the acquisition module is used for acquiring hydrological forecast data;
the correction module is used for correcting the acquired hydrologic forecast data based on a pre-established correction model; wherein, the first and the second end of the pipe are connected with each other,
the pre-established correction model comprises: and clustering and generating the K-means value of the percentile interval based on the historical hydrologic prediction value and the prediction error matrix.
Further: the device also comprises a formulating module used for formulating the correction model in advance.
Further, the method comprises the following steps: the formulation module comprises:
the calculation submodule is used for calculating a forecast value and a forecast error matrix percentile interval in the hydrological forecasting process according to the historical hydrological forecasting data and the historical hydrological actually-measured data;
the clustering submodule is used for carrying out K-means clustering on the forecasting values of the hydrologic forecasting process and the forecasting error matrix percentile interval;
the first establishing submodule is used for establishing a typical category regression model of the prediction value after the K-means clustering and the prediction error matrix percentile interval;
and the second establishing submodule is used for establishing a correction model according to the typical category regression model.
And further, the method also comprises a construction submodule for constructing the hydrologic prediction process forecast value and the forecast error matrix before the hydrologic prediction process forecast value and the forecast error matrix percentile interval are calculated according to the obtained hydrologic prediction.
Further: the construction submodule, comprising:
the first determining unit is used for determining a forecast value and a forecast error in a hydrological forecasting process according to historical hydrological forecasting data and historical hydrological actual measurement data;
the first establishing unit is used for constructing a forecast value and a forecast error matrix according to the forecast value and the forecast error;
a collecting unit for forecasting a value P 'in the hydrological forecasting process according to the forecasting value and the forecasting error matrix of the percentile method' ij And (5) grouping by partitions.
Further, the method comprises the following steps: the correction module is further used for correcting the acquired hydrologic forecast based on a pre-established correction model, and comprises the following steps of:
P' t ＝f(P' t )
wherein: p 'on the left' t Representing a specific forecast value in a forecast value interval of the hydrologic forecast process; f (P' t ) Representing the correction values for a typical subset of the forecasted time series tracks given by K-means clustering.
Compared with the closest prior art, the technical scheme provided by the invention has the beneficial effects that:
the invention provides a Japanese hydrologic forecast error correction method, which comprises the steps of obtaining hydrologic forecast data; correcting the acquired hydrological forecast data based on a pre-established correction model; the pre-established correction model comprises: based on the historical hydrologic prediction value and the K-means clustering of the percentile interval of the prediction error matrix, the problems that different error characteristics are not generally regarded as each configuration of the whole error dynamic process in the prior art, so that the same processing strategy with different characteristics such as systematic errors, nonlinear errors and the like is easy to appear in the aspect of error correction, the error suppression capability is reduced, and the whole performance of a hydrologic prediction model is weakened are solved.
On the basis of a traditional hydrological forecasting method, diagnosis research is carried out on time sequence tracks of error features of the hydrological forecasting in the future, the method is different from a black box type error statistical correction, a machine learning algorithm for precision optimization and the like, a method for identifying the error features in the future is adopted, error time series modal decomposition and error feature classification are taken into consideration, the dynamic process of the errors is decomposed, identified and positioned, and then feature matching is carried out in practical application, so that the purpose of reducing the error of the hydrological forecasting in the future is achieved.
Drawings
FIG. 1 is a simple block diagram of a method for correcting a Japanese hydrological prediction error based on time series characteristic clustering according to the present invention;
FIG. 2 is a detailed block diagram of the flow of the method for correcting the error of the Japanese hydrological prediction based on the time series characteristic clustering provided by the invention;
fig. 3 is a structural block diagram of a system for correcting the error of the Japanese hydrologic prediction based on the time series feature clustering provided by the invention.
Detailed Description
The following describes embodiments of the present invention in further detail with reference to the accompanying drawings.
The following description and the drawings sufficiently illustrate specific embodiments of the invention to enable those skilled in the art to practice them. Other embodiments may incorporate structural, logical, electrical, process, and other changes. The examples merely typify possible variations. Individual components and functions are optional unless explicitly required, and the sequence of operations may vary. Portions and features of some embodiments may be included in or substituted for those of others. The scope of embodiments of the invention encompasses the full ambit of the claims, as well as all available equivalents of the claims. Embodiments of the invention may be referred to, individually or collectively, herein by the term "invention" merely for convenience and without intending to voluntarily limit the scope of this application to any single invention or inventive concept if more than one is in fact disclosed.
The first embodiment,
The invention provides a method for identifying the day-ahead error characteristics by taking the analysis of the error time sequence track characteristics as the premise and considering the modal decomposition of the error time sequence and the classification of the error characteristics, thereby realizing the error correction of the day-ahead hydrological prediction. The invention provides a flow chart of a Japanese hydrologic forecast error correction method based on time sequence feature clustering, as shown in figures 1 and 2, the method comprises the following steps:
step 11, acquiring hydrologic forecast data;
step 12, correcting the acquired hydrological forecast data based on a pre-established correction model; wherein, the first and the second end of the pipe are connected with each other,
the pre-established correction model comprises: and clustering and generating the percentile interval K mean value based on the historical hydrological prediction value and the prediction error matrix.
The acquiring of the hydrologic forecast data in step 11 includes acquiring historical hydrologic forecast data and historical hydrologic actual measurement data.
The step 12 of correcting the acquired hydrological forecast data by the pre-established correction model includes:
1. and (4) collecting hydrologic forecast time sequence process data, constructing a forecast value and a forecast error matrix, and calculating a forecast value percentile interval.
2. And clustering the prediction value with the prediction error matrix K mean value.
3. And establishing a typical category regression model of the forecast value and the forecast error matrix.
4. A prediction value correction model based on the prediction error estimate.
The specific content is as follows:
to accurately illustrate the method and steps of practicing the present invention, the following examples are given:
(1) Hydrologic forecasting process data collection
Usually, the forecast period of hydrologic forecast is the accumulation of convergence time given by statistical data, and can reach the day ahead and above under the condition of forecasting rainfall by application.
The embodiment adopts hydrologic forecasting process and actual measurement warehousing flow corresponding to the hydrologic forecasting process to form a forecasting time sequence, and the forecasting value is defined as P' ij And measured value is P ij Forecast error of E ij ，E ij ＝P ij -P' ij
Wherein i is more than or equal to 1 and less than or equal to 96, j is more than or equal to 1 and less than or equal to n
N is the forecast day number of the input analysis, i.e. the day number of the input analysis samples is N, the time resolution between the day-by-day samples is 15min, and the total number of samples is N = N · 96
Wherein: e ij To predict errors, P ij Is measured value, P' ij Predicting a value for the hydrologic prediction process, wherein i is more than or equal to 1 and less than or equal to a set threshold, the set threshold =24 hours/time resolution between day-by-day samples, and j is more than or equal to 1 and less than or equal to n; n is the forecast day number counted in the analysis, and the total number of samples N = N · set threshold.
First, a total of N historical samples are subjected to data collection.
The forecast value and the forecast error form a matrix which is defined as X (P' ij ，E ij )
From P' ij P 'is carried out according to a percentile method on the premise of ascending order' ij Inter-division collection
Thus, X (P' ij ，E ij ) Further rewritten as X (T) t ，E ij )，
t＝[1,100,1]
(2)X(T t ，E ij ) K-means clustering of
By randomly chosen interval T t And taking the K samples as initial center points, classifying the rest samples into the cluster where the center point with the highest similarity is located, then determining the mean value of the coordinates of the samples in the current cluster as a new center point, and sequentially and circularly iterating until the categories of all the samples do not change.
Thus, the original data set X (T) t ，E ij ) By using the K-means clustering method, X (T) can be obtained t ，E ij ) Treated as a clustered data set and one potentially each cluster satisfying the integer K. Outputting one of the partitions P k ＝{C 1 ，…，C k }, set x y Is X (T) t ，E ij ) Then the problem of K-means clustering as described in this example is to find such a partition P k ＝{C 1 ，…，C k Such that the objective function
And minimum.
Wherein the content of the first and second substances,
indicates the center position of the nth cluster, n =1, \8230;, k, d (x) y ，m n ) Represents x y To m n A distance.
(3)X(P' ij ，E ij ) Regression modeling of canonical classes
In the interval T t In, X (T) t ，E ij ) The method is divided into clusters with similar object characteristics by the application of a K-means clustering algorithm, and objects are different among different clusters.
Obtaining a certain typical characteristic subset of an original prediction value and a prediction error value matrix with similar characteristics through a clustering algorithm, and establishing a regression equation aiming at the subset of the typical prediction time sequence track:
E ij ＝a·P' ij + b type (1.1)
Solving the linear relation between the two by using a least square method to give P' ij Satisfies the interval T t And the values of a and b under the condition of the typical feature subset, namely P is given ij And E ij Satisfies the interval T t And a typical feature subset, there is a given, typical relationship based on historical statistics.
(4) Based on E ij Estimated P' ij And (3) calculating a correction value:
using the formula (1.1), interval T t And in the characteristic feature subset, corresponding E ij From the measured value in the history time sequence track as P ij And predicted value is P' ij The difference is equivalent to an estimate in a linear relationship.
Therefore, in the interval T t And in the characteristic feature subset, P ij Can be regarded as P' ij I.e. corresponding to an interval, a characteristic feature subset, there is a certain P ij ＝f(P' ij )。
At a future moment, the hydrologic forecast model gives a specific forecast value P' t
Is P' t Subject to T t In a certain interval, and P' t In single day-ahead prediction, the belonged interval and the exhibited error statistics accord with a typical prediction time sequence track subset given by K-means clustering, and then
P' t ＝f(P' t ) Formula (1.2)
Equation 1.2 is the error correction model given in this example.
Example II,
Based on the same inventive concept, the invention also provides a time series characteristic clustering-based Japanese hydrologic forecast error correction system, the structure diagram of which is shown in fig. 3, and the system comprises:
an obtaining module 201, configured to obtain hydrologic forecast data;
a correction module 202, configured to correct the acquired hydrologic forecast data based on a pre-established correction model; wherein the content of the first and second substances,
the pre-established correction model comprises: and (4) clustering and generating the percentile interval K mean value based on the forecast value in the historical hydrologic forecast process and the forecast error matrix.
Further: the device also comprises a formulation module which is used for formulating the correction model in advance.
Further: the formulation module comprises:
the calculation submodule is used for calculating a forecast value and a forecast error matrix percentile interval in the hydrologic forecast process according to the historical hydrologic forecast data and the historical hydrologic actual measurement data;
the clustering submodule is used for carrying out K-means clustering on the forecasting values of the hydrologic forecasting process and the forecasting error matrix percentile interval;
the first establishing submodule is used for establishing a typical category regression model of the prediction value after the K-means clustering and the prediction error matrix percentile interval;
and the second establishing submodule is used for establishing a correction model according to the typical category regression model.
And further, the method also comprises a construction submodule for constructing the forecasting value and the forecasting error matrix in the hydrologic forecasting process before the hydrologic forecasting value and the forecasting error matrix percentile interval are calculated according to the obtained hydrologic forecasting.
Further: the construction submodule, comprising:
the first determining unit is used for determining a forecast value and a forecast error in a hydrological forecasting process according to historical hydrological forecasting data and historical hydrological actual measurement data;
the first establishing unit is used for constructing a forecast value and a forecast error matrix according to the forecast value and the forecast error;
a collecting unit for forecasting value P 'in hydrologic forecasting process according to percentile forecasting value and forecasting error matrix' ij And (5) grouping the components in different regions.
Further, the method comprises the following steps: the correction module is further used for correcting the acquired hydrologic forecast based on a pre-established correction model, and comprises the following steps of:
P' t ＝f(P' t )
wherein: p 'on the left' t Representing a specific forecast value in a forecast value interval of the hydrologic forecast process; f (P' t ) Representing the correction values for a typical subset of the forecasted time series tracks given by K-means clustering.
The method adopts K-means clustering to carry out typical feature subset division on a prediction value and prediction error value matrix, and realizes prediction value correction by using the steps and the given equation of the method through screening the numerical value interval to which the prediction value belongs and the corresponding feature subset.
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 so forth) 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.
Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art can make modifications and equivalents to the embodiments of the present invention without departing from the spirit and scope of the present invention, which is set forth in the claims of the present application.
Claims (12)
1. A Japanese hydrologic forecast error correction method is characterized in that:
acquiring hydrologic forecast data;
correcting the acquired hydrological forecast data based on a pre-established correction model;
wherein the pre-established correction model comprises: clustering generation is carried out on the basis of a percentile interval K mean value of a historical hydrological forecast value and a forecast error matrix;
the construction process of the pre-established correction model comprises the following steps:
calculating a forecast value and a forecast error matrix percentile interval in the hydrological forecasting process according to the historical hydrological forecasting data and the historical hydrological actually-measured data;
performing K-means clustering on the prediction value of the hydrologic prediction process and the prediction error matrix percentile interval;
establishing a typical category regression model of the prediction value after the K-means clustering and the prediction error matrix percentile interval;
formulating a correction model according to the typical category regression model;
the typical class regression model is represented as:
E ij ＝a·P' ij +b
wherein: a and b are preset regression coefficients, P' ij Forecasting values for the hydrologic forecasting process; e ij The regression model is a typical category regression model and represents a forecast error;
the making of the correction model according to the typical category regression model comprises:
determining a correction model between a hydrologic prediction process predicted value and a hydrologic prediction process measured value of a typical category regression model;
the calibration model for determining the distance between the predicted value of the hydrologic prediction process and the measured value of the hydrologic prediction process is expressed by the following formula:
P ij ＝f(P' ij )
wherein: p' ij Predicting a value, P, for a hydrologic forecasting process ij Is a hydrologic forecast process measured value f (P' ij ) Is a function taking a hydrologic prediction process prediction value as an independent variable;
correcting the acquired hydrologic forecast based on a pre-established correction model, comprising: when the forecast value of the hydrologic forecast process is a specific forecast value, correcting according to the following formula:
P' t ＝f(P' t )
wherein: p 'on the left' t A correction result representing a specific prediction value of the hydrologic prediction process; f (P' t ) Representing a function with hydrologically predicted process specific prediction values as arguments.
2. The method of correcting a Japanese hydrologic prediction error as set forth in claim 1, wherein: before calculating the forecasting value of the hydrologic forecasting process and the forecasting error matrix percentile interval according to the acquired historical hydrologic forecasting data and the historical hydrologic actual measurement data, the method further comprises the following steps:
determining a forecast value and a forecast error in a hydrologic forecast process according to historical hydrologic forecast data and historical hydrologic actual measurement data;
constructing a forecast value and a forecast error matrix according to the forecast value and the forecast error;
and (4) performing interval collection of forecast values in the hydrologic forecast process according to the forecast values and the forecast error matrix by a percentile method.
3. The method of correcting errors in forecasting hydrology before day according to claim 2, characterized in that: the hydrologic prediction error calculation formula is as follows:
E ij ＝P ij -P' ij ；
wherein E is ij To predict error, P ij Is actually measured value, P' ij Predicting a value for the hydrologic forecasting process, wherein i is more than or equal to 1 and less than or equal to a set threshold, the set threshold =24 hours/time resolution between day-by-day samples, and j is more than or equal to 1 and less than or equal to n; n is the number of days predicted for which analysis is to be accounted.
4. The method of correcting a Japanese hydrologic prediction error as claimed in claim 3, characterized in that: the hydrologic forecasting process forecast value and forecast error matrix are expressed as:
A＝(P′ ij ,E ij )
wherein: x is a prediction value and prediction error matrix, A i1 、A i2 ...A ij Both represent the hydrologic prediction process prediction value and the 1 st column of the prediction error matrix at row i and the 2 nd column of the prediction error matrix at row i.
5. The method of correcting a Japanese hydrologic prediction error as claimed in claim 4, characterized in that: the step of grouping the forecast values in the hydrologic forecast process according to the percentile method forecast values and the forecast error matrix comprises the following steps:
forecasting value P 'through hydrologic forecasting process' ij Taking ascending order as a premise, and carrying out P 'according to a percentile method' ij Percentile inter-partition collection, P' ij The percentile inter-partition collection is expressed as: x (T) t ，E ij ) Represents when P' ij An error sample corresponding to the prediction value interval Tt of the hydrologic prediction process selected at random, wherein the prediction value interval of the hydrologic prediction process is P' ij The error samples are classified under the condition of each percentile interval,
t＝[1,100,1]。
6. the method of correcting an error in a japanese hydrologic forecast according to claim 1, characterized by: the K-means clustering generation of the prediction value and the prediction error matrix percentile interval in the hydrologic prediction process is represented as follows:
wherein: f (P) k ) K mean value clustering representing prediction values in the hydrologic prediction process and prediction error matrix percentile intervals; p k To make f (P) k ) One division at a minimum, P k ＝{C 1 ，…，C k }；
Denotes the center position of the nth cluster, n =1, \8230;, k, d (x) y ,m n ) Denotes x y To m n A distance; x is the number of y Is X (T) t ，E ij ) The elements of (1); m is n Represents the center position of the nth cluster; t is a unit of t And representing the forecast value interval of the randomly selected hydrological forecasting process.
7. A japanese hydrologic forecast error correction system using the japanese hydrologic forecast error correction method according to any one of claims 1-6, characterized by: the method comprises the following steps:
the acquisition module is used for acquiring hydrologic forecast data;
the correction module is used for correcting the acquired hydrologic forecast data based on a pre-established correction model; wherein the content of the first and second substances,
the pre-established correction model comprises: and clustering and generating the percentile interval K mean value based on the historical hydrological prediction value and the prediction error matrix.
8. The japanese hydrologic forecast error correction system of claim 7, characterized by: the device also comprises a formulating module used for formulating the correction model in advance.
9. The japanese hydrologic forecast error correction system of claim 8, characterized by: the formulation module comprises:
the calculation submodule is used for calculating a forecast value and a forecast error matrix percentile interval in the hydrological forecasting process according to the historical hydrological forecasting data and the historical hydrological actually-measured data;
the clustering submodule is used for carrying out K-means clustering on the forecasting values of the hydrologic forecasting process and the forecasting error matrix percentile interval;
the first establishing submodule is used for establishing a typical category regression model of the prediction value after the K-means clustering and the prediction error matrix percentile interval;
and the second establishing submodule is used for establishing a correction model according to the typical category regression model.
10. The system of correcting for future hydrographic prediction error of claim 7, further comprising a construction sub-module for constructing a hydrographic prediction process prediction value and a prediction error matrix before calculating a hydrographic prediction process prediction value and a prediction error matrix percentile interval based on the acquired hydrographic prediction.
11. The japanese hydrologic forecast error correction system of claim 10, characterized by: the construction submodule, comprising:
the first determination unit is used for determining a prediction value and a prediction error in a hydrologic prediction process according to historical hydrologic prediction data and historical hydrologic actual measurement data;
the first establishing unit is used for constructing a hydrologic prediction process forecast value and a forecast error matrix according to the hydrologic prediction process forecast value and the forecast error;
a collecting unit for forecasting a value P 'in the hydrological forecasting process according to the forecasting value and the forecasting error matrix of the percentile method' ij And (5) grouping the components in different regions.
12. The japanese hydrologic forecast error correction system of claim 7, characterized by: the correction module is further used for correcting the hydrologic forecast process forecast value according to the following formula when the forecast value is a specific forecast value:
P' t ＝f(P' t )
wherein: p 'on the left' t A correction result representing a specific prediction value of the hydrologic prediction process; f (P' t ) Representing a function with hydrologically predicted process specific prediction values as arguments.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810085197.1A CN110110339B (en) | 2018-01-29 | 2018-01-29 | Japanese hydrologic forecast error correction method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810085197.1A CN110110339B (en) | 2018-01-29 | 2018-01-29 | Japanese hydrologic forecast error correction method and system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110110339A CN110110339A (en) | 2019-08-09 |
| CN110110339B true CN110110339B (en) | 2022-10-11 |
Family
ID=67483022
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810085197.1A Active CN110110339B (en) | 2018-01-29 | 2018-01-29 | Japanese hydrologic forecast error correction method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110110339B (en) |
Families Citing this family (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113158394B (en) * | 2020-01-22 | 2022-08-19 | 河海大学 | Basin hydrological model error correction method and device based on evaporation error compensation |
| CN111401666B (en) * | 2020-04-28 | 2021-07-27 | 中国水利水电科学研究院 | Method for forecasting influence of upstream reservoir group on runoff by utilizing forecasting errors |
| CN112415635B (en) * | 2020-11-19 | 2022-03-29 | 中山大学 | Gamma and Gaussian distribution coupled monthly scale rainfall forecast correction method |
| CN116595945B (en) * | 2023-07-14 | 2023-10-20 | 浙江大华技术股份有限公司 | High-precision simulation scattering parameter extraction method, electronic equipment and storage medium |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106650767A (en) * | 2016-09-20 | 2017-05-10 | 河海大学 | Flood forecasting method based on cluster analysis and real time correction |
-
2018
- 2018-01-29 CN CN201810085197.1A patent/CN110110339B/en active Active
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106650767A (en) * | 2016-09-20 | 2017-05-10 | 河海大学 | Flood forecasting method based on cluster analysis and real time correction |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110110339A (en) | 2019-08-09 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Oprea et al. | Machine learning algorithms for short-term load forecast in residential buildings using smart meters, sensors and big data solutions | |
| CN110110339B (en) | Japanese hydrologic forecast error correction method and system | |
| Ceci et al. | Predictive modeling of PV energy production: How to set up the learning task for a better prediction? | |
| CN110232483B (en) | Deep learning load prediction method and device and terminal equipment | |
| WO2017076154A1 (en) | Method and apparatus for predicting network event and establishing network event prediction model | |
| CN112734128B (en) | 7-day power load peak prediction method based on optimized RBF | |
| CN111027775A (en) | Step hydropower station generating capacity prediction method based on long-term and short-term memory network | |
| CN110570012B (en) | Storm-based power plant production equipment fault early warning method and system | |
| CN116307291B (en) | Distributed photovoltaic power generation prediction method and prediction terminal based on wavelet decomposition | |
| CN116596044B (en) | Power generation load prediction model training method and device based on multi-source data | |
| CN114819374A (en) | Regional new energy ultra-short term power prediction method and system | |
| Efendi et al. | Maximum-minimum temperature prediction using fuzzy random auto-regression time series model | |
| CN116187835A (en) | Data-driven-based method and system for estimating theoretical line loss interval of transformer area | |
| CN115577752A (en) | Ultra-short-term photovoltaic power prediction method and system | |
| CN115271253A (en) | Water-wind power generation power prediction model construction method and device and storage medium | |
| CN115186923A (en) | Photovoltaic power generation power prediction method and device and electronic equipment | |
| CN114186733A (en) | Short-term load prediction method and device | |
| CN113919610A (en) | ARIMA model construction method and evaluation method for low-voltage transformer area line loss prediction | |
| CN116191412A (en) | Power load prediction method | |
| WO2022222230A1 (en) | Indicator prediction method and apparatus based on machine learning, and device and storage medium | |
| CN115809719A (en) | Short-term load prediction correction method based on morphological clustering | |
| CN114936590A (en) | Photovoltaic power station abandoned light data identification method and device and storage medium | |
| Sinitsyna et al. | Some practical aspects of electric power consumption time series analysis | |
| CN110323743B (en) | Clustering method and device for transient power angle stability evaluation historical data | |
| CN110175705B (en) | Load prediction method and memory and system comprising same |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |