CN115587761A - Comprehensive evaluation method for wind power prediction error - Google Patents
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Abstract
According to the comprehensive evaluation method, device and storage medium for the wind power prediction error, power prediction data and actual power data in a preset time period of a wind power plant are obtained, MAPE of wind power plant power prediction is obtained through calculation according to the power prediction data and the actual power data in the preset time period, an error qualification rate index of wind power prediction is obtained through calculation according to the power prediction data and the actual power data in the preset time period, and the wind power prediction error is comprehensively evaluated through the MAPE and the error qualification rate index. The method and the device improve the accuracy of the power prediction error, have wide application range, and simultaneously solve the problem of artificial subjectivity, so that the obtained evaluation result is more objective and accurate.
Description
Technical Field
The application relates to the technical field of wind power generation power prediction, in particular to a comprehensive evaluation method and device for a wind power prediction error and a storage medium.
Background
For guaranteeing safe and stable operation of a power grid and full utilization of wind power, in the wind power dispatching work, the construction of a wind power forecasting and real-time monitoring system is promoted, conventional energy and wind power are planned according to the wind power forecasting condition, the wind power is brought into monthly electric quantity balance and day-ahead dispatching plan management, and the safe and stable operation of the power grid and the full utilization of the wind power are guaranteed. However, the quality and the availability of the uploaded power prediction data of the existing wind power plant with the power prediction capability are not high. Therefore, it is necessary to determine whether the data of the wind farm power prediction can be used by determining the error of the wind farm power prediction, so that when the error of the power prediction is large, a rectification requirement needs to be made for the wind farm, a wind farm owner is promoted to continuously reduce the error of the wind farm power prediction, and a reference is provided for determining a power generation plan for a power grid.
In the related technology, the error of wind power prediction has no related systematic research and unified evaluation index system. Most new energy power generation enterprises determine the error conditions of power prediction data and actual power data by means of self experience, and the method only subjectively evaluates the error of power prediction, so that the evaluation result of the power prediction error is inaccurate.
Disclosure of Invention
The application provides a comprehensive evaluation method and device for a wind power prediction error and a storage medium, and aims to solve the technical problem that an evaluation result of the power prediction error is inaccurate in the related technology.
An embodiment of the first aspect of the present application provides a comprehensive evaluation method for a wind power prediction error, including:
acquiring power prediction data and actual power data in a preset time period of a wind power plant;
calculating to obtain an average absolute percentage error MAPE of the power prediction of the wind power plant according to the power prediction data and the actual power data in the preset time period;
calculating to obtain an error qualified rate index of the wind power prediction according to the power prediction data and the actual power data in the preset time period;
and comprehensively evaluating the wind power prediction error through the MAPE and the error qualification rate index.
Optionally, the calculating, according to the predicted power data and the actual power data in the preset time period, the MAPE of the power prediction of the wind farm includes: calculating to obtain the MAPE predicted by the power of the wind power plant through a first formula according to the predicted power data and the actual power data in the preset time period, wherein the first formula is as follows:
wherein n represents the time within the preset time periodTotal number, A i Representing actual power data of said wind park at moment i, F i And representing power prediction data of the wind power plant at the ith moment, wherein epsilon represents a parameter value.
Optionally, the calculating, according to the power prediction data and the actual power data in the preset time period, an error yield index of the wind power prediction is obtained, and the calculating includes: calculating an error qualification rate index of the wind power plant power prediction through a second formula according to the power prediction data and the actual power data in the preset time period, wherein the second formula is as follows:
wherein n represents the total number of time within the preset time period, Q i And the error qualification rate of the power prediction data and the actual power data at the ith moment in the preset time period is represented.
Optionally, the comprehensively evaluating the wind power prediction error through the MAPE and the error yield index includes: according to the MAPE and the error qualification rate index, comprehensively evaluating the wind power prediction error through a calculation result of a third formula, wherein the third formula is as follows:
E＝α×MAPE+(1-α)×R
wherein α represents a weight coefficient of the MAPE, and 1- α represents a weight coefficient of the error yield index.
The embodiment of the second aspect of the present application provides a comprehensive evaluation device for a wind power prediction error, including:
the acquisition module is used for acquiring power prediction data and actual power data in a preset time period of the wind power plant;
the first calculation module is used for calculating MAPE of the power prediction of the wind power plant according to the power prediction data and the actual power data in the preset time period;
the second calculation module is used for calculating to obtain an error qualification rate index of the wind power prediction according to the power prediction data and the actual power data in the preset time period;
and the third calculation module is used for comprehensively evaluating the wind power prediction error through the MAPE and the error qualification rate index.
Optionally, the first computing module is further configured to:
calculating to obtain the MAPE predicted by the power of the wind power plant through a first formula according to the predicted power data and the actual power data in the preset time period, wherein the first formula is as follows:
wherein n represents the total number of time within the preset time period, A t Representing actual power data at the t-th moment in a preset time period of the wind power plant, F t And representing power prediction data at the t moment in a preset time period of the wind power plant, wherein epsilon represents a parameter value.
Optionally, the second computing module is further configured to:
calculating an error qualification rate index of the wind power plant power prediction through a second formula according to the power prediction data and the actual power data in the preset time period, wherein the second formula is as follows:
wherein n represents the total number of time within the preset time period, Q i And the error qualification rate of the power prediction data and the actual power data at the ith moment in the preset time period is represented.
Optionally, the third computing module is further configured to:
according to the MAPE and the error qualification rate index, comprehensively evaluating the wind power prediction error through a calculation result of a third formula, wherein the third formula is as follows:
E＝α×MAPE+(1-α)×R
wherein α represents a weight coefficient of the MAPE, and 1- α represents a weight coefficient of the error yield indicator.
A computer storage medium provided in an embodiment of the third aspect of the present application, where the computer storage medium stores computer-executable instructions; the computer executable instructions, when executed by a processor, are capable of performing the method of the first aspect as described above.
A computer device according to an embodiment of a fourth aspect of the present application includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the method according to the first aspect is implemented.
The technical scheme provided by the embodiment of the application at least has the following beneficial effects:
according to the comprehensive evaluation method, device and storage medium for the wind power prediction error, power prediction data and actual power data in a preset time period of a wind power plant are obtained, MAPE of wind power plant power prediction is obtained through calculation according to the power prediction data and the actual power data in the preset time period, an error qualification rate index of wind power prediction is obtained through calculation according to the power prediction data and the actual power data in the preset time period, and the wind power prediction error is comprehensively evaluated through the MAPE and the error qualification rate index. The wind power prediction error is comprehensively evaluated by utilizing the power prediction data and the actual power data, and the problem of human subjectivity is eliminated, so that the obtained evaluation result is more objective and accurate. Meanwhile, the root mean square error of the power prediction of the wind power station is obtained through calculation, so that the difference of the wind power station with different starting capacities in the magnitude of deviation is avoided, and the accuracy of the power prediction error is improved.
In addition, when the MAPE predicted by the wind power plant power is obtained through calculation of the second formula according to the power prediction data and the actual power data in the preset time period, the denominator in the first formula is processed, so that the method and the device are applicable to a scene with the actual power being 0, and the application range is wide.
Additional aspects and advantages of the present application will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the present application.
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The foregoing and/or additional aspects and advantages of the present application will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
fig. 1 is a schematic flow chart of a comprehensive evaluation method for wind power prediction errors according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of a comprehensive evaluation device for wind power prediction errors according to an embodiment of the present application.
Detailed Description
Reference will now be made in detail to embodiments of the present application, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are exemplary and intended to be used for explaining the present application and should not be construed as limiting the present application.
The comprehensive evaluation method and device for the wind power prediction error according to the embodiment of the application are described below with reference to the accompanying drawings.
Example one
Fig. 1 is a schematic flow diagram of a comprehensive evaluation method for a wind power prediction error according to an embodiment of the present application, and as shown in fig. 1, the method may include:
In an embodiment of the present application, the preset time period may be 4 hours or 1 day. And, in an embodiment of the present application, a plurality of times may be included within the preset time period. For example, assuming that the preset time period is 4 hours in the past and 15 minutes are used as an interval, 16 time instants may be included in the 4 hours in the past.
In one embodiment of the application, different wind farms can use different power prediction methods to perform power prediction, so that when the wind farm power prediction methods are different, the corresponding obtained power prediction data are also different.
Further, in an embodiment of the present application, the actual power data within the preset time period may be obtained by the power monitoring system.
In an embodiment of the application, the power monitoring system may monitor, in real time, actual output power of the wind farm corresponding to each time within a preset time period. In an embodiment of the application, the actual power data at each moment in the preset time period of the wind farm can be obtained through the power monitoring system.
In an embodiment of the application, the method for calculating the MAPE of the wind farm power prediction according to the power prediction data and the actual power data in the preset time period may include: calculating MAPE of wind power plant power prediction through a first formula according to power prediction data and actual power data in a preset time period, wherein the first formula is as follows:
wherein n represents the total number of time within a preset time period, A i Representing actual power data at the ith moment in a preset time period of the wind farm, F i And the power prediction data at the ith moment in a preset time period of the wind power plant are represented, and epsilon represents a parameter value.
It should be noted that in one embodiment of the present application, MAPE may be used to evaluate the predicted performance, but the existing MAPE corresponds to the following formula:
wherein, A i Representing the actual value at time i, F i Indicates the predicted value at the i-th time, and n indicates the total number of times. The formula corresponding to the existing MAPE does not consider A i The case of 0 occurs. However, in practical industrial application scenarios, there may be a large number of 0's in the set of actual values, such as in the case of actual power versus predicted power, and there may be a large number of 0's in the actual power, and thus it is impossible to calculate according to the above existing MAPE formula. When the first formula calculates the MAPE, the denominator is processed, so that the first formula can be applied to a scenario where the actual power is 0.
And in one embodiment of the present application, epsilon in the first formula can be set artificially according to actual conditions. For example, ε =10 -9 Since the value of ε is extremely small, based on this when A i When the value is non-zero, the influence of epsilon on MAPE calculation is negligible, and the first formula can be considered as equivalent to the MAPE calculation formula, and when A is i When the value is zero, the denominator is not zero due to the epsilon, so that the problem of expression failure caused by the denominator being zero in the formula is solved, and therefore, the first formula can be applied to various practical application scenes and is wider in application range.
And 103, calculating to obtain an error qualification rate index of the wind power plant power prediction according to the power prediction data and the actual power data in the preset time period.
In an embodiment of the disclosure, the error yield index may represent an error yield of the power prediction, and the smaller the error yield is, the smaller the error of the power prediction is, and the more accurate the result of the power prediction is.
Specifically, in an embodiment of the present application, the method for calculating the error yield index of the wind power prediction according to the power prediction data and the actual power data within the preset time period may include: calculating an error qualification rate index of the wind power plant power prediction through a second formula according to the power prediction data and the actual power data in the preset time period, wherein the second formula is as follows:
wherein n represents the total number of time within a preset time period, Q i And the error qualification rate of the power prediction data and the actual power data at the ith moment in the preset time period is represented.
Wherein, in one embodiment of the present application, the Q is determined by i The calculation formula is specifically as follows:
wherein A is i Representing actual power data at the ith moment in a preset time period of the wind farm, F i And representing power prediction data at the ith moment in a preset time period of the wind power plant.
And, in one embodiment of the present application, the above Q i When the error rate of the power prediction data and the actual power data at the ith moment is greater than the error rate threshold (such as 0.1), the error rate of the power prediction data at the ith moment is considered to be too large to be directly used.
Further, in an embodiment of the present application, the error yield of the power prediction data and the actual power data within the preset time period obtained by the calculation of the second formula may reflect an occupation ratio of the power prediction data and the power prediction data satisfying the error rate threshold in the actual power data at each time within the preset time period, and when the occupation ratio satisfying the error rate threshold within the preset time period is smaller, it indicates that the times smaller than the error rate threshold in the power prediction data and the actual power data within the preset time period are more, and further indicates that the overall prediction trend of the power prediction data is better, and the generated error is smaller.
And step 104, comprehensively evaluating the wind power prediction error through the MAPE and the error qualification rate index.
In an embodiment of the present application, a method for comprehensively evaluating a wind power prediction error through MAPE and an error yield index may include: according to the MAPE and the error qualification rate index, comprehensively evaluating the wind power prediction error through a calculation result of a third formula, wherein the third formula is as follows:
E＝α×MAPE+(1-α)×R。
where α represents a weight coefficient of MAPE, and 1- α represents a weight coefficient of the error yield index.
In an embodiment of the disclosure, the method for comprehensively evaluating the wind power prediction error through the calculation result of the third formula may include that when the calculation result E of the third formula is greater than a preset threshold, it may be determined that the deviation of the wind farm power prediction is large, and an adjustment and modification request needs to be made for the wind farm.
In summary, in the comprehensive evaluation method for the wind power prediction error provided by the application, the power prediction data and the actual power data in the preset time period of the wind farm are obtained, the MAPE of the wind farm power prediction is calculated according to the power prediction data and the actual power data in the preset time period, the error qualification rate index of the wind power prediction is calculated according to the power prediction data and the actual power data in the preset time period, and the wind power prediction error is comprehensively evaluated through the MAPE and the error qualification rate index. The wind power prediction error is comprehensively evaluated by utilizing the power prediction data and the actual power data, and the problem of human subjectivity is eliminated, so that the obtained evaluation result is more objective and accurate. Meanwhile, the root mean square error of the power prediction of the wind power station is obtained through calculation, so that the difference of the wind power station with different starting capacities in the magnitude of deviation is avoided, and the accuracy of the power prediction error is improved.
In addition, when the MAPE predicted by the wind power plant power is obtained through calculation of the second formula according to the power prediction data and the actual power data in the preset time period, the denominator in the first formula is processed, so that the method and the device are applicable to a scene with the actual power being 0, and the application range is wide.
Example two
Fig. 2 is a schematic structural diagram of a comprehensive evaluation device for wind power prediction error according to an embodiment of the present application, and as shown in fig. 2, the device may include:
the obtaining module 201 is configured to obtain power prediction data and actual power data in a preset time period of a wind farm;
the first calculation module 202 is configured to calculate a MAPE of the power prediction of the wind farm according to the power prediction data and the actual power data in a preset time period;
the second calculation module 203 is used for calculating an error qualification rate index of wind power prediction according to the power prediction data and the actual power data in the preset time period;
and the third calculation module 204 is used for comprehensively evaluating the wind power prediction error through the MAPE and the error qualification rate index.
Optionally, the first calculating module 202 is further configured to:
calculating MAPE of wind power plant power prediction through a first formula according to power prediction data and actual power data in a preset time period, wherein the first formula is as follows:
wherein n represents the total number of time within a preset time period, A i Representing actual power data at the i-th moment of the wind farm, F i And representing power prediction data of the wind power plant at the ith moment, wherein epsilon represents a parameter value.
Optionally, the second calculating module 203 is further configured to:
calculating an error qualification rate index of the wind power plant power prediction through a second formula according to the power prediction data and the actual power data in the preset time period, wherein the second formula is as follows:
wherein n represents the total number of time within a preset time period, Q i And the error qualified rate of the power prediction data and the actual power data at the ith moment in the preset time period is represented.
Optionally, the third calculating module 204 is further configured to:
according to the MAPE and the error qualification rate index, comprehensively evaluating the wind power prediction error through a calculation result of a third formula, wherein the third formula is as follows:
E＝α×MAPE+(1-α)×R
where α represents a weight coefficient corresponding to the root mean square error, and 1- α represents a weight coefficient of the error yield index.
In summary, in the comprehensive evaluation device for the wind power prediction error provided by the application, the power prediction data and the actual power data in the preset time period of the wind farm are obtained, the MAPE of the wind farm power prediction is calculated according to the power prediction data and the actual power data in the preset time period, the error qualification rate index of the wind power prediction is calculated according to the power prediction data and the actual power data in the preset time period, and the wind power prediction error is comprehensively evaluated through the MAPE and the error qualification rate index. The wind power prediction error is comprehensively evaluated by utilizing the power prediction data and the actual power data, and the problem of human subjectivity is eliminated, so that the obtained evaluation result is more objective and accurate. Meanwhile, the root mean square error of the power prediction of the wind power station is obtained through calculation, so that the difference of the wind power station with different starting capacities in the magnitude of deviation is avoided, and the accuracy of the power prediction error is improved.
In addition, when the MAPE predicted by the wind power plant power is obtained through calculation of the second formula according to the power prediction data and the actual power data in the preset time period, the denominator in the first formula is processed, so that the method and the device can be suitable for a scene with the actual power being 0, and the application range is wide.
In order to implement the above embodiments, the present disclosure also provides a computer storage medium.
The computer storage medium provided by the embodiment of the disclosure stores an executable program; the executable program, when executed by a processor, enables the method as shown in figure 1 to be implemented.
In order to implement the above embodiments, the present disclosure also provides a computer device.
The computer equipment provided by the embodiment of the disclosure comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor; the processor, when executing the program, is capable of implementing the method as shown in any of fig. 1.
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 application. 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.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing steps of a custom logic function or process, and alternate implementations are included within the scope of the preferred embodiment of the present application in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present application.
Although embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present application, and that variations, modifications, substitutions and alterations may be made to the above embodiments by those of ordinary skill in the art within the scope of the present application.
Claims (10)
1. A comprehensive evaluation method for wind power prediction errors is characterized by comprising the following steps:
acquiring power prediction data and actual power data in a preset time period of a wind power plant;
calculating to obtain an average absolute percentage error MAPE of the power prediction of the wind power plant according to the power prediction data and the actual power data in the preset time period;
calculating to obtain an error qualification rate index of the wind power prediction according to the power prediction data and the actual power data in the preset time period;
and comprehensively evaluating the wind power prediction error through the MAPE and the error qualification rate index.
2. The method according to claim 1, wherein the calculating the MAPE of the wind farm power prediction from the predicted power data and the actual power data over the preset time period comprises: calculating to obtain the MAPE predicted by the power of the wind power plant through a first formula according to the predicted power data and the actual power data in the preset time period, wherein the first formula is as follows:
wherein n represents the total number of time within the preset time period, A i Representing actual power data of said wind park at moment i, F i And representing power prediction data of the wind power plant at the ith moment, and epsilon represents a parameter value.
3. The method according to claim 1, wherein the calculating an error qualification rate index of the wind power prediction according to the power prediction data and the actual power data in the preset time period comprises: calculating an error qualification rate index of the wind power plant power prediction through a second formula according to the power prediction data and the actual power data in the preset time period, wherein the second formula is as follows:
wherein n represents the total number of time within the preset time period, Q i And the error qualification rate of the power prediction data and the actual power data at the ith moment in the preset time period is represented.
4. The method of claim 1, wherein said comprehensively evaluating said wind power prediction error via said MAPE and said error qualification rate indicator comprises: according to the MAPE and the error qualification rate index, comprehensively evaluating the wind power prediction error through a calculation result of a third formula, wherein the third formula is as follows:
E＝α×MAPE+(1-α)×R
wherein α represents a weight coefficient of the MAPE, and 1- α represents a weight coefficient of the error yield indicator.
5. A comprehensive evaluation device for wind power prediction errors is characterized by comprising:
the acquisition module is used for acquiring power prediction data and actual power data in a preset time period of the wind power plant;
the first calculation module is used for calculating MAPE of the power prediction of the wind power plant according to the power prediction data and the actual power data in the preset time period;
the second calculation module is used for calculating to obtain an error qualification rate index of the wind power prediction according to the power prediction data and the actual power data in the preset time period;
and the third calculation module is used for comprehensively evaluating the wind power prediction error through the MAPE and the error qualification rate index.
6. The apparatus of claim 5, wherein the first computing module is further configured to:
calculating to obtain MAPE of the wind power plant power prediction through a first formula according to the power prediction data and the actual power data in the preset time period, wherein the first formula is as follows:
wherein n represents the total number of time within the preset time period, A i Representing actual power data of said wind park at moment i, F i And representing power prediction data of the wind power plant at the ith moment, and epsilon represents a parameter value.
7. The apparatus of claim 5, wherein the second computing module is further configured to:
calculating an error qualification rate index of the wind power plant power prediction through a second formula according to the power prediction data and the actual power data in the preset time period, wherein the second formula is as follows:
wherein n represents the total number of time within the preset time period, Q i And the error qualification rate of the power prediction data and the actual power data at the ith moment in the preset time period is represented.
8. The apparatus of claim 5, wherein the third computing module is further configured to:
according to the MAPE and the error qualification rate index, comprehensively evaluating the wind power prediction error through a calculation result of a third formula, wherein the third formula is as follows:
E＝α×MAPE+(1-α)×R
where α represents a weight coefficient corresponding to the root mean square error, and 1- α represents a weight coefficient of the error yield index.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor being capable of implementing the method of any one of claims 1 to 4 when the program is executed by the processor.
10. A computer storage medium, wherein the computer storage medium stores computer-executable instructions; the computer-executable instructions, when executed by a processor, are capable of performing the method of any one of claims 1-4.
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