CN115358495B - Calculation method for wind power prediction comprehensive deviation rate - Google Patents
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Abstract
According to the calculation method, the calculation device and the storage medium for the wind power prediction comprehensive deviation rate, power prediction data and actual power data in a preset time period of a wind power plant are obtained, a correlation deviation value of wind power prediction is calculated according to the power prediction data and the actual power data in the preset time period, MAPE of wind power plant power prediction is calculated according to the power prediction data and the actual power data in the preset time period, and CROB of the wind power plant power prediction is calculated according to the correlation deviation value and the MAPE. The accuracy of the prediction precision is improved, and the application range is wide.
Description
Technical Field
The application relates to the technical field of wind power plant energy storage, in particular to a method and a device for calculating a wind power prediction comprehensive deviation rate 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, the deviation of the wind power plant power prediction needs to be determined to determine the prediction precision of the wind power plant power prediction, so that when the prediction precision of the power prediction does not meet the standard, a rectification requirement needs to be provided for the wind power plant, a wind power plant owner is promoted to continuously improve the wind power prediction accuracy, and a reference is provided for the power grid to determine a power generation plan.
In the related technology, the deviation of wind power prediction has no related systematic research and unified evaluation index system. Most new energy power generation enterprises determine the deviation condition of power prediction data and actual power data by depending on self experience, and determine the prediction precision of power prediction based on the deviation condition, so that the method has the problems of inaccuracy and strong subjectivity, and the determined prediction precision is inaccurate; and determining the deviation between the power prediction data and the actual power data by using a conventional MAPE (Mean Absolute Percentage Error), which is meaningless due to being unable to calculate in some specific scenarios, for example, in a scenario where the actual power is 0, the Percentage Error between the power prediction data and the actual power data is unable to be calculated by using the conventional MAPE, so that the application range is limited.
Disclosure of Invention
The application provides a method and a device for calculating a wind power prediction comprehensive deviation rate and a storage medium, which are used for solving the technical problems of inaccurate prediction precision and limited application range in the related technologies.
An embodiment of the first aspect of the present application provides a method for calculating a wind power prediction comprehensive deviation ratio, including:
acquiring power prediction data and actual power data in a preset time period of a wind power plant;
calculating to obtain a correlation deviation value 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 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;
and calculating to obtain a comprehensive deviation rate CROB of the wind power plant power prediction according to the correlation deviation value and the MAPE.
Optionally, the calculating, according to the power prediction data and the actual power data in the preset time period, to obtain a correlation deviation value of the wind farm power prediction includes: calculating to obtain a correlation deviation value 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, the
A correlation deviation value representing the power prediction, the
Representing a covariance of predicted power data and actual power data over the predetermined time period, the
A variance representing power prediction data over the preset time period, the
Representing the variance of the actual power data over the preset time period.
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 MAPE 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, and the time is the total number of the time within the preset time period
Actual power data representing the t-th moment within a preset time period of the wind farm, the
Representing power prediction data at the t-th moment in a preset time period of the wind power plant, the
Representing the parameter value.
Optionally, the calculating, according to the correlation deviation value and the MAPE, the CROB of the wind farm power prediction includes: according to the correlation deviation value and the MAPE, calculating to obtain a CROB of the wind power plant power prediction through a third formula, wherein the third formula is as follows:
wherein, the
And representing the weight coefficient corresponding to the MAPE.
An embodiment of a second aspect of the present application provides a device for calculating a wind power prediction comprehensive deviation ratio, including:
the acquisition module is used for acquiring corrected power prediction data at each moment in a first preset time period of the wind power plant;
the first 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 to obtain a correlation deviation value 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 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;
and the third calculating module is used for calculating the CROB of the wind power plant power prediction according to the correlation deviation value and the MAPE.
Optionally, the first computing module is further configured to: calculating to obtain a correlation deviation value 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, the
A correlation deviation value representing a prediction of said wind farm power, said
Representing a covariance of predicted power data and actual power data over the predetermined time period, the
A variance representing power prediction data for the preset time period, the
Representing the variance of the actual power data over the preset time period.
Optionally, the second calculating module is further configured to:
calculating to obtain MAPE of the wind power plant power prediction through a second formula based on 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, and the time is the total number of the time within the preset time period
Actual power data representing the t-th moment in the preset time period of the wind power plant, wherein
Representing power prediction data at the t-th moment in a preset time period of the wind power plant, the
Representing the parameter value.
Optionally, the third computing module is further configured to:
according to the correlation deviation value and the MAPE, calculating to obtain the CROB of the wind power plant power prediction through a third formula, wherein the third formula is as follows:
wherein, the
And representing the weight coefficient corresponding to the MAPE.
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, enable the method of the first aspect as described above.
A computer device according to an embodiment of the 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 of the present application can be implemented.
The technical scheme provided by the embodiment of the application at least has the following beneficial effects:
in the method and the device for calculating the wind power prediction comprehensive deviation rate and the storage medium,
the method comprises the steps of obtaining power prediction data and actual power data of a wind power plant in a preset time period, calculating to obtain a correlation deviation value of wind power prediction according to the power prediction data and the actual power data in the preset time period, calculating to obtain MAPE of the wind power plant power prediction according to the power prediction data and the actual power data in the preset time period, and calculating to obtain CROB of the wind power plant power prediction according to the correlation deviation value and the MAPE. 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 second 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. Meanwhile, according to the correlation deviation value and the MAPE, the CROB of the power prediction of the wind power plant is obtained through calculation, the comprehensive deviation rate is obtained through calculation by utilizing the power prediction data and the actual power data, the problem of artificial subjectivity is eliminated, the obtained result is more objective and accurate, and the accuracy of the prediction precision is improved.
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 above 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 flowchart of a method for calculating a wind power prediction comprehensive deviation ratio according to an embodiment of the application;
fig. 2 is a schematic structural diagram of a computing device for predicting a comprehensive deviation ratio of wind power 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 method and the device for calculating the wind power prediction comprehensive deviation rate in the embodiment of the application are described below with reference to the accompanying drawings.
Example one
Fig. 1 is a schematic flowchart of a method for calculating a wind power prediction comprehensive deviation ratio 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 in the preset time period may be obtained through a power monitoring system.
In an embodiment of the application, the power monitoring system can monitor the actual output power of the wind farm in real time at each moment in 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.
And step 102, calculating to obtain a correlation deviation value of the power prediction of the wind power plant according to the power prediction data and the actual power data in a preset time period.
In an embodiment of the present disclosure, the correlation deviation value may reflect the correlation between two variables. Specifically, the more strongly two variables are correlated, the more information one of the variables reflects about the other.
And, in one embodiment of the present application, predicting data based on power over a predetermined time period
The method for calculating the correlation deviation value of the prediction model by using the actual power data can comprise the following steps: according to the power prediction data and the actual power data in the preset time period, calculating through a first formula to obtain a correlation deviation value of the wind power plant power prediction, wherein the first formula is as follows:
wherein, the first and the second end of the pipe are connected with each other,
a correlation deviation value representing the power prediction,
represents the covariance of the predicted power data and the actual power data within a preset time period,
represents the variance of the power prediction data over a preset time period,
indicating a preset time periodThe variance of the actual power data in.
And, in one embodiment of the present application, in the first formula above,
wherein n is the total number of time within a preset time period,
is the actual power data at the moment i of the wind farm,
represents the power forecast data at the moment i of the wind farm,
represents the variance of the actual power data over a preset period of time,
representing the covariance of the actual power data over a preset time period.
Further, in an embodiment of the present application, a correlation deviation value between the actual power data and the predicted power data can be obtained through step 102, and when the obtained correlation deviation value is larger, it indicates that the correlation between the actual power data and the predicted power data is stronger, thereby indicating that the predicted power data is closer to the actual power data, and further indicating that the accuracy of the predicted power data is higher.
For example, table 1 shows the correspondence relationship between 16 past moments (each moment is 15 minutes) of a wind farm and a correlation deviation value.
TABLE 1
| Time of day | Correlation deviation value |
| Time 1 | 0.976477 |
| Time 2 | 0.998205 |
| Time 3 | 0.974902 |
| Time 4 | 0.945927 |
| Time 5 | 0.918284 |
| Time 6 | 0.892062 |
| Time 7 | 0.867294 |
| Time 8 | 0.847403 |
| Time 9 | 0.83021 |
| Time 10 | 0.81483 |
| Time 11 | 0.800189 |
| Time 12 | 0.786246 |
| Time 13 | 0.774228 |
| Time 14 | 0.763485 |
| Time 15 | 0.753071 |
| Time 16 | 0.744099 |
Referring to table 1, the corresponding correlation deviation value can be obtained according to the time, for example, the corresponding correlation deviation value can be obtained by table 1 at the 1 st time, which is 0.976477.
Among them, in an embodiment of the present application, it is shown with reference to table 1 that as the time between the predicted time and the current time is longer, the smaller the correlation coefficient, the lower the accuracy of power prediction is, which is consistent with the actual situation, based on which it is described that the accuracy of the above-proposed correlation deviation value for identifying the predicted power is objectively accurate.
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 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,
representing actual power data at a time t within a preset time period of the wind farm,
representing power prediction data at the t moment in a preset time period of the wind farm,
representing the value of the parameter.
It should be noted that in one embodiment of the present application, MAPE can be used to evaluate the predicted performance, but the existing formula for MAPE is:
wherein, the first and the second end of the pipe are connected with each other,
which represents the actual value at the time t,
indicating time tAnd n represents the total time. The formula corresponding to the existing MAPE does not consider
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. And the second formula processes the denominator when calculating the MAPE, so that the second formula can be applied to a scenario where the actual power is 0.
And, in one embodiment of the present application, in the second formula
The setting can be manually carried out according to the actual situation. For example,
=10 -9 due to the fact that
Is extremely small, based on
In the case of a non-zero value,
for calculation
The effect of (a) can be ignored, and the second formula can be considered to be AND
Is equivalent to
In the case of a value of zero,
the denominator is not zero, so that the problem of expression failure caused by zero denominator in the formula is solved, and the second formula can be applied to various practical application scenes and is wider in application range.
And step 104, calculating to obtain a CROB (Comprehensive Ratio of Bias) of the wind power plant power prediction according to the correlation deviation value and the MAPE.
In an embodiment of the present application, the above-mentioned CROB may be used to indicate a prediction accuracy of the wind farm power prediction.
In an embodiment of the present application, a method for calculating a CROB of a wind farm power prediction according to the correlation deviation value and the MAPE may include: and calculating to obtain a wind power plant power prediction CROB through a third formula according to the correlation deviation value and the MAPE, wherein the third formula is as follows:
wherein the content of the first and second substances,
representing the weight coefficient corresponding to the MAPE.
And, in one embodiment of the present application,
the absolute value of the deviation value of the correlation between the power prediction data and the actual power data is obtained. Wherein, when the deviation between the power prediction data and the actual power data is smaller, the absolute value of the corresponding correlation deviation value is closer to 1, thereby enabling to obtain the power prediction data with the smaller deviation
The closer to 0 the result of (a); when in use
Smaller values of (a) indicate less overall deviation of the power prediction data from the actual power data. Based on this, it can be found that the smaller the value of the above-mentioned CROB, the smaller the deviation between the power prediction data obtained by the wind farm power prediction and the actual power data is, and thus the higher the accuracy of the wind farm power prediction is.
Further, in an embodiment of the present disclosure, a comprehensive deviation rate of the wind farm power prediction may be obtained by the above method, and the prediction accuracy of the wind farm power prediction is represented by the comprehensive deviation rate.
Specifically, in an embodiment of the disclosure, when the comprehensive deviation rate of the power prediction of the wind farm is greater than a preset threshold, it may be determined that the deviation of the power prediction of the wind farm is relatively large, and a rectification requirement needs to be provided for the wind farm, so that a wind farm owner is promoted to continuously improve the accuracy of the wind power prediction, and a reference is provided for determining a power generation plan for a power grid.
In summary, in the calculation method for the wind power prediction comprehensive deviation rate 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 correlation deviation value of the wind power prediction is calculated according to the power prediction data and the actual power data in the preset time period, 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, and the CROB of the wind farm power prediction is calculated according to the correlation deviation value and the MAPE. 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 second 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. Meanwhile, according to the correlation deviation value and the MAPE, the CROB of the power prediction of the wind power plant is obtained through calculation, the comprehensive deviation rate is obtained through calculation by utilizing the power prediction data and the actual power data, the problem of artificial subjectivity is eliminated, the obtained result is more objective and accurate, and the accuracy of the prediction precision is improved.
Example two
Fig. 2 is a schematic structural diagram of a device for calculating a wind power prediction comprehensive deviation ratio according to an embodiment of the present application, and as shown in fig. 2, the device may include:
the first 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 calculating module 202 is used for calculating a correlation deviation value of the power prediction of the wind power plant according to the power prediction data and the actual power data in a preset time period;
the second calculation module 203 is configured to calculate, according to the power prediction data and the actual power data in the preset time period, to obtain a MAPE of the power prediction of the wind farm;
and a third calculating module 204, configured to calculate a CROB of the wind farm power prediction according to the correlation deviation value and the MAPE.
Optionally, the first calculating module 202 is further configured to: according to the power prediction data and the actual power data in the preset time period, calculating through a first formula to obtain a correlation deviation value of the wind power plant power prediction, wherein the first formula is as follows:
wherein the content of the first and second substances,
a correlation deviation value representing the power prediction,
represents the covariance of the predicted power data and the actual power data within a predetermined time period,
represents the variance of the power prediction data over a preset time period,
to representVariance of actual power data within a preset time period.
Optionally, the second calculating module 203 is further configured to:
calculating MAPE of wind power plant power prediction through a second formula based on power prediction data and actual power data in a preset time period, wherein the second formula is as follows:
wherein n represents the total number of time within a preset time period,
representing actual power data at a time t within a preset time period of the wind farm,
representing power prediction data at the t moment in a preset time period of the wind farm,
representing the value of the parameter.
Optionally, the third calculating module 204 is further configured to:
according to the correlation deviation value and the MAPE, calculating to obtain a wind power plant power prediction CROB through a third formula, wherein the third formula is as follows:
wherein the content of the first and second substances,
representing the weight coefficient corresponding to the MAPE.
In summary, in the calculation device for the wind power prediction comprehensive deviation ratio 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 correlation deviation value of the wind power prediction is calculated according to the power prediction data and the actual power data in the preset time period, 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, and the CROB of the wind farm power prediction is calculated according to the correlation deviation value and the MAPE. According to the method and the device, when the MAPE of the wind power plant power prediction is obtained through the 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 second formula is processed, so that the method and the device can be applied to the scene with the actual power being 0, and the application range is wide. Meanwhile, according to the method and the device, the CROB of the wind power plant power prediction is obtained through calculation according to the correlation deviation value and the MAPE, the comprehensive deviation rate is obtained through calculation according to the power prediction data and the actual power data, the problem of artificial subjectivity is solved, the obtained result is objective and accurate, and the accuracy of the prediction precision is improved.
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.
While embodiments of the present application have been shown and described above, it will be understood that the above embodiments are exemplary and should not be construed as limiting the present application and that changes, modifications, substitutions and alterations in the above embodiments may be made by those of ordinary skill in the art within the scope of the present application.
Claims (4)
1. A method for calculating a wind power plant power prediction comprehensive deviation rate 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 a correlation deviation value 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 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 a comprehensive deviation rate CROB of the wind power plant power prediction according to the correlation deviation value and the average absolute percentage error MAPE;
the calculating to obtain the correlation deviation value of the wind power plant power prediction according to the power prediction data and the actual power data in the preset time period comprises the following steps: calculating to obtain a correlation deviation value 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, the
A correlation deviation value representing the power prediction, the
Represents the covariance of the predicted power data and the actual power data within the predetermined time period, the
A variance representing power prediction data for the preset time period, the
Representing a variance of actual power data over the preset time period;
the calculating to obtain the 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 comprises the following steps: calculating to obtain an average absolute percentage error MAPE of the power prediction of the wind farm according to the power prediction data and the actual power data in the preset time period by using a second formula, wherein the second formula is as follows:
wherein n represents the total time within the preset time periodNumber, the
Actual power data representing the t-th moment within a preset time period of the wind farm, the
Representing power prediction data at the t moment in a preset time period of the wind power plant,
representing a parameter value;
the step of calculating a comprehensive deviation rate CROB of the wind power plant power prediction according to the correlation deviation value and the average absolute percentage error MAPE comprises the following steps: according to the correlation deviation value and the average absolute percentage error MAPE, calculating a comprehensive deviation rate CROB of the wind power plant power prediction through a third formula, wherein the third formula is as follows:
wherein, the
And representing the weight coefficient corresponding to the mean absolute percentage error MAPE.
2. A device for calculating a wind farm power prediction integrated deviation ratio, the device 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 a correlation deviation value 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 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;
the third calculation module is used for calculating a comprehensive deviation rate CROB of the wind power plant power prediction according to the correlation deviation value and the average absolute percentage error MAPE;
wherein the first computing module is further configured to: calculating a correlation deviation value of the wind power plant power prediction according to the power prediction data and the actual power data in the preset time period by using a first formula, wherein the first formula is as follows:
wherein, the
A correlation deviation value representing a prediction of said wind farm power, said
Representing a covariance of predicted power data and actual power data over the predetermined time period, the
A variance representing power prediction data for the preset time period, the
Representing a variance of actual power data over the preset time period;
the second computing module is further to:
calculating to obtain an average absolute percentage error MAPE of the power prediction of the wind farm through a second formula based on 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, and the time is the total number of the time within the preset time period
Actual power data representing the t-th moment in the preset time period of the wind power plant, wherein
Representing power prediction data at the t moment in a preset time period of the wind power plant, wherein the power prediction data are obtained by calculating the power prediction data
Representing a parameter value;
the third computing module is further to:
according to the correlation deviation value and the average absolute percentage error MAPE, calculating a comprehensive deviation rate CROB of the wind power plant power prediction through a third formula, wherein the third formula is as follows:
wherein, the
And representing the weight coefficient corresponding to the mean absolute percentage error MAPE.
3. 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 claim 1 when executing the program.
4. 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 claim 1.
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