CN114325877B - Assessment method and device for weather forecast data - Google Patents
Assessment method and device for weather forecast data Download PDFInfo
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Abstract
A method and a device for evaluating weather forecast data are provided, wherein the method comprises the following steps: acquiring real-time weather forecast data of a wind power plant in a first preset time period; searching historical similar forecast data which is most similar to the acquired real-time weather forecast data from the historical weather forecast data of the wind farm in a first preset time period, wherein the historical similar forecast data are provided by a plurality of weather sources; an evaluation index for evaluating the forecast accuracy of each weather source is determined based on the forecast accuracy index of the historical similarity forecast data. By adopting the method and the device for evaluating the weather forecast data, which are disclosed by the embodiment of the invention, the forecast accuracy of each weather source can be evaluated based on the historical forecast effect of the real-time weather system, so that more accurate forecast results of target weather elements can be obtained.
Description
Technical Field
The present disclosure relates generally to the field of wind power generation technology, and more particularly, to a method and apparatus for evaluating weather forecast data.
Background
At present, the data such as wind speed, wind direction and the like of numerical weather forecast can be used as input quantity, and the forecast meteorological elements are converted into output power forecast of a wind farm and a photovoltaic through a forecast algorithm. Therefore, the accurate forecast of the numerical weather forecast can provide important decision support for power dispatching, and is one of important factors for determining the prediction accuracy of the new energy generated power.
The existing numerical forecasting system can generally provide multiple forecasting times per day, and forecasting aging can reach 5-6 days. Taking wind speed forecasting as an example, the power forecasting requirement of the wind power plant is that the wind speed is forecasted every 15 minutes, namely, the numerical weather forecast is required to forecast the wind speed every 15 minutes.
However, at present, for wind speed prediction of a wind power plant, the following two difficulties exist in numerical weather prediction: firstly, because the numerical weather forecast has the condition of advancing or lagging to the forecast of the movement of the weather system, the time point of sudden rise and suddenly fall of the forecast wind speed is difficult to accurately position. Secondly, the numerical weather forecast is called as mesoscale numerical weather forecast, the weather system is only predicted on the mesoscale, the small-scale strong wind gust phenomenon at the position of the wind power plant is difficult to capture, and the wind speed mutation on the small scale cannot be accurately predicted.
Disclosure of Invention
It is an object of exemplary embodiments of the present disclosure to provide a method and apparatus for evaluating weather forecast data, which overcomes at least one of the above-mentioned drawbacks.
In one general aspect, there is provided a method of evaluating weather forecast data, the method comprising: acquiring real-time weather forecast data of a wind power plant in a first preset time period; searching historical similar forecast data which is most similar to the acquired real-time weather forecast data from the historical weather forecast data of the wind farm in a first preset time period, wherein the historical similar forecast data are provided by a plurality of weather sources; an evaluation index for evaluating the forecast accuracy of each weather source is determined based on the forecast accuracy index of the historical similarity forecast data.
Optionally, the first predetermined time period includes a plurality of time intervals, and the historical similarity forecast data most similar to the real-time weather forecast data for any of the time intervals may be determined by: and acquiring historical weather forecast data of each weather source in the same time period as any time interval, and searching a plurality of pieces of historical similar forecast data which are similar to the real-time forecast data of any time interval from the acquired historical weather forecast data.
Alternatively, the prediction accuracy index of any segment of historical similar prediction data having similarity to the real-time prediction data of any time interval searched from any one of each of the weather sources may be determined by: acquiring historical observation data which are in the same time period with the searched historical similar forecast data of any section; determining at least one similarity index of the acquired historical observation data and any section of historical similarity forecast data; and determining the prediction accuracy of the historical similarity prediction data of any section based on the at least one similarity index, and taking the determined prediction accuracy as the prediction accuracy index of the historical similarity prediction data of any section.
Alternatively, an evaluation index for evaluating the forecast accuracy of any one of the meteorological sources may be determined by: and determining an evaluation index for evaluating the prediction accuracy of any weather source in any time interval based on the prediction accuracy index of the multiple pieces of historical similar prediction data searched from any weather source.
Optionally, the step of determining an evaluation index for evaluating the forecast accuracy of the arbitrary source of the gas at the arbitrary time interval may include: the step of determining the average value of the forecast accuracy index of the plurality of pieces of historical similarity forecast data as an evaluation index for evaluating the forecast accuracy of the arbitrary source of the air image in the arbitrary time interval, or determining an evaluation index for evaluating the forecast accuracy of the arbitrary source of the air image may include: and determining a weight value corresponding to each section of historical similar forecast data based on the similarity degree of each section of historical similar forecast data and the corresponding real-time weather forecast data, and determining an evaluation index for evaluating the forecast accuracy of any one weather source in any time interval based on the forecast accuracy index of each section of historical similar forecast data and the corresponding weight value.
Optionally, the higher the similarity degree between any section of historical similar forecast data and the corresponding real-time weather forecast data is, the larger the corresponding weight value of any section of historical similar forecast data is.
Optionally, the evaluation method may further include: extracting predicted values of target weather elements from real-time weather forecast data provided by each weather source within a second preset time period; and determining a weight value corresponding to each weather source in the time interval according to an evaluation index of the prediction accuracy of each weather source in the time interval aiming at each time interval contained in the second preset time interval, and obtaining a final predicted value of the target weather element in the time interval based on the predicted value of the target weather element in the time interval extracted from each weather source and the corresponding weight value.
In another general aspect, there is provided an apparatus for evaluating weather forecast data, the apparatus comprising: the real-time forecast data acquisition module acquires real-time weather forecast data of the wind power plant in a first preset time period; a historical similar data acquisition module for searching historical similar forecast data which is most similar to the acquired real-time weather forecast data from the historical weather forecast data of the wind farm in a first preset time period, wherein the historical similar data are provided by a plurality of weather sources; the forecast evaluation index determination module determines an evaluation index for evaluating the forecast accuracy of each weather source based on the forecast accuracy index of the historical similar forecast data.
In another general aspect, there is provided a controller comprising: a processor; and the memory is used for storing a computer program which is used for realizing the weather forecast data evaluation method when being executed by the processor.
In another general aspect, there is provided a computer-readable storage medium storing a computer program which, when executed by a processor, implements a method of evaluating weather forecast data as described above.
By adopting the method and the device for evaluating the weather forecast data, which are disclosed by the embodiment of the invention, the forecast accuracy of each weather source can be evaluated based on the historical forecast effect of the real-time weather system, so that more accurate forecast results of target weather elements can be obtained.
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The foregoing and other objects, features and advantages of exemplary embodiments of the present disclosure will become more apparent from the following detailed description, taken in conjunction with the accompanying drawings that illustrate exemplary embodiments.
FIG. 1 illustrates a flow chart of a method of evaluating weather forecast data, according to an exemplary embodiment of the present disclosure;
FIG. 2 illustrates a schematic diagram of an assessment for weather sources according to an exemplary embodiment of the present disclosure;
FIG. 3 illustrates a flowchart of steps for determining an evaluation index for evaluating the forecast accuracy of each meteorological source, according to an exemplary embodiment of the present disclosure;
FIG. 4 illustrates a flowchart of steps to obtain final predicted values of a target weather element over time intervals, according to an exemplary embodiment of the present disclosure;
FIG. 5 illustrates a block diagram of an apparatus for evaluating weather forecast data, according to an exemplary embodiment of the present disclosure;
fig. 6 shows a block diagram of a controller according to an exemplary embodiment of the present disclosure.
Detailed Description
Various example embodiments will now be described more fully with reference to the accompanying drawings, in which some example embodiments are shown.
Wind farms are usually located in mountainous areas with complex topography and topography, most of the wind farms are affected by canyon wind, and meteorological elements such as surface wind speed are difficult to forecast, and in the exemplary embodiment of the disclosure, weather forecast data of a plurality of international authorities are accessed to conduct aggregate forecast, so that accuracy of weather forecast is improved.
When the historical weather forecast data is duplicated, the optimal weather source can actually report weather processes of sudden rise and dip of weather elements (such as wind speed), but the existing weather source selection strategy is as follows: according to the manual selection of experience, or setting an evaluation period, evaluating the weather element prediction accuracy of each weather source in the evaluation period, and automatically selecting the weather source with the highest accuracy. That is, the weather source selection strategy based on the above cannot select the optimal weather source from among the plurality of weather sources at the time of real-time weather forecast.
In order to solve the above problems, the disclosure proposes a method for evaluating the prediction accuracy of each weather source, which evaluates the prediction accuracy of each weather source based on the historically prediction effect of the real-time weather system, and may generate an optimized aggregate prediction result based on the evaluation result, so as to improve the prediction accuracy of the target weather element.
FIG. 1 illustrates a flow chart of a method of evaluating weather forecast data, according to an exemplary embodiment of the present disclosure.
Referring to fig. 1, in step S10, real-time weather forecast data of a wind farm over a first predetermined period of time is acquired.
Here, the real-time weather forecast data may be obtained through a numerical weather forecast, for example, the numerical weather forecast may refer to a method of calculating a numerical value by a large computer under a certain initial value and edge value condition according to the actual condition of the atmosphere, solving a system of equations describing the hydrodynamic and thermodynamic of the weather evolution process, and predicting the atmospheric motion state and weather phenomenon for a certain period of time in the future, that is, a method of making a weather forecast by using the current weather condition as input data.
In exemplary embodiments of the present disclosure, the real-time weather forecast data for the wind farm over the first predetermined period of time may be real-time weather forecast data obtained from any of a plurality of weather sources.
In step S20, historical similar forecast data most similar to the acquired real-time forecast data is searched for from historical weather forecast data of wind farms provided by a plurality of weather sources over a first predetermined period of time.
Here, the searched historical similarity forecast data refers to historical weather forecast data which is in the same time period as the acquired real-time weather forecast data and has similarity with the acquired real-time weather forecast data.
In an example, real-time weather forecast data of a wind farm provided by all weather sources is obtained, and taking site M as an example, real-time weather forecast data of time period site M from eight points on the day to five points on the day provided by each weather source can be obtained. Here, the location where the center of the wind farm is located may be determined as the location where the station M is located.
In this case, historical weather forecast data provided by all weather sources of the wind farm for at least 1 year are obtained, historical weather forecast data of a period from eight points in the day to five points in the day are respectively extracted from each weather source, and historical similar forecast data is searched from the extracted historical weather forecast data.
In a preferred example, the first predetermined time period may be refined, e.g., the first predetermined time period may be divided into a plurality of time intervals. In one example, the time period of the eighth to fifth of the day may be divided at three-hour intervals to obtain three time intervals in total.
Taking a time interval of 8:00-11:00 (hereinafter referred to as a first time interval) as an example, acquiring real-time weather forecast data of the wind power plant in the first time interval, and acquiring historical weather forecast data of the wind power plant in the first time interval for each weather source. That is, a plurality of pieces of historical weather forecast data in the first time interval may be acquired from any one of the weather sources, and each acquired piece of historical weather forecast data is respectively subjected to similarity judgment with the real-time weather forecast data, so that the historical weather forecast data having similarity with the real-time weather forecast data is determined as historical similar forecast data.
Those skilled in the art may utilize various similarity determination methods to determine historical weather forecast data having similarities to real-time weather forecast data, which is not limited in this disclosure.
In one example, historical weather forecast data having similarities to real-time weather forecast data may be determined by:
In the formula (1), E represents a correlation coefficient between real-time weather forecast data and historical weather forecast data, F i represents ith historical weather forecast data, Represents the average value of the historical weather forecast data, O i represents the ith real-time weather forecast data,The average value of the real-time weather forecast data is represented, X represents the number of the historical forecast data, and Y represents the number of the historical observation data.
It should be understood that the above-listed similarity determination method is only an example, and the disclosure is not limited thereto.
For other time intervals, the same manner as described above can be used to obtain historical similar forecast data of real-time weather forecast data for each time interval. As an example, the weather elements may include, but are not limited to, at least one of the following: temperature, humidity, wind speed, wind direction, atmospheric pressure at different heights.
It should be understood that the above-listed embodiments are only examples, and the present disclosure is not limited thereto, and those skilled in the art can adjust the specific parameters listed according to actual needs.
In step S30, an evaluation index for evaluating the forecast accuracy of each weather source is determined based on the forecast accuracy index of the searched historical similar forecast data.
For example, historical observation data that is in the same period as the historical similarity forecast data may be acquired, and the forecast accuracy of the historical similarity forecast data during that period may be determined based on the acquired historical observation data.
Taking the site M as an example, for the first time interval, after each weather source obtains a plurality of pieces of historical similar forecast data in the first time interval, the historical observation data corresponding to each piece of historical weather forecast data in the first time interval can be obtained respectively, and the forecast accuracy of each piece of historical similar forecast data in the first time interval is determined by comparing the historical observation data with the corresponding historical similar forecast data.
And obtaining an evaluation index of the weather source in the first time interval based on the prediction accuracy of the historical similar prediction data of each period searched from the same weather source in the first time interval. That is, in exemplary embodiments of the present disclosure, an evaluation index for each weather source at different time intervals may be obtained for different weather sources.
In the exemplary embodiments of the present disclosure, the historical observation data, the historical weather forecast data, the weather elements contained in the real-time forecast data, and the time resolution of the data should be consistent.
A specific procedure for determining an evaluation index for evaluating the accuracy of the forecast for each weather source is described below in conjunction with fig. 2 and 3.
FIG. 2 illustrates a schematic diagram of an assessment for weather sources according to an exemplary embodiment of the present disclosure. FIG. 3 illustrates a flowchart of steps for determining an evaluation index for evaluating the forecast accuracy of each meteorological source, according to an exemplary embodiment of the present disclosure.
Referring to fig. 3, in step S301, real-time weather forecast data of an ith time interval into which a first predetermined time period is divided is acquired.
For example, the first predetermined period may be divided at preset intervals.
In step S302, historical weather forecast data of the jth weather source in the same period as the ith time interval is acquired, and a plurality of pieces of historical similar forecast data having similarity with the real-time forecast data of the ith time interval are searched from the acquired historical weather forecast data.
For example, taking the site M as an example, assuming that the ith time interval is the first time interval, historical weather forecast data of the jth weather source in the time interval 8:00-11:00 can be acquired, and assuming that historical weather forecast data of 360 days in the time interval 8:00-11:00 is acquired, multiple pieces of historical similar forecast data are searched from the acquired historical weather forecast data of 360 days.
In step S303, history observation data in the same period as the searched kth period history similar forecast data is acquired.
For example, the kth period of historical similar forecast data is historical weather forecast data of one day of 360 days, and at this time, historical observation data of the same day in a time interval of 8:00-11:00 can be obtained.
In step S304, at least one similarity index of the acquired historical observation data and the kth segment of historical similarity forecast data is determined.
As an example, the at least one similarity indicator may include, but is not limited to, at least one of: RMSE (root mean square error), R (correlation coefficient), MAE (mean absolute error). The method for determining the similarity index is common knowledge in the art, and the disclosure will not be repeated in this section.
In step S305, the prediction accuracy of the kth segment of historical similarity prediction data is determined based on at least one similarity index, and the determined prediction accuracy is used as the prediction accuracy index of the kth segment of historical similarity prediction data.
For example, a corresponding weight may be set for each similarity index, and the prediction accuracy of the kth segment of historical similarity prediction data may be obtained based on the value of at least one similarity index and the respective corresponding weight. As an example, a weighted sum of the numerical value of the at least one similarity index and the respective corresponding weight may be determined as the forecast accuracy of the kth segment of historical similarity forecast data.
In an example, assuming that the wind farm assessment standard is R, if the weight corresponding to the similarity index R is set to be 1, the weight corresponding to the other similarity indexes is 0, and if the correlation coefficient r=0.68 of the prediction of the kth-stage historical similarity prediction data and the real-time weather prediction data, the prediction accuracy of the kth-stage historical similarity prediction data is 68%. Or if the wind power plant assessment standard is RMSE, if the weight corresponding to the set similarity index RMSE is 1, the weight corresponding to other similarity indexes is 0, and if the root mean square error RMSE=2.36 of the prediction of the kth historical similar prediction data and the real-time weather prediction data, the prediction accuracy of the kth historical similar prediction data is 1-1/2.36=57%. That is, the prediction accuracy of one section of 0% to 100% can be obtained by using the weight ratios set for different similarity indexes.
In step S306, it is determined whether k is equal to P. Here, P is the number of pieces of historical similar forecast data having similarity to the real-time forecast data of the ith time interval, which are searched from the jth weather source. In this example, the initial value of k is 1.
If it is determined that k is not equal to P, step S307 is performed: so that k=k+1, and returns to at least step S303.
If it is determined that k is equal to P, step S308 is performed: and determining the evaluation index of the jth weather source in the ith time interval.
For example, an evaluation index for evaluating the forecast accuracy of the jth weather source over the ith time interval may be determined based on the forecast accuracy index of the pieces of historical similar forecast data searched from the jth weather source.
In one example, the evaluation index may be obtained by taking an arithmetic average.
For example, for the jth weather source, an average value of the forecast accuracy indexes of the multiple pieces of historical similar forecast data may be determined as an evaluation index for evaluating the forecast accuracy of the jth weather source in the ith time interval.
In another example, the evaluation index may be obtained by means of weighted summation.
For example, based on the similarity degree of each section of history similar forecast data and the corresponding real-time weather forecast data, a weight value corresponding to each section of history similar forecast data is determined, and based on the forecast accuracy index of each section of history similar forecast data and the corresponding weight value, an evaluation index for evaluating the forecast accuracy of any one weather source in any time interval is determined. Here, the higher the similarity degree between any piece of historical similar forecast data and the corresponding real-time weather forecast data is, the larger the corresponding weight value of any piece of historical similar forecast data is.
For example, for the jth weather source, the prediction accuracy index of the multiple pieces of historical similar prediction data may be weighted and summed with various corresponding weight values, and the sum value may be determined as an evaluation index for evaluating the prediction accuracy of the jth weather source in the ith time interval.
In step S309, it is determined whether j is equal to n. Here, n is the number of meteorological sources. In this example, the initial value of j is 1.
If it is determined that j is not equal to n, step S310 is performed: so that j=j+1, and returns to step S302.
If it is determined that j is equal to n, step S311 is performed: it is determined whether i is equal to m. Here, m is the number of the plurality of time intervals included in the first predetermined period. In this example, the initial value of i is 1.
If it is determined that i is not equal to m, step S312 is performed: so that i=i+1, and returns to step S301.
If it is determined that i is equal to m, step S313 is performed: and obtaining the evaluation indexes of the n meteorological sources in m time intervals respectively.
Based on the mode, the evaluation indexes of different meteorological sources in a plurality of time intervals can be obtained, and the evaluation indexes can be calculated into a prediction accuracy of 0-100%. In one example, assuming a total of three weather sources, table 1 shows an example of the evaluation index of the three weather sources at different time intervals:
TABLE 1
| Time interval | Meteorological source 1 | Meteorological source 2 | Meteorological source 3 |
| 2020-07-25_00:00 To 2020-07-25_03:00:00 | 56％ | 38％ | 89％ |
| 2020-07-25_03:00 To 2020-07-25_06:00:00:00 | 22％ | 46％ | 7％ |
| 2020-07-25_06:00 To 2020-07-25_09:00:00 | 78％ | 50％ | 50％ |
| 2020-07-25_09:00 To 2020-07-25_12:00:00 | 76％ | 93％ | 59％ |
| 2020-07-25_12:00 To 2020-07-25_15:00:00 | 34％ | 45％ | 61％ |
| 2020-07-25_15:00:00 To 2020-07-25_18:00:00 | 23％ | 76％ | 79％ |
| …… | …… | …… | …… |
According to the method for evaluating weather forecast data, according to the exemplary embodiment of the disclosure, a historical similar weather system is searched for real-time forecast according to the similarity criteria, an evaluation index capable of representing the real-time forecast level of a weather source is generated by integrating forecast accuracy of a plurality of historical similar weather systems, and an optimized aggregate forecast is generated according to the evaluation index.
In an example, according to the method for evaluating weather forecast data of the exemplary embodiment of the present disclosure, after determining an evaluation index for evaluating the forecast accuracy of each weather source, a weather source may be selected to acquire a predicted value of a weather element based on the evaluation index to be applied in the control of a wind farm or a photovoltaic using the predicted value of the weather element provided by the selected weather source.
FIG. 4 illustrates a flowchart of steps to obtain final predicted values of a target weather element over time intervals, according to an exemplary embodiment of the present disclosure.
Referring to FIG. 4, in step S401, a predicted value of a target weather element is extracted from real-time weather forecast data provided by each weather source over a second predetermined period of time.
For example, one weather element may be selected as the target weather element from a plurality of weather elements included in the real-time weather forecast data.
In step S402, the time intervals included in the second predetermined period of time are determined, that is, the number a of time intervals included in the second predetermined period of time is determined.
For example, the first predetermined period is divided into a plurality of time intervals, in which case it is determined in step S402 which of the divided plurality of time intervals the second predetermined period includes.
In step S403, a weight value corresponding to each weather source in the a-th time interval is determined based on the evaluation index of the forecast accuracy of each weather source in the a-th time interval.
For example, the higher the evaluation index of the prediction accuracy of any weather source in the a-th time interval is, the larger the weight value corresponding to any weather source in the a-th time interval is, and the lower the evaluation index of the prediction accuracy of any weather source in the a-th time interval is, the smaller the weight value corresponding to any weather source in the a-th time interval is.
In step S404, a final predicted value of the target weather element in the a-th time interval is obtained based on the predicted value of the target weather element in the a-th time interval extracted from each weather source and the corresponding weight value.
For example, the predicted value of the target weather element at the a-th time interval extracted from each weather source may be weighted and summed with the corresponding weight value, and the sum value may be determined as the final predicted value of the target weather element at the a-th time interval.
Taking the example shown in table 1 as an example, assuming that the a-th time interval is a time interval from 2020-07-25_00:00:00 to 2020-07-25_03:00:00, the evaluation indexes of the three weather sources in the time interval are 56%, 38% and 89% respectively, taking the target weather element as an example of wind speed, the predicted values of the target weather element in the a-th time interval extracted from each weather source are v1, v2 and v3 respectively, and in this case, the final predicted value of the target weather element in the a-th time interval is: v=v1×56% +v2×38% +v3×89%.
In step S405, it is determined whether a is equal to a. Here, a is the number of time intervals included in the second predetermined period. In this example, the initial value of a is 1.
If it is determined that a is not equal to A, step S406 is performed: so that a=a+1, and returns to step S403.
If it is determined that a is equal to A, step S407 is performed: and obtaining the final predicted value of the target meteorological element in each time interval.
According to the method for evaluating the weather forecast data of the exemplary embodiment of the disclosure, the target weather elements in each weather source forecast data are extracted, weight values are set according to the obtained evaluation indexes of the weather sources, and all weather source forecasts are mixed into an optimized set forecast result.
After obtaining the final predicted value of the target meteorological element in each time interval, the obtained final predicted value of the target meteorological element can be applied to wind power plant or photovoltaic control, for example, taking the target meteorological element as an example, power prediction can be performed based on the final predicted value of the wind speed in each time interval, so as to improve the accuracy of power prediction.
FIG. 5 illustrates a block diagram of an apparatus for evaluating weather forecast data, according to an exemplary embodiment of the present disclosure.
As shown in fig. 5, an apparatus 100 for evaluating weather forecast data according to an exemplary embodiment of the present disclosure includes: a real-time forecast data acquisition module 101, a historical similar data acquisition module 102, and a forecast evaluation index determination module 103.
Specifically, the real-time forecast data acquisition module 101 acquires real-time weather forecast data of the wind farm over a first predetermined period of time.
For example, the real-time weather forecast data for the wind farm over the first predetermined period of time may refer to real-time weather forecast data obtained from any of a plurality of weather sources.
The historical similar data acquisition module 102 searches historical similar forecast data most similar to the acquired real-time forecast data from historical weather forecast data of wind farms provided by a plurality of weather sources over a first predetermined period of time.
For example, the searched historical similarity forecast data may refer to historical weather forecast data that is in the same time period as and has similarity to the acquired real-time weather forecast data.
In an example, the historical similarity data acquisition module 102 may determine historical similarity forecast data for the real-time weather forecast data for any of the time intervals by: for each weather source, historical weather forecast data of the weather source in the same time period as any time interval is acquired, and multiple pieces of historical similar forecast data with similarity with real-time forecast data of any time interval are searched from the acquired historical weather forecast data.
The forecast evaluation index determination module 103 determines an evaluation index for evaluating the forecast accuracy of each weather source based on the forecast accuracy index of the searched historical similar forecast data.
For example, the forecast evaluation index determination module 103 may acquire historical observation data that is in the same period as the historical similarity forecast data, and determine the forecast accuracy of the historical similarity forecast data during that period based on the acquired historical observation data.
In an example, the forecast evaluation index determination module 103 can determine the forecast accuracy index for any segment of historical similar forecast data having similarity to the real-time forecast data for any time interval searched from any of each of the weather sources by: acquiring historical observation data which are in the same time period with any section of searched historical similar forecast data; determining at least one similarity index of the acquired historical observation data and any section of historical similarity forecast data; and determining the prediction accuracy of any section of historical similar prediction data based on at least one similarity index, and taking the determined prediction accuracy as the prediction accuracy index of any section of historical similar prediction data.
For example, the forecast evaluation index determination module 103 may determine an evaluation index for evaluating the forecast accuracy of any of the meteorological sources by: an evaluation index for evaluating the forecast accuracy of any one of the sources at any one of the time intervals is determined based on the forecast accuracy index of the pieces of historical similar forecast data searched from any one of the sources.
In one example, the forecast evaluation index determination module 103 may determine an average of forecast accuracy indexes of the plurality of pieces of historical similar forecast data as an evaluation index for evaluating the forecast accuracy of any one of the sources of air at any one of the time intervals.
In another example, the forecast evaluation index determination module 103 may determine a weight value corresponding to each piece of historical similar forecast data based on the degree of similarity of each piece of historical similar forecast data to the corresponding real-time weather forecast data, and determine an evaluation index for evaluating the forecast accuracy of any one of the weather sources at any one of the time intervals based on the forecast accuracy index of each piece of historical similar forecast data and the corresponding weight value.
Here, the higher the similarity degree between any piece of historical similar forecast data and the corresponding real-time weather forecast data is, the larger the corresponding weight value of any piece of historical similar forecast data is.
In an example, the apparatus for evaluating weather forecast data according to an exemplary embodiment of the present disclosure may further include: the weather element prediction module extracts a predicted value of a target weather element from real-time weather forecast data provided by each weather source in a second preset time period, determines a weight value corresponding to each weather source in the time period according to an evaluation index of the forecast accuracy of each weather source in the time period aiming at each time period contained in the second preset time period, and obtains a final predicted value of the target weather element in the time period based on the predicted value of the target weather element extracted from each weather source in the time period and the corresponding weight value.
Fig. 6 shows a block diagram of a controller according to an exemplary embodiment of the present disclosure.
As shown in fig. 6, the controller 200 according to an exemplary embodiment of the present disclosure includes: a processor 201 and a memory 202.
Specifically, the memory 202 is configured to store a computer program that, when executed by the processor 201, implements the weather forecast data evaluation method described above.
Here, the method of evaluating weather forecast data shown in fig. 1 may be performed in the processor 201 shown in fig. 6. That is, each module shown in fig. 5 may be implemented by a general-purpose hardware processor such as a digital signal processor, a field programmable gate array, or the like, or may be implemented by a special-purpose hardware processor such as a special-purpose chip, or may be implemented in a software manner entirely by a computer program, for example, may be implemented as each module in the processor 201 shown in fig. 6.
There is also provided, in accordance with an exemplary embodiment of the present disclosure, a computer-readable storage medium storing a computer program. The computer readable storage medium stores a computer program which, when executed by a processor, causes the processor to perform the above-described weather forecast data evaluation method. The computer readable recording medium is any data storage device that can store data which can be read out by a computer system. Examples of the computer-readable recording medium include: read-only memory, random access memory, compact disc read-only, magnetic tape, floppy disk, optical data storage device, and carrier waves (such as data transmission through the internet via wired or wireless transmission paths).
In the existing weather source selection strategy, the optimal weather source is selected according to the recent wind speed forecasting effect, but the atmosphere is a chaotic system, and the evaluation of a single weather element cannot represent the forecasting effect of one weather source on the whole weather system at the time. In the method and the device for evaluating the weather forecast data in the exemplary embodiment of the disclosure, the single weather elements are not scored, but the weather sources are evaluated, so that the weather sources which are accurate for real-time weather forecast are obtained.
In the method and the device for evaluating the weather forecast data in the exemplary embodiment of the disclosure, the accuracy of the real-time weather system is evaluated by searching for the historical weather system similar to the real-time weather system and evaluating the forecast accuracy of the similar historical weather system, so that the method and the device are more scientific and reasonable compared with the existing aggregate forecast mode.
While the present disclosure has been particularly shown and described with reference to exemplary embodiments thereof, it will be understood by those of ordinary skill in the art that various changes in form and details may be made therein without departing from the spirit and scope of the present disclosure as defined by the following claims.
Claims (10)
1. A method of evaluating weather forecast data, the method comprising:
Acquiring real-time weather forecast data of a wind power plant in a first preset time period;
Searching historical similar forecast data which is most similar to the acquired real-time weather forecast data from the historical weather forecast data of the wind farm in a first preset time period, wherein the historical similar forecast data are provided by a plurality of weather sources;
Determining an evaluation index for evaluating the forecast accuracy of each weather source based on the forecast accuracy index of the historical similarity forecast data;
Wherein the first predetermined time period includes a plurality of time intervals,
The prediction accuracy index is obtained by the following steps: acquiring historical observation data which are in the same time interval as the historical similar forecast data, and determining the forecast accuracy of the historical similar forecast data in the time interval based on the historical observation data to obtain the forecast accuracy index;
The evaluation index of the forecast accuracy is obtained by the following method: and obtaining an evaluation index of the forecast accuracy of the meteorological source in a corresponding time interval based on the forecast accuracy index of the historical similar forecast data of each section searched from the same meteorological source in any time interval.
2. The method of claim 1, wherein the historical similarity forecast data most similar to the real-time weather forecast data for any of the time intervals is determined by:
And acquiring historical weather forecast data of each weather source in the same time period as any time interval, and searching a plurality of pieces of historical similar forecast data which are similar to the real-time forecast data of any time interval from the acquired historical weather forecast data.
3. The method of claim 2, wherein the prediction accuracy index of any segment of historical similarity prediction data having similarity to the real-time prediction data of any time interval searched from any one of each of the weather sources is determined by:
Acquiring historical observation data which are in the same time period with the searched historical similar forecast data of any section;
determining at least one similarity index of the acquired historical observation data and any section of historical similarity forecast data;
And determining the prediction accuracy of the historical similarity prediction data of any section based on the at least one similarity index, and taking the determined prediction accuracy as the prediction accuracy index of the historical similarity prediction data of any section.
4. The evaluation method according to claim 2, wherein the evaluation index for evaluating the forecast accuracy of any one of the meteorological sources is determined by:
And determining an evaluation index for evaluating the prediction accuracy of any weather source in any time interval based on the prediction accuracy index of the multiple pieces of historical similar prediction data searched from any weather source.
5. The evaluation method according to claim 4, wherein the step of determining an evaluation index for evaluating a forecast accuracy of the arbitrary source of the gas at the arbitrary time interval includes:
Determining an average value of forecast accuracy indexes of the plurality of pieces of historical similar forecast data as an evaluation index for evaluating the forecast accuracy of the arbitrary weather source in the arbitrary time interval,
Or the step of determining an evaluation index for evaluating the forecast accuracy of the arbitrary source of air image includes:
Determining a weight value corresponding to each section of historical similar forecast data based on the similarity degree of each section of historical similar forecast data and the corresponding real-time weather forecast data,
And determining an evaluation index for evaluating the prediction accuracy of any one of the meteorological sources in any time interval based on the prediction accuracy index of each section of historical similar prediction data and the corresponding weight value.
6. The method of claim 5, wherein the higher the similarity between any one piece of historical similarity forecast data and the corresponding real-time weather forecast data, the greater the corresponding weight value of the any one piece of historical similarity forecast data.
7. The evaluation method according to claim 4, characterized in that the evaluation method further comprises:
extracting predicted values of target weather elements from real-time weather forecast data provided by each weather source within a second preset time period;
And determining a weight value corresponding to each weather source in the time interval according to an evaluation index of the prediction accuracy of each weather source in the time interval aiming at each time interval contained in the second preset time interval, and obtaining a final predicted value of the target weather element in the time interval based on the predicted value of the target weather element in the time interval extracted from each weather source and the corresponding weight value.
8. An apparatus for evaluating weather forecast data, the apparatus comprising:
the real-time forecast data acquisition module acquires real-time weather forecast data of the wind power plant in a first preset time period;
a historical similar data acquisition module for searching historical similar forecast data which is most similar to the acquired real-time weather forecast data from the historical weather forecast data of the wind farm in a first preset time period, wherein the historical similar data are provided by a plurality of weather sources;
a forecast evaluation index determination module that determines an evaluation index for evaluating a forecast accuracy of each weather source based on the forecast accuracy index of the historical similarity forecast data;
Wherein the first predetermined time period includes a plurality of time intervals,
The prediction accuracy index is obtained by the following steps: acquiring historical observation data which are in the same time interval as the historical similar forecast data, and determining the forecast accuracy of the historical similar forecast data in the time interval based on the historical observation data to obtain the forecast accuracy index;
The evaluation index of the forecast accuracy is obtained by the following method: and obtaining an evaluation index of the forecast accuracy of the meteorological source in a corresponding time interval based on the forecast accuracy index of the historical similar forecast data of each section searched from the same meteorological source in any time interval.
9. A controller, comprising:
A processor;
a memory for storing a computer program which when executed by the processor implements a method of assessing weather forecast data as claimed in any one of claims 1 to 7.
10. A computer readable storage medium storing a computer program, wherein the computer program when executed by a processor implements a method of assessing weather forecast data as claimed in any one of claims 1 to 7.
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