CN112600189A - Method and device for predicting generated power - Google Patents
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Abstract
The invention provides a method and a device for predicting generated power, wherein the method comprises the following steps: (a) acquiring weather forecast data of a target area at any moment; (b) measuring actual meteorological data of the target area at any moment; (c) performing data assimilation processing on the weather forecast data by using the actual weather data to acquire corrected weather forecast data of the target area at any time; (d) predicting weather forecast data of the target area in a preset time period from the any moment according to the corrected weather forecast data of the target area at the any moment; (e) and inputting the predicted weather forecast data into a power prediction model of the wind generating set in the target area so as to output the generated power of the wind generating set in the target area within a preset time period from the any moment.
Description
Technical Field
The present application relates to the field of wind power generation technologies, and in particular, to a method and an apparatus for predicting a generated power.
Background
With the increase of the number and the capacity of wind power stations in China, the requirements of the wind power stations on weather forecast are increased. In the new energy power prediction service, the accuracy of weather forecast greatly affects the accuracy of the generated power prediction result of the wind farm (especially, the ultra-short-term generated power prediction result of the wind farm, which generally refers to the generated power of the wind farm within 4 hours in the future from the current time, and the time resolution of the ultra-short-term generated power prediction result is 15 minutes).
Therefore, a method and an apparatus for improving the prediction accuracy of ultra-short-term power generation power prediction are urgently needed.
Disclosure of Invention
The invention aims to provide a method and a device for predicting generated power.
According to an aspect of the present invention, there is provided a method for predicting generated power, the method comprising: (a) acquiring weather forecast data of a target area at any moment; (b) measuring actual meteorological data of the target area at any moment; (c) performing data assimilation processing on the weather forecast data by using the actual weather data to acquire corrected weather forecast data of the target area at any time; (d) predicting weather forecast data of the target area in a preset time period from the any moment according to the corrected weather forecast data of the target area at the any moment; (e) and inputting the predicted weather forecast data into a power prediction model of the wind generating set in the target area so as to output the generated power of the wind generating set in the target area within a preset time period from the any moment.
Preferably, the weather forecast data for the target area at said arbitrary time is from a global weather forecast system.
Preferably, a plurality of times separated by predetermined time intervals are included in the predetermined period of time from the arbitrary time.
Preferably, the method further comprises: and (e) repeatedly executing the steps (a) to (e) for each moment in the plurality of moments according to the chronological order so as to output the generated power of the wind generating set in the target area within a preset time period from each moment in the plurality of moments.
Preferably, when the steps (a) to (e) are repeatedly performed for each of the plurality of time instants, the weather forecast data of the target area at the arbitrary time instant is derived from the weather forecast data of the target area for a predetermined period of time from a last time instant adjacent to the arbitrary time instant among the plurality of time instants.
Preferably, the generated power of the wind turbine generator set of the target area in the predetermined time period from the arbitrary time is the ultra-short-term generated power of the wind turbine generator set of the target area in the future 4 hours from the arbitrary time.
According to another aspect of the present invention, there is provided an apparatus for predicting generated power, the apparatus comprising: the weather acquiring unit is used for acquiring weather forecast data of the target area at any time; the meteorological measurement unit is used for measuring actual meteorological data of the target area at any moment; the data assimilation unit is used for carrying out data assimilation processing on the weather forecast data by using the actual weather data so as to acquire weather forecast data of the corrected target area at any time; a weather prediction unit for predicting weather forecast data of the target area in a predetermined time period from the arbitrary time, based on the corrected weather forecast data of the target area at the arbitrary time; and the power output unit is used for inputting the predicted weather forecast data into a power prediction model of the wind generating set in the target area so as to output the generated power of the wind generating set in the target area within a preset time period from the any moment.
Preferably, the weather forecast data for the target area at said arbitrary time is from a global weather forecast system.
Preferably, a plurality of times separated by predetermined time intervals are included in the predetermined period of time from the arbitrary time.
Preferably, the apparatus further comprises: and the cyclic assimilation unit is used for repeatedly executing the weather obtaining unit, the weather measuring unit, the data assimilation unit, the weather prediction unit and the power output unit aiming at each moment in the plurality of moments according to the chronological order so as to output the generated power of the wind generating set in the target area within a preset time period from each moment in the plurality of moments.
Preferably, when the weather acquisition unit, the weather measurement unit, the data assimilation unit, the weather prediction unit, and the power output unit are repeatedly executed for each of the plurality of time instants, the weather forecast data of the target area at the arbitrary time instant is derived from the weather forecast data of the target area within a predetermined period of time from a last time instant adjacent to the arbitrary time instant among the plurality of time instants.
Preferably, the generated power of the wind turbine generator set of the target area in the predetermined time period from the arbitrary time is the ultra-short-term generated power of the wind turbine generator set of the target area in the future 4 hours from the arbitrary time.
According to another aspect of the invention, a computer-readable storage medium is provided, in which a computer program is stored, which, when being executed by a processor, carries out the method for predicting power generation as described above.
According to another aspect of the present invention, there is provided a computer apparatus comprising: a processor; a memory storing a computer program which, when executed by the processor, implements the method for predicting generated power as described above.
The method and the device for predicting the generating power provided by the invention can effectively improve the prediction precision of ultra-short-term generating power prediction of the wind power plant, thereby realizing safe scheduling of a power grid.
Drawings
The above and other objects and features of the present invention will become more apparent from the following description taken in conjunction with the accompanying drawings, in which:
fig. 1 is a flowchart illustrating a process for predicting power generation according to an exemplary embodiment of the present invention;
fig. 2 is a block diagram illustrating a structure of an apparatus for predicting power generation according to an exemplary embodiment of the present invention;
fig. 3 is a schematic diagram illustrating a rapid assimilation process for ultra-short term generation power prediction according to an exemplary embodiment of the present invention;
fig. 4 is another diagram illustrating a rapid assimilation process for ultra-short term generation power prediction according to an exemplary embodiment of the present invention.
Detailed Description
Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings.
Fig. 1 is a flowchart illustrating a process 100 for predicting generated power according to an exemplary embodiment of the present invention. Process 100 may be performed by any end processing device that includes a processor.
Referring to FIG. 1, a process 100 may be initiated.
In block 110, the process 100 may obtain weather forecast data for the target area at any time. The weather forecast data may be obtained by a global weather forecast system (GFS). However, the weather forecast data may not be limited to being obtained by the aforementioned GFS. Therefore, the weather forecast data can also be obtained by other weather forecast systems or other means.
In block 120, the process 100 may measure actual weather data for the target area at the arbitrary time. These actual meteorological data include, but are not limited to, wind farm anemometer tower meteorological data, wind turbine anemometer data, ground station observation data, sounding observation data, and the like.
In block 130, the process 100 may use the actual weather data to perform data assimilation on the weather forecast data to obtain weather forecast data for the modified target area at the arbitrary time.
In block 140, the process 100 may predict weather forecast data for the target area for a predetermined period of time from the arbitrary time based on the modified weather forecast data for the target area at the arbitrary time.
In block 150, the process 100 may input the predicted weather forecast data into a power prediction model of the wind generating sets of the target area to output the generated power of the wind generating sets of the target area for a predetermined period of time from the arbitrary time.
After block 150, process 100 ends.
This manner of correcting the weather forecast data by the assimilation process can provide reliable weather forecast data for the prediction of ultra-short-term power generation for a predetermined period of time (particularly, for 4 hours in the future) from any time point in a target area such as, but not limited to, a wind farm. However, since the weather forecast result needs to be updated every 4 to 6 hours or more, the low-frequency assimilation processing method cannot effectively grasp the weather change of the target area in a predetermined time period from any time.
In view of this, in another example, a plurality of times may be set within a predetermined period from an arbitrary time so that a plurality of times separated by a predetermined time interval may be included within the predetermined period from the arbitrary time, and the process 100 may perform an assimilation process on weather forecast data for each of the plurality of times, thereby implementing a high-frequency fast-cycle assimilation process on weather forecast data for a target area, thereby further improving the accuracy of the weather forecast data. In this example, process 100 may take place in chronological order, with pinsRepeating the blocks 110 to 150 for each of the plurality of time instants to output the generated power of the wind turbine generator set of the target area within a predetermined time period from each of the plurality of time instants. In this example, when blocks 110 to 150 are repeatedly executed for each of the plurality of time instants, the weather forecast data for the target area at the arbitrary time instant may be obtained from the weather forecast data for the target area within a predetermined period of time from a last time instant adjacent to the arbitrary time instant among the plurality of time instants. For example, in the rapid assimilation process 400 for ultra-short term power generation prediction shown in FIG. 4, at time t00The predetermined time period from the time of day may include t separated by a predetermined time interval0Time of day t1Time of day t2Time instant … … tnAt time, process 100 may be at time t from the target region00Acquiring target area t from weather forecast data within dt hours forecasted backward from moment0Weather forecast data of time from target area to t0Acquiring target area t from weather forecast data within dt hours forecasted backward from moment1Weather forecast data of time from target area to t1Acquiring target area t from weather forecast data within dt hours forecasted backward from moment2Weather forecast data for a time of day, and so on, until process 100 may be at time t from the target arean-1Acquiring target area t from weather forecast data within dt hours forecasted backward from momentnWeather forecast data at a time. Accordingly, in the rapid assimilation process 400 for ultra-short term power generation prediction shown in FIG. 4, the process 100 may also measure the target area at t separately0Actual meteorological data at time, target area at t1Actual meteorological data at time, target area at t2Actual weather data at time, … … target area at tnActual meteorological data of time, so as to respectively compare the measured actual meteorological data of the target area at each time with the previously acquired actual meteorological data of the target area at t0Weather forecast data at time, target area at t1Weather forecast data at time, target area at t2Weather forecast data at time, … … target area at tnThe weather forecast data at the time is subjected to fusion analysis (i.e., assimilation processing).
The weather forecast data of the target area can be updated in time by means of high-frequency rapid cyclic assimilation processing of the weather forecast data, and therefore better and more accurate weather forecast data are provided for ultra-short-term power generation power prediction of the target area.
Fig. 2 is a block diagram illustrating a structure of an apparatus 200 for predicting power generation according to an exemplary embodiment of the present invention.
Referring to FIG. 2, the apparatus 200 shown in FIG. 2 may include a weather acquisition unit 210, a weather measurement unit 220, a data assimilation unit 230, a weather prediction unit 240, and a power output unit 250. The weather obtaining unit 210 can be used to obtain weather forecast data of the target area at any time. The weather forecast data may be obtained by a global weather forecast system (GFS). However, the weather forecast data may not be limited to being obtained by the aforementioned GFS. Therefore, the weather forecast data can also be obtained by other weather forecast systems or other means. The weather measurement unit 220 can be used to measure the actual weather data of the target area at the arbitrary time. These actual meteorological data include, but are not limited to, wind farm anemometer tower meteorological data, wind turbine anemometer data, ground station observation data, sounding observation data, and the like. The data assimilation unit 230 is operable to use the actual weather data to perform data assimilation processing on the weather forecast data to obtain weather forecast data of the modified target area at the arbitrary time. The weather prediction unit 240 may be configured to predict weather forecast data of the target area within a predetermined time period from the arbitrary time, based on the corrected weather forecast data of the target area at the arbitrary time. The power output unit 250 may be configured to input the predicted weather forecast data into a power prediction model of the wind turbine generator set of the target area to output the generated power of the wind turbine generator set of the target area for a predetermined period of time from the arbitrary time.
In another example, a plurality of times may be set within a predetermined period from an arbitrary time such that a plurality of times separated by a predetermined time interval may be included within the predetermined period from the arbitrary time. Accordingly, the device 200 shown in FIG. 2 may also include a recycling assimilation unit (not shown). The cyclic assimilation unit may be configured to repeatedly execute the weather obtaining unit 210, the weather measuring unit 220, the data assimilation unit 230, the weather prediction unit 240, and the power output unit 250 of the apparatus 200 shown in fig. 2 for each of the plurality of time instants in chronological order. In this example, when the weather acquisition unit 210, the weather measurement unit 220, the data assimilation unit 230, the weather prediction unit 240, and the power output unit 250 in the apparatus 200 shown in fig. 2 are repeatedly executed for each of the plurality of time instants, the weather forecast data of the target area at the arbitrary time instant may be obtained from the weather forecast data of the target area within a predetermined period of time from the last time instant adjacent to the arbitrary time instant among the plurality of time instants.
The device 200 shown in fig. 2 can update the weather forecast data of the target area in time, so as to provide better and more accurate weather forecast data for ultra-short-term power generation prediction of the target area.
Fig. 3 is a schematic diagram illustrating a fast assimilation process 300 for ultra-short term generation power prediction according to an exemplary embodiment of the present invention.
Referring to fig. 3, the rapid assimilation process 300 for ultra-short term power generation prediction illustrated in fig. 3 may include only 1 cold start, wherein the cold start is from t00The forecast dt hours backward from time.
In the rapid assimilation process 300 shown in FIG. 3, process 100 may utilize a WRF mode pre-processing (WPS) system for t00To t00And analyzing the GFS forecast data in the period of + dt to extract the required meteorological elements to generate a meteorological data file in a specified format, and fusing the meteorological data file and the topographic data file to generate the gridded meteorological data of the target area. Next, the process 100 may generate the original initial field and boundary conditions needed for data assimilation based on the gridded meteorological data for the target area, and may align the field and boundary conditions at t00Implementation of time measurementThe interpersonal/observation data is pre-processed to be processed into a format accessible by a data assimilation (WRFDA) system. Process 100 may then utilize the WRFDA system pair at t00The actual meteorological/observation data measured at the time of day is fusion-analyzed with the previously generated original initial field to generate an analysis field that can be used as the initial field for numerical forecasting and update the boundary conditions. Finally, process 100 may utilize a WRF mesoscale weather forecasting system to perform weather forecasting to obtain a wind farm in the target area at t based on the generated analytic site and the updated boundary conditions00To t00Weather forecast data in the period of + dt for the target area from t00And predicting the ultra-short-term generated power in a dt time period from the moment.
Fig. 4 is another schematic diagram illustrating a fast assimilation process 400 for ultra-short term generation power prediction according to an exemplary embodiment of the present invention.
Referring to fig. 4, the rapid assimilation process 300 for ultra-short term power generation prediction illustrated in fig. 4 may include 1 cold start and n +1 hot starts, wherein the cold start is from t00Forecasting dt hours of data from time to time, 1 st hot start from t0Forecasting dt hours of data from time to time, 2 nd hot start from t1Forecast dt hours of data from time to time, 3 rd hot start from t2Forecasting dt hours of data backwards from time, … … n +1 th hot start from tnForecasting dt hours of data backwards from time, and in subsequent thermal startups, t0Time of day t1Time of day t2Time instant … … tnThe time instants may be separated by a predetermined time interval (e.g., 1 hour).
In the rapid assimilation process 400 shown in FIG. 4, when running a cold start, process 100 may utilize the WPS system for t00To t00And analyzing the GFS forecast data in the period of + dt to extract the required meteorological elements to generate a meteorological data file in a specified format, and fusing the meteorological data file and the topographic data file to generate the gridded meteorological data of the target area. Next, the process 100 may generate data assimilation needs based on the gridded meteorological data for the target areaAnd may be aligned to the original initial field and boundary conditions at t00The actual meteorological/observation data measured at the time is pre-processed to be processed into a format accessible by the WRFDA system. Process 100 may then utilize the WRFDA system pair at t00The actual meteorological data/observation data measured at the time are fusion-analyzed with the generated original initial field to generate an analysis field that can be used as the initial field for numerical prediction and update the boundary conditions. Finally, process 100 may utilize a WRF mesoscale weather forecasting system to perform weather forecasting to obtain a wind farm in the target area at t based on the generated analytic site and the updated boundary conditions00To t00Weather forecast data in the period of + dt for the target area from t00Ultra-short term generation power prediction in dt periods from the moment, and t00The + dt time may be the start time t of the 1 st hot start0。
In the rapid assimilation process 400 shown in FIG. 4, process 100 may utilize the WPS system for t when running the 1 st warm boot0To t0And analyzing the GFS forecast data at the time of + dt to extract required meteorological elements to generate a meteorological data file in a specified format, and fusing the meteorological data file and the topographic data file to generate a gridded meteorological data file of the target area. Next, process 100 may generate only boundary conditions based on the gridded GFS forecast data at the target area, and from t generated by the cold start00To t00Extracting t from forecast result file in period of + dt0Time of day (which may correspond to t in this example)00Moment + dt) as the original assimilation initial field for numerical assimilation, and for the prediction at t0The actual meteorological/observation data measured at the time is pre-processed to be processed into a format accessible by the WRFDA system. Process 100 may then utilize the WRFDA system pair at t0Actual meteorological data/observation data measured at the moment and t extracted previously0The prediction results of the moments are fusion analyzed to generate an analysis field that can be used as an initial field for numerical prediction and update the boundary conditions. Finally, process 100 may utilize the WRF's in the analysis field based on the generated analysis field and updated boundary conditionsThe scale weather forecasting system carries out weather forecasting to obtain the wind power plant at t0To t0Weather forecast data in the period of + dt for the target area from t0Ultra-short term generation power prediction in dt periods from the moment, and t0The + dt time may be the start time t of the 2 nd hot start1。
In the rapid assimilation process 400 shown in FIG. 4, process 100 may utilize the WPS system for t when running the 2 nd warm start1To t1And analyzing the GFS forecast data at the time of + dt to extract required meteorological elements to generate a meteorological data file in a specified format, and fusing the meteorological data file and the topographic data file to generate a gridded meteorological data file of the target area. Next, process 100 may generate only boundary conditions based on the gridded GFS forecast data at the target area, and from t generated by the 1 st hot start0To t0Extracting t from forecast result file in period of + dt1Time of day (which may correspond to t in this example)0Moment + dt) as the original assimilation initial field for numerical assimilation, and for the prediction at t1The actual meteorological/observation data measured at the time is pre-processed to be processed into a format accessible by the WRFDA system. Process 100 may then utilize the WRFDA system pair at t1Actual meteorological data/observation data measured at the moment and t extracted previously1The prediction results of the moments are fusion analyzed to generate an analysis field that can be used as an initial field for numerical prediction and update the boundary conditions. Finally, process 100 may utilize a WRF mesoscale weather forecasting system to perform weather forecasting to obtain wind farm at t based on the generated analytic site and the updated boundary conditions1To t1Weather forecast data in the period of + dt for the target area from t1Ultra-short term generation power prediction in dt periods from the moment, and t1The + dt time may be the starting time t of the 3 rd hot start2。
By analogy, when running the n +1 th warm boot, process 100 may utilize the WPS system for tnTo tnAnalyzing the GFS forecast data at + dt to extract the required gasThe elephant elements generate a weather data file in a specified format and fuse the weather data file with the terrain data file to generate a gridded weather data file for the target area. Next, process 100 may generate only boundary conditions based on the gridded GFS forecast data at the target area, and from t generated by the nth hot startnTo tnExtracting t from forecast result file in period of + dtnTime of day (which may correspond to t in this example)nMoment + dt) as the original assimilation initiation field of the numerical assimilation and can be used for the prediction at tnThe actual meteorological/observation data measured at the time is pre-processed to be processed into a format accessible by the WRFDA system. Process 100 may then utilize the WRFDA system pair at tnActual meteorological data/observation data measured at the moment and t extracted previouslynThe prediction results of the moments are fusion analyzed to generate an analysis field that can be used as an initial field for numerical prediction and update the boundary conditions. Finally, process 100 may utilize a WRF mesoscale weather forecasting system to perform weather forecasting to obtain wind farm at t based on the generated analytic site and the updated boundary conditionsnTo tnWeather forecast data in the period of + dt for the target area from tnAnd predicting the ultra-short-term generated power in a dt time period from the moment.
By adopting the implementation process, the forecasting precision of the ultra-short-term power generation power forecasting of the wind power plant can be effectively improved, and the safe dispatching of the power grid is further realized.
There is also provided, in accordance with an exemplary embodiment of the present invention, a computer-readable storage medium storing a computer program. The computer readable storage medium stores a computer program that, when executed by a processor, causes the processor to perform a method for predicting power generation according to the present invention. The computer readable recording medium is any data storage device that can store data read by a computer system. Examples of the computer-readable recording medium include: read-only memory, random access memory, read-only optical disks, magnetic tapes, floppy disks, optical data storage devices, and carrier waves (such as data transmission through the internet via wired or wireless transmission paths).
There is also provided, in accordance with an exemplary embodiment of the present invention, a computer apparatus. The computer device includes a processor and a memory. The memory is for storing a computer program. The computer program is executed by a processor causing the processor to execute a computer program for a method for predicting power generation according to the present invention.
While the present application has been shown and described with reference to preferred embodiments, it will be understood by those skilled in the art that various changes and modifications may be made to these embodiments without departing from the spirit and scope of the present application as defined by the following claims.
Claims (14)
1. A method for predicting generated power, the method comprising:
(a) acquiring weather forecast data of a target area at any moment;
(b) measuring actual meteorological data of the target area at any moment;
(c) performing data assimilation processing on the weather forecast data by using the actual weather data to acquire corrected weather forecast data of the target area at any time;
(d) predicting weather forecast data of the target area in a preset time period from the any moment according to the corrected weather forecast data of the target area at the any moment;
(e) and inputting the predicted weather forecast data into a power prediction model of the wind generating set in the target area so as to output the generated power of the wind generating set in the target area within a preset time period from the any moment.
2. The method of claim 1, wherein the weather forecast data for the target area at said arbitrary time is from a global weather forecast system.
3. The method of claim 1, comprising a plurality of time instants separated by predetermined time intervals within a predetermined time period from the arbitrary time instant.
4. The method of claim 3, wherein the method further comprises:
and (e) repeatedly executing the steps (a) to (e) for each moment in the plurality of moments according to the chronological order so as to output the generated power of the wind generating set in the target area within a preset time period from each moment in the plurality of moments.
5. The method of claim 4, wherein when steps (a) through (e) are repeatedly performed for each of the plurality of time instants, the weather forecast data for the target area at the arbitrary time instant is derived from the weather forecast data for the target area for a predetermined period of time from a last time instant adjacent to the arbitrary time instant in the plurality of time instants.
6. The method according to any one of claims 1 to 5, wherein the generated power of the wind generating set of the target area in the predetermined time period from the any time is the ultra-short-term generated power of the wind generating set of the target area in the future 4 hours from the any time.
7. An apparatus for predicting generated power, the apparatus comprising:
the weather acquiring unit is used for acquiring weather forecast data of the target area at any time;
the meteorological measurement unit is used for measuring actual meteorological data of the target area at any moment;
the data assimilation unit is used for carrying out data assimilation processing on the weather forecast data by using the actual weather data so as to acquire weather forecast data of the corrected target area at any time;
a weather prediction unit for predicting weather forecast data of the target area in a predetermined time period from the arbitrary time, based on the corrected weather forecast data of the target area at the arbitrary time;
and the power output unit is used for inputting the predicted weather forecast data into a power prediction model of the wind generating set in the target area so as to output the generated power of the wind generating set in the target area within a preset time period from the any moment.
8. The apparatus of claim 7, wherein the weather forecast data for the target area at said arbitrary time is from a global weather forecast system.
9. The apparatus of claim 7, comprising a plurality of times separated by predetermined time intervals within a predetermined time period from the arbitrary time.
10. The apparatus of claim 9, wherein the apparatus further comprises:
and the cyclic assimilation unit is used for repeatedly executing the weather obtaining unit, the weather measuring unit, the data assimilation unit, the weather prediction unit and the power output unit aiming at each moment in the plurality of moments according to the chronological order so as to output the generated power of the wind generating set in the target area within a preset time period from each moment in the plurality of moments.
11. The apparatus of claim 10, wherein when the weather acquisition unit, the weather measurement unit, the data assimilation unit, the weather prediction unit, and the power output unit are repeatedly executed for each of the plurality of time instants, the weather forecast data for the target area at the arbitrary time instant is derived from the weather forecast data for the target area during a predetermined period of time from a last time instant adjacent to the arbitrary time instant among the plurality of time instants.
12. The device according to any one of claims 7 to 11, wherein the generated power of the wind generating set of the target area in the predetermined time period from the any time is the ultra-short-term generated power of the wind generating set of the target area in the future 4 hours from the any time.
13. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the method for predicting power generation according to any one of claims 1 to 6.
14. A computer device, characterized in that the computer device comprises:
a processor;
memory storing a computer program which, when executed by a processor, implements a method for predicting generated power as claimed in any one of claims 1-6.
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Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102269124A (en) * | 2011-06-30 | 2011-12-07 | 内蒙古电力勘测设计院 | Ultra-short term wind power station generated power forecasting system |
| CN102628876A (en) * | 2012-02-13 | 2012-08-08 | 甘肃省电力公司风电技术中心 | Ultra-short term prediction method comprising real-time upstream and downstream effect monitoring |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102269124A (en) * | 2011-06-30 | 2011-12-07 | 内蒙古电力勘测设计院 | Ultra-short term wind power station generated power forecasting system |
| CN102628876A (en) * | 2012-02-13 | 2012-08-08 | 甘肃省电力公司风电技术中心 | Ultra-short term prediction method comprising real-time upstream and downstream effect monitoring |
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