LU503687B1 - A wind power prediction method and system for wind farm - Google Patents
A wind power prediction method and system for wind farm Download PDFInfo
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- LU503687B1 LU503687B1 LU503687A LU503687A LU503687B1 LU 503687 B1 LU503687 B1 LU 503687B1 LU 503687 A LU503687 A LU 503687A LU 503687 A LU503687 A LU 503687A LU 503687 B1 LU503687 B1 LU 503687B1
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- 238000000034 method Methods 0.000 title claims abstract description 20
- 230000003993 interaction Effects 0.000 claims abstract description 9
- 238000005259 measurement Methods 0.000 claims abstract description 8
- 230000003203 everyday effect Effects 0.000 claims abstract description 6
- 238000005192 partition Methods 0.000 claims abstract description 4
- 230000005684 electric field Effects 0.000 claims description 13
- 230000002354 daily effect Effects 0.000 claims description 12
- 238000012544 monitoring process Methods 0.000 claims description 12
- 238000010248 power generation Methods 0.000 claims description 10
- 238000012423 maintenance Methods 0.000 claims description 9
- 238000007619 statistical method Methods 0.000 claims description 6
- 238000012876 topography Methods 0.000 claims description 6
- 230000008520 organization Effects 0.000 claims description 5
- 230000002452 interceptive effect Effects 0.000 claims description 4
- 230000001932 seasonal effect Effects 0.000 abstract description 2
- 230000005611 electricity Effects 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 4
- 230000004075 alteration Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000007774 longterm Effects 0.000 description 1
- 230000035515 penetration Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Classifications
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/004—Generation forecast, e.g. methods or systems for forecasting future energy generation
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/381—Dispersed generators
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/20—The dispersed energy generation being of renewable origin
- H02J2300/28—The renewable source being wind energy
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
Abstract
A wind power prediction method and system for wind farm is disclosed in the invention, relating to the technical field of the wind power prediction, and comprises: a data acquisition module, a partition modeling module, a generation prediction module, a data sending module, a human-machine interaction module, a data statistic module and an information reporting module. In the invention, according to the seasonal change of climate, extreme weather conditions and the change of operation state of wind power equipment, the prediction service center not only sends simulated weather forecast every day, but also sends the parameters of the power field prediction model adjusted according to the actual meteorological measurement data to adjust the prediction model timely.
Description
A wind power prediction method and system for wind farm 0503687
The invention relates to the technical field of wind power prediction, in particular to a wind power prediction method and system for wind farm.
Background technology
The wind power prediction technology refers to the prediction of the output power of wind farm in the future to arrange the dispatching plan. Wind power is an unstable energy source with random fluctuations, new challenges are brought to the stability of the system when large-scale wind power is integrated. The wind power output in the next few hours is needed to be known by the power generation and dispatching organization. According to the time scale of wind farm output prediction, the prediction comprises: long-term prediction, medium-term prediction, short-term prediction and ultra-short-time prediction. As the mature of wind power technology and the expanding of wind power unit capacity and grid-connected wind farms scale, the proportion of wind power in the total power system also increases year by year. With the continuous increase of the penetration power of the wind farm, a series of problems brought to the power system have become increasingly prominent, such as serious threat, power system security, stability, economy and reliable operation. Timely and accurate prediction of wind power can significantly enhance the security, stability, economy and controllability of the power system.
The wind power prediction system currently used has some problems in the accuracy of day-ahead/day-ahead ultra-short-term prediction data, reporting rate of day-ahead/day-ahead ultra-short-term prediction data, qualified rate of meteorological data, day-ahead correlation coefficient and so on, resulting in a large amount of on-grid electricity is assessed by the grid every month in the aspect of wind power prediction, which causes huge losses in economy.
Whereby a wind power prediction method and system for wind farm is provided to solve the above problems.
The object of the invention is to solve the problems of the accuracy of day-ahead/day-ahead ultra-short-term prediction data, reporting rate of day-ahead/day-ahead ultra-short-term prediction data, qualified rate of meteorological data, day-ahead correlation coefficient and so on, resulting in a large amount of on-grid electricity is assessed by the grid every month in the aspect of wind power prediction, which causes huge losses in economy.
Whereby a wind power prediction method and system for wind farm is provided to solve the above problems.
To achieve the aim above, the technical scheme as follows is provided in the invention: LU503687
A wind power prediction method for wind farm, comprising the following steps:
The data of topography, geomorphology, wind turbine generator distribution, wind turbine generation characteristics and climate characteristics of the power station are acquired,
According to the acquired data, the wind farm is divided into three or more areas, and an electric field prediction model is established for each area separately;
The power prediction of the wind farm is performed according to the climate characteristics of the geographical location, numerical weather prediction and historical data of wind farm, operation status of wind turbine;
The prediction result and the electric field prediction model parameters adjusted according to the actual meteorological measurement data covering the whole country are sent by the prediction service center;
The real-time information is shown in a human-machine interaction interface and is analyzed and input;
The statistic is made on the information displayed, analyzed and input;
The statistic information data is reported.
Preferably, the power prediction of the wind farm is performed according to the climate characteristic of the geographical location, numerical weather prediction and historical data of wind farm, operation status of wind turbine;
Daily forecast of wind farm;
Forecast of wind farm real-time forecast and prediction;
Forecast of power change curve prediction and the time resolution is 15min.
Preferably, the daily forecast: the active power of wind power in 96 nodes every 15 minutes from 0:15 to 24:00 the next day is submitted to the grid dispatching organization before the specified time every day.
Real-time forecast: according to the requirement of grid dispatching, the prediction data of the wind power in next 15 minutes to 4 hours is reported every 15min.
Preferably, the real-time information is shown in a human-machine interaction interface and is analyzed and input;
The real-time display information comprises: the display of real-time wind resource monitoring information, real-time power, real-time wind generation set status and the predicted power content.
Statistical analysis comprises: the statistical analysis of wind resource monitoring information, measured power, prediction power information, prediction accuracy and so on.
Analysis and input comprise: analysis and input of expected startup capacity, shutdown maintenance plan, the content of new energy power generation plan for grid dispatching. LUS03687
Preferably, the statistic is made on the information displayed, analyzed and input;
The statistic comprises:
The historical power data statistic comprises data integrity statistic, frequency distribution statistic and rate of change statistic;
The historical meteorological data statistic comprises data integrity statistic, frequency distribution statistic of wind speed and frequency distribution statistic of wind direction;
Wind farm operation parameter statistic comprises the statistic of power generation, effective generation time, maximum output and occurrence time thereof, coincidence rate, utilization hours and average load rate parameter;
Error statistic: the error statistic can be made on the prediction results in any time interval,
The error index comprises root mean square error and mean absolute error rate.
Preferably, the statistic information data is reported;
According to the technical requirement of the grid dispatching, the standard format of short-term power prediction, ultra-short-term power prediction, wind tower real-time monitoring, maintenance capacity of wind turbine, installed capacity of wind farm, operation capacity and the maximum output information are reported.
Preferably, the result of short-term power prediction is reported before 11:00 every morning in the form of power curves in 96 time nodes from 0:15 to 24:00 the next day every 15 minutes; The ultra-short-term power prediction is reported every 15min, and the real-time monitoring data of wind tower is reported every Smin; The maintenance capacity, installed capacity, operation capacity and maximum output information of wind farms are uniformly reported in file format as the header of the short-term power prediction report data.
A wind power prediction system for wind farm, comprising:
A data acquisition module: is used to acquire the data of topography, geomorphology, wind turbine generator distribution, wind turbine generation characteristics and climate characteristics of the power station;
A partition modeling module: according to the acquired data, the wind farm is divided into three or more areas, and an electric field prediction model is established for each area separately;
A power generation prediction module: the power prediction of the wind farm is performed according to the climate characteristics of the geographical location, numerical weather prediction and historical data of wind farm, operation status of wind turbine;
A data sending module: the prediction result and the electric field prediction model parameters adjusted according to the actual meteorological measurement data covering the whole country are sent by the prediction service center; LUS03687
A human-machine interactive module: the real-time information is shown in a human-machine interaction interface and is analyzed and input;
A data statistic module: the statistic is made on the information displayed, analyzed and input;
An information reporting module: the statistical information data is reported.
Compared with the prior art, the beneficial effect of the invention is:
In the invention, through selecting of multi-meteorological sources, meteorological sources more suitable for the station for collective power prediction is selected and combined. According to the seasonal change of climate, the interaction of regional climate, extreme weather conditions and the change of operation state of wind power equipment, the prediction service center not only sends simulated weather forecast every day, but also sends the parameters of the electric field prediction model adjusted according to the actual meteorological measurement data to adjust the prediction model timely; Whereby the prediction accuracy of the system is guaranteed, which saves resources and reduces economic losses.
Description of attached drawings
Figure 1 is an overall flow diagram of a wind power prediction method and system for wind farm provided in the invention;
Specific embodiments
In combination with the attached drawings of the examples in the invention, the technical scheme of the examples in the invention is described clearly and completely. It is obvious that the described examples are only part in the invention, not all.
Referring to figure 1, a wind power prediction method for wind farm, comprising the following steps:
S1: The data of topography, geomorphology, wind turbine generator distribution, wind turbine generation characteristics and climate characteristics of the power station are acquired,
S2: According to the acquired data, the wind farm is divided into three or more areas, and an electric field prediction model is established for each area separately;
S3: The power prediction of the wind farm is performed according to the climate characteristic of the geographical location, numerical weather prediction and historical data of wind farm and operation status of wind turbine; The prediction comprises:
Wind farm daily forecast, the daily forecast: the active power of wind power in 96 nodes every 15 minutes from 0:15 to 24:00 the next day is submitted to the grid dispatching organization before the specified time every day; The day-ahead power prediction accuracy of the wind farm from O to 24h the next day should be greater than or equal to 80%, if less than
80%, it should be assessed according to the following formula: LUS03687
VX (Pi —P,)
Day —ahead accuracy =11-—— |x100%
Y 7 Cap x Jn
Daily assessment of electric quantity based on day-ahead accuracy= (80%-accuracy)x Py x 1 (hour); 5 In the formula: Pui the actual power at time 1; Pr, is the day-ahead power prediction value at time i; Cap is the available capacity of the wind farm; n is the number of samples and
By is the rated capacity of the wind farm;
The forecast of wind farm real-time forecast and prediction, real-time forecast: according to the requirement of grid dispatching, the prediction data of the wind power in next 15 minutes to 4 hours is reported every 15min;
The correlation coefficient between the day-ahead prediction of the wind farm from 0-24h the next day and the actual power should be greater than or equal to 0.68, and less than 0.68 shall be regarded as a disqualification, and it shall be assessed according to 0.1% of the on-grid electricity of the wind farm in that month each time. The correlation coefficient of wind power prediction is calculated as follows:
HP -P,)e (P, -P,)]
Correlation coefficient (r) = Fa = =v < —=\2 nate PR) XP -P,) i=l i=l
In the formula: n is the number of samples; Pua is the measured power at time 1; Pr is the prediction power at time 1; Lu is the average measured power of all samples, Pb js the average prediction power of all samples;
The forecast of power change curve prediction and the time resolution is 15min;
The accuracy of the ultra-short-term power prediction of wind farm in the 4th hour should be greater than or equal to 85%, and is assessed according to the following formula when less than 85%:
ALY PA)
Ultra — short — term accuracy =11-—=— |x100% } Cap x Jn
Daily assessment of electric quantity based on ultra-short-terk}503687 accuracy=(85%-accuracy)x Py x 1 (hour);
In the formula: Pua is the actual power at time 1; Pri is the prediction value at the 4th hour (time 1) of ultra-short-term power prediction; Cap is the available capacity of the wind farm; n is the number of samples in the generation period and Py is the rated capacity of the wind farm;
S4: The prediction result and the electric field prediction model parameters adjusted according to the actual meteorological measurement data covering the whole country are sent by the prediction service center;
S5: The real-time information is displayed in the human-machine interactive interface, and is analyzed and input; The real-time display information comprises: the display of real-time wind resource monitoring information, real-time power, real-time wind generation set status and predicted power content.
Statistical analysis comprises: the statistical analysis of wind resource monitoring information, measured power, prediction power information, prediction accuracy and so on.
Analysis and input comprise: analysis and input of expected startup capacity, shutdown maintenance plan and the content of new energy power generation plan for grid dispatching;
S6: The statistic is made on the information displayed, analyzed and input;
The statistic comprises:
The historical power data statistic comprises data integrity statistic, frequency distribution statistic and rate of change statistic;
The historical meteorological data statistic comprises data integrity statistic, frequency distribution statistic of wind speed and frequency distribution statistic of wind direction;
Wind farm operation parameter statistic comprises the statistic of power generation, effective generation time, maximum output and occurrence time thereof, coincidence rate, utilization hours and average load rate parameter;
Error statistic: the error statistic can be made on the prediction results in any time interval,
The error index comprises root mean square error and mean absolute error rate;
S7: The statistical information data is reported; According to the requirement of the grid dispatching, the standard format of short-term power prediction, ultra-short-term power prediction, wind tower real-time monitoring, maintenance capacity of wind turbine, installed capacity of wind farm, operation capacity and the maximum output information are reported.
The result of short-term power prediction is reported before 11:00 every morning in the form of power curves in 96 time nodes from 0:15 to 24:00 the next day every 15 minutes; The ultra-short-term power prediction is reported every 15min, and the real-time monitoring data bF503687 wind tower is reported every Smin; The maintenance capacity, installed capacity, operation capacity and maximum output information of wind farms are uniformly reported in file format as the header of the short-term power prediction report data.
A wind power prediction system for wind farm, comprising:
A data acquisition module: is used to acquire the data of topography, geomorphology, wind turbine generator distribution, wind turbine generation characteristics and climate characteristics of the power station;
A partition modeling module: according to the acquired data, the wind farm is divided into three or more areas, and an electric field prediction model is established for each area separately;
A power generation prediction module: the power prediction of the wind farm is performed according to the climate characteristic of the geographical location, numerical weather prediction and historical data of wind farm and operation status of wind turbine; The prediction comprises:
Wind farm daily forecast, the daily forecast: the active power of wind power in 96 nodes every 15 minutes from 0:15 to 24:00 the next day is submitted to the grid dispatching organization before the specified time every day; The day-ahead power prediction accuracy of the wind farm from 0 to 24h the next day should be greater than or equal to 80%, if less than 80%, it should be assessed according to the following formula:
ZA -F,)
Day — ahead accuracy = dr x 100%
Day-ahead accuracy daily assessment of electric quantity=(80%-accuracy)x Py x 1 (hour);
In the formula: Puis the actual power at time 1; Pa is the day-ahead power prediction value at time i; Cap is the available capacity of the wind farm; n is the number of samples and
Py is the rated capacity of the wind farm;
The forecast of wind farm real-time forecast and prediction, real-time forecast: according to the requirement of grid dispatching, the prediction data of the wind power in next 15 minutes to 4 hours is reported every 15min;
The correlation coefficient between the day-ahead prediction of the wind farm from 0-24h the next day and the actual power should be greater than or equal to 0.68, and less than 0.68 shall be regarded as a disqualification, and it shall be assessed according to 0.1% of the on-grid electricity of the wind farm in that month each time. The correlation coefficient of wind power prediction is calculated as follows: LU503687
Yln,-P.)e(,-7]
Correlation coefficient(r)= —
JE PJ + 20-2)
In the formula: n is the number of samples; Pua is the measured power at time 1; Pa is the prediction power at time 1; Pu is the average measured power of all samples, Pb js the average prediction power of all samples;
The forecast of power change curve prediction and the time resolution is 15min;
The accuracy of the ultra-short-term power prediction of wind farm in the 4th hour should be greater than or equal to 85%, and is assessed according to the following formula when less than 85%:
JR Eu -P,)
Ultra — short — term accuracy =| 1-42 |x100%
Cap x Jn
Ultra-short-term accuracy daily assessment of electric quantity=(85%-accuracy)x Pa x 1(hour);
In the formula: Pa is the actual power at time 1; Pa is the prediction value at the 4th hour (time 1) of ultra-short-term power prediction; Cap 1s the available capacity of the wind farm; n is the number of samples in the generation period and Py is the rated capacity of the wind farm;
A data sending module: the prediction result and the electric field prediction model parameters adjusted according to the actual meteorological measurement data covering the whole country are sent by the prediction service center;
A human-machine interactive module: the real-time information is shown in a human-machine interaction interface and is analyzed and input;
A data statistic module: the statistic is made on the information displayed, analyzed and input;
An information reporting module: the statistical information data is reported.
An intelligent computer device, comprising a memory and a processor; The memory has computer-readable instruction stored therein, and when the processor performs the computer-readable instruction, the steps of the wind power prediction method for wind farm are implemented. LUS03687
A computer readable storage medium having computer readable instruction stored therein, and when the computer readable instruction is performed by the processor, the steps of the wind power prediction method for wind farm are implemented.
What is said above is only the better specific embodiments, but the protection scope of the invention is not limited to this. Within the scope of technology disclosed by the invention, equivalent substitution or alteration according to the technical scheme of the invention and inventive concept thereof shall be covered by the protection scope of the invention.
Claims (10)
1. A wind power prediction method for wind farm, which characterized by comprising the following steps: The data of topography, geomorphology, wind turbine generator distribution, wind turbine generation characteristics and climate characteristics of the power station are acquired, According to the acquired data, the wind farm is divided into three or more areas, and an electric field prediction model is established for each area separately; The power prediction of the wind farm is performed according to the climate characteristics of the geographical location, numerical weather prediction and historical data of wind farm, operation status of wind turbine; The prediction result and the electric field prediction model parameters adjusted according to the actual meteorological measurement data covering the whole country are sent by the prediction service center; The real-time information is shown in a human-machine interaction interface and is analyzed and input; The statistic is made on the information displayed, analyzed and input; The statistic information data is reported.
2. A wind power prediction method for wind farm of claim 1, characterized by, the power prediction of the wind farm is performed by the model according to the climate characteristics of the geographical location, numerical weather prediction and historical data of wind farm, operation status of wind turbine, and the prediction comprises: Daily forecast of wind farm; Forecast of wind farm real-time forecast and prediction; Forecast of power change curve prediction and the time resolution is 15min.
3. A wind power prediction method for wind farm of claim 2, characterized by, the daily forecast: the active power of wind power in 96 nodes every 15 minutes from 0:15 to 24:00 the next day is submitted to the grid dispatching organization before the specified time every day; Real-time forecast: according to the requirement of grid dispatching, the prediction data of the wind power in next 15 minutes to 4 hours is reported every 15min.
4. A wind power prediction method for wind farm of claim 1, characterized by, the real-time information is shown in a human-machine interaction interface and is analyzed and input; The real-time display information comprises: the display of real-time wind resource monitoring information, real-time power, real-time wind generation set status and the prediction power content; LUS03687 Statistical analysis comprises: the statistical analysis of wind resource monitoring information, measured power, prediction power information, prediction accuracy and so on; Analysis and input comprise: analysis and input of expected startup capacity, shutdown maintenance plan, the content of new energy power generation plan for grid dispatching.
5. À wind power prediction method for wind farm of claim 1, characterized by, the statistic is made on the information displayed, analyzed and input; The statistic comprises: The historical power data statistic comprises data integrity statistic, frequency distribution statistic and rate of change statistic; The historical meteorological data statistic comprises data integrity statistic, frequency distribution statistic of wind speed and frequency distribution statistic of wind direction; Wind farm operation parameter statistic comprises the statistic of power generation, effective generation time, maximum output and occurrence time thereof, coincidence rate, utilization hours and average load rate parameter; Error statistic: the error statistic can be made on the prediction results in any time interval; The error index comprises root mean square error and mean absolute error rate.
6. A wind power prediction method for wind farm of claim 1, characterized by, the statistical information data is reported; According to the technical requirement of the grid dispatching, the standard format of short-term power prediction, ultra-short-term power prediction, wind tower real-time monitoring, maintenance capacity of wind turbine, installed capacity of wind farm, operation capacity and the maximum output information are reported.
7. A wind power prediction method for wind farm of claim 6, characterized by, the result of short-term power prediction is reported before 11:00 every morning in the form of power curves in 96 time nodes from 0:15 to 24:00 the next day every 15 minutes; The ultra-short-term power prediction is reported every 15min, and the real-time monitoring data of wind tower is reported every Smin; The maintenance capacity, installed capacity, operation capacity and maximum output information of wind farms are uniformly reported in file format as the header of the short-term power prediction report data.
8. A wind power prediction system for wind farm, characterized by, comprising: A data acquisition module: is used to acquire the data of topography, geomorphology, wind turbine generator distribution, wind turbine generation characteristics and climate characteristics of the power station; A partition modeling module: according to the acquired data, the wind farm is divided into three or more areas, and an electric field prediction model is established for each area separately:U>03687 A power generation prediction module: the power prediction of the wind farm is performed according to the climate characteristics of the geographical location, numerical weather prediction and historical data of wind farm, and operation status of wind turbine; A data sending module: the prediction result and the electric field prediction model parameters adjusted according to the actual meteorological measurement data covering the whole country are sent by the prediction service center; A human-machine interactive module: the real-time information is shown in a human-machine interaction interface and is analyzed and input; A data statistic module: the statistic is made on the information displayed, analyzed and input; An information reporting module: the statistical information data is reported.
9. An intelligent computer device, characterized by, comprising a memory and a processor; The memory has computer-readable instruction stored therein, and when the processor performs the computer-readable instruction, the steps of the wind power prediction method for wind farm described in any of claims 1 to 7 are implemented.
10. A computer readable storage medium having computer readable instruction stored therein, and when the computer readable instruction is performed by the processor, the steps of the wind power prediction method for wind farm described in any of claims 1 to 7 are implemented.
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