CN114662800A - Wind power prediction method and system based on artificial neural network - Google Patents
Wind power prediction method and system based on artificial neural network Download PDFInfo
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Abstract
The invention provides a wind power prediction method and a wind power prediction system based on an artificial neural network, wherein the prediction system comprises a wind speed model establishing module, a single-machine power prediction module and a regional power prediction module; the wind speed model building module is used for acquiring and processing annual wind speed data of a prediction area; the wind speed model building module comprises a wind speed classification unit, a single-day wind speed processing unit, a single-month wind speed summarizing unit and a whole-year wind speed summarizing unit; the wind speed classification unit is used for classifying effective wind speeds; the single-day wind speed processing unit is used for summarizing the wind speed duration of a single day; the method can predict annual generated power of a certain region based on the annual wind speed model and the power conversion efficiency of the wind driven generator, and solves the problems that the existing wind power generation power prediction is more general and the prediction is not accurate enough.
Description
Technical Field
The invention relates to the technical field of wind power prediction, in particular to a wind power prediction method and a wind power prediction system based on an artificial neural network.
Background
The wind power prediction technology is used for predicting the power output by a wind power place in a future period of time so as to arrange a scheduling plan. This is because wind energy belongs to unstable energy with random fluctuation, and large-scale wind power is incorporated into the system, which inevitably brings new challenges to the stability of the system. The power generation dispatching mechanism needs to know the wind power output power for hours in the future, the artificial neural network abstracts the human brain neural network from the information processing perspective, establishes a certain simple model, and forms different networks according to different connection modes. The neural network is an operational model, which is formed by connecting a large number of nodes (or called neurons). Each node represents a particular output function.
In the prior art, in the process of predicting the wind power, single wind speed conversion is generally adopted to obtain, and in the process, the data of the wind speed is not comprehensive enough, and meanwhile, the power conversion of a final generator is not accurate enough, so that the final power generation prediction result is not accurate enough, and misleading is caused to the power generation construction in the early stage.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide a wind power prediction method and system based on an artificial neural network, which can predict annual generated power of a certain region based on an annual wind speed model and the power conversion efficiency of a wind driven generator, so as to solve the problems that the existing wind power prediction is more general and the prediction is not accurate enough.
In order to achieve the purpose, the invention is realized by the following technical scheme: a wind power prediction system based on an artificial neural network comprises a wind speed model building module, a single machine power prediction module and an area power prediction module; the wind speed model building module is used for acquiring and processing annual wind speed data of a prediction area; the wind speed model building module comprises a wind speed classification unit, a single-day wind speed processing unit, a single-month wind speed summarizing unit and a whole-year wind speed summarizing unit; the wind speed classification unit is used for classifying effective wind speeds; the single-day wind speed processing unit is used for summarizing the wind speed duration of a single day; the single-month wind speed summarizing unit is used for summarizing the whole-month wind speed based on the single-day wind speed processing result; the annual wind speed summarizing unit is used for summarizing the comprehensive wind speed based on the summarizing result of the monthly wind speed summarizing unit;
the single-machine power prediction module is used for predicting the annual power generation power of the single machine in the prediction region based on annual wind speed data; the regional power prediction module is used for predicting regional generated power in a prediction region based on the annual generated power of the single machine.
Further, the wind speed classification unit is configured with a wind speed classification strategy comprising: dividing the effective wind speed into a first level to a tenth level according to the grade of the wind speed;
the single-day wind speed processing unit is configured with a single-day wind speed summarizing strategy, and the single-day wind speed summarizing strategy comprises the following steps: respectively counting the effective wind speed durations of the first level to the tenth level in a single day, and respectively marking the effective wind speed durations of the first level to the tenth level in the single day as Vd 1-Vd 12;
the single-month wind speed summarizing unit is configured with a single-month wind speed summarizing strategy, and the single-month wind speed summarizing strategy comprises the following steps: respectively accumulating the wind speed durations from the first level to the twelfth level every day in a single month, and respectively marking the wind speed durations from the first level to the twelfth level in the single month as Vm 1-Vm 12;
the annual wind speed summarizing unit is configured with an annual wind speed summarizing strategy, and the annual wind speed summarizing strategy comprises the following steps: wind speed durations of the first level to the tenth level in each month in the whole year are respectively accumulated, and the wind speed durations of the first level to the tenth level in a single month are respectively marked as Vy1 to Vy 12.
Further, the stand-alone power prediction module includes a wind speed conversion unit configured with a wind speed conversion strategy, the wind speed conversion strategy including: when the annual generated power of a single machine is predicted, the wind speed time from the first level to the twelfth level of the year is respectively substituted into a wind speed power conversion formula to obtain the wind speed conversion power from the first level to the twelfth level;
substituting the wind speed conversion power from the first stage to the twelfth stage into a single-machine power accumulation formula to obtain the single-machine standard annual power generation power;
the stand-alone power prediction module further comprises a loss cancellation unit configured with a loss cancellation strategy comprising: placing a single-machine wind driven generator in a wind power generation laboratory, blowing the single-machine wind driven generator to the wind driven generator at the wind speeds of the first stage to the twelfth stage respectively, continuously generating power for a first test duration in the wind speed test state of each stage, and acquiring the actual power generation power of each stage of wind driven generator in the first test duration;
substituting the wind speed duration from the first stage to the twelfth stage of the whole year and the actual generated power corresponding to the wind speed of each stage into a loss elimination power conversion formula to obtain the single-machine actual whole year generated power.
Further, the wind speed power conversion formula is configured to:
(ii) a The stand-alone power accumulation formula is configured to:
(ii) a Wfz is wind speed conversion power, Wfz 1-Wfz 12 is wind speed conversion power of first-level to twelfth-level wind speeds all year around, Vi is a representative character of wind speed duration of the first-level to twelfth-level all year around, Wbdq is single-machine standard all year electricity generation power, a1 is smaller than zero, b1 is larger than zero, the absolute value of a1 is smaller than b1, and w1 is a single-machine all year around month compensation coefficient;
the loss cancellation power conversion equation is configured to:
(ii) a Wherein, Wsdq is the actual annual generated power of the single machine, Vc1 is the first test duration, and Wsi represents the actual generated power of each stage of wind driven generator under the first test duration.
Further, the area power prediction module is configured with an area power prediction strategy, which includes: acquiring installed power of wind driven generators needing to be arranged in a prediction region, and substituting the installed power of each wind driven generator, the test power of the wind driven generators in the single-machine power prediction module and the actual annual generating power of the single machine into a single-machine power conversion formula to obtain the predicted power of each wind driven generator;
and accumulating the predicted power of each wind driven generator to obtain the regional predicted power generation power.
Further, the stand-alone power conversion formula is configured to:
(ii) a Wyc is the predicted power of each generator, Wzjn is the installed power of each generator, and Wyzj is the test power of the wind driven generator in the single-machine power prediction module.
A wind power prediction method based on an artificial neural network comprises the following steps:
step S1, classifying the wind speeds, respectively acquiring the wind speed duration of a single day, then acquiring the wind speed duration of a single month, and finally summarizing the wind speed duration of the whole year;
step S2, predicting annual generated power of the single machines in the prediction area based on annual wind speed data;
step S3, the regional generated power in the predicted region is predicted based on the annual generated power of the single machine.
Further, the step S1 further includes: dividing the effective wind speed into a first level to a tenth level according to the grade of the wind speed; respectively counting the effective wind speed durations of the first level to the tenth level in a single day, and respectively marking the effective wind speed durations of the first level to the tenth level in the single day as Vd 1-Vd 12; respectively accumulating the wind speed durations from the first level to the twelfth level every day in a single month, and respectively marking the wind speed durations from the first level to the twelfth level in the single month as Vm 1-Vm 12; wind speed durations from the first level to the twelfth level of each month in the whole year are respectively accumulated, and the wind speed durations from the first level to the eleventh level in a single month are respectively marked as Vy1 to Vy 12.
Further, the step S2 further includes:
when the annual generated power of a single machine is predicted, the wind speed time from the first level to the twelfth level of the year is respectively substituted into a wind speed power conversion formula to obtain the wind speed conversion power from the first level to the twelfth level;
substituting the wind speed conversion power from the first stage to the twelfth stage into a single-machine power accumulation formula to obtain the single-machine standard annual power generation power;
placing a single-machine wind driven generator in a wind power generation laboratory, blowing the single-machine wind driven generator to the wind driven generator at the wind speeds of the first stage to the twelfth stage respectively, continuously generating power for a first test duration in the wind speed test state of each stage, and acquiring the actual power generation power of each stage of wind driven generator in the first test duration;
substituting the wind speed duration from the first stage to the twelfth stage of the whole year and the actual generated power corresponding to the wind speed of each stage into a loss elimination power conversion formula to obtain the single-machine actual whole year generated power.
Further, the step S3 further includes: obtaining the installed power of the wind driven generators needing to be arranged in the prediction region, and substituting the installed power of each wind driven generator, the predicted test power of the wind driven generators and the actual annual generating power of the single machine into a single machine power conversion formula to obtain the predicted power of each wind driven generator; and accumulating the predicted power of each wind driven generator to obtain the regional predicted power generation power.
The invention has the beneficial effects that: in the process of acquiring annual wind speed data, the grade of the wind speed is classified firstly, then the classified wind speed is subjected to cascade accumulation, the wind speed data of a single day is acquired firstly, then the wind speed data of a single month is summarized, and finally the annual wind speed data is summarized, so that a relatively accurate annual wind speed model can be obtained;
according to the method, after annual wind speed data are acquired, firstly, the generated power of the single machine is subjected to prediction conversion, and then the regional generated power in the prediction region is predicted based on the annual generated power of the single machine, so that the prediction accuracy of the wind power generation efficiency in the region can be improved.
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Other features, objects and advantages of the invention will become more apparent upon reading of the detailed description of non-limiting embodiments with reference to the following drawings:
FIG. 1 is a schematic block diagram of the system of the present invention.
FIG. 2 is a flow chart of the method of the present invention.
Detailed Description
In order to make the technical means, the creation characteristics, the achievement purposes and the effects of the invention easy to understand, the invention is further explained by combining the specific embodiments.
Referring to fig. 1, a wind power prediction system based on an artificial neural network includes a wind speed model building module, a single-machine power prediction module, and a regional power prediction module; the invention utilizes the artificial neural network calculation model to accumulate the wind power generation power of the cascade, thereby accurately predicting the wind power generation power in the prediction region.
The wind speed model building module is used for acquiring and processing annual wind speed data of a prediction area; the wind speed model building module comprises a wind speed classification unit, a single-day wind speed processing unit, a single-month wind speed summarizing unit and a whole-year wind speed summarizing unit; the wind speed classification unit is used for classifying effective wind speeds; the wind speed classification unit is configured with a wind speed classification strategy, and the wind speed classification strategy comprises the following steps: dividing the effective wind speed into a first level to a tenth level according to the grade of the wind speed; in the process of counting the wind speed, wind speed sampling is carried out in the area in advance, wind with certain wind speed in a certain wind direction range is divided into effective wind speeds, and then the duration of the effective wind speeds is counted, so that the effectiveness of wind speed statistical data can be improved.
The single-day wind speed processing unit is used for summarizing the wind speed duration of a single day; the single-day wind speed processing unit is configured with a single-day wind speed summarizing strategy, and the single-day wind speed summarizing strategy comprises the following steps: respectively counting the effective wind speed duration from the first level to the tenth level in a single day, and respectively marking the effective wind speed duration from the first level to the tenth level in the single day as Vd 1-Vd 12; firstly, single-day wind speed data are counted, and a starting point is set according to a minimum counting base point.
The single-month wind speed summarizing unit is used for summarizing the whole-month wind speed based on the single-day wind speed processing result; the single-month wind speed summarizing unit is configured with a single-month wind speed summarizing strategy, and the single-month wind speed summarizing strategy comprises the following steps: respectively accumulating the wind speed durations from the first level to the twelfth level every day in a single month, and respectively marking the wind speed durations from the first level to the twelfth level in the single month as Vm 1-Vm 12; the annual wind speed summarizing unit is used for summarizing the comprehensive wind speed based on the summarizing result of the monthly wind speed summarizing unit; the annual wind speed summarizing unit is configured with an annual wind speed summarizing strategy, and the annual wind speed summarizing strategy comprises the following steps: wind speed durations from the first level to the twelfth level of each month in the whole year are respectively accumulated, and the wind speed durations from the first level to the eleventh level in a single month are respectively marked as Vy1 to Vy 12.
The single-machine power prediction module is used for predicting the annual power generation power of the single machine in the prediction region based on annual wind speed data; the single-machine power prediction module comprises a wind speed conversion unit, the wind speed conversion unit is configured with a wind speed conversion strategy, and the wind speed conversion strategy comprises the following steps: when the annual generated power of a single machine is predicted, the wind speed time from the first level to the twelfth level of the year is respectively substituted into a wind speed power conversion formula to obtain the wind speed conversion power from the first level to the twelfth level;
substituting the wind speed conversion power from the first stage to the twelfth stage into a single-machine power accumulation formula to obtain the single-machine standard annual power generation power; the wind speed power conversion formula is configured to:
(ii) a The stand-alone power accumulation formula is configured to:
(ii) a w1 is a single machine annual month compensation coefficient, w1 is between 0 and 2, wherein Wfz is windThe method comprises the steps of converting speed into power, wherein Wfz 1-Wfz 12 are the annual wind speed conversion power of first-level to twelfth-level wind speeds, Vi is a representative character of annual wind speed duration of the first-level to the twelfth-level, Wbdq is single-machine standard annual power generation, the value of a1 is smaller than zero, the value of b1 is larger than zero, and the absolute value of a1 is smaller than b 1; in the wind speed power conversion formula, it can be obviously known that when the wind speed is higher or lower, the obtained wind speed power is also lower, and the wind speed has an obvious peak value in the middle interval.
The stand-alone power prediction module further comprises a loss cancellation unit configured with a loss cancellation strategy comprising: placing a single-machine wind driven generator in a wind power generation laboratory, blowing the single-machine wind driven generator to the wind driven generator at the wind speeds of the first stage to the twelfth stage respectively, continuously generating power for a first test duration in the wind speed test state of each stage, and acquiring the actual power generation power of each stage of wind driven generator in the first test duration; substituting the wind speed duration from the first stage to the twelfth stage of the whole year and the actual generated power corresponding to the wind speed of each stage into a loss elimination power conversion formula to obtain the single-machine actual whole year generated power.
The loss cancellation power conversion equation is configured to:
(ii) a Wherein, Wsdq is the actual annual generated power of the single machine, Vc1 is the first test duration, and Wsi represents the actual generated power of each stage of wind driven generator under the first test duration.
The regional power prediction module is used for predicting regional generated power in a prediction region based on the annual generated power of a single machine, and is configured with a regional power prediction strategy, wherein the regional power prediction strategy comprises the following steps: acquiring installed power of wind driven generators needing to be arranged in a prediction region, and substituting the installed power of each wind driven generator, the test power of the wind driven generators in the single-machine power prediction module and the actual annual generating power of the single machine into a single-machine power conversion formula to obtain the predicted power of each wind driven generator; accumulating the predicted power of each wind driven generator to obtain a regionPredicting the generated power by the domain; the stand-alone power conversion formula is configured to:
(ii) a Wyc is the predicted power of each generator, Wzjn is the installed power of each generator, Wyzj is the test power of the wind driven generator in the single-machine power prediction module, the installed power and the test power belong to the standard output power of the generator in the early stage, no loss is calculated, and therefore the actual output power needs to be referred.
Referring to fig. 2, a wind power prediction method based on an artificial neural network includes the following steps:
step S1, classifying the wind speeds, respectively acquiring the wind speed duration of a single day, then acquiring the wind speed duration of a single month, and finally summarizing the wind speed duration of the whole year; dividing the effective wind speed into a first level to a tenth level according to the grade of the wind speed; respectively counting the effective wind speed duration from the first level to the tenth level in a single day, and respectively marking the effective wind speed duration from the first level to the tenth level in the single day as Vd 1-Vd 12; respectively accumulating the wind speed durations from the first level to the twelfth level in a single month every day, and respectively marking the wind speed durations from the first level to the twelfth level in the single month as Vm 1-Vm 12; wind speed durations from the first level to the twelfth level of each month in the whole year are respectively accumulated, and the wind speed durations from the first level to the eleventh level in a single month are respectively marked as Vy1 to Vy 12.
Step S2, predicting annual generated power of the single machine in the prediction area based on annual wind speed data; when the annual generated power of a single machine is predicted, the wind speed time from the first level to the twelfth level of the year is respectively substituted into a wind speed power conversion formula to obtain the wind speed conversion power from the first level to the twelfth level;
substituting the wind speed conversion power from the first stage to the twelfth stage into a single-machine power accumulation formula to obtain the single-machine standard annual power generation power;
placing a single-machine wind driven generator in a wind power generation laboratory, blowing the single-machine wind driven generator to the wind driven generator at the wind speeds of the first stage to the twelfth stage respectively, continuously generating power for a first test duration in the wind speed test state of each stage, and acquiring the actual power generation power of each stage of wind driven generator in the first test duration;
substituting the wind speed duration from the first stage to the twelfth stage of the whole year and the actual generated power corresponding to the wind speed of each stage into a loss elimination power conversion formula to obtain the single-machine actual whole year generated power.
Step S3, predicting the regional power generation power in the prediction region based on the annual power generation power of the single machine, acquiring the installed power of the wind driven generators needing to be arranged in the prediction region, and substituting the installed power of each wind driven generator, the predicted test power of the wind driven generator and the actual annual power generation power of the single machine into a single machine power conversion formula to obtain the predicted power of each wind driven generator; and accumulating the predicted power of each wind driven generator to obtain the regional predicted power generation power.
Finally, it should be noted that: the above-mentioned embodiments are only specific embodiments of the present invention, which are used for illustrating the technical solutions of the present invention and not for limiting the same, and the protection scope of the present invention is not limited thereto, although the present invention is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: those skilled in the art can still make modifications or changes to the embodiments described in the foregoing embodiments, or make equivalent substitutions for some features, within the scope of the disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the embodiments of the present invention, and they should be construed as being included therein. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (10)
1. A wind power prediction system based on an artificial neural network is characterized by comprising a wind speed model building module, a single-machine power prediction module and a regional power prediction module; the wind speed model building module is used for acquiring and processing annual wind speed data of a prediction area; the wind speed model building module comprises a wind speed classification unit, a single-day wind speed processing unit, a single-month wind speed summarizing unit and a whole-year wind speed summarizing unit; the wind speed classification unit is used for classifying effective wind speeds; the single-day wind speed processing unit is used for summarizing the wind speed duration of a single day; the single-month wind speed summarizing unit is used for summarizing the whole-month wind speed based on the single-day wind speed processing result; the annual wind speed summarizing unit is used for summarizing the comprehensive wind speed based on the summarizing result of the monthly wind speed summarizing unit;
the single-machine power prediction module is used for predicting the annual power generation power of the single machines in the prediction area based on annual wind speed data; the regional power prediction module is used for predicting regional generated power in a prediction region based on the annual generated power of the single machine.
2. The artificial neural network-based wind power prediction system of claim 1, wherein the wind speed classification unit is configured with a wind speed classification strategy comprising: dividing the effective wind speed into a first level to a tenth level according to the grade of the wind speed;
the single-day wind speed processing unit is configured with a single-day wind speed summarizing strategy, and the single-day wind speed summarizing strategy comprises the following steps: respectively counting the effective wind speed duration from the first level to the tenth level in a single day, and respectively marking the effective wind speed duration from the first level to the tenth level in the single day as Vd 1-Vd 12;
the single-month wind speed summarizing unit is configured with a single-month wind speed summarizing strategy, and the single-month wind speed summarizing strategy comprises the following steps: respectively accumulating the wind speed durations from the first level to the twelfth level in a single month every day, and respectively marking the wind speed durations from the first level to the twelfth level in the single month as Vm 1-Vm 12;
the annual wind speed summarizing unit is configured with an annual wind speed summarizing strategy, and the annual wind speed summarizing strategy comprises the following steps: wind speed durations of the first level to the tenth level in each month in the whole year are respectively accumulated, and the wind speed durations of the first level to the tenth level in a single month are respectively marked as Vy1 to Vy 12.
3. The artificial neural network-based wind power prediction system of claim 2, wherein the stand-alone power prediction module comprises a wind speed conversion unit configured with a wind speed conversion strategy, and the wind speed conversion strategy comprises: when the annual generated power of a single machine is predicted, the wind speed time from the first level to the twelfth level of the year is respectively substituted into a wind speed power conversion formula to obtain the wind speed conversion power from the first level to the twelfth level;
substituting the wind speed conversion power from the first stage to the twelfth stage into a single-machine power accumulation formula to obtain the single-machine standard annual power generation power;
the stand-alone power prediction module further comprises a loss cancellation unit configured with a loss cancellation strategy comprising: placing a single-machine wind driven generator in a wind power generation laboratory, blowing the single-machine wind driven generator to the wind driven generator at the wind speeds of the first stage to the twelfth stage respectively, continuously generating power for a first test duration in the wind speed test state of each stage, and acquiring the actual power generation power of each stage of wind driven generator in the first test duration;
substituting the wind speed duration from the first stage to the twelfth stage of the whole year and the actual generated power corresponding to the wind speed of each stage into a loss elimination power conversion formula to obtain the single-machine actual whole year generated power.
4. The artificial neural network-based wind power prediction system of claim 3, wherein the wind speed power conversion formula is configured to:
(ii) a The stand-alone power accumulation formula is configured to:
(ii) a Wherein Wfz is wind speed conversion power, Wfz 1-Wfz 12 is the wind speed conversion power of the first-level to twelfth-level wind speed all year, Vi is the wind speed duration of the first-level to twelfth-level all yearThe Wbdq is single-machine standard annual power generation power, the value of a1 is smaller than zero, the value of b1 is larger than zero, the absolute value of a1 is smaller than b1, and w1 is a single-machine annual month compensation coefficient;
the loss cancellation power conversion equation is configured to:
(ii) a Wherein, Wsdq is the actual annual generated power of the single machine, Vc1 is the first test duration, and Wsi represents the actual generated power of each stage of wind driven generator under the first test duration.
5. The artificial neural network-based wind power prediction system of claim 4, wherein the regional power prediction module is configured with a regional power prediction strategy, the regional power prediction strategy comprising: acquiring installed power of wind driven generators needing to be arranged in a prediction region, and substituting the installed power of each wind driven generator, the test power of the wind driven generators in the single-machine power prediction module and the actual annual generating power of the single machine into a single-machine power conversion formula to obtain the predicted power of each wind driven generator;
and accumulating the predicted power of each wind driven generator to obtain the regional predicted power generation power.
6. The artificial neural network-based wind power prediction system of claim 5, wherein the stand-alone power conversion formula is configured to:
(ii) a Wyc is the predicted power of each generator, Wzjn is the installed power of each generator, and Wyzj is the test power of the wind driven generator in the single-machine power prediction module.
7. The method of the wind power prediction system based on the artificial neural network as claimed in any one of claims 1 to 6, wherein the method comprises the following steps:
step S1, classifying the wind speeds, respectively acquiring the wind speed duration of a single day, then acquiring the wind speed duration of a single month, and finally summarizing the wind speed duration of the whole year;
step S2, predicting annual generated power of the single machine in the prediction area based on annual wind speed data;
step S3, the regional generated power in the predicted region is predicted based on the annual generated power of the single machine.
8. The method for predicting wind power based on artificial neural network as claimed in claim 7, wherein the step S1 further includes: dividing the effective wind speed into a first level to a tenth level according to the grade of the wind speed; respectively counting the effective wind speed duration from the first level to the tenth level in a single day, and respectively marking the effective wind speed duration from the first level to the tenth level in the single day as Vd 1-Vd 12; respectively accumulating the wind speed durations from the first level to the twelfth level in a single month every day, and respectively marking the wind speed durations from the first level to the twelfth level in the single month as Vm 1-Vm 12; wind speed durations from the first level to the twelfth level of each month in the whole year are respectively accumulated, and the wind speed durations from the first level to the eleventh level in a single month are respectively marked as Vy1 to Vy 12.
9. The method for predicting wind power based on artificial neural network as claimed in claim 8, wherein said step S2 further includes:
when the annual generated power of a single machine is predicted, the wind speed time from the first level to the twelfth level of the year is respectively substituted into a wind speed power conversion formula to obtain the wind speed conversion power from the first level to the twelfth level;
substituting the wind speed conversion power from the first stage to the twelfth stage into a single-machine power accumulation formula to obtain the single-machine standard annual power generation power;
placing a single-machine wind driven generator in a wind power generation laboratory, blowing the single-machine wind driven generator to the wind driven generator at the wind speeds of the first stage to the twelfth stage respectively, continuously generating power for a first test duration in the wind speed test state of each stage, and acquiring the actual power generation power of each stage of wind driven generator in the first test duration;
substituting the wind speed duration from the first stage to the twelfth stage of the whole year and the actual generated power corresponding to the wind speed of each stage into a loss elimination power conversion formula to obtain the single-machine actual whole year generated power.
10. The method for predicting wind power based on artificial neural network as claimed in claim 9, wherein the step S3 further includes: obtaining the installed power of the wind driven generators needing to be arranged in the prediction region, and substituting the installed power of each wind driven generator, the predicted test power of the wind driven generators and the actual annual generating power of the single machine into a single machine power conversion formula to obtain the predicted power of each wind driven generator; and accumulating the predicted power of each wind driven generator to obtain the regional predicted power generation power.
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