CN111667093A - Medium-and-long-term wind power generation calculation method and device - Google Patents
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Abstract
The invention provides a medium-long term wind power generation calculation method and a medium-long term wind power generation calculation device, wherein the method comprises the following steps: acquiring meteorological historical data of an area where a wind power plant is located, historical operating data of the wind power plant, meteorological prediction data and power limiting factors of a time period corresponding to the historical operating data; establishing a statistical model according to historical operating data through a statistical principle, and calculating by using the statistical model, the historical operating data and the electricity limiting factor to obtain annual prediction power; training through a machine learning algorithm according to meteorological historical data and historical operating data to obtain a monthly generated power prediction model; calculating according to the meteorological prediction data, the historical operation data and the monthly power generation power prediction model to obtain monthly prediction power, and adjusting the statistical model according to the incidence relation between the monthly prediction power and the annual prediction power to obtain an annual power generation power prediction model; and calculating the future annual power generation power or the future monthly power generation power of the wind power plant through the annual power generation power prediction model or the monthly power generation power prediction model.
Description
Technical Field
The invention relates to the field of wind power, in particular to a medium-long term wind power generation calculation method and device.
Background
At present, wind power is predictedThe research of measurement mainly focuses on short-term and ultra-short-term, and the purpose is mainly to carry out real-time power generation planning and arrangement for power system scheduling so as to ensure the stable operation of the power system. The accuracy of wind power prediction is directly related to the stability of power grid supply and demand balance, a great deal of research aiming at the aspect of wind power prediction is carried out by experts and scholars of all countries in the world, and the predicted value of wind power is taken as an important index of power system scheduling by part of the countries. Like some wind power strong countries, wind power prediction is used as one of conditions for large-scale wind field grid connection[1]。
The medium-long term electricity quantity prediction is an important basis for distribution and pricing of power generation indexes by a dispatching department and a trading center, and the medium-long term resource prediction is a necessary condition for realizing the medium-long term electricity quantity prediction. At the present stage, only the local meteorological bureau has a medium-long term prediction function of wind resources, but the prediction data has a limitation of low resolution, and region accurate prediction cannot be realized for the wind power plant, so that accurate medium-long term electric quantity prediction data is difficult to obtain.
Disclosure of Invention
The invention aims to provide a medium-and-long-term wind power generation calculation method and device, which can improve medium-and-long-term prediction precision by combining the advantages of a physical model and a statistical model, and can provide precondition for medium-and-long-term electric quantity evaluation and prediction of a power grid and a wind power plant.
In order to achieve the above object, the present invention provides a method for calculating wind power generation in medium and long periods, the method comprising: acquiring meteorological historical data of an area where a wind power plant is located, historical operating data of the wind power plant, meteorological prediction data and power limiting factors of a time period corresponding to the historical operating data; establishing a statistical model according to the historical operating data through a statistical principle, and calculating by using the statistical model, the historical operating data and the electricity limiting factor to obtain annual prediction power; training through a machine learning algorithm according to the meteorological historical data and the historical operating data to obtain a monthly generated power prediction model; calculating according to the meteorological prediction data, the historical operation data and the monthly power generation power prediction model to obtain monthly prediction power, and adjusting the statistical model according to the incidence relation between the monthly prediction power and the annual prediction power to obtain an annual power generation power prediction model; and calculating the future annual power generation power or the future monthly power generation power of the wind power plant through the annual power generation power prediction model or the monthly power generation power prediction model.
In the above method for calculating medium-and-long-term wind power generation, preferably, the obtaining of annual predicted power by using the statistical model, the historical operating data and the electricity limiting factor includes: removing influences in the historical operation data through a power limiting factor, and restoring a historical output sequence in the historical operation data into a theoretical output sequence; calculating according to the theoretical output sequence and the monthly wind power grid-connected capacity to obtain an annual normalized power sequence; acquiring a future year wind power normalization sequence according to the average value of a plurality of year normalization power sequences in a preset period; and acquiring annual prediction power according to the future annual wind power normalization sequence.
In the method for calculating medium-and-long-term wind power generation, preferably, the historical output sequence and the theoretical output sequence are the ratio of the actual output value to the installed capacity of the wind power plant; the historical operation data comprises historical measurement data of the production anemometer tower, historical output sequences of wind power actual output of the wind power plant and monthly grid-connected capacity of the wind power.
In the above method for calculating wind power generation in medium and long periods, preferably, the obtaining of the monthly power generation power prediction model through machine learning algorithm training according to the meteorological historical data and the historical operating data includes: screening effective associated meteorological variables in the meteorological historical data according to the correlation between the meteorological historical data and historical wind power data in the historical operating data; establishing a generating power prediction model through an iterative decision tree algorithm according to the effective associated meteorological variables; and adjusting the generating power prediction model according to the meteorological historical data and the historical operating data to obtain a monthly generating power prediction model.
In the above mid-and-long-term wind power generation calculation method, preferably, the iterative decision tree algorithm is a GBDT model algorithm.
The invention also provides a medium-long term wind power generation computing device, which comprises: the system comprises a data acquisition module, a model construction module and a calculation module; the data acquisition module is used for acquiring meteorological historical data of an area where the wind power plant is located, historical operating data of the wind power plant, meteorological prediction data and power limiting factors of a time period corresponding to the historical operating data; the model construction module is used for establishing a statistical model according to the historical operating data through a statistical principle, and calculating by using the statistical model, the historical operating data and the electricity limiting factor to obtain annual prediction power; training through a machine learning algorithm according to the meteorological historical data and the historical operating data to obtain a monthly generated power prediction model; calculating according to the meteorological prediction data, the historical operation data and the monthly power generation power prediction model to obtain monthly prediction power, and adjusting the statistical model according to the incidence relation between the monthly prediction power and the annual prediction power to obtain an annual power generation power prediction model; the calculation module is used for calculating the future annual power generation power or the future monthly power generation power of the wind power plant through the annual power generation power prediction model or the monthly power generation power prediction model.
In the medium-and-long-term wind power generation computing device, preferably, the model construction module includes a statistical model unit, and the statistical unit is configured to remove influences in the historical operating data through a power limiting factor, and restore a historical output sequence in the historical operating data to a theoretical output sequence; calculating according to the theoretical output sequence and the monthly wind power grid-connected capacity to obtain an annual normalized power sequence; acquiring a future year wind power normalization sequence according to the average value of a plurality of year normalization power sequences in a preset period; and acquiring annual prediction power according to the future annual wind power normalization sequence.
In the above mid-and-long-term wind power generation computing device, preferably, the model construction module includes a physical model unit, and the physical model unit is configured to screen and obtain effective associated meteorological variables in the meteorological historical data according to a correlation between the meteorological historical data and historical wind power data in the historical operating data; establishing a generating power prediction model through an iterative decision tree algorithm according to the effective associated meteorological variables; and adjusting the generating power prediction model according to the meteorological historical data and the historical operating data to obtain a monthly generating power prediction model.
The invention also provides an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method when executing the computer program.
The present invention also provides a computer-readable storage medium storing a computer program for executing the above method.
The result predicted by the medium-and-long-term wind power generation calculation method and device provided by the invention can be used for providing medium-and-long-term marketized trading power quantity declaration decision service for the new energy station and evaluating after power grid marketized trading, and making a scheduling auxiliary decision suitable for marketization.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the principles of the invention. In the drawings:
fig. 1 is a schematic flow chart of a medium-and-long-term wind power generation calculation method according to an embodiment of the present invention;
FIG. 2 is a schematic diagram illustrating a process for calculating annual predicted power according to an embodiment of the present invention;
fig. 3 is a schematic flow chart illustrating a process of establishing a monthly generated power prediction model according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of a medium-and-long-term wind power generation computing device according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
The following detailed description of the embodiments of the present invention will be provided with reference to the drawings and examples, so that how to apply the technical means to solve the technical problems and achieve the technical effects can be fully understood and implemented. It should be noted that, unless otherwise specified, the embodiments and features of the embodiments of the present invention may be combined with each other, and the technical solutions formed are within the scope of the present invention.
Additionally, the steps illustrated in the flow charts of the figures may be performed in a computer system such as a set of computer-executable instructions and, although a logical order is illustrated in the flow charts, in some cases, the steps illustrated or described may be performed in an order different than here.
Referring to fig. 1, the method for calculating the medium-and-long-term wind power generation provided by the present invention includes:
s101, acquiring meteorological historical data of an area where a wind power plant is located, historical operating data of the wind power plant, meteorological prediction data and power limiting factors of a time period corresponding to the historical operating data;
s102, establishing a statistical model according to the historical operating data through a statistical principle, and calculating by using the statistical model, the historical operating data and the electricity limiting factor to obtain annual prediction power;
s103, training through a machine learning algorithm according to the meteorological historical data and the historical operating data to obtain a monthly generated power prediction model;
s104, calculating according to the meteorological predicted data, the historical operating data and the monthly power generation power prediction model to obtain monthly predicted power, and adjusting the statistical model according to the incidence relation between the monthly predicted power and the annual predicted power to obtain an annual power generation power prediction model;
s105, calculating the future annual power generation power or the future monthly power generation power of the wind power plant through the annual power generation power prediction model or the monthly power generation power prediction model.
In the embodiment, the annual power generation power prediction model is refined by using a monthly power generation power prediction model with higher precision, so that after the method for calculating the medium-and-long-term wind power generation is used for multiple times, the precision of the annual power generation power prediction model is closer to the real condition and accords with the actual power generation power; in the actual work, a person skilled in the art can select and adjust according to actual needs, for example, when the statistical model data is more detailed and the error between the statistical model and the actual data is lower, the statistical model can be used to calibrate the lunar power generation prediction model, so as to further accurately predict the accuracy of the lunar power generation prediction model; the calibration process is an existing model calibration method, and is not described in detail herein.
Referring to fig. 2, in the above embodiment, the obtaining of the annual predicted power by using the statistical model, the historical operating data and the electricity limiting factor includes:
s201, removing influences in the historical operation data through a power limiting factor, and restoring a historical output sequence in the historical operation data into a theoretical output sequence;
s202, calculating according to the theoretical output sequence and the monthly wind power grid-connected capacity to obtain an annual normalized power sequence;
s203, acquiring a future year wind power normalization sequence according to the average value of the year normalization power sequences in a preset period;
s204, annual prediction power is obtained according to the future annual wind power normalization sequence.
The historical output sequence and the theoretical output sequence are the ratio of the actual output value to the installed capacity of the wind power plant; the historical operation data comprises historical measurement data of the production anemometer tower, historical output sequences of wind power actual output of the wind power plant and monthly grid-connected capacity of the wind power.
In actual work, the meteorological historical data can be basic element historical live analysis products (comprising five elements of wind speed, irradiance, air temperature, relative humidity, air pressure and precipitation) with the resolution of 1 hour and the historical measurement data of the past five years of a wind measuring tower for producing a wind power plant in the past 5 years of the area by 30 km; the historical output sequence can be the actual output sequence of wind power in the last five years of the forecast area; the weather forecast data comprises forecast weather data with a resolution of 25km × 25km6 hours for the future 45 days of the forecast region. Therefore, when the annual generating capacity of the wind power is predicted, a wind resource annual prediction method can be researched based on a statistical model, and the historical power data is used for predicting the wind power generation power of the whole network in the future year. Specifically, the influence of electricity limiting factors can be removed according to historical operation data, and the annual historical output sequence of the whole network is restored to obtain an annual theoretical output sequence (actual output/installed capacity); calculating an annual normalized power sequence of the whole network by combining the monthly grid-connected capacity of the new energy; and obtaining the whole-network wind power normalization sequence of the next year according to the normalization sequence average value (average value of power values corresponding to each moment point) of the last five years, and further obtaining the whole-network predicted power sequence and the annual predicted electric quantity.
Referring to fig. 3, in an embodiment of the invention, the obtaining of the monthly generated power prediction model by training the machine learning algorithm according to the meteorological historical data and the historical operating data includes:
s301, screening effective associated meteorological variables in the meteorological historical data according to the correlation between the meteorological historical data and historical wind power data in the historical operating data;
s302, establishing a power generation power prediction model through an iterative decision tree algorithm according to the effective associated meteorological variables; wherein the iterative decision tree algorithm may be a GBDT model algorithm;
s303, adjusting the generating power prediction model according to the meteorological historical data and the historical operating data to obtain a monthly generating power prediction model.
To more clearly illustrate the above-mentioned monthly generated power prediction model establishing process, the following takes the process adopted in the actual work as an example to further illustrate the above-mentioned embodiment, and it should be understood by those skilled in the art that this example is only provided for the convenience of understanding the present invention, and is not further limited.
The embodiment mainly aims to research a wind power monthly prediction method based on a physical model, and predict future monthly full-network wind power generation power by combining historical power, meteorological data and future meteorological data; (the input data is the ratio of the generated power to the installed capacity, and the output is the one-hour power of the whole network in the next month), the specific implementation can be implemented as follows:
(1) firstly, historical meteorological data and historical wind power data are used as objects, and meteorological variables effective for wind power generation amount prediction are screened out through causality and correlation analysis among different variables. For example: according to the data of the near three calendar history data of a certain region, the meteorological variables which are strongly related to the wind power generation amount are screened out to be the height wind speed of a hub, and the weakly related meteorological variables comprise the wind direction, the air temperature and the air pressure. Therefore, the height and the wind speed of the hub can be used as effective meteorological variables as modeling input, weakly related meteorological variables can be used as correlation coefficients to adjust the model precision, and related technicians in the field can select adjustment according to actual needs, and the invention is not limited to the method.
(2) Then, reconstructing and analyzing the time sequence of the effective meteorological variables, determining data structures of the variables in the model training and predicting processes, respectively identifying the model types and parameters of different meteorological variables, and selecting a proper prediction model, wherein an iterative decision tree model can be adopted, and other models can be selected according to the identification condition; compared with a general Decision tree algorithm, the method has the advantages of overfitting prevention, strong generalization capability and the like by taking the GBDT model as a fully-named Gradient boost Decision tree algorithm as an example; the basic idea is to construct M weak classifiers, and finally combine the M weak classifiers into a strong classifier through multiple iterations, wherein each iteration is to improve the last result, reduce the residual error of the last model, and establish a new combination model in the gradient direction of residual error reduction. The algorithm consists of a number of decision trees, typically hundreds of trees, each of which is of a small size (i.e., the depth of the tree is shallow, typically 4 to 6). When the model predicts, an input sample instance is given an initial value firstly, then each decision tree is traversed, each decision tree adjusts and corrects the predicted value, and the final result is the final predicted result obtained by accumulating the results of each decision tree.
(3) And finally, performing prediction analysis on the test set sample by adopting a prediction model, and retraining and evaluating the model through error analysis of a predicted value and an actual observed value until the prediction precision meets the given requirement. And finally, the prediction of the wind power generation amount in the future in the middle and long term can be completed by combining the prediction model and the future meteorological data.
Referring to fig. 4, the present invention further provides a medium-and long-term wind power generation computing device, including: the system comprises a data acquisition module, a model construction module and a calculation module; the data acquisition module is used for acquiring meteorological historical data of an area where the wind power plant is located, historical operating data of the wind power plant, meteorological prediction data and power limiting factors of a time period corresponding to the historical operating data; the model construction module is used for establishing a statistical model according to the historical operating data through a statistical principle, and calculating by using the statistical model, the historical operating data and the electricity limiting factor to obtain annual prediction power; training through a machine learning algorithm according to the meteorological historical data and the historical operating data to obtain a monthly generated power prediction model; calculating according to the meteorological prediction data, the historical operation data and the monthly power generation power prediction model to obtain monthly prediction power, and adjusting the statistical model according to the incidence relation between the monthly prediction power and the annual prediction power to obtain an annual power generation power prediction model; the calculation module is used for calculating the future annual power generation power or the future monthly power generation power of the wind power plant through the annual power generation power prediction model or the monthly power generation power prediction model.
In the above embodiment, the model construction module includes a statistical model unit and a physical model unit, and the statistical unit is configured to remove influences in the historical operating data by a power limiting factor, and restore a historical output sequence in the historical operating data to a theoretical output sequence; calculating according to the theoretical output sequence and the monthly wind power grid-connected capacity to obtain an annual normalized power sequence; acquiring a future year wind power normalization sequence according to the average value of a plurality of year normalization power sequences in a preset period; and acquiring annual prediction power according to the future annual wind power normalization sequence. The physical model unit is used for screening effective associated meteorological variables from the meteorological historical data according to the correlation between the meteorological historical data and the historical wind power data in the historical operating data; establishing a generating power prediction model through an iterative decision tree algorithm according to the effective associated meteorological variables; and adjusting the generating power prediction model according to the meteorological historical data and the historical operating data to obtain a monthly generating power prediction model.
The result predicted by the medium-and-long-term wind power generation calculation method and device provided by the invention can be used for providing medium-and-long-term marketized trading power quantity declaration decision service for the new energy station and evaluating after power grid marketized trading, and making a scheduling auxiliary decision suitable for marketization.
The invention also provides an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method when executing the computer program.
The present invention also provides a computer-readable storage medium storing a computer program for executing the above method.
As shown in fig. 5, the electronic device 600 may further include: communication module 110, input unit 120, audio processing unit 130, display 160, power supply 170. It is noted that the electronic device 600 does not necessarily include all of the components shown in fig. 5; furthermore, the electronic device 600 may also comprise components not shown in fig. 5, which may be referred to in the prior art.
As shown in fig. 5, the central processor 100, sometimes referred to as a controller or operational control, may include a microprocessor or other processor device and/or logic device, the central processor 100 receiving input and controlling the operation of the various components of the electronic device 600.
The memory 140 may be, for example, one or more of a buffer, a flash memory, a hard drive, a removable media, a volatile memory, a non-volatile memory, or other suitable device. The information relating to the failure may be stored, and a program for executing the information may be stored. And the central processing unit 100 may execute the program stored in the memory 140 to realize information storage or processing, etc.
The input unit 120 provides input to the cpu 100. The input unit 120 is, for example, a key or a touch input device. The power supply 170 is used to provide power to the electronic device 600. The display 160 is used to display an object to be displayed, such as an image or a character. The display may be, for example, an LCD display, but is not limited thereto.
The memory 140 may be a solid state memory such as Read Only Memory (ROM), Random Access Memory (RAM), a SIM card, or the like. There may also be a memory that holds information even when power is off, can be selectively erased, and is provided with more data, an example of which is sometimes called an EPROM or the like. The memory 140 may also be some other type of device. Memory 140 includes buffer memory 141 (sometimes referred to as a buffer). The memory 140 may include an application/function storage section 142, and the application/function storage section 142 is used to store application programs and function programs or a flow for executing the operation of the electronic device 600 by the central processing unit 100.
The memory 140 may also include a data store 143, the data store 143 for storing data, such as contacts, digital data, pictures, sounds, and/or any other data used by the electronic device. The driver storage portion 144 of the memory 140 may include various drivers of the electronic device for communication functions and/or for performing other functions of the electronic device (e.g., messaging application, address book application, etc.).
The communication module 110 is a transmitter/receiver 110 that transmits and receives signals via an antenna 111. The communication module (transmitter/receiver) 110 is coupled to the central processor 100 to provide an input signal and receive an output signal, which may be the same as in the case of a conventional mobile communication terminal.
Based on different communication technologies, a plurality of communication modules 110, such as a cellular network module, a bluetooth module, and/or a wireless local area network module, may be provided in the same electronic device. The communication module (transmitter/receiver) 110 is also coupled to a speaker 131 and a microphone 132 via an audio processor 130 to provide audio output via the speaker 131 and receive audio input from the microphone 132 to implement general telecommunications functions. Audio processor 130 may include any suitable buffers, decoders, amplifiers and so forth. In addition, an audio processor 130 is also coupled to the central processor 100, so that recording on the local can be enabled through a microphone 132, and so that sound stored on the local can be played through a speaker 131.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are only exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (10)
1. A medium-long term wind power generation calculation method is characterized by comprising the following steps:
acquiring meteorological historical data of an area where a wind power plant is located, historical operating data of the wind power plant, meteorological prediction data and power limiting factors of a time period corresponding to the historical operating data;
establishing a statistical model according to the historical operating data through a statistical principle, and calculating by using the statistical model, the historical operating data and the electricity limiting factor to obtain annual prediction power;
training through a machine learning algorithm according to the meteorological historical data and the historical operating data to obtain a monthly generated power prediction model;
calculating according to the meteorological prediction data, the historical operation data and the monthly power generation power prediction model to obtain monthly prediction power, and adjusting the statistical model according to the incidence relation between the monthly prediction power and the annual prediction power to obtain an annual power generation power prediction model;
and calculating the future annual power generation power or the future monthly power generation power of the wind power plant through the annual power generation power prediction model or the monthly power generation power prediction model.
2. The medium-long term wind power generation calculation method of claim 1, wherein calculating the annual predicted power using the statistical model, the historical operating data, and the electricity limiting factor comprises:
removing influences in the historical operation data through a power limiting factor, and restoring a historical output sequence in the historical operation data into a theoretical output sequence;
calculating according to the theoretical output sequence and the monthly wind power grid-connected capacity to obtain an annual normalized power sequence;
acquiring a future year wind power normalization sequence according to the average value of a plurality of year normalization power sequences in a preset period;
and acquiring annual prediction power according to the future annual wind power normalization sequence.
3. The medium-and-long-term wind power generation calculation method of claim 2, wherein the historical output sequence and the theoretical output sequence are ratios of actual output values to installed capacity of a wind farm; the historical operation data comprises historical measurement data of the production anemometer tower, historical output sequences of wind power actual output of the wind power plant and monthly grid-connected capacity of the wind power.
4. The method for calculating the medium-long term wind power generation according to claim 1, wherein the obtaining of the monthly power generation power prediction model through machine learning algorithm training according to the meteorological historical data and the historical operating data comprises:
screening effective associated meteorological variables in the meteorological historical data according to the correlation between the meteorological historical data and historical wind power data in the historical operating data;
establishing a generating power prediction model through an iterative decision tree algorithm according to the effective associated meteorological variables;
and adjusting the generating power prediction model according to the meteorological historical data and the historical operating data to obtain a monthly generating power prediction model.
5. The mid-and-long-term wind power generation calculation method according to claim 4, wherein the iterative decision tree algorithm is a GBDT model algorithm.
6. A medium-long term wind power generation computing device, the device comprising: the system comprises a data acquisition module, a model construction module and a calculation module;
the data acquisition module is used for acquiring meteorological historical data of an area where the wind power plant is located, historical operating data of the wind power plant, meteorological prediction data and power limiting factors of a time period corresponding to the historical operating data;
the model construction module is used for establishing a statistical model according to the historical operating data through a statistical principle, and calculating by using the statistical model, the historical operating data and the electricity limiting factor to obtain annual prediction power; training through a machine learning algorithm according to the meteorological historical data and the historical operating data to obtain a monthly generated power prediction model; calculating according to the meteorological prediction data, the historical operation data and the monthly power generation power prediction model to obtain monthly prediction power, and adjusting the statistical model according to the incidence relation between the monthly prediction power and the annual prediction power to obtain an annual power generation power prediction model;
the calculation module is used for calculating the future annual power generation power or the future monthly power generation power of the wind power plant through the annual power generation power prediction model or the monthly power generation power prediction model.
7. The medium-long term wind power generation computing device of claim 6, wherein the model building module comprises a statistical model unit, and the statistical unit is configured to remove influences in the historical operating data by a power limiting factor, and restore a historical output sequence in the historical operating data to a theoretical output sequence; calculating according to the theoretical output sequence and the monthly wind power grid-connected capacity to obtain an annual normalized power sequence; acquiring a future year wind power normalization sequence according to the average value of a plurality of year normalization power sequences in a preset period; and acquiring annual prediction power according to the future annual wind power normalization sequence.
8. The mid-long term wind power generation computing device of claim 6, wherein the model building module comprises a physical model unit, and the physical model unit is configured to obtain valid associated meteorological variables by screening in the meteorological historical data according to the correlation between the meteorological historical data and historical wind power data in the historical operating data; establishing a generating power prediction model through an iterative decision tree algorithm according to the effective associated meteorological variables; and adjusting the generating power prediction model according to the meteorological historical data and the historical operating data to obtain a monthly generating power prediction model.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of any of claims 1 to 5 when executing the computer program.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program for executing the method of any one of claims 1 to 5.
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