Annual power generation method and device based on probability prediction

Abstract

The invention relates to the technical field of data processing, and discloses an annual power generation method and device based on probability prediction, and the method comprises the steps: obtaining original data, carrying out the wind speed correction of the original data, and determining the corrected wind speed data; probability modeling is carried out based on the corrected wind speed data, and a time sequence is determined; and inputting the time sequence into a grey prediction model, and determining an annual generating capacity prediction result. The method comprises the following steps: acquiring original data, performing wind speed correction on the original data, and determining corrected wind speed data; probability modeling is carried out based on the corrected wind speed data, and a time sequence is determined; and inputting the time sequence into a grey prediction model, determining an annual power generation prediction result, realizing annual wind power generation prediction, and improving prediction accuracy.

Description

Technical Field

[0001] The invention relates to the technical field of data processing, and in particular to an annual power generation method based on probability prediction. Background Art

[0002] At present, the grid dispatching department requires power stations (including wind power and photovoltaic power) to report power forecasts including power generation forecasts for system power generation planning and real-time dispatching of the power system.

[0003] The existing power generation forecasts are not accurate enough, so a better solution is urgently needed. Summary of the invention

[0004] In view of this, the present invention provides an annual power generation method based on probability prediction to solve the technical defects existing in the prior art.

[0005] In a first aspect, the present invention provides an annual power generation method based on probability prediction, comprising:

[0006] Obtaining original data, performing wind speed correction on the original data, and determining corrected wind speed data;

[0007] Probabilistic modeling is performed based on the corrected wind speed data to determine the time series; specifically, the corrected wind speed data corresponding to the historical years are determined; the corrected wind speed data corresponding to the historical years are fitted through the Weibull distribution curve to obtain the time series corresponding to the Weibull distribution parameters of the wind speed in the historical years;

[0008] The time series is input into the grey forecasting model to determine the annual power generation forecast results.

[0009] In a possible implementation, performing wind speed correction on original data to determine corrected wind speed data includes:

[0010] The wind speed is corrected for the original data based on the spatial correlation method to determine the corrected wind speed data.

[0011] In one possible implementation, the time series is input into the grey forecasting model to determine the annual power generation forecast result, including:

[0012] Establish a mapping relationship between wind energy and wind farm power generation;

[0013] The annual power generation forecast results are determined based on the time series using the moving average method.

[0014] In a possible implementation, the method further includes:

[0015] Determine the forecast accuracy of annual power generation forecast results;

[0016] Determine whether the prediction accuracy reaches the set accuracy threshold and determine the accuracy qualification result;

[0017] Re-predictions are made based on the accuracy qualified results.

[0018] In a possible implementation, re-prediction is performed based on the accuracy qualified result, including:

[0019] In the case where the accuracy result is unqualified, the time series is smoothed to obtain a smoothed time series;

[0020] Based on the smoothed time series input grey forecasting model, the annual power generation forecast results are determined and updated.

[0021] In a possible implementation, the annual power generation forecast result is determined based on the time series using a moving average method, including:

[0022] Based on the time series, the moving average method is used to determine the prediction results of the power generation of a single machine;

[0023] Determine the annual installed capacity, and determine the annual power generation forecast results based on the annual installed capacity and single-unit power generation forecast results.

[0024] In a second aspect, the present invention provides an annual power generation device based on probability prediction, comprising:

[0025] A data correction module is configured to obtain raw data, perform wind speed correction on the raw data, and determine corrected wind speed data;

[0026] A sequence determination module is configured to perform probability modeling based on the corrected wind speed data to determine a time series;

[0027] The result prediction module is configured to input the time series into the grey prediction model to determine the annual power generation prediction result.

[0028] According to a third aspect of the present invention, there is provided a computing device, comprising:

[0029] Memory and processor;

[0030] The memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions. When the computer executable instructions are executed by the processor, the steps of the annual power generation method based on probability prediction are implemented.

[0031] According to a fourth aspect of the present invention, a computer-readable storage medium is provided, which stores computer-executable instructions, and when the instructions are executed by a processor, the steps of the annual power generation method based on probability prediction are implemented.

[0032] According to a fifth aspect of the present invention, a computer program is provided, wherein when the computer program is executed in a computer, the computer is caused to execute the steps of the above-mentioned annual power generation method based on probability prediction.

[0033] The present invention provides a method and device for annual power generation based on probability prediction, wherein the method for annual power generation based on probability prediction includes: obtaining original data, performing wind speed correction on the original data, and determining corrected wind speed data; performing probability modeling based on the corrected wind speed data and determining a time series; inputting the time series into a gray prediction model and determining an annual power generation prediction result. By obtaining original data, performing wind speed correction on the original data, and determining corrected wind speed data; performing probability modeling based on the corrected wind speed data and determining a time series; inputting the time series into a gray prediction model and determining an annual power generation prediction result, annual wind power generation prediction is achieved, and the prediction accuracy is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 It is a scenario diagram of an annual power generation method based on probability prediction provided by an embodiment of this specification;

[0035] Figure 2 is a flow chart of an annual power generation method based on probability prediction provided by an embodiment of this specification;

[0036] Figure 3 It is a structural schematic diagram of an annual power generation device based on probability prediction provided by an embodiment of this specification;

[0037] Figure 4 It is a structural block diagram of a computing device provided by an embodiment of this specification. DETAILED DESCRIPTION

[0038] Many specific details are described in the following description to facilitate a full understanding of this specification. However, this specification can be implemented in many other ways than those described herein, and those skilled in the art can make similar generalizations without violating the connotation of this specification, so this specification is not limited to the specific implementation disclosed below.

[0039] The terms used in one or more embodiments of this specification are only for the purpose of describing specific embodiments, and are not intended to limit one or more embodiments of this specification. The singular forms of "a" and "the" used in one or more embodiments of this specification and the appended claims are also intended to include plural forms, unless the context clearly indicates other meanings. It should also be understood that the term "and / or" used in one or more embodiments of this specification refers to and includes any or all possible combinations of one or more associated listed items.

[0040] It should be understood that although the terms first, second, etc. may be used to describe various information in one or more embodiments of this specification, this information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of one or more embodiments of this specification, the first may also be referred to as the second, and similarly, the second may also be referred to as the first. Depending on the context, the word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0041] First, the terms involved in one or more embodiments of this specification are explained.

[0042] In this specification, a method for annual power generation based on probability prediction is provided. This specification also relates to an annual power generation device based on probability prediction, a computing device, and a computer-readable storage medium, which are described in detail one by one in the following embodiments.

[0043] See also Figure 1 , Figure 1 A schematic diagram of a scenario of an annual power generation method based on probability prediction provided according to an embodiment of the present specification is shown.

[0044] exist Figure 1 In the application scenario, the computing device 101 can obtain raw data 102. Then, the computing device 101 can perform wind speed correction on the raw data 102 to determine the corrected wind speed data 103. After that, the computing device 101 can perform probability modeling based on the corrected wind speed data 103 to determine the time series 104. The computing device 101 can input the time series 104 into the gray prediction model to determine the annual power generation prediction result, as shown by the reference numeral 105.

[0045] It should be noted that the computing device 101 can be hardware or software. When the computing device 101 is hardware, it can be implemented as a distributed cluster consisting of multiple servers or terminal devices, or it can be implemented as a single server or a single terminal device. When the computing device 101 is embodied as software, it can be installed in the hardware devices listed above. It can be implemented as multiple software or software modules for providing distributed services, for example, or it can be implemented as a single software or software module. No specific limitation is made here.

[0046] See also Figure 2 , Figure 2 A flow chart of a method for annual power generation based on probability prediction provided according to an embodiment of the present specification is shown, which specifically includes the following steps.

[0047] Step 201: Acquire original data, perform wind speed correction on the original data, and determine corrected wind speed data.

[0048] In a possible implementation, performing wind speed correction on the original data to determine the corrected wind speed data includes: performing wind speed correction on the original data based on a spatial correlation method to determine the corrected wind speed data.

[0049] Among them, the original data includes wind tower data, wind turbine data and power generation data.

[0050] In practical applications, first, the wind speed of the wind farm is corrected. Considering the certain distance between the meteorological wind measuring device and the wind farm in space, the wind speed data of the wind tower is corrected by the meteorological wind measuring data within the geographical range of the wind farm.

[0051] Furthermore, when there is a lot of missing data or the data duration does not meet the requirements, benchmark stations in the same area are introduced to deduce the missing data based on the differences in existing data.

[0052] Step 202: Probabilistic modeling is performed based on the corrected wind speed data to determine the time series.

[0053] In a possible implementation, a Weibull distribution curve is fitted based on the corrected wind speed data to determine a time series, including: determining the corrected wind speed data corresponding to the historical years; fitting the corrected wind speed data corresponding to the historical years through the Weibull distribution curve to obtain a time series corresponding to the Weibull distribution parameters of the wind speed in the historical years.

[0054] In practical applications, probability modeling is performed on the corrected wind speed of the wind farm, and the Weibull distribution curve is used to fit the corrected wind speed data of the reference wind farm in historical years to obtain the time series of Weibull distribution parameters k and c of the wind speed in historical years, thereby predicting the Weibull parameters of the wind speed in the forecast year.

[0055] Step 203: Input the time series into the grey prediction model to determine the annual power generation prediction result.

[0056] In a possible implementation, the time series is input into the grey prediction model to determine the annual power generation prediction result, including: establishing a mapping relationship between wind energy and wind farm power generation; and determining the annual power generation prediction result based on the time series using a moving average method.

[0057] In practical applications, a mapping relationship between wind energy and wind farm power generation is established. The moving average method is used to predict the average utilization hours of wind power in the forecast year to obtain the annual power generation forecast result of a single machine.

[0058] In a possible implementation, an annual power generation forecast result is determined based on a time series using a moving average method, including: determining a single-machine power generation forecast result based on a time series using a moving average method; determining an annual installed capacity, and determining an annual power generation forecast result based on the annual installed capacity and the single-machine power generation forecast result.

[0059] In practical applications, the mapping relationship between wind energy and wind farm power generation is established above. After the average utilization hours of wind power in the forecast year are predicted using the moving average method, the predicted result is multiplied by the planned installed capacity in the forecast year to obtain the equivalent power generation of the newly added installed capacity in the forecast year.

[0060] In a possible implementation, it also includes: determining the prediction accuracy of the annual power generation prediction result; judging whether the prediction accuracy reaches a set accuracy threshold, and determining a qualified accuracy result; and re-predicting based on the qualified accuracy result.

[0061] In practical applications, it is also possible to determine whether the prediction accuracy has reached the set requirements. If it has reached the set requirements, the final prediction result can be obtained. If it has not reached the set requirements, the data is processed again and the prediction is performed again. Please refer to the following steps for details.

[0062] In a possible implementation, re-forecasting is performed based on the accuracy qualified result, including: when the accuracy qualified result is unqualified, smoothing the time series to obtain a smoothed time series; inputting a gray prediction model based on the smoothed time series to determine the updated annual power generation forecast result.

[0063] In actual applications, if the set requirements are not met, the time series of the Weibull distribution parameters k and c of the wind speed in historical years are smoothed, and the processed data are input into the gray prediction model to determine a more accurate annual power generation forecast result. Then, it is judged whether the prediction accuracy meets the set requirements, until it meets the set requirements, and the final k and c prediction values ​​are obtained.

[0064] The embodiments of this specification provide a method and device for annual power generation based on probability prediction, wherein the method for annual power generation based on probability prediction includes: obtaining original data, performing wind speed correction on the original data, and determining corrected wind speed data; performing probability modeling based on the corrected wind speed data and determining a time series; inputting the time series into a gray prediction model and determining an annual power generation prediction result. By obtaining original data, performing wind speed correction on the original data, and determining corrected wind speed data; performing probability modeling based on the corrected wind speed data and determining a time series; inputting the time series into a gray prediction model and determining an annual power generation prediction result, annual wind power generation prediction is achieved and the prediction accuracy is improved.

[0065] Corresponding to the above method embodiment, this specification also provides an annual power generation device embodiment based on probability prediction, Figure 3 A schematic diagram of the structure of an annual power generation device based on probability prediction provided by an embodiment of this specification is shown.

[0066] like Figure 3 As shown, the device comprises:

[0067] The data correction module 301 is configured to obtain raw data, perform wind speed correction on the raw data, and determine corrected wind speed data;

[0068] A sequence determination module 302 is configured to perform probability modeling based on the corrected wind speed data to determine a time sequence;

[0069] The result prediction module 303 is configured to input the time series into the grey prediction model to determine the annual power generation prediction result.

[0070] In a possible implementation, the data correction module 301 is further configured to:

[0071] The wind speed is corrected for the original data based on the spatial correlation method to determine the corrected wind speed data.

[0072] In a possible implementation, the sequence determination module 302 is further configured to:

[0073] Determine the corrected wind speed data corresponding to the historical years;

[0074] The corrected wind speed data corresponding to the historical years are fitted through the Weibull distribution curve to obtain the time series corresponding to the Weibull distribution parameters of the wind speed in the historical years.

[0075] In a possible implementation, the result prediction module 303 is further configured to:

[0076] Establish a mapping relationship between wind energy and wind farm power generation;

[0077] The annual power generation forecast results are determined based on the time series using the moving average method.

[0078] In a possible implementation, the result prediction module 303 is further configured to:

[0079] Determine the forecast accuracy of annual power generation forecast results;

[0080] Determine whether the prediction accuracy reaches the set accuracy threshold and determine the accuracy qualification result;

[0081] Re-predictions are made based on the accuracy qualified results.

[0082] In a possible implementation, the result prediction module 303 is further configured to:

[0083] In the case where the accuracy result is unqualified, the time series is smoothed to obtain a smoothed time series;

[0084] Based on the smoothed time series input grey forecasting model, the annual power generation forecast results are determined and updated.

[0085] In a possible implementation, the result prediction module 303 is further configured to:

[0086] Based on the time series, the moving average method is used to determine the prediction results of the power generation of a single machine;

[0087] Determine the annual installed capacity, and determine the annual power generation forecast results based on the annual installed capacity and single-unit power generation forecast results.

[0088] The embodiments of this specification provide a method and device for annual power generation based on probability prediction, wherein the annual power generation device based on probability prediction includes: obtaining original data, performing wind speed correction on the original data, and determining corrected wind speed data; performing probability modeling based on the corrected wind speed data to determine a time series; inputting the time series into a gray prediction model to determine an annual power generation prediction result. By obtaining original data, performing wind speed correction on the original data, and determining corrected wind speed data; performing probability modeling based on the corrected wind speed data to determine a time series; inputting the time series into a gray prediction model to determine an annual power generation prediction result, annual wind power generation prediction is achieved, and the prediction accuracy is improved.

[0089] The above is a schematic scheme of an annual power generation device based on probability prediction of this embodiment. It should be noted that the technical scheme of the annual power generation device based on probability prediction and the technical scheme of the annual power generation method based on probability prediction belong to the same concept, and the details of the technical scheme of the annual power generation device based on probability prediction that are not described in detail can all be referred to the description of the technical scheme of the annual power generation method based on probability prediction.

[0090] Figure 4 The block diagram of a computing device 400 according to one embodiment of the present specification is shown. The components of the computing device 400 include but are not limited to a memory 410 and a processor 420. The processor 420 is connected to the memory 410 via a bus 430, and the database 450 is used to store data.

[0091] The computing device 400 also includes an access device 440 that enables the computing device 400 to communicate via one or more networks 460. Examples of these networks include a public switched telephone network (PSTN), a local area network (LAN), a wide area network (WAN), a personal area network (PAN), or a combination of communication networks such as the Internet. The access device 440 may include one or more of any type of network interface (e.g., a network interface card (NIC)) that is wired or wireless, such as an IEEE 802.11 wireless local area network (WLAN) wireless interface, a world-wide interoperability for microwave access (Wi-MAX) interface, an Ethernet interface, a universal serial bus (USB) interface, a cellular network interface, a Bluetooth interface, and a near field communication (NFC).

[0092] In one embodiment of the present specification, the above components of the computing device 400 and Figure 4 Other components not shown in the figure may also be connected to each other, for example, via a bus. It should be understood that Figure 4 The computing device structure block diagram shown is only for the purpose of illustration, and is not intended to limit the scope of this specification. Those skilled in the art can add or replace other components as needed.

[0093] The computing device 400 may be any type of stationary or mobile computing device, including a mobile computer or mobile computing device (e.g., a tablet computer, a personal digital assistant, a laptop computer, a notebook computer, a netbook, etc.), a mobile phone (e.g., a smart phone), a wearable computing device (e.g., a smart watch, smart glasses, etc.), or other types of mobile devices, or a stationary computing device such as a desktop computer or a personal computer (PC). The computing device 400 may also be a mobile or stationary server.

[0094] The processor 420 is used to execute the following computer executable instructions, which, when executed by the processor, implement the steps of the above-mentioned annual power generation method based on probability prediction. The above is a schematic scheme of a computing device of this embodiment. It should be noted that the technical scheme of the computing device and the technical scheme of the above-mentioned annual power generation method based on probability prediction belong to the same concept, and the details not described in detail in the technical scheme of the computing device can be found in the description of the technical scheme of the above-mentioned annual power generation method based on probability prediction.

[0095] An embodiment of the present specification also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the annual power generation method based on probability prediction.

[0096] The above is a schematic scheme of a computer-readable storage medium of this embodiment. It should be noted that the technical scheme of the storage medium and the technical scheme of the annual power generation method based on probability prediction described above belong to the same concept, and the details not described in detail in the technical scheme of the storage medium can be referred to the description of the technical scheme of the annual power generation method based on probability prediction described above.

[0097] An embodiment of the present specification also provides a computer program, wherein when the computer program is executed in a computer, the computer is instructed to execute the steps of the annual power generation method based on probability prediction.

[0098] The above is a schematic scheme of a computer program of this embodiment. It should be noted that the technical scheme of the computer program and the technical scheme of the annual power generation method based on probability prediction described above are of the same concept, and the details not described in detail in the technical scheme of the computer program can be found in the description of the technical scheme of the annual power generation method based on probability prediction described above.

[0099] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0100] The computer instructions include computer program codes, which may be in source code form, object code form, executable files or some intermediate forms, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0101] It should be noted that, for the above-mentioned method embodiments, for the sake of simplicity of description, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the embodiments of this specification are not limited by the order of the actions described, because according to the embodiments of this specification, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the embodiments of this specification.

[0102] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0103] The preferred embodiments of this specification disclosed above are only used to help explain this specification. The optional embodiments do not describe all the details in detail, nor do they limit the invention to the specific implementation methods described. Obviously, many modifications and changes can be made according to the content of the embodiments of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the embodiments of this specification, so that technicians in the relevant technical field can understand and use this specification well. This specification is limited only by the claims and their full scope and equivalents.

Claims

1. A method for annual power generation based on probability prediction, characterized in that: include: Acquire original data, perform wind speed correction on the original data, and determine corrected wind speed data; Performing probability modeling based on the corrected wind speed data to determine a time series; Specifically, the corrected wind speed data corresponding to the historical years are determined; the corrected wind speed data corresponding to the historical years are fitted through the Weibull distribution curve to obtain the time series corresponding to the Weibull distribution parameters of the wind speed in the historical years; The time series is input into the grey prediction model to determine the annual power generation prediction result.

2. The method according to claim 1, characterized in that The performing wind speed correction on the original data to determine the corrected wind speed data includes: The original data is subjected to wind speed correction based on a spatial correlation method to determine corrected wind speed data.

3. The method according to claim 1, characterized in that The step of inputting the time series into a grey prediction model to determine an annual power generation prediction result includes: Establish a mapping relationship between wind energy and wind farm power generation; The annual power generation forecast result is determined based on the time series using a moving average method.

4. The method according to claim 1, characterized in that: Also includes: Determining the prediction accuracy of the annual power generation prediction result; Determine whether the prediction accuracy reaches a set accuracy threshold and determine a qualified accuracy result; A re-prediction is performed based on the qualified accuracy results.

5. The method according to claim 4, characterized in that The re-prediction based on the qualified accuracy result includes: In the case where the accuracy qualification result is unqualified, performing smoothing processing on the time series to obtain a smoothed time series; Based on the smoothed time series input grey prediction model, the annual power generation prediction result is determined and updated.

6. The method according to claim 5, characterized in that Determining the annual power generation forecast result based on the time series by using the moving average method includes: Determine the prediction result of the power generation of a single machine by using the moving average method based on the time series; Determine the annual installed capacity, and determine the annual power generation forecast result based on the annual installed capacity and the single-machine power generation forecast result.

7. An annual power generation device based on probability prediction, characterized in that: include: A data correction module is configured to obtain raw data, perform wind speed correction on the raw data, and determine corrected wind speed data; A sequence determination module is configured to perform probability modeling based on the corrected wind speed data to determine a time sequence; Specifically, the corrected wind speed data corresponding to the historical years are determined; the corrected wind speed data corresponding to the historical years are fitted through the Weibull distribution curve to obtain the time series corresponding to the Weibull distribution parameters of the wind speed in the historical years; The result prediction module is configured to input the time series into a grey prediction model to determine an annual power generation prediction result.

8. A computing device, characterized in that include: Memory and processor; The memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions. When the computer executable instructions are executed by the processor, the steps of the annual power generation method based on probability prediction according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the annual power generation method based on probability prediction as described in any one of claims 1 to 6.

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