Method and device for predicting power generation data of wind power plant based on space-time correlation

Abstract

The invention provides a wind power plant power generation data prediction method and device based on space-time correlation. According to the historical actual power generation data of each wind driven generator in the wind power plant and the historical actual wind speed of each wind driven generator in the wind power plant, the wind driven generators in the wind power plant are divided into a plurality of wind driven generator clusters; and determining a reference wind driven generator in the wind driven generator cluster according to the historical actual power generation data of each wind driven generator in the wind driven generator cluster and the historical actual power generation total data of the wind driven generator cluster. And according to the historical actual power generation total data of the wind power plant, the current actual power generation total data of the wind power plant in the upwind direction of the wind power plant and the current actual power generation data of the reference wind driven generator, predicting future expected power generation total data of the wind power plant. Therefore, the accuracy of the predicted future expected power generation total data of the wind power plant can be higher.

Description

Technical Field

[0001] The present application relates to the field of wind energy technology, and in particular to a method and device for predicting wind farm power generation data based on spatiotemporal correlation. Background Art

[0002] As a clean and renewable energy, wind energy is of great significance in alleviating the shortage of traditional energy and reducing environmental pollution. Therefore, the development and utilization of wind power has received more and more attention.

[0003] The development of wind power requires the use of wind power technology, which refers to the technology of using wind energy to generate electricity. Due to the cleanliness and renewable nature of wind energy, the proportion of wind power in the power system is increasing.

[0004] In the scenario of developing wind power, a wind farm is usually used, in which a plurality of wind turbines are provided to generate electricity.

[0005] However, the power generation data of wind farms is affected by many factors and has strong randomness, intermittency and volatility, which brings great challenges to the stable operation of the power grid and power dispatching.

[0006] Therefore, in this context, it is crucial to accurately predict the power generation data of wind farms. For example, the predicted power generation data of wind farms is of great guiding significance in making long-term dispatching plans for power systems, formulating trading strategies for power markets, and formulating operation and maintenance plans for wind farms. In this way, it is possible to optimize the allocation of power resources, improve the operating efficiency and reliability of the power grid, reduce economic losses caused by power fluctuations, and help improve the market competitiveness of wind power. Summary of the invention

[0007] The present application shows a method and device for predicting wind farm power generation data based on spatiotemporal correlation.

[0008] In a first aspect, the present application shows a method for predicting wind farm power generation data based on spatiotemporal correlation, the method comprising: Acquire historical actual power generation data of each wind turbine in the target wind farm, and acquire historical actual wind speed of each wind turbine in the target wind farm; According to the historical actual power generation data of each wind turbine in the target wind farm and the historical actual wind speed of each wind turbine in the target wind farm, the wind turbines in the target wind farm are divided into a plurality of wind turbine clusters, each wind turbine cluster including more than two wind turbines; For any wind turbine cluster, determining a reference wind turbine in the wind turbine cluster according to historical actual power generation data of each wind turbine in the wind turbine cluster and historical actual power generation total data of the wind turbine cluster; Obtain the current actual power generation data of the reference wind turbines in each wind turbine cluster, and obtain the current actual total power generation data of the wind farm in the upwind direction of the target wind farm; obtain the historical actual total power generation data of the target wind farm based on the historical actual power generation data of each wind turbine in the target wind farm; The future expected total power generation data of the target wind farm is predicted based on the historical total actual power generation data of the target wind farm, the current total actual power generation data of the wind farm upwind of the target wind farm, and the current actual power generation data of the reference wind turbine.

[0009] In a second aspect, the present application shows a method for training a prediction model for wind farm power generation data based on spatiotemporal correlation, the method comprising: A training data set is obtained, wherein the training data set includes a plurality of training data, wherein the training data includes sample data and annotated data, wherein the sample data includes sample total power generation data of a first historical time period of a sample wind farm, sample total power generation data of a second historical time period of a wind farm upwind of the sample wind farm, and sample power generation data of a second historical time period of a reference wind turbine in a plurality of wind turbine clusters in the sample wind farm, wherein the annotated data includes actual total power generation data of a third historical time period of the sample wind farm, wherein the first historical time period is earlier than the second historical time period, and the second historical time period is earlier than the third historical time period, wherein each wind turbine cluster includes more than two wind turbines, and wherein the plurality of wind turbine clusters include more than two wind turbines. The machine cluster is obtained by dividing the wind turbines in the sample wind farm according to the sample power generation data of the first historical time period of each wind turbine in the sample wind farm and the sample wind speed of the first historical time period of each wind turbine in the sample wind farm. For any wind turbine cluster, the reference wind turbine in the wind turbine cluster is determined in the wind turbine cluster according to the sample power generation data of the first historical time period of each wind turbine in the wind turbine cluster and the total sample power generation data of the first historical time period of the wind turbine cluster; the total historical actual power generation data of the target wind farm is obtained according to the historical actual power generation data of each wind turbine in the target wind farm; The model to be trained is trained according to the training data set until the model to be trained converges, thereby obtaining a prediction model for wind farm power generation data based on spatiotemporal correlation.

[0010] In a third aspect, the present application shows a wind farm power generation data prediction device based on spatiotemporal correlation, the device comprising: A first acquisition module is used to acquire historical actual power generation data of each wind turbine in the target wind farm, and to acquire historical actual wind speed of each wind turbine in the target wind farm; a division module, for dividing the wind turbines in the target wind farm into a plurality of wind turbine clusters according to the historical actual power generation data of each wind turbine in the target wind farm and the historical actual wind speed of each wind turbine in the target wind farm, each wind turbine cluster including more than two wind turbines; A determination module, for determining, for any one wind turbine cluster, a reference wind turbine in the wind turbine cluster according to historical actual power generation data of each wind turbine in the wind turbine cluster and historical actual power generation total data of the wind turbine cluster; The second acquisition module is used to acquire the current actual power generation data of the reference wind turbines in each wind turbine cluster, and acquire the current actual total power generation data of the wind farm in the upwind direction of the target wind farm; and acquire the historical actual total power generation data of the target wind farm based on the historical actual power generation data of each wind turbine in the target wind farm; The prediction module is used to predict the future expected total power generation data of the target wind farm based on the historical total actual power generation data of the target wind farm, the current total actual power generation data of the wind farm upwind of the target wind farm, and the current actual power generation data of the benchmark wind turbine.

[0011] In a fourth aspect, the present application shows a device for training a prediction model of wind farm power generation data based on spatiotemporal correlation, the device comprising: The third acquisition module is used to acquire a training data set, wherein the training data set includes multiple training data, and the training data includes sample data and annotated data. The sample data includes the total sample power generation data of the sample wind farm in the first historical time period, the total sample power generation data of the wind farm upwind of the sample wind farm in the second historical time period, and the sample power generation data of the benchmark wind turbines in multiple wind turbine clusters in the sample wind farm in the second historical time period. The annotated data includes the actual total power generation data of the sample wind farm in the third historical time period. The first historical time period is earlier than the second historical time period, and the second historical time period is earlier than the third historical time period. Each wind turbine cluster includes more than two wind turbines. A wind turbine cluster is obtained by dividing the wind turbines in the sample wind farm according to the sample power generation data of the first historical time period of each wind turbine in the sample wind farm and the sample wind speed of the first historical time period of each wind turbine in the sample wind farm. For any wind turbine cluster, the reference wind turbine in the wind turbine cluster is determined in the wind turbine cluster according to the sample power generation data of the first historical time period of each wind turbine in the wind turbine cluster and the total sample power generation data of the first historical time period of the wind turbine cluster; the total historical actual power generation data of the target wind farm is obtained according to the historical actual power generation data of each wind turbine in the target wind farm; The training module is used to train the model to be trained according to the training data set until the model to be trained converges, thereby obtaining a prediction model for wind farm power generation data based on spatiotemporal correlation.

[0012] In a fifth aspect, the present application illustrates an electronic device, comprising: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to execute the method described in any of the above aspects.

[0013] In a sixth aspect, the present application illustrates a non-temporary computer-readable storage medium, which, when instructions in the storage medium are executed by a processor of an electronic device, enables the electronic device to execute a method as described in any of the above aspects.

[0014] In a seventh aspect, the present application illustrates a computer program product. When instructions in the computer program product are executed by a processor of an electronic device, the electronic device is enabled to execute the method described in any of the above aspects.

[0015] The technical solution provided by this application may have the following beneficial effects: In the present application, the historical actual power generation data of each wind turbine in the target wind farm is obtained, and the historical actual wind speed of each wind turbine in the target wind farm is obtained. According to the historical actual power generation data of each wind turbine in the target wind farm and the historical actual wind speed of each wind turbine in the target wind farm, the wind turbines in the target wind farm are divided into a plurality of wind turbine clusters, each wind turbine cluster including more than two wind turbines. For any wind turbine cluster, the reference wind turbine in the wind turbine cluster is determined according to the historical actual power generation data of each wind turbine in the wind turbine cluster and the total historical actual power generation data of the wind turbine cluster. The current actual power generation data of the reference wind turbine in each wind turbine cluster is obtained, and the total current actual power generation data of the wind farm in the upwind direction of the target wind farm is obtained; the total historical actual power generation data of the target wind farm is obtained according to the historical actual power generation data of each wind turbine in the target wind farm. The future expected total power generation data of the target wind farm is predicted based on the historical total actual power generation data of the target wind farm, the current total actual power generation data of the wind farm upwind of the target wind farm, and the current actual power generation data of the reference wind turbine.

[0016] Among them, the present application takes into account spatial factors when predicting the future expected total power generation data of the target wind farm. For example, the application takes into account the impact of the current actual total power generation data of the wind farm upwind of the target wind farm on the future expected total power generation data of the target wind farm. Since the wind first passes through the wind farm upwind of the target wind farm and then passes through the target wind farm, the wind farm upwind of the target wind farm will absorb part of the wind energy, for example, the wind speed may be reduced and / or the wind direction may be changed, which may lead to a reduction in the wind energy that the target wind farm can obtain, which may lead to a reduction in the total power generation data of the target wind farm. In this way, by considering the impact of the current actual total power generation data of the wind farm upwind of the target wind farm on the future expected total power generation data of the target wind farm, the accuracy of the predicted future expected total power generation data of the target wind farm can be improved.

[0017] In addition, when predicting the future expected total power generation data of the target wind farm, the present application also takes into account the time factor, for example, the impact of the historical actual total power generation data of the target wind farm on the future expected total power generation data of the target wind farm is taken into account. For example, the wind speed and / or wind direction in the area occupied by the wind farm cluster in adjacent time periods are often stable and generally do not change significantly. Therefore, the difference between the historical actual total power generation data of the target wind farm in a time period and the future expected total power generation data of the target wind farm in an adjacent time period is often not large. Therefore, by considering the impact of the historical actual total power generation data of the target wind farm on the future expected total power generation data of the target wind farm, the accuracy of the predicted future expected total power generation data of the target wind farm can be improved.

[0018] Furthermore, when predicting the expected total power generation data of the target wind farm in the future, the present application also takes into account the influence of the current actual power generation data of the benchmark wind turbines in each wind turbine cluster on the expected total power generation data of the target wind farm in the future. For example, the wind turbines in the target wind farm are divided into multiple wind turbine clusters. The historical actual power generation data of the wind turbines in the same wind turbine cluster are very close, and / or the historical actual wind speeds are very close. The contribution of the historical actual power generation data of each wind turbine in the wind turbine cluster to the historical actual power generation data of the wind turbine cluster can be determined. The greater the contribution, the greater the correlation between the historical actual power generation data of the wind turbine cluster and the historical actual power generation data of the wind turbine cluster. The multiple wind turbines with the largest correlation in a wind turbine cluster are determined as the benchmark wind turbines in the wind turbine cluster. The benchmark wind turbines in each wind turbine cluster are used to process the historical actual power generation data of each wind turbine in the target wind farm, so as to improve the accuracy of the predicted expected total power generation data of the target wind farm in the future. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 This is a flowchart of the steps of a method for predicting wind farm power generation data based on spatiotemporal correlation in the present application.

[0020] Figure 2 This is a flowchart of the steps of a method for training a prediction model for wind farm power generation data based on spatiotemporal correlation in the present application.

[0021] Figure 3 It is a schematic diagram of a prediction model of wind farm power generation data based on spatiotemporal correlation in the present application.

[0022] Figure 4 This is a structural block diagram of a wind farm power generation data prediction device based on time-space correlation in the present application.

[0023] Figure 5 It is a structural block diagram of a device for training a prediction model of wind farm power generation data based on spatiotemporal correlation in the present application.

[0024] Figure 6 It is a block diagram of an electronic device of the present application.

[0025] Figure 7 It is a block diagram of an electronic device of the present application. DETAILED DESCRIPTION

[0026] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0027] Reference Figure 1 , shows a flowchart of a method for predicting wind farm power generation data based on spatiotemporal correlation of the present application, the method can be applied to an electronic device, the electronic device can include a terminal or a server, etc., wherein the method can specifically include the following steps: In step S101, historical actual power generation data of each wind turbine in the target wind farm is obtained, and historical actual wind speed of each wind turbine in the target wind farm is obtained.

[0028] In the present application, a wind farm has multiple wind turbines, or multiple wind turbines constitute a wind farm.

[0029] A wind farm occupies an area, and the wind turbines in the wind farm are located at different positions in the area.

[0030] There are other wind farms around a wind farm, and multiple wind farms can form a wind farm cluster.

[0031] The wind speed and / or wind direction in the area occupied by the wind farm cluster is often stable in adjacent time periods and generally does not change significantly.

[0032] The target wind farm is one of the wind farms in the wind farm cluster.

[0033] The historical actual power generation data of a wind turbine can be understood as: the actual power generation power (in kilowatts) or actual power generation (in kilowatt-hours or degrees) of the wind turbine during a historical period in the historical process.

[0034] The length of the historical period can be 6 months, 5 months, 4 months, 3 months, 2 months, 1 month, 2 weeks, 1 week, 48 hours, 36 hours, 24 hours, 12 hours, 6 hours, 2 hours, 1 hour or half an hour, etc., and can be determined according to actual conditions, and this application does not limit this.

[0035] The end time of the historical duration may be the current time, or may be a time earlier and closer than the current time.

[0036] The power generation of the wind turbine generator in the historical period during the historical process includes: the real power generation of the wind turbine generator in the historical period during the historical process (the real situation that actually occurred).

[0037] Alternatively, the power generation of the wind turbine during the historical period in the historical process includes: the real power generation of the wind turbine during the historical period in the historical process (the real situation that actually occurred).

[0038] The actual power generation or actual power generation of the wind turbine in the historical period of the historical process can be counted and stored in the electronic device in advance, and can be updated in real time.

[0039] In this way, for any wind turbine generator in the target wind farm, the electronic device can directly obtain the actual power generation or actual power generation of the wind turbine generator in the historical period of the historical process, and use it as the historical actual power generation data of the wind turbine generator.

[0040] The same is done for each other wind turbine generator in the target wind farm, thereby obtaining the historical actual power generation data of each wind turbine generator in the target wind farm.

[0041] The actual power generation power of the wind turbine generator in the historical period during the historical process may be the actual average power of the wind turbine generator in the historical period during the historical process.

[0042] The actual power generation of the wind turbine in the historical period during the historical process may be the actual total power generation of the wind turbine in the historical period during the historical process.

[0043] In addition, each wind turbine in each wind farm is equipped with a wind speed measuring device, such as a wind tower. The wind speed measuring device on each wind turbine can measure the wind speed. The wind speed actually measured by a wind speed measuring device can be regarded as the actual wind speed of the wind turbine where the wind speed measuring device is located.

[0044] The historical actual wind speed of the wind turbine can be understood as: the wind speed in the historical period of the historical process measured by the wind speed measuring device on the wind turbine, for example, it can be the average wind speed in the historical period.

[0045] In step S102, the wind turbines in the target wind farm are divided into a plurality of wind turbine clusters according to the historical actual power generation data of the wind turbines in the target wind farm and the historical actual wind speed of the wind turbines in the target wind farm, each wind turbine cluster including more than two wind turbines.

[0046] In one embodiment of the present application, the wind turbines in the target wind farm can be divided into multiple wind turbine clusters using a DBSCAN (Density-Based Spatial Clustering of Applications with Noise, a representative density-based clustering) algorithm based on the historical actual power generation data of each wind turbine in the target wind farm and the historical actual wind speed of each wind turbine in the target wind farm.

[0047] Of course, it is understandable that other clustering algorithms may also be used to divide the wind turbines in the target wind farm into multiple wind turbine clusters, such as the k-means algorithm or the Clara algorithm, etc. The present application does not limit the specific clustering algorithm.

[0048] The wind turbines in different wind turbine clusters are different.

[0049] The historical actual power generation data of the wind turbines in the same wind turbine cluster are very close, and / or the historical actual wind speeds are very close.

[0050] The historical actual power generation data of wind turbines in different wind turbine clusters have certain differences, or even have large differences, and / or the historical actual wind speeds of wind turbines in different wind turbine clusters have certain differences, or even have large differences.

[0051] In step S103, for any wind turbine cluster, a reference wind turbine in the wind turbine cluster is determined according to the historical actual power generation data of each wind turbine in the wind turbine cluster and the historical actual power generation total data of the wind turbine cluster.

[0052] The historical actual power generation data of the wind turbine can refer to the above description. For example, it can be the actual power generation of the wind turbine in the historical period of the historical process (the actual situation that actually occurred), or the actual power generation of the wind turbine in the historical period of the historical process (the actual situation that actually occurred), which will not be elaborated here.

[0053] The total historical actual power generation data of the wind turbine cluster can be understood as: the sum of the historical actual power generation data of each generator in the wind turbine cluster.

[0054] In an example, assuming that the historical actual power generation data of a wind turbine is the actual average power generation of the wind turbine during a historical period in a historical process, the total historical actual power generation data of the wind turbine cluster can be understood as: the sum of the actual average power generation of each wind turbine in the wind turbine cluster during a historical period in the historical process.

[0055] Alternatively, in another example, assuming that the historical actual power generation data of a wind turbine is the actual power generation of the wind turbine during a historical period in a historical process, the total historical actual power generation data of the wind turbine cluster can be understood as: the sum of the actual power generation of each wind turbine in the wind turbine cluster during a historical period in the historical process.

[0056] In one embodiment of the present application, step S103 may be implemented by the following process, including: 1031. Determine the correlation between the historical actual power generation data of each wind turbine in the wind turbine cluster and the historical actual power generation data of the wind turbine cluster based on the historical actual power generation data of each wind turbine in the wind turbine cluster and the historical actual power generation data of the wind turbine cluster.

[0057] In this way, the contribution degree of the historical actual power generation data of each wind turbine in the wind turbine cluster to the total historical actual power generation data of the wind turbine cluster can be determined.

[0058] The greater the contribution of a wind turbine, the greater the correlation between the historical actual power generation data and the total historical actual power generation data of the wind turbine cluster; or, the smaller the contribution of a wind turbine, the smaller the correlation between the historical actual power generation data and the total historical actual power generation data of the wind turbine cluster.

[0059] In the present application, various methods are supported for determining the correlation between the historical actual power generation data of each wind turbine in the wind turbine cluster and the historical actual power generation data of the wind turbine cluster. The present application does not limit the specific method for determining the correlation between the historical actual power generation data of each wind turbine in the wind turbine cluster and the historical actual power generation data of the wind turbine cluster.

[0060] In an optional embodiment, the correlation relationship between the historical actual power generation data of a wind turbine in the wind turbine cluster and the historical actual power generation total data of the wind turbine cluster may be determined in the following manner: For example, the historical actual power generation data of the wind turbine and the historical actual power generation data of the wind turbine cluster are expressed as time series vectors, which are recorded as A and B respectively: The MI value between A and B is defined as: I(A,B) = H(A) + H(B) - H(A,B) H(A) and H(B) represent the entropy values ​​of variable A and variable B respectively.

[0061] H(A, B) represents the joint entropy between variables A and B.

[0062] They are defined as follows:

[0063]

[0064] p is the probability density of a single variable.

[0065] p joint is the joint probability density between the two variables.

[0066] From the above formula, we can see that the calculation process of I (A, B) is related to the probability density p and the joint probability density p joint Closely related.

[0067] However, in practice, it is often difficult to obtain an accurate probability density function.

[0068] To solve this problem, we can use nonparametric density estimation method to make an approximate estimate, which is more efficient and practical.

[0069] Taking the continuous random variable A as an example, the value range of the variable is divided into K F fragments of equal length, each fragment length is defined as Δ F .

[0070] In order to reduce the error of the estimation method, the number of fragments can be determined according to the following given formula, where the value of n represents the total number of data contained in A:

[0071]

[0072] Based on this, the entropy value of variable A can be expressed as:

[0073]

[0074] p i is the probability that sample A belongs to the i-th segment.

[0075] n i is the number of samples of sample A belonging to the i-th segment.

[0076] Then H(A) can be rewritten as follows:

[0077] Similarly, the entropy value of variable B can be expressed as:

[0078] n j is the number of samples of sample B belonging to the jth segment.

[0079] After determining the entropy values ​​of variables A and B, it is necessary to estimate the joint entropy value of variables A and B.

[0080] The range of its joint space is divided into K parts using the histogram estimation method F *K V cells of equal size, the cell size is Δ F *Δ V , each cell position is defined by (i, j). By counting the number of samples n belonging to the cell (i, j) ij After statistics, the joint entropy value can be expressed as follows:

[0081] Then the MI values ​​of variables A and B can be calculated as follows:

[0082] 1032. Determine a reference wind turbine in the wind turbine cluster according to the correlation between the historical actual power generation data of each wind turbine in the wind turbine cluster and the historical actual power generation total data of the wind turbine cluster.

[0083] The historical actual power generation data of the benchmark wind turbines in a wind turbine cluster often have a high degree of contribution to the total historical actual power generation data of the wind turbine cluster, and are often the highest one, two, or several.

[0084] Alternatively, the correlation between the historical actual power generation data of the benchmark wind turbines in a wind turbine cluster and the historical actual power generation total data of the wind turbine cluster is often high, and is often the highest one, two, or several.

[0085] Thus, in one embodiment, the wind turbines in the wind turbine cluster can be sorted in descending order according to the correlation between the historical actual power generation data of each wind turbine in the wind turbine cluster and the historical actual power generation total data of the wind turbine cluster. For example, the wind turbines in the wind turbine cluster are sorted. In the wind turbine cluster, the wind turbines with the greater correlation between their historical actual power generation data and the historical actual power generation total data of the wind turbine cluster are ranked higher, and the wind turbines with the smaller correlation between their historical actual power generation data and the historical actual power generation total data of the wind turbine cluster are ranked lower. The correlation relationship can be represented by a numerical value, and the larger the numerical value, the closer the correlation relationship between the two is. Alternatively, the smaller the numerical value, the more distant the correlation relationship between the two is. Then, at least one wind turbine can be selected from the wind turbines in the wind turbine cluster according to the sorting order. For example, the TopN wind turbines can be selected from the wind turbines in the wind turbine cluster according to the sorting order. N is a positive integer greater than or equal to 1. The specific value of N can be determined according to the actual situation, and this application does not limit this. Afterwards, the reference wind turbine in the wind turbine cluster may be determined according to the selected wind turbine. For example, the selected wind turbine may be used as the reference wind turbine in the wind turbine cluster.

[0086] The number of reference wind turbines in a wind turbine cluster may be 1, 2, 3, or the like.

[0087] In step S104, the current actual power generation data of the reference wind turbines in each wind turbine cluster is obtained, the current total actual power generation data of the wind farm upwind of the target wind farm is obtained, and the historical total actual power generation data of the target wind farm is obtained based on the historical actual power generation data of each wind turbine in the target wind farm.

[0088] The wind speed and / or wind direction of the wind farm cluster where the target wind farm is located is often stable and generally does not change significantly.

[0089] For any wind farm other than the non-target wind farm in the wind farm cluster, if the wind passes through the wind farm first and then passes through the target wind farm, the wind farm may be the wind farm upwind of the target wind farm, and the target wind farm is the wind farm downwind of the wind farm.

[0090] In the present application, the wind first passes through a wind farm upwind of the target wind farm, and then passes through the target wind farm.

[0091] The current actual power generation data of the wind turbine can be understood as: the actual power generation power (in kilowatts) or the actual power generation (in kilowatt-hours / degrees) of the wind turbine in the current time period at the current moment.

[0092] The duration of the current time period can be 5 minutes, 10 minutes, half an hour, 1 hour, 2 hours, 6 hours, 12 hours, 24 hours, 36 hours or 48 hours, etc., which can be determined according to actual conditions and is not limited in this application.

[0093] The power generated by the wind turbine in the current time period at the current moment includes: the real power generated by the wind turbine in the current time period at the current moment (the actual situation that occurs). Alternatively, the power generated by the wind turbine in the current time period at the current moment includes: the real power generated by the wind turbine in the current time period at the current moment (the actual situation that occurs).

[0094] The actual power generation or actual power generation of the wind turbine in the current time period at the current moment can be counted and stored in the electronic device in real time, and can be updated in real time.

[0095] In this way, for any one of the benchmark wind turbines in each wind turbine cluster, the electronic device can directly obtain the actual power generation or actual power generation of the benchmark wind turbine in the current time period at the current moment, and use it as the current actual power generation data of the benchmark wind turbine.

[0096] The same is true for each other reference wind turbine in the reference wind turbine in each wind turbine cluster, so as to obtain the current actual power generation data of the reference wind turbine in each wind turbine cluster.

[0097] The actual power generation of the wind turbine in the current time period at the current moment may be the actual average power generation of the wind turbine in the current time period at the current moment.

[0098] The actual power generation of the wind turbine in the current time period at the current moment may be the actual total power generation of the wind turbine in the current time period at the current moment.

[0099] The actual power generation or actual power generation of the wind turbines in the wind farm upwind of the target wind farm in the current time period at the current moment can be counted and stored in the electronic device in real time, and can be updated in real time.

[0100] In this way, for any wind turbine in the wind farm upwind of the target wind farm, the electronic device can directly obtain the actual power generation or actual power generation of the wind turbine in the current time period at the current moment, and use it as the current actual power generation data of the wind turbine.

[0101] The same is done for each other wind turbine in the wind farm upwind of the target wind farm, thereby obtaining the current actual power generation data of each wind turbine in the wind farm upwind of the target wind farm, and then summing up the current actual power generation data of each wind turbine in the wind farm upwind of the target wind farm to obtain the total current actual power generation data of the wind farm upwind of the target wind farm.

[0102] In addition, the historical actual power generation data of each wind turbine in the target wind farm may be summed to obtain the total historical actual power generation data of the target wind farm.

[0103] In step S105, the target wind farm's future expected total power generation data is predicted based on the target wind farm's historical total actual power generation data, the current total actual power generation data of wind farms upwind of the target wind farm, and the current actual power generation data of the reference wind turbine.

[0104] The total future expected power generation data of the target wind farm can be understood as: the sum of the future expected power generation data of each generator in the target wind farm.

[0105] In an example, assuming that the total expected future power generation data of a wind turbine is the expected average power generation of the wind turbine in a certain period of time in the future, the total expected future power generation data of the target wind farm can be understood as: the sum of the expected average power generation of each wind turbine in the target wind farm in a certain period of time in the future.

[0106] Alternatively, in another example, assuming that the total expected future power generation data of the wind turbine is the expected power generation of the wind turbine in a certain period of time in the future, the total expected future power generation data of the target wind farm can be understood as: the sum of the expected power generation of each wind turbine in the target wind farm in a certain period of time in the future.

[0107] The length of a certain period of time can be 5 minutes, 10 minutes, half an hour, 1 hour, 2 hours, 6 hours, 12 hours, 24 hours, 36 hours or 48 hours, etc., which can be determined according to actual conditions and is not limited in this application.

[0108] In the present application, the historical actual power generation data of each wind turbine in the target wind farm is obtained, and the historical actual wind speed of each wind turbine in the target wind farm is obtained. According to the historical actual power generation data of each wind turbine in the target wind farm and the historical actual wind speed of each wind turbine in the target wind farm, the wind turbines in the target wind farm are divided into a plurality of wind turbine clusters, each wind turbine cluster including more than two wind turbines. For any wind turbine cluster, the reference wind turbine in the wind turbine cluster is determined according to the historical actual power generation data of each wind turbine in the wind turbine cluster and the total historical actual power generation data of the wind turbine cluster. The current actual power generation data of the reference wind turbine in each wind turbine cluster is obtained, and the total current actual power generation data of the wind farm in the upwind direction of the target wind farm is obtained; the total historical actual power generation data of the target wind farm is obtained according to the historical actual power generation data of each wind turbine in the target wind farm. The future expected total power generation data of the target wind farm is predicted based on the historical total actual power generation data of the target wind farm, the current total actual power generation data of the wind farm upwind of the target wind farm, and the current actual power generation data of the reference wind turbine.

[0109] Among them, the present application takes into account spatial factors when predicting the future expected total power generation data of the target wind farm. For example, the application takes into account the impact of the current actual total power generation data of the wind farm upwind of the target wind farm on the future expected total power generation data of the target wind farm. Since the wind first passes through the wind farm upwind of the target wind farm and then passes through the target wind farm, the wind farm upwind of the target wind farm will absorb part of the wind energy, for example, the wind speed may be reduced and / or the wind direction may be changed, which may lead to a reduction in the wind energy that the target wind farm can obtain, which may lead to a reduction in the total power generation data of the target wind farm. In this way, by considering the impact of the current actual total power generation data of the wind farm upwind of the target wind farm on the future expected total power generation data of the target wind farm, the accuracy of the predicted future expected total power generation data of the target wind farm can be improved.

[0110] In addition, when predicting the future expected total power generation data of the target wind farm, the present application also takes into account the time factor, for example, the impact of the historical actual total power generation data of the target wind farm on the future expected total power generation data of the target wind farm is taken into account. For example, the wind speed and / or wind direction in the area occupied by the wind farm cluster in adjacent time periods are often stable and generally do not change significantly. Therefore, the difference between the historical actual total power generation data of the target wind farm in a time period and the future expected total power generation data of the target wind farm in an adjacent time period is often not large. Therefore, by considering the impact of the historical actual total power generation data of the target wind farm on the future expected total power generation data of the target wind farm, the accuracy of the predicted future expected total power generation data of the target wind farm can be improved.

[0111] Furthermore, when predicting the expected total power generation data of the target wind farm in the future, the present application also takes into account the influence of the current actual power generation data of the benchmark wind turbines in each wind turbine cluster on the expected total power generation data of the target wind farm in the future. For example, the wind turbines in the target wind farm are divided into multiple wind turbine clusters. The historical actual power generation data of the wind turbines in the same wind turbine cluster are very close, and / or the historical actual wind speeds are very close. The contribution of the historical actual power generation data of each wind turbine in the wind turbine cluster to the historical actual power generation data of the wind turbine cluster can be determined. The greater the contribution, the greater the correlation between the historical actual power generation data of the wind turbine cluster and the historical actual power generation data of the wind turbine cluster. The multiple wind turbines with the largest correlation in a wind turbine cluster are determined as the benchmark wind turbines in the wind turbine cluster. The benchmark wind turbines in each wind turbine cluster are used to process the historical actual power generation data of each wind turbine in the target wind farm, so as to improve the accuracy of the predicted expected total power generation data of the target wind farm in the future.

[0112] In one embodiment of the present application, a prediction model for wind farm power generation data based on spatiotemporal correlation may be trained in advance, and then the prediction model for wind farm power generation data based on spatiotemporal correlation may be used to predict the future expected total power generation data of the target wind farm.

[0113] Among them, see Figure 2 , the specific training process is as follows: In step S201, a training data set is obtained. The training data set includes a plurality of training data. The training data includes sample data and annotated data. The sample data includes the total sample power generation data of the sample wind farm in the first historical time period, the total sample power generation data of the wind farm upwind of the sample wind farm in the second historical time period, and the sample power generation data of the benchmark wind turbines in the plurality of wind turbine clusters in the sample wind farm in the second historical time period. The annotated data includes the total real power generation data of the sample wind farm in the third historical time period. The first historical time period is earlier than the second historical time period, and the second historical time period is earlier than the third historical time period.

[0114] Each wind turbine cluster includes more than two wind turbines.

[0115] The multiple wind turbine clusters are obtained by dividing the wind turbines in the sample wind farm according to the sample power generation data of the first historical time period of each wind turbine in the sample wind farm and the sample wind speed of the first historical time period of each wind turbine in the sample wind farm.

[0116] For any wind turbine cluster, the benchmark wind turbine in the wind turbine cluster is determined in the wind turbine cluster based on the sample power generation data of the first historical time period of each wind turbine in the wind turbine cluster and the sample total power generation data of the first historical time period of the wind turbine cluster.

[0117] The total historical actual power generation data of the target wind farm is obtained based on the historical actual power generation data of each wind turbine in the target wind farm.

[0118] The explanations involved in this step can refer to the explanations in steps S101 to S104, and will not be described in detail here.

[0119] In step S202, the model to be trained is trained according to the training data set until the model to be trained converges, thereby obtaining a prediction model for wind farm power generation data based on spatiotemporal correlation.

[0120] Both the sample data and the labeled data are real data that occurred in history. Therefore, the prediction model of wind farm power generation data trained based on spatiotemporal correlation can be close to the actual situation, with a high degree of generalization, and the prediction results are closer to the actual situation.

[0121] In this way, when predicting the future expected total power generation data of the target wind farm in step S105, the historical actual total power generation data of the target wind farm, the current actual total power generation data of the wind farm upwind of the target wind farm, and the current actual power generation data of the benchmark wind turbine can be input into the prediction model of wind farm power generation data based on spatiotemporal correlation, so that the prediction model of wind farm power generation data based on spatiotemporal correlation processes the historical actual total power generation data of the target wind farm, the current actual total power generation data of the wind farm upwind of the target wind farm, and the current actual power generation data of the benchmark wind turbine to obtain a processing result; then, the future expected total power generation data of the target wind farm can be obtained according to the processing result obtained by the prediction model of wind farm power generation data based on spatiotemporal correlation.

[0122] For example, in one embodiment of the present application, there is one prediction model for wind farm power generation data based on spatiotemporal correlation, and a processing result obtained by the prediction model for wind farm power generation data based on spatiotemporal correlation is the future expected total power generation data of the target wind farm predicted by the prediction model for wind farm power generation data based on spatiotemporal correlation. In this way, the future expected total power generation data of the target wind farm predicted by the prediction model for wind farm power generation data based on spatiotemporal correlation can be determined as the future expected total power generation data of the target wind farm.

[0123] In another embodiment of the present application, when training a prediction model for wind farm power generation data based on spatiotemporal correlation, see Figure 3, you can use the bootstrapping sampling method to perform S rounds of extraction on the training data set (extracting 80% of the data in the training data set each time). The training data extracted in each round is used as the training data subset, that is, each round of extraction will get a training data subset, and a total of S training data subsets will be obtained.

[0124] Bootstrapping sampling is a statistical method used to deal with small sample data sets. It reconstructs the original distribution by sampling with replacement (there may be repeated observations in the sample).

[0125] For any training data subset, the model to be trained can be trained according to the training data subset until the model to be trained converges, thereby obtaining a prediction model for wind farm power generation data based on spatiotemporal correlation. The same is true for each other training data subset. In this way, a total of S prediction models for wind farm power generation data based on spatiotemporal correlation are obtained.

[0126] S is a positive integer greater than or equal to 2; the training data in any two training data subsets are not all the same or all different.

[0127] Afterwards, S prediction models of wind farm power generation data based on spatiotemporal correlation can be put into use, for example, to predict the total expected power generation data of the target wind farm in the future.

[0128] For example, in another embodiment of the present application, there are more than two prediction models for wind farm power generation data based on spatiotemporal correlation, and more than two prediction models for wind farm power generation data based on spatiotemporal correlation can be trained in advance, and the training data used when training different prediction models for wind farm power generation data based on spatiotemporal correlation are not all the same or completely different; the specific training method can be found in the above description and will not be described in detail here.

[0129] In this way, when predicting the future expected total power generation data of the target wind farm in step S105, the historical actual total power generation data of the target wind farm, the current actual total power generation data of the wind farm upwind of the target wind farm, and the current actual power generation data of the benchmark wind turbine generator can be respectively input into two or more prediction models of wind farm power generation data based on spatiotemporal correlation. That is, each prediction model of wind farm power generation data based on spatiotemporal correlation will obtain the historical actual total power generation data of the target wind farm, the current actual total power generation data of the wind farm upwind of the target wind farm, and the current actual power generation data of the benchmark wind turbine generator. More than one prediction model for wind farm power generation data based on spatiotemporal correlation respectively processes the historical actual total power generation data of the target wind farm, the current actual total power generation data of the wind farm upwind of the target wind farm, and the current actual power generation data of the benchmark wind turbine to obtain respective processing results; the processing results obtained by each prediction model for wind farm power generation data based on spatiotemporal correlation are the respective predicted future expected total power generation data of the target wind farm, so that the future expected total power generation data of the target wind farm can be obtained according to the processing results obtained by the two or more prediction models for wind farm power generation data based on spatiotemporal correlation.

[0130] For example, the processing results obtained by the prediction models of wind farm power generation data based on spatiotemporal correlation are: the future expected total power generation data of the target wind farm predicted by the prediction models of wind farm power generation data based on spatiotemporal correlation; in this way, the average value between the future expected total power generation data of the target wind farm predicted by each prediction model of wind farm power generation data based on spatiotemporal correlation can be calculated to obtain the future expected total power generation data of the target wind farm, that is, the average value is determined as the future expected total power generation data of the target wind farm.

[0131] Among them, the models in this application may include Transformer models, etc., and of course, may also include CNN (Convolutional Neural Networks), RNN (Recurrent Neural Network) and LSTM (Long Short-Term Memory), etc. This application does not limit the structure of the specific model.

[0132] It should be noted that, for the method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the order of the actions described, because according to the present application, certain 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 optional embodiments, and the actions involved are not necessarily required by the present application.

[0133] Reference Figure 4 , shows a structural block diagram of a wind farm power generation data prediction device based on spatiotemporal correlation of the present application, the device comprising: A first acquisition module 11 is used to acquire historical actual power generation data of each wind turbine in the target wind farm, and to acquire historical actual wind speed of each wind turbine in the target wind farm; A division module 12 is used to divide the wind turbines in the target wind farm into a plurality of wind turbine clusters according to the historical actual power generation data of each wind turbine in the target wind farm and the historical actual wind speed of each wind turbine in the target wind farm, each wind turbine cluster including more than two wind turbines; A determination module 13, for determining, for any one wind turbine cluster, a reference wind turbine in the wind turbine cluster according to historical actual power generation data of each wind turbine in the wind turbine cluster and historical actual power generation total data of the wind turbine cluster; The second acquisition module 14 is used to acquire the current actual power generation data of the reference wind turbines in each wind turbine cluster, and acquire the current actual total power generation data of the wind farm in the upwind direction of the target wind farm; and acquire the historical actual total power generation data of the target wind farm according to the historical actual power generation data of each wind turbine in the target wind farm; The prediction module 15 is used to predict the expected future total power generation data of the target wind farm based on the historical total actual power generation data of the target wind farm, the current total actual power generation data of the wind farm upwind of the target wind farm, and the current actual power generation data of the reference wind turbine.

[0134] In an optional implementation, the prediction module includes: An input unit is used to input the historical actual total power generation data of the target wind farm, the current actual total power generation data of the wind farm upwind of the target wind farm, and the current actual power generation data of the reference wind turbine into a prediction model of wind farm power generation data based on spatiotemporal correlation, so that the prediction model of wind farm power generation data based on spatiotemporal correlation processes the historical actual total power generation data of the target wind farm, the current actual total power generation data of the wind farm upwind of the target wind farm, and the current actual power generation data of the reference wind turbine to obtain a processing result; The acquisition unit is used to acquire the total expected future power generation data of the target wind farm according to the processing result obtained by the prediction model of the wind farm power generation data based on the spatiotemporal correlation.

[0135] In an optional implementation, there are more than two prediction models for wind farm power generation data based on spatiotemporal correlation, and the training data used when training different prediction models for wind farm power generation data based on spatiotemporal correlation are not all the same or are not all the same; The input unit comprises: An input subunit is used to input the historical actual total power generation data of the target wind farm, the current actual total power generation data of the wind farm upwind of the target wind farm, and the current actual power generation data of the reference wind turbine respectively into two or more prediction models of wind farm power generation data based on spatiotemporal correlation, so that the two or more prediction models of wind farm power generation data based on spatiotemporal correlation respectively process the historical actual total power generation data of the target wind farm, the current actual total power generation data of the wind farm upwind of the target wind farm, and the current actual power generation data of the reference wind turbine to obtain respective processing results; Accordingly, the acquisition unit includes: The acquisition subunit is used to acquire the total expected future power generation data of the target wind farm according to the processing results respectively obtained by two or more prediction models of wind farm power generation data based on time-space correlation.

[0136] In an optional implementation, the prediction model for wind farm power generation data based on spatiotemporal correlation respectively obtains the following processing results: total future expected power generation data of the target wind farm predicted by the prediction model for wind farm power generation data based on spatiotemporal correlation; The acquisition subunit is specifically used to calculate the average value of the total future power generation data of the target wind farm predicted by the prediction models of wind farm power generation data based on spatiotemporal correlation, so as to obtain the total future power generation data of the target wind farm.

[0137] In an optional implementation, the division module includes: The division unit is used to divide the wind turbines in the target wind farm into a plurality of wind turbine clusters by using a representative density-based clustering algorithm DBSCAN according to the historical actual power generation data of each wind turbine in the target wind farm and the historical actual wind speed of each wind turbine in the target wind farm.

[0138] In an optional implementation, the determining module includes: A first determining unit is used to determine the correlation relationship between the historical actual power generation data of each wind turbine in the wind turbine cluster and the historical actual power generation total data of the wind turbine cluster according to the historical actual power generation data of each wind turbine in the wind turbine cluster and the historical actual power generation total data of the wind turbine cluster; The second determination unit is used to determine the reference wind turbine in the wind turbine cluster according to the correlation relationship between the historical actual power generation data of each wind turbine in the wind turbine cluster and the historical actual power generation total data of the wind turbine cluster.

[0139] In an optional implementation, the second determining unit includes: a sorting subunit, for sorting the wind turbines in the wind turbine cluster, wherein the wind turbines with a greater correlation between their historical actual power generation data and the total historical actual power generation data of the wind turbine cluster are sorted higher, and the wind turbines with a smaller correlation between their historical actual power generation data and the total historical actual power generation data of the wind turbine cluster are sorted lower; A selection subunit is used to select TopN wind turbines from among the wind turbines in the wind turbine cluster in a sorted order; N is a positive integer greater than or equal to 1; The determination subunit is used to determine the reference wind turbine in the wind turbine cluster according to the selected wind turbine.

[0140] Reference Figure 5 , showing a structural block diagram of a device for training a prediction model of wind farm power generation data based on spatiotemporal correlation in the present application, the device comprising: The third acquisition module 21 is used to acquire a training data set, wherein the training data set includes multiple training data, and the training data includes sample data and annotated data. The sample data includes the total sample power generation data of the sample wind farm in the first historical time period, the total sample power generation data of the wind farm upwind of the sample wind farm in the second historical time period, and the sample power generation data of the benchmark wind turbines in the multiple wind turbine clusters in the sample wind farm in the second historical time period. The annotated data includes the actual total power generation data of the sample wind farm in the third historical time period. The first historical time period is earlier than the second historical time period, and the second historical time period is earlier than the third historical time period. Each wind turbine cluster includes more than two wind turbines. The plurality of wind turbine clusters are obtained by dividing the wind turbines in the sample wind farm according to the sample power generation data of the first historical time period of each wind turbine in the sample wind farm and the sample wind speed of the first historical time period of each wind turbine in the sample wind farm. For any wind turbine cluster, the reference wind turbine in the wind turbine cluster is determined in the wind turbine cluster according to the sample power generation data of the first historical time period of each wind turbine in the wind turbine cluster and the total sample power generation data of the first historical time period of the wind turbine cluster; the total historical actual power generation data of the target wind farm is obtained according to the historical actual power generation data of each wind turbine in the target wind farm; The training module 22 is used to train the model to be trained according to the training data set until the model to be trained converges, thereby obtaining a prediction model of wind farm power generation data based on spatiotemporal correlation.

[0141] In an optional implementation, the training module includes: The extraction unit is used to perform S rounds of extraction on the training data set using the bootstrapping sampling method, and the training data extracted in each round is used as the training data subset; S is a positive integer greater than or equal to 2; A training unit is used to train a to-be-trained model according to any training data subset until the to-be-trained model converges, thereby obtaining a prediction model of wind farm power generation data based on spatiotemporal correlation, and a total of S prediction models of wind farm power generation data based on spatiotemporal correlation are obtained; the training data in any two training data subsets are not completely the same or completely different.

[0142] As for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0143] Optionally, an embodiment of the present application further provides an electronic device, comprising: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, the various processes of the above-mentioned method embodiment are implemented, and the same technical effect can be achieved. To avoid repetition, it will not be described here.

[0144] The embodiment of the present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, each process of the above method embodiment is implemented, and the same technical effect can be achieved. To avoid repetition, it is not repeated here. The computer-readable storage medium is, for example, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0145] FIG6 is a block diagram of an electronic device 800 shown in the present application. For example, the electronic device 800 may be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, etc.

[0146] Reference Figure 6, the electronic device 800 may include one or more of the following components: a processing component 802 , a memory 804 , a power component 806 , a multimedia component 808 , an audio component 810 , an input / output (I / O) interface 812 , a sensor component 814 , and a communication component 816 .

[0147] The processing component 802 generally controls the overall operation of the electronic device 800, such as operations associated with display, phone calls, data communications, camera operations, and recording operations. The processing component 802 may include one or more processors 820 to execute instructions to complete all or part of the steps of the above method. In addition, the processing component 802 may include one or more modules to facilitate the interaction between the processing component 802 and other components. For example, the processing component 802 may include a multimedia module to facilitate the interaction between the multimedia component 808 and the processing component 802.

[0148] The memory 804 is configured to store various types of data to support operations on the device 800. Examples of such data include instructions for any application or method operating on the electronic device 800, contact data, phone book data, messages, images, videos, etc. The memory 804 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0149] The power supply component 806 provides power to the various components of the electronic device 800. The power supply component 806 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to the electronic device 800.

[0150] The multimedia component 808 includes a screen that provides an output interface between the electronic device 800 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundaries of the touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 808 includes a front camera and / or a rear camera. When the device 800 is in an operating mode, such as a shooting mode or a video mode, the front camera and / or the rear camera may receive external multimedia data. Each front camera and rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.

[0151] The audio component 810 is configured to output and / or input audio signals. For example, the audio component 810 includes a microphone (MIC), and when the electronic device 800 is in an operating mode, such as a call mode, a recording mode, and a voice recognition mode, the microphone is configured to receive an external audio signal. The received audio signal can be further stored in the memory 804 or sent via the communication component 816. In some embodiments, the audio component 810 also includes a speaker for outputting audio signals.

[0152] I / O interface 812 provides an interface between processing component 802 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include but are not limited to: home button, volume button, start button, and lock button.

[0153] The sensor assembly 814 includes one or more sensors for providing various aspects of status assessment for the electronic device 800. For example, the sensor assembly 814 can detect the open / closed state of the device 800, the relative positioning of components, such as the display and keypad of the electronic device 800, and the sensor assembly 814 can also detect the position change of the electronic device 800 or a component of the electronic device 800, the presence or absence of contact between the user and the electronic device 800, the orientation or acceleration / deceleration of the electronic device 800, and the temperature change of the electronic device 800. The sensor assembly 814 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. The sensor assembly 814 may also include an optical sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 814 may also include an accelerometer, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

[0154] The communication component 816 is configured to facilitate wired or wireless communication between the electronic device 800 and other devices. The electronic device 800 can access a wireless network based on a communication standard, such as WiFi, a carrier network (such as 2G, 3G, 4G or 5G), or a combination thereof. In an exemplary embodiment, the communication component 816 receives a broadcast signal or broadcast operation information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 816 also includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.

[0155] In an exemplary embodiment, the electronic device 800 may be implemented by one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above methods.

[0156] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 804 including instructions, and the instructions can be executed by a processor 820 of an electronic device 800 to perform the above method. For example, the non-transitory computer-readable storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

[0157] Figure 7 1 is a block diagram of an electronic device 1900 shown in the present application. For example, the electronic device 1900 may be provided as a server.

[0158] Reference Figure 7 , the electronic device 1900 includes a processing component 1922, which further includes one or more processors, and a memory resource represented by a memory 1932 for storing instructions executable by the processing component 1922, such as an application. The application stored in the memory 1932 may include one or more modules, each corresponding to a set of instructions. In addition, the processing component 1922 is configured to execute instructions to perform the above method.

[0159] The electronic device 1900 may also include a power supply component 1926 configured to perform power management of the electronic device 1900, a wired or wireless network interface 1950 configured to connect the electronic device 1900 to a network, and an input / output (I / O) interface 1958. The electronic device 1900 may operate based on an operating system stored in the memory 1932, such as Windows Server™, Mac OS X™, Unix™, Linux™, FreeBSD™ or the like.

[0160] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0161] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, a magnetic disk, or an optical disk), and includes a number of instructions for a terminal (which can be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to execute the methods described in each embodiment of the present application.

[0162] The embodiments of the present application are described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present application, ordinary technicians in this field can also make many forms without departing from the purpose of the present application and the scope of protection of the claims, all of which are within the protection of the present application.

[0163] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present application can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0164] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0165] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0166] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0167] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0168] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard drives, ROM, RAM, magnetic disks, or optical disks.

[0169] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for predicting wind farm power generation data based on spatiotemporal correlation, characterized in that: The method comprises: Acquire historical actual power generation data of each wind turbine in the target wind farm, and acquire historical actual wind speed of each wind turbine in the target wind farm; According to the historical actual power generation data of each wind turbine in the target wind farm and the historical actual wind speed of each wind turbine in the target wind farm, the wind turbines in the target wind farm are divided into a plurality of wind turbine clusters, each wind turbine cluster including more than two wind turbines; For any wind turbine cluster, determining a reference wind turbine in the wind turbine cluster according to historical actual power generation data of each wind turbine in the wind turbine cluster and historical actual power generation total data of the wind turbine cluster; Obtain the current actual power generation data of the reference wind turbines in each wind turbine cluster, and obtain the current actual total power generation data of the wind farm in the upwind direction of the target wind farm; obtain the historical actual total power generation data of the target wind farm based on the historical actual power generation data of each wind turbine in the target wind farm; The future expected total power generation data of the target wind farm is predicted based on the historical total actual power generation data of the target wind farm, the current total actual power generation data of the wind farm upwind of the target wind farm, and the current actual power generation data of the reference wind turbine.

2. The method according to claim 1, characterized in that The method of predicting the expected future total power generation data of the target wind farm based on the historical total actual power generation data of the target wind farm, the current total actual power generation data of the wind farm upwind of the target wind farm, and the current actual power generation data of the reference wind turbine generator comprises: Inputting the historical actual total power generation data of the target wind farm, the current actual total power generation data of the wind farm upwind of the target wind farm, and the current actual power generation data of the reference wind turbine into a prediction model of wind farm power generation data based on spatiotemporal correlation, so that the prediction model of wind farm power generation data based on spatiotemporal correlation processes the historical actual total power generation data of the target wind farm, the current actual total power generation data of the wind farm upwind of the target wind farm, and the current actual power generation data of the reference wind turbine to obtain a processing result; According to the processing results obtained by the prediction model of wind farm power generation data based on spatiotemporal correlation, the total expected power generation data of the target wind farm in the future is obtained.

3. The method according to claim 2, characterized in that There are more than two prediction models for wind farm power generation data based on spatiotemporal correlation, and the training data used when training different prediction models for wind farm power generation data based on spatiotemporal correlation are not all the same or are completely different; The historical actual power generation total data of the target wind farm, the current actual power generation total data of the wind farm upwind of the target wind farm, and the current actual power generation data of the reference wind turbine generator are input into the prediction model of wind farm power generation data based on spatiotemporal correlation, so that the prediction model of wind farm power generation data based on spatiotemporal correlation processes the historical actual power generation total data of the target wind farm, the current actual power generation total data of the wind farm upwind of the target wind farm, and the current actual power generation data of the reference wind turbine generator to obtain a processing result, including: Inputting the historical actual total power generation data of the target wind farm, the current actual total power generation data of the wind farm upwind of the target wind farm, and the current actual power generation data of the reference wind turbine into two or more prediction models of wind farm power generation data based on spatiotemporal correlation, respectively, so that the two or more prediction models of wind farm power generation data based on spatiotemporal correlation respectively process the historical actual total power generation data of the target wind farm, the current actual total power generation data of the wind farm upwind of the target wind farm, and the current actual power generation data of the reference wind turbine to obtain respective processing results; Accordingly, the processing result obtained according to the prediction model of wind farm power generation data based on spatiotemporal correlation to obtain the total expected future power generation data of the target wind farm includes: According to the processing results respectively obtained from two or more prediction models of wind farm power generation data based on spatiotemporal correlation, the total expected power generation data of the target wind farm in the future is obtained.

4. The method according to claim 3, characterized in that The processing results obtained by the prediction model of wind farm power generation data based on spatiotemporal correlation are respectively as follows: the total future expected power generation data of the target wind farm predicted by the prediction model of wind farm power generation data based on spatiotemporal correlation; The method of obtaining the total expected future power generation data of the target wind farm according to the processing results obtained from the two or more wind farm power generation data prediction models based on spatiotemporal correlation includes: The average value of the total future power generation data of the target wind farm predicted by the prediction models of wind farm power generation data based on spatiotemporal correlation is calculated to obtain the total future power generation data of the target wind farm.

5. The method according to claim 1, characterized in that The wind turbines in the target wind farm are divided into a plurality of wind turbine clusters according to the historical actual power generation data of each wind turbine in the target wind farm and the historical actual wind speed of each wind turbine in the target wind farm, including: According to the historical actual power generation data of each wind turbine in the target wind farm and the historical actual wind speed of each wind turbine in the target wind farm, the wind turbines in the target wind farm are divided into multiple wind turbine clusters using a representative density-based clustering algorithm DBSCAN.

6. The method according to claim 1, characterized in that Determining a reference wind turbine in the wind turbine cluster according to the historical actual power generation data of each wind turbine in the wind turbine cluster and the total historical actual power generation data of the wind turbine cluster includes: Determine the correlation between the historical actual power generation data of each wind turbine in the wind turbine cluster and the historical actual power generation total data of the wind turbine cluster according to the historical actual power generation data of each wind turbine in the wind turbine cluster and the historical actual power generation total data of the wind turbine cluster; The reference wind turbine in the wind turbine cluster is determined according to the correlation between the historical actual power generation data of each wind turbine in the wind turbine cluster and the total historical actual power generation data of the wind turbine cluster.

7. The method according to claim 6, characterized in that The step of determining the reference wind turbine in the wind turbine cluster according to the correlation between the historical actual power generation data of each wind turbine in the wind turbine cluster and the total historical actual power generation data of the wind turbine cluster comprises: Sorting the wind turbines in the wind turbine cluster, wherein the wind turbines with a greater correlation between their historical actual power generation data and the total historical actual power generation data of the wind turbine cluster are ranked higher, and the wind turbines with a smaller correlation between their historical actual power generation data and the total historical actual power generation data of the wind turbine cluster are ranked lower; Selecting TopN wind turbines from among the wind turbines in the wind turbine cluster in a sorted order; N is a positive integer greater than or equal to 1; A reference wind turbine in the wind turbine cluster is determined according to the selected wind turbine.

8. A method for training a prediction model for wind farm power generation data based on spatiotemporal correlation, characterized in that: The method comprises: Get the training dataset; The training data set includes multiple training data; The training data includes sample data and labeled data; The sample data includes sample total power generation data of the sample wind farm in a first historical time period, sample total power generation data of a wind farm upwind of the sample wind farm in a second historical time period, and sample power generation data of a benchmark wind turbine in a plurality of wind turbine clusters in the sample wind farm in a second historical time period; The annotated data includes the actual total power generation data of the sample wind farm in the third historical period; The first historical time period is earlier than the second historical time period, and the second historical time period is earlier than the third historical time period. Each wind turbine cluster includes more than two wind turbines. The multiple wind turbine clusters are obtained by dividing the wind turbines in the sample wind farm according to the sample power generation data of the first historical time period of each wind turbine in the sample wind farm and the sample wind speed of the first historical time period of each wind turbine in the sample wind farm. For any wind turbine cluster, the reference wind turbine in the wind turbine cluster is determined in the wind turbine cluster according to the sample power generation data of the first historical time period of each wind turbine in the wind turbine cluster and the total sample power generation data of the first historical time period of the wind turbine cluster; the total historical actual power generation data of the target wind farm is obtained according to the historical actual power generation data of each wind turbine in the target wind farm; The model to be trained is trained according to the training data set until the model to be trained converges, thereby obtaining a prediction model for wind farm power generation data based on spatiotemporal correlation.

9. The method according to claim 8, characterized in that The training of the model to be trained according to the training data set until the model to be trained converges, thereby obtaining a prediction model for wind farm power generation data based on spatiotemporal correlation, includes: Use the bootstrapping sampling method to extract the training data set in S rounds, and the training data extracted in each round is used as the training data subset; S is a positive integer greater than or equal to 2; For any training data subset, the model to be trained is trained according to the training data subset until the model to be trained converges, so as to obtain a prediction model of wind farm power generation data based on spatiotemporal correlation, and a total of S prediction models of wind farm power generation data based on spatiotemporal correlation are obtained; the training data in any two training data subsets are not completely the same or completely different.

10. A wind farm power generation data prediction device based on spatiotemporal correlation, characterized in that: The device comprises: A first acquisition module is used to acquire historical actual power generation data of each wind turbine in the target wind farm, and to acquire historical actual wind speed of each wind turbine in the target wind farm; a division module, for dividing the wind turbines in the target wind farm into a plurality of wind turbine clusters according to the historical actual power generation data of each wind turbine in the target wind farm and the historical actual wind speed of each wind turbine in the target wind farm, each wind turbine cluster including more than two wind turbines; A determination module, for determining, for any one wind turbine cluster, a reference wind turbine in the wind turbine cluster according to historical actual power generation data of each wind turbine in the wind turbine cluster and historical actual power generation total data of the wind turbine cluster; The second acquisition module is used to acquire the current actual power generation data of the reference wind turbines in each wind turbine cluster, and acquire the current actual total power generation data of the wind farm in the upwind direction of the target wind farm; and acquire the historical actual total power generation data of the target wind farm based on the historical actual power generation data of each wind turbine in the target wind farm; The prediction module is used to predict the future expected total power generation data of the target wind farm based on the historical total actual power generation data of the target wind farm, the current total actual power generation data of the wind farm upwind of the target wind farm, and the current actual power generation data of the benchmark wind turbine.

11. A device for training a prediction model for wind farm power generation data based on spatiotemporal correlation, characterized in that: The device comprises: The third acquisition module is used to acquire a training data set, wherein the training data set includes multiple training data, and the training data includes sample data and annotated data. The sample data includes the total sample power generation data of the sample wind farm in the first historical time period, the total sample power generation data of the wind farm upwind of the sample wind farm in the second historical time period, and the sample power generation data of the benchmark wind turbines in multiple wind turbine clusters in the sample wind farm in the second historical time period. The annotated data includes the actual total power generation data of the sample wind farm in the third historical time period. The first historical time period is earlier than the second historical time period, and the second historical time period is earlier than the third historical time period. Each wind turbine cluster includes more than two wind turbines. A wind turbine cluster is obtained by dividing the wind turbines in the sample wind farm according to the sample power generation data of the first historical time period of each wind turbine in the sample wind farm and the sample wind speed of the first historical time period of each wind turbine in the sample wind farm. For any wind turbine cluster, the reference wind turbine in the wind turbine cluster is determined in the wind turbine cluster according to the sample power generation data of the first historical time period of each wind turbine in the wind turbine cluster and the total sample power generation data of the first historical time period of the wind turbine cluster; the total historical actual power generation data of the target wind farm is obtained according to the historical actual power generation data of each wind turbine in the target wind farm; The training module is used to train the model to be trained according to the training data set until the model to be trained converges, thereby obtaining a prediction model for wind farm power generation data based on spatiotemporal correlation.

12. An electronic device, characterized in that: include: A processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the computer program implements the method according to any one of claims 1 to 9 when executed by the processor.

13. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 9 is implemented.

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