Feature extraction and deep learning-based wind power climbing event prediction method and system, medium and equipment

Abstract

The invention relates to the field of digital power grids and artificial intelligence, and discloses a wind power climbing event prediction method, system, medium and equipment based on feature extraction and deep learning, and the method comprises the steps: selecting historical meteorological data related to wind power prediction and corresponding historical wind power data, taking the historical meteorological data as a wind power prediction feature, and carrying out the prediction of a wind power climbing event; forming a wind power prediction feature set with historical wind power data; extracting a wind power climbing event from the feature set by adopting a parameter self-adaptive revolving door algorithm, and processing historical data into various climbing features; taking meteorological data, historical wind power data and the obtained climbing features as input of a prediction model, and performing secondary feature extraction through a convolutional neural network to obtain a feature map; inputting the feature map into a long-short-term memory neural network to obtain forecast wind power; and extracting a climbing event from the forecasting result of the wind power through a parameter self-adaptive revolving door algorithm to obtain a forecasting result of the wind power climbing event.

Description

Technical Field

[0001] The present invention relates to the field of digital power grid and artificial intelligence technology, and in particular to a method, system, medium and equipment for predicting wind power ramp events based on feature extraction and deep learning. Background Art

[0002] Wind power ramping events refer to the phenomenon of rapid fluctuations in wind speed in a short period of time caused by meteorological factors such as strong low pressure, thunderstorms or gusts during the actual grid connection process. Wind power ramping events can easily destroy the power balance of the system and threaten the reliability, safety and economy of the power system. Compared with wind power prediction, ramping prediction is significantly different. Ramp is a low-probability event that occurs in the wind power sequence. Its prediction object is these events themselves. The prediction methods are mainly divided into direct prediction and indirect prediction. Direct prediction usually relies on a large amount of historical ramping data to train the model, while current research on wind power ramping event prediction mostly uses indirect prediction methods, that is, the ramping event is identified based on the wind power prediction results to finally obtain the prediction results. Since ramping events increase the difficulty of analyzing the characteristics of wind power output, existing studies mostly use wind power-related characteristic data as model input to improve prediction accuracy. However, these methods usually lack feature engineering support, which may cause the model to fall into local optimality and ignore the influence of historical training status. Summary of the invention

[0003] In view of the above problems, the purpose of the present invention is to provide a wind power ramp event prediction method, system, medium and equipment based on feature extraction and deep learning, so as to solve the technical problems that the existing wind power ramp prediction model lacks feature engineering, is prone to fall into local optimality and ignores historical training status, and improves the accuracy of wind power ramp event prediction.

[0004] To achieve the above-mentioned purpose, in the first aspect, the technical solution adopted by the present invention is: a method for predicting wind power climbing events based on feature extraction and deep learning, which includes: selecting historical meteorological data related to wind power prediction, and corresponding historical wind power data, using the historical meteorological data as wind power prediction features, and forming a wind power prediction feature set with the historical wind power data; using a parameter-adaptive revolving door algorithm to extract wind power climbing events from the feature set, and processing the historical data into a variety of climbing features; using meteorological data, historical wind power data and the obtained climbing features as inputs to the prediction model, and extracting features secondary through a convolutional neural network to obtain a feature map; inputting the feature map into a long short-term memory neural network to obtain predicted wind power; using the forecast result of wind power to extract climbing events through a parameter-adaptive revolving door algorithm to obtain a prediction result of wind power climbing events.

[0005] Furthermore, the parameter-adaptive revolving door algorithm includes: Calculate the upper door opening slope and the lower door opening slope between the detection point and the previous storage point; The upper door opening slope greater than the slope threshold and the lower door opening slope less than the slope threshold are saved by using a discrimination rule, and according to the newly saved door opening slope, it is determined whether to continue traversing the data or temporarily store the wind power point; Calculate the compression error between the original data and the temporarily stored data, compare the compression error with the set threshold, if the compression error is greater than the set threshold, reduce the gating parameter and return to the previous storage point, and re-perform the revolving door algorithm to determine whether the power point is within the open gate range; if the compression error is less than the set threshold, add the gating parameter, and then perform the same operation as greater than the set threshold; if the compression error is within the threshold range, the point will be finally stored; Determine the ramp start time of wind power ramp-up events and ramp-down events based on the clear definition and conditions of wind power ramp events , climbing rate , climbing duration Three characteristic parameters.

[0006] Furthermore, the judgment rule is:

[0007] in:

[0008] In the formula, The upper door opening slope stored for point n; The upper door opening slope calculated for point n; The upward door opening slope stored for point n-1; The lower door opening slope stored for point n; The slope of the lower door opening calculated for point n; The lower door opening slope stored for point n-1; represents the wind power at point a; represents the wind power at point b; represents the moment of point a; represents the moment of point b; Represents the gating parameters of the revolving door algorithm.

[0009] Further, the compression error between the original data and the temporarily stored data is calculated, including:

[0010] In the formula, Represents the compression error of the algorithm; is the original number of data points; is the original power of the kth point, is the power of the kth point after the compressed signal is restored.

[0011] Furthermore, the compression error is compared with the set threshold. If the compression error is greater than the set threshold, the gating parameter is reduced and the previous storage point is returned to re-perform the revolving door algorithm to determine whether the power point is within the open gate range; if the compression error is less than the set threshold, the gating parameter is added, and then the same operation is performed as if it is greater than the set threshold; if the compression error is within the threshold range, the point will be finally stored as:

[0012] In the formula, Indicates the setting of the initial gating parameter value; For a single parameter growth; represents the gating parameter value after adaptive adjustment; Represents the gating parameter value before adaptive adjustment; is the process coefficient, and its initial value is 1; Represents the compression error of the algorithm; To set the threshold; is the maximum allowable deviation.

[0013] Furthermore, the convolutional neural network model is:

[0014] In the formula, Indicates Layer feature map output; Represents the convolution operation; To connect Tier The feature map and Tier The convolution kernel weight matrix between feature maps; is the bias matrix; is the set of input feature maps; For the Layer feature map output; is the activation function.

[0015] Furthermore, the long short-term memory neural network model is:

[0016]

[0017]

[0018] in, as well as Respectively represent the weights and biases of the forget gate, input gate, and output gate; Represent the outputs of the forget gate, input gate, and output gate respectively; Represents the sigmoid function; Represents vector concatenation; represents the hidden state of the long short-term memory neural network model at time t-1, Represents the feature map at time t.

[0019] In the second aspect, the technical solution adopted by the present invention is: a wind power climbing event prediction system based on feature extraction and deep learning, which includes: a data acquisition unit, which selects historical meteorological data related to wind power prediction, and corresponding historical wind power data, uses the historical meteorological data as wind power prediction features, and forms a wind power prediction feature set with the historical wind power data; a climbing event extraction unit, which uses a parameter-adaptive revolving door algorithm to extract wind power climbing events from the feature set, and processes the historical data into a variety of climbing features; a feature extraction unit, which uses meteorological data, historical wind power data and the obtained climbing features as inputs of the prediction model, and extracts features secondary through a convolutional neural network to obtain a feature map; a wind power prediction unit, which inputs the feature map into a long short-term memory neural network to obtain predicted wind power; the forecast result of wind power is used to extract climbing events through a parameter-adaptive revolving door algorithm to obtain a prediction result of wind power climbing events.

[0020] Furthermore, the parameter-adaptive revolving door algorithm includes: Calculate the upper door opening slope and the lower door opening slope between the detection point and the previous storage point; The upper door opening slope greater than the slope threshold and the lower door opening slope less than the slope threshold are saved by using a discrimination rule, and according to the newly saved door opening slope, it is determined whether to continue traversing the data or temporarily store the wind power point; Calculate the compression error between the original data and the temporarily stored data, compare the compression error with the set threshold, if the compression error is greater than the set threshold, reduce the gating parameter and return to the previous storage point, and re-perform the revolving door algorithm to determine whether the power point is within the open gate range; if the compression error is less than the set threshold, add the gating parameter, and then perform the same operation as greater than the set threshold; if the compression error is within the threshold range, the point will be finally stored; Determine the ramp start time of wind power ramp-up events and ramp-down events based on the clear definition and conditions of wind power ramp events , climbing rate , climbing duration Three characteristic parameters.

[0021] Furthermore, the judgment rule is:

[0022] in:

[0023] In the formula, The upper door opening slope stored for point n; The upper door opening slope calculated for point n; The upward door opening slope stored for point n-1; The lower door opening slope stored for point n; The slope of the lower door opening calculated for point n; The lower door opening slope stored for point n-1; represents the wind power at point a; represents the wind power at point b; represents the moment of point a; represents the moment of point b; Represents the gating parameters of the revolving door algorithm.

[0024] Further, the compression error between the original data and the temporarily stored data is calculated, including:

[0025] In the formula, Represents the compression error of the algorithm; is the original number of data points; is the original power of the kth point, is the power of the kth point after the compressed signal is restored.

[0026] Furthermore, the compression error is compared with the set threshold. If the compression error is greater than the set threshold, the gating parameter is reduced and the previous storage point is returned to re-perform the revolving door algorithm to determine whether the power point is within the open gate range; if the compression error is less than the set threshold, the gating parameter is added, and then the same operation is performed as if it is greater than the set threshold; if the compression error is within the threshold range, the point will be finally stored as:

[0027] In the formula, Indicates the setting of the initial gating parameter value; For a single parameter growth; represents the gating parameter value after adaptive adjustment; Represents the gating parameter value before adaptive adjustment; is the process coefficient, and its initial value is 1; Represents the compression error of the algorithm; To set the threshold; is the maximum allowable deviation.

[0028] Furthermore, the convolutional neural network model is:

[0029] In the formula, Indicates Layer feature map output; Represents the convolution operation; To connect Tier The feature map and Tier The convolution kernel weight matrix between feature maps; is the bias matrix; is the set of input feature maps; For the Layer feature map output; is the activation function.

[0030] Furthermore, the long short-term memory neural network model is:

[0031]

[0032]

[0033] in, as well as Respectively represent the weights and biases of the forget gate, input gate, and output gate; Represent the outputs of the forget gate, input gate, and output gate respectively; Represents the sigmoid function; Represents vector concatenation; represents the hidden state of the long short-term memory neural network model at time t-1, Represents the feature map at time t.

[0034] In a third aspect, the technical solution adopted by the present invention is: a computer-readable storage medium storing one or more programs, wherein the one or more programs include instructions, and when the instructions are executed by a computing device, the computing device executes any one of the above methods.

[0035] In a fourth aspect, the technical solution adopted by the present invention is: a computing device, comprising: one or more processors, a memory and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include instructions for executing any of the above methods.

[0036] The present invention adopts the above technical solution, which has the following advantages: The wind power ramp event prediction method, system, device and medium based on feature extraction and deep learning of the present invention use historical meteorological data as input data, detect wind power ramp events through parameter adaptive revolving door algorithm, and obtain ramp features. The historical wind power data and ramp features are used as input of the prediction model, and then the features are extracted secondary through convolutional neural network, and then the relationship in the time series is learned through long short-term memory neural network. The predicted wind power is used as the output of the model, and after ramp detection, the prediction result of the ramp event is obtained. The present invention can accurately capture the complex spatiotemporal dependencies and uncertainties in wind power and meteorological data, and provide accurate and information-rich forecast results of wind power ramp events, providing a reference basis for the development of new energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 This is a schematic diagram of the steps of a method for predicting wind power ramp events based on feature extraction and deep learning provided by a preferred embodiment of the present application; Figure 2 It is a schematic diagram of the steps of a parameter adaptive revolving door algorithm provided by a preferred embodiment of the present application; Figure 3 This is a schematic diagram of a wind power ramp event prediction system based on feature extraction and deep learning provided by a preferred embodiment of the present application; Figure 4 It is a schematic diagram of a computer device provided by a preferred embodiment of the present application. DETAILED DESCRIPTION

[0038] In order to solve the technical problems that the existing wind power prediction model needs to rely on a large amount of feature engineering, is prone to fall into local optimality, and ignores the historical training status, the present invention provides a wind power climbing event prediction method, system, equipment and medium based on feature extraction and deep learning, taking historical meteorological data as input data, and detecting wind power climbing events through parameter adaptive revolving door algorithm to obtain climbing features. The historical wind power data and climbing features will be used as the input of the prediction model, and then the features will be extracted twice through the convolutional neural network, and then the relationship in the time series will be learned through the long short-term memory neural network. The predicted wind power is used as the output of the model, and after climbing detection, the prediction result of the climbing event is obtained. The present invention can accurately capture the complex spatiotemporal dependence and uncertainty in wind power and meteorological data, and provide accurate and information-rich forecast results of wind power climbing events, providing a reference basis for the development of new energy.

[0039] In order to make the purpose, technical solution and advantages of the embodiment of the present invention clearer, the technical solution of the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings of the embodiment of the present invention. Obviously, the described embodiment is a part of the embodiment of the present invention, not all of the embodiments. Based on the described embodiment of the present invention, all other embodiments obtained by ordinary technicians in this field belong to the scope of protection of the present invention.

[0040] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0041] In one embodiment of the present invention, a method for predicting wind power ramp events based on feature extraction and deep learning is provided to solve the technical problems that the existing wind power prediction model needs to rely on a large amount of feature engineering, is prone to fall into local optimality, and ignores historical training status, thereby effectively improving the accuracy of wind power ramp event prediction. Figure 1 As shown, the method comprises the following steps: S1. Select historical meteorological data related to wind power prediction and corresponding historical wind power data, use the historical meteorological data as wind power prediction features, and form a wind power prediction feature set with the historical wind power data; S2, using parameter adaptive revolving door algorithm, extracting wind power ramp events from feature set and processing historical data into multiple ramp features; S3, using meteorological data, historical wind power data and the obtained climbing characteristics as inputs of the prediction model, extracting features through a convolutional neural network to obtain a feature map; S4, inputting the feature map into the long short-term memory neural network to obtain the predicted wind power; S5. The forecast result of wind power is used to extract the ramp event through the parameter adaptive revolving door algorithm to obtain the prediction result of wind power ramp event.

[0042] In this embodiment, numerical weather forecasts related to wind power forecasts are selected from the output of a global-scale numerical weather forecast model, including 100-meter vertical wind speed, 100-meter horizontal wind speed, temperature, humidity, air pressure, and corresponding historical wind power data, and the original meteorological parameters and historical wind power data are cleaned to construct a wind power prediction feature set.

[0043] In the above step S2, the parameter adaptive revolving door algorithm includes the following steps: S21, data processing based on the original SDA algorithm: Calculate the upper door opening slope and the lower door opening slope between the detection point and the previous storage point.

[0044] S22, using a discrimination rule to save an upper door opening slope greater than a slope threshold and a lower door opening slope less than a slope threshold, and determining whether to continue traversing the data or temporarily store the wind power point according to the newly saved door opening slope; S23, data processing based on parameter adaptive selection. Calculate the compression error between the original data and the temporarily stored data, and compare the compression error with the set threshold: If the compression error is greater than the set threshold, the gating parameter is reduced and the previous storage point is returned, and the revolving door algorithm is re-performed to determine whether the power point is within the open gate range; if the compression error is less than the set threshold, the gating parameter is added, and then the same operation is performed as when it is greater than the set threshold; if the compression error is within the threshold range, the point will be finally stored; S24, identify based on the definition of wind power ramp events. According to the clear definition and conditions of wind power ramp events, determine the ramp start time of wind power ramp-up events and ramp-down events. , climbing rate , climbing duration Three characteristic parameters.

[0045] In the above step S22, a discrimination rule is used to save the upper door opening slope greater than the slope threshold and the lower door opening slope less than the slope threshold, wherein the discrimination rule is:

[0046] in:

[0047] In the formula, The upper door opening slope stored for point n; The upper door opening slope calculated for point n; The upward door opening slope stored for point n-1; The lower door opening slope stored for point n; The slope of the lower door opening calculated for point n; The lower door opening slope stored for point n-1; represents the wind power at point a; represents the wind power at point b; represents the moment of point a; represents the moment of point b; Represents the gating parameters of the revolving door algorithm.

[0048] In the above step S23, the compression error between the original data and the temporarily stored data is calculated as:

[0049] In the formula, Represents the compression error of the algorithm; is the original number of data points; is the original power of the kth point, is the power of the kth point after the compressed signal is restored.

[0050] In this embodiment, the compression error is compared with the set threshold. If the compression error is greater than the set threshold, the gating parameter is reduced and the previous storage point is returned to re-perform the revolving door algorithm to determine whether the power point is within the open gate range; if the compression error is less than the set threshold, the gating parameter is added, and then the same operation as that greater than the set threshold is performed; if the compression error is within the threshold range, the point will be finally stored as:

[0051] In the formula, Indicates the setting of the initial gating parameter value; For a single parameter growth; represents the gating parameter value after adaptive adjustment; Represents the gating parameter value before adaptive adjustment; is the process coefficient, and its initial value is 1; Represents the compression error of the algorithm; To set the threshold; is the maximum allowable deviation.

[0052] In the above step S3, the meteorological data, historical wind power data and the obtained ramp characteristics are used as the input of the prediction model, and then the features are extracted again through the convolutional neural network to obtain the feature map. The convolutional neural network model is:

[0053] In the formula, Indicates Layer feature map output; Represents the convolution operation; To connect Tier The feature map and Tier The convolution kernel weight matrix between feature maps; is the bias matrix; is the set of input feature maps; For the Layer feature map output; is the activation function.

[0054] The pooling layer performs secondary feature extraction and information filtering on the output of the convolutional layer, thereby retaining the most significant features. The amount of parameters and data compressed by the pooling layer can effectively reduce overfitting and reduce the complexity of the network. The calculation process of the pooling layer is shown in the formula:

[0055] In the formula, For the Layer feature map output; Indicates Layer feature map output; represents the downsampling function.

[0056] In the above step S4, the long short-term memory neural network model is:

[0057]

[0058]

[0059] in, as well as Respectively represent the weights and biases of the forget gate, input gate, and output gate; Represent the outputs of the forget gate, input gate, and output gate respectively; Represents the sigmoid function; Represents vector concatenation; represents the hidden state of the long short-term memory neural network model at time t-1, Represents the feature map at time t.

[0060] In this embodiment, the wind power forecast results are used to extract the ramp events through the parameter adaptive revolving door algorithm to obtain the prediction results of the wind power ramp events.

[0061] The wind power ramp event prediction method based on feature extraction and deep learning proposed in the present invention takes historical meteorological data as input data, detects wind power ramp events through parameter adaptive revolving door algorithm, and obtains ramp features. The historical wind power data and ramp features are used as inputs of the prediction model, and then the features are extracted secondary through convolutional neural network, and then the relationship in the time series is learned through long short-term memory neural network. The predicted wind power is used as the output of the model, and after ramp detection, the prediction result of the ramp event is obtained. The present invention can accurately capture the complex spatiotemporal dependencies and uncertainties in wind power and meteorological data, and provide accurate and information-rich forecast results of wind power ramp events, providing a reference basis for the development of new energy.

[0062] In one embodiment of the present invention, a wind power ramp event prediction system based on feature extraction and deep learning is provided. Figure 3 As shown, it includes: The data collection unit 1 selects historical meteorological data related to wind power prediction and corresponding historical wind power data, uses the historical meteorological data as wind power prediction features, and forms a wind power prediction feature set with the historical wind power data; The ramp event extraction unit 2 uses a parameter-adaptive revolving door algorithm to extract wind power ramp events from the feature set and processes the historical data into a variety of ramp features; The feature extraction unit 3 uses the meteorological data, historical wind power data and the obtained climbing characteristics as the input of the prediction model, extracts the features again through the convolutional neural network, and obtains the feature map; The wind power prediction unit 4 inputs the feature map into the long short-term memory neural network to obtain the predicted wind power; the forecast result of the wind power is used to extract the climbing event through the parameter adaptive revolving door algorithm to obtain the prediction result of the wind power climbing event.

[0063] In the above embodiment, the parameter adaptive revolving door algorithm includes: Calculate the upper door opening slope and the lower door opening slope between the detection point and the previous storage point; The upper door opening slope greater than the slope threshold and the lower door opening slope less than the slope threshold are saved by using a discrimination rule, and according to the newly saved door opening slope, it is determined whether to continue traversing the data or temporarily store the wind power point; Calculate the compression error between the original data and the temporarily stored data, compare the compression error with the set threshold, if the compression error is greater than the set threshold, reduce the gating parameter and return to the previous storage point, and re-perform the revolving door algorithm to determine whether the power point is within the open gate range; if the compression error is less than the set threshold, add the gating parameter, and then perform the same operation as greater than the set threshold; if the compression error is within the threshold range, the point will be finally stored; Determine the ramp start time of wind power ramp-up events and ramp-down events based on the clear definition and conditions of wind power ramp events , climbing rate , climbing duration Three characteristic parameters.

[0064] In the above embodiment, the discrimination rule is:

[0065] in:

[0066] In the formula, The upper door opening slope stored for point n; The upper door opening slope calculated for point n; The upward door opening slope stored for point n-1; The lower door opening slope stored for point n; The slope of the lower door opening calculated for point n; The lower door opening slope stored for point n-1; represents the wind power at point a; represents the wind power at point b; represents the moment of point a; represents the moment of point b; Represents the gating parameters of the revolving door algorithm.

[0067] In the above embodiment, calculating the compression error between the original data and the temporarily stored data includes:

[0068] In the formula, Represents the compression error of the algorithm; is the original number of data points; is the original power of the kth point, is the power of the kth point after the compressed signal is restored.

[0069] Among them, the compression error is compared with the set threshold. If the compression error is greater than the set threshold, the gating parameter is reduced and the previous storage point is returned, and the revolving door algorithm is re-performed to determine whether the power point is within the open gate range; if the compression error is less than the set threshold, the gating parameter is added, and then the same operation is performed as if it is greater than the set threshold; if the compression error is within the threshold range, the point will be finally stored as:

[0070] In the formula, Indicates the setting of the initial gating parameter value; For a single parameter growth; represents the gating parameter value after adaptive adjustment; Represents the gating parameter value before adaptive adjustment; is the process coefficient, and its initial value is 1; Represents the compression error of the algorithm; To set the threshold; is the maximum allowable deviation.

[0071] In the above embodiment, the convolutional neural network model is:

[0072] In the formula, Indicates Layer feature map output; Represents the convolution operation; To connect Tier The feature map and Tier The convolution kernel weight matrix between feature maps; is the bias matrix; is the set of input feature maps; For the Layer feature map output; is the activation function.

[0073] In the above embodiment, the long short-term memory neural network model is:

[0074]

[0075]

[0076] in, as well as Respectively represent the weights and biases of the forget gate, input gate, and output gate; Represent the outputs of the forget gate, input gate, and output gate respectively; Represents the sigmoid function; Represents vector concatenation; represents the hidden state of the long short-term memory neural network model at time t-1, Represents the feature map at time t.

[0077] The system provided in this embodiment is used to execute the above-mentioned method embodiments. Please refer to the above-mentioned embodiments for specific processes and detailed contents, which will not be repeated here.

[0078] In summary, the present invention aims at the technical problems that the existing wind power ramp event prediction model lacks feature engineering, is prone to fall into local optimality, and ignores the historical training status. The present invention uses historical meteorological data as input data, detects wind power ramp events through a parameter adaptive revolving door algorithm, and obtains the ramp characteristics. The historical wind power data and the ramp characteristics are used as the input of the prediction model, and then the features are extracted secondary through a convolutional neural network, and then the relationship in the time series is learned through a long short-term memory neural network. The predicted wind power is used as the output of the model, and after the ramp detection, the prediction result of the ramp event is obtained. The present invention can accurately capture the complex spatiotemporal dependencies and uncertainties in wind power and meteorological data, and provide accurate and information-rich forecast results of wind power ramp events, providing a reference basis for the development of new energy.

[0079] like Figure 4 As shown, in one embodiment of the present invention, a computing device structure is provided, and the computing device can be a terminal, which can include: a processor, a communication interface, a memory, a display screen and an input device. Among them, the processor, the communication interface and the memory complete mutual communication through a communication bus. The processor is used to provide computing and control capabilities. The memory includes a non-volatile storage medium and an internal memory, and the non-volatile storage medium stores an operating system and a computer program. When the computer program is executed by the processor, the method in each of the above embodiments is implemented; the internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be realized through WIFI, a management network, NFC (near field communication) or other technologies. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device can be a touch layer covered on the display screen, or a key, trackball or touchpad set on the housing of the computing device, or an external keyboard, touchpad or mouse. The processor can call the logical instructions in the memory.

[0080] In addition, the logic instructions in the above-mentioned memory can be implemented in the form of software functional units and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on such an understanding, the technical solution of the present invention, in essence, 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, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0081] In one embodiment of the present invention, a computer program product is provided, wherein the computer program product includes a computer program stored on a non-transitory computer-readable storage medium, wherein the computer program includes program instructions. When the program instructions are executed by a computer, the computer can execute the methods provided by the above-mentioned method embodiments.

[0082] In one embodiment of the present invention, a non-transitory computer-readable storage medium is provided, wherein the non-transitory computer-readable storage medium stores server instructions, and the computer instructions enable a computer to execute the methods provided in the above embodiments.

[0083] The above embodiment provides a computer-readable storage medium, whose implementation principle and technical effect are similar to those of the above method embodiment, and will not be repeated here.

[0084] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0085] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0086] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0087] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A wind power ramp event prediction method based on feature extraction and deep learning, characterized in that: include: Selecting historical meteorological data related to wind power prediction and corresponding historical wind power data, taking the historical meteorological data as wind power prediction features, and forming a wind power prediction feature set with the historical wind power data; Adopting parameter adaptive revolving door algorithm, wind power ramp events are extracted from feature sets, and historical data are processed into multiple ramp features; The meteorological data, historical wind power data and the obtained climbing characteristics are used as the input of the prediction model, and the features are extracted again through the convolutional neural network to obtain the feature map; The feature map is input into the long short-term memory neural network to obtain the predicted wind power; The forecast results of wind power are used to extract the ramp events through the parameter adaptive revolving door algorithm to obtain the prediction results of wind power ramp events.

2. The method for predicting wind power ramp events based on feature extraction and deep learning as claimed in claim 1, characterized in that: Parameter-adaptive revolving door algorithm, including: Calculate the upper door opening slope and the lower door opening slope between the detection point and the previous storage point; The upper door opening slope greater than the slope threshold and the lower door opening slope less than the slope threshold are saved by using a discrimination rule, and according to the newly saved door opening slope, it is determined whether to continue traversing the data or temporarily store the wind power point; Calculate the compression error between the original data and the temporarily stored data, compare the compression error with the set threshold, if the compression error is greater than the set threshold, reduce the gating parameter and return to the previous storage point, and re-perform the revolving door algorithm to determine whether the power point is within the open gate range; if the compression error is less than the set threshold, add the gating parameter, and then perform the same operation as greater than the set threshold; if the compression error is within the threshold range, the point will be finally stored; Determine the ramp start time of wind power generation up-ramp event and down-ramp event based on the clear definition and conditions of wind power ramp event , climbing rate , climbing duration Three characteristic parameters.

3. The method for predicting wind power ramp events based on feature extraction and deep learning as claimed in claim 2, characterized in that: The judgment rules are: in: In the formula, The upward door opening slope stored for point n; The upper door opening slope calculated for point n; The upward door opening slope stored for point n-1; The lower door opening slope stored for point n; The lower door opening slope calculated for point n; The lower door opening slope stored for point n-1; represents the wind power at point a; represents the wind power at point b; represents the moment of point a; represents the moment of point b; Represents the gating parameters of the revolving door algorithm.

4. The method for predicting wind power ramp events based on feature extraction and deep learning as claimed in claim 2, characterized in that: Calculate the compression error between the original data and the temporarily stored data, including: In the formula, Represents the compression error of the algorithm; is the original number of data points; is the original power of the kth point, is the power of the kth point after the compressed signal is restored.

5. The method for predicting wind power ramp events based on feature extraction and deep learning as claimed in claim 4, characterized in that: Compare the compression error with the set threshold. If the compression error is greater than the set threshold, reduce the gating parameter and return to the previous storage point, and re-perform the revolving door algorithm to determine whether the power point is within the open gate range; if the compression error is less than the set threshold, add the gating parameter, and then perform the same operation as the one greater than the set threshold; if the compression error is within the threshold range, the point will be finally stored as: In the formula, Indicates the setting of the initial gating parameter value; For a single parameter growth; represents the gating parameter value after adaptive adjustment; Represents the gating parameter value before adaptive adjustment; is the process coefficient, and its initial value is 1; Represents the compression error of the algorithm; To set the threshold; is the maximum allowable deviation.

6. The method for predicting wind power ramp events based on feature extraction and deep learning as claimed in claim 1, characterized in that: The convolutional neural network model is: In the formula, Indicates Layer feature map output; Represents the convolution operation; To connect Tier The feature map and Tier The convolution kernel weight matrix between feature maps; is the bias matrix; is the set of input feature maps; For the Layer feature map output; is the activation function.

7. The method for predicting wind power ramp events based on feature extraction and deep learning as claimed in claim 1, characterized in that: The long short-term memory neural network model is: in, as well as Respectively represent the weights and biases of the forget gate, input gate, and output gate; Represent the outputs of the forget gate, input gate, and output gate respectively; Represents the sigmoid function; Represents vector concatenation; represents the hidden state of the long short-term memory neural network model at time t-1, Represents the feature map at time t.

8. A wind power ramp event prediction system based on feature extraction and deep learning, characterized in that: include: The data collection unit selects historical meteorological data related to wind power prediction and corresponding historical wind power data, uses the historical meteorological data as wind power prediction features, and forms a wind power prediction feature set with the historical wind power data; The ramp event extraction unit uses a parameter-adaptive revolving door algorithm to extract wind power ramp events from the feature set and process historical data into multiple ramp features; The feature extraction unit uses the meteorological data, historical wind power data and the obtained climbing characteristics as the input of the prediction model, extracts the features again through the convolutional neural network, and obtains the feature map; The wind power prediction unit inputs the feature map into the long short-term memory neural network to obtain the predicted wind power; the forecast result of wind power is used to extract the climbing event through the parameter adaptive revolving door algorithm to obtain the prediction result of wind power climbing event.

9. A computer-readable storage medium storing one or more programs, characterized in that: The one or more programs include instructions, which, when executed by a computing device, cause the computing device to perform any one of the methods of claims 1 to 7.

10. A computing device, characterized in that include: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include instructions for executing any one of the methods described in claims 1 to 7.

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