A method and system for predicting the evolution process of photovoltaic power generation power probability density function

Abstract

The present invention discloses a prediction method and system for the evolution process of photovoltaic power generation power probability density function, which is used to solve the technical problem that the existing technology cannot accurately capture the fluctuation and long-term trend of power over time, thereby affecting the reliability and accuracy of the prediction, and belongs to the field of deep learning technology. A prediction method for the evolution process of photovoltaic power generation power probability density function includes the following steps: step 1, obtaining the historical probability density function; step 2, obtaining the coefficient vector of the basis function; step 3, enhancing the representation of meteorological feature information; step 4, reconstructing the meteorological feature; step 5, predicting the evolution of the probability density function; S51, splicing the coefficient vector and the reconstructed meteorological feature to form a more comprehensive and information-rich splicing feature; weighting the splicing feature with the photovoltaic historical power generation power input multi-head cross attention mechanism, reconstructing the splicing feature, and obtaining the predicted coefficient vector through the fully connected layer; S52, the predicted coefficient vector is obtained by inverse solution of cubic B-spline interpolation to obtain the predicted photovoltaic power generation power probability density function.

Description

Technical Field

[0001] The present invention belongs to the field of deep learning technology, and in particular relates to a method and system for predicting the evolution process of a photovoltaic power generation power probability density function. Background Art

[0002] Photovoltaic power generation forecasts provide a crucial basis for grid scheduling and operation, helping to promote the effective integration of renewable energy into the grid and the development of a green, low-carbon power system. Because photovoltaic power generation is affected by a variety of uncertainties, such as weather conditions and seasonal variations, its output power is highly random and uncertain. Therefore, accurate photovoltaic power forecasting is crucial for grid stability and energy management. Traditional point forecasts only provide a single power value prediction and fail to quantify the uncertainty of the forecast value. This makes it difficult to effectively address power fluctuations and forecast errors caused by abnormal weather conditions, leading to difficulties in grid scheduling and increased profit and loss risks. In contrast, probabilistic forecasting provides a probability distribution of power output, quantifying potential forecast errors and comprehensively describing the likelihood of different power levels. Prediction accuracy and accurate description of the distribution shape are key to reliable forecasting.

[0003] Current probabilistic forecasting methods have the following problems: 1. Probabilistic forecasts such as quantile and interval forecasts lack a description of the continuity of the overall probability distribution, making it difficult to accurately describe extreme situations that may occur when uncertainty is high. 2. Prior probability distribution assumptions are made about power, and these assumed distributions are often inconsistent with the distribution of actual data. This prior assumption limits the flexibility of the model and can lead to forecast bias under extreme weather conditions. 3. Many probabilistic forecasting methods ignore the dependencies of time series. When dealing with scenarios where photovoltaic power generation is significantly affected by weather and time of year, ignoring the temporal characteristics and dynamic evolution of power changes can result in the model being unable to accurately capture power fluctuations and long-term trends over time, thereby affecting the reliability and accuracy of the forecast. Summary of the Invention

[0004] The purpose of the present invention is to provide a method and system for predicting the evolution process of the probability density function of photovoltaic power generation, which is used to solve the technical problem that the existing technology cannot accurately capture the fluctuations and long-term trends of power over time, thereby affecting the reliability and accuracy of the prediction.

[0005] The present invention is achieved by adopting the following technical solutions:

[0006] A method for predicting the evolution process of a photovoltaic power generation power probability density function comprises the following steps:

[0007] Step 1: Obtain the historical probability density function;

[0008] S11, taking the historical photovoltaic power generation power as input, and using kernel density estimation to obtain the probability density function (PDF) of the photovoltaic power generation power.

[0009] Step 2: Obtain the coefficient vector of the basis function;

[0010] S21, after defining the control points, the PDF of photovoltaic power generation is fitted using the cubic B-spline interpolation method;

[0011] S22, calculate the coefficient vector of the basis function corresponding to the control point.

[0012] Step three: enhance the representation of meteorological characteristic information;

[0013] S31, denoising the meteorological features;

[0014] S32, encoding the time information of the meteorological characteristics;

[0015] S33, performing feature splicing on the denoised meteorological features and the encoded time information to form a first spliced feature;

[0016] S34, the first concatenated features are input into iTransform to obtain new meteorological features, which capture the temporal relationship between different meteorological features and enhance the representation of meteorological information.

[0017] Step 4: Reconstruct meteorological characteristics;

[0018] S41, uses a multi-head cross-attention mechanism to perform weighted summation on the meteorological features output by iTransformer, so that the meteorological features have the same dimension as the coefficient vector, thereby realizing the reconstruction of the meteorological features;

[0019] S42, the multi-head cross attention mechanism input is the coefficient vector obtained in step S22 and the meteorological features obtained in step S34;

[0020] Step 5: predict the evolution of probability density function;

[0021] S51: The coefficient vector and the reconstructed meteorological features are spliced together to form a more comprehensive and information-rich spliced feature. The spliced feature is weighted with the historical photovoltaic power generation input into the multi-head cross-attention mechanism to reconstruct the spliced feature. The predicted coefficient vector is obtained through the fully connected layer. The coefficient vector and the historical photovoltaic power generation input into the multi-head cross-attention mechanism are obtained after self-attention.

[0022] S52 , performing an inverse solution of the predicted coefficient vector through cubic B-spline interpolation to obtain a predicted photovoltaic power generation power probability density function.

[0023] Further preferably, the meteorological characteristics include direct irradiance, temperature, humidity, wind speed and atmospheric pressure.

[0024] Further preferably, the kernel density estimation formula is: in, represents the probability density estimate at point x, where x is the photovoltaic power generation value to be estimated; n is the total number of data points; κ(·) represents the kernel function; h is the bandwidth parameter used to control the width of the kernel; x i is the actual power value of the i-th photovoltaic.

[0025] More preferably, the kernel function adopts the Gaussian kernel function, and the formula is: in is a standardized variable, indicating the actual x i The relative distance to x.

[0026] More preferably, discrete wavelet transform (DWT) is used to perform noise reduction on the meteorological features.

[0027] Further preferably, the control points are selected from the PDF curve in equal proportions.

[0028] Further preferably, S21 uses cubic B-spline to fit the obtained PDF, and the fitting formula is as follows: Where S(x) is the fitted PDF, P i is the i-th control point, B i,3 (x) is the cubic B-spline basis function corresponding to the i-th control point, which is constructed through a recursive relationship and has the following form:

[0029]

[0030] Among them, x i is the i-th node vector, and under a given x value, the coefficient vector M is solved by the interpolation condition.

[0031] The relationship between the coefficient vector of the S22 cubic B-spline and the control points is expressed by the following linear equation system: M×{P0,P1,…,P n}={S0,S1,…,S n}, the coefficient vector M is obtained by satisfying the conditions of value continuity, first-order derivative continuity and second-order derivative continuity of the basis function, which represents the relationship between the control point and the interpolation curve, and the coefficient vector is extracted as the coefficient vector.

[0032] A prediction system for the evolution process of photovoltaic power generation power probability density function is used to implement any of the above-mentioned prediction methods for the evolution process of photovoltaic power generation power probability density function, including a weight extraction module, a noise reduction module, a multivariate attention mechanism and a prediction module.

[0033] The weight extraction module realizes the extraction of basis function coefficient vectors. Specifically, in order to obtain the coefficient vectors corresponding to the control points on the PDF curve, the kernel density estimation with the Gaussian kernel function is first used to obtain the PDF of the historical photovoltaic power generation power. Then, the PDF is fitted using cubic B-spline interpolation, and the coefficient vectors corresponding to the control points calculated during the fitting process are extracted as the coefficient vectors.

[0034] The denoising module realizes meteorological feature denoising and time information encoding. The collected meteorological information is denoised using DWT to minimize the impact of noise. At the same time, the time information is encoded and the meteorological information is concatenated with the encoded time information. Finally, the meteorological features containing time series information are obtained in the denoising module.

[0035] The multivariate attention mechanism realizes the reconstruction of meteorological features and the reconstruction after splicing the coefficient vector and meteorological features.

[0036] Specifically, iTransformer is used to embed each feature independently, ensuring that each feature is considered equally while preventing cross-channel confusion and information loss. iTransformer can also learn fine-grained relationships and multi-dimensional dependency structures between different features, as well as deep connections between temporal information and meteorological features.

[0037] By considering the correlation between the coefficient vector and meteorological information, a multi-head cross-attention mechanism is adopted to reconstruct the features of meteorological information according to the correlation, so that the meteorological features are aligned with the coefficient vector in dimension and time, ensuring that they can be effectively fused in the same space.

[0038] The reconstructed meteorological features are spliced with the coefficient vector after the self-attention mechanism, and then input into the multi-head cross attention mechanism again. The spliced features are reconstructed again using the correlation between the coefficient vector and the historical power.

[0039] The multivariate attention mechanism performs weighted combination of these features according to different attention heads to achieve more accurate feature representation.

[0040] The prediction module obtains the predicted coefficient vector and the predicted photovoltaic power generation power PDF.

[0041] The final reconstructed features are input into a linear fully connected layer, which performs a linear transformation on the input features to predict the coefficient vector. The predicted coefficient vector is then inversely solved using cubic B-spline interpolation to obtain the predicted photovoltaic power PDF.

[0042] This system can capture and predict the time-varying characteristics of photovoltaic power PDF and dynamically adjust the probability distribution of power generation to obtain the global probability distribution of photovoltaic power, providing a more comprehensive risk assessment tool for the power grid.

[0043] The present invention has the following beneficial effects:

[0044] (1) Significant results have been achieved in the prediction of photovoltaic power PDF, which can more comprehensively reflect the randomness and uncertainty caused by changes in meteorological conditions in the photovoltaic power generation process. The predicted photovoltaic power PDF can define the possible range and probability distribution of power generation, so that the power grid dispatching center can better estimate the fluctuation range of photovoltaic power generation, providing reliable data support for dispatch optimization and energy management.

[0045] (2) This innovative method can improve the accuracy and reliability of photovoltaic power generation predictions, allowing the power grid to not only access renewable energy more efficiently, but also take timely response measures when photovoltaic power generation suddenly increases or decreases, thereby ensuring the stable operation of the power grid.

[0046] (3) iTransformer is used to model temporal relationships, and a multi-head cross-attention mechanism is used to effectively capture and utilize the complex patterns and relationships in time series. PDF provides a more flexible and detailed characterization of power uncertainty with its comprehensive probabilistic information description. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0048] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0049] Figure 1 A structural diagram showing the photovoltaic power generation power probability density function evolution model of the present invention.

[0050] Figure 2 The flowchart of the photovoltaic power generation power probability density function of the present invention is shown.

[0051] Figure 3 Represents the photovoltaic power generation power and meteorological characteristic data of the present invention.

[0052] Figure 4 A diagram showing the prediction results of the photovoltaic power generation power probability density function evolution model of the present invention.

[0053] Figure 5 It represents the actual evolution diagram of the photovoltaic power generation power probability density function of the present invention.

[0054] Figure 6 It represents the predicted evolution diagram of the photovoltaic power generation power probability density function of the present invention. DETAILED DESCRIPTION

[0055] In order to more clearly understand the above-mentioned objectives, features and advantages of the present invention, the scheme of the present invention will be further described below. It should be noted that, in the absence of conflict, the embodiments of the present invention and the features therein can be combined with each other.

[0056] In the description, it should be noted that the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance. It should be noted that, unless otherwise expressly specified or limited, the terms "installed," "connected," and "connected" should be understood broadly. For example, they can refer to fixed connections, removable connections, or integral connections; mechanical connections or electrical connections; direct connections or indirect connections through an intermediate medium; and internal connections between two components. Those skilled in the art will understand the specific meanings of the above terms based on specific circumstances.

[0057] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein; it is obvious that the embodiments in the specification are only part of the embodiments of the present invention, rather than all the embodiments.

[0058] The specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0059] Example 1: A method for predicting the evolution of photovoltaic power generation power probability density function, such as Figure 1-6 As shown, the following steps are included:

[0060] Step 1: Get the historical PDF;

[0061] S11, taking the historical photovoltaic power generation as input, and using kernel density estimation to obtain the PDF of photovoltaic power generation;

[0062] The kernel density estimation formula is: in, represents the probability density estimate at point x, where x is the photovoltaic power generation value to be estimated; n is the total number of data points; κ(·) represents the kernel function; h is the bandwidth parameter used to control the width of the kernel; x i is the actual power value of the i-th photovoltaic.

[0063] The kernel function uses the Gaussian kernel function, and the formula is in is a standardized variable, indicating the actual x i The relative distance to x.

[0064] The dataset used in this example comes from a photovoltaic power plant in Hebei Province, China. The dataset was collected from July 2018 to June 2019. The dataset includes photovoltaic power generation and seven meteorological characteristics: global horizontal irradiance (GHI), direct irradiance (DNI), temperature, relative humidity, wind speed, wind direction, and atmospheric pressure. The temporal resolution of the data is 15 minutes.

[0065] Step 2: Obtain the coefficient vector of the basis function;

[0066] S21, after defining the control points, use the cubic B-spline interpolation method to fit the PDF of the photovoltaic power generation power; in this embodiment, the control points are selected from the PDF curve in equal proportion.

[0067] After obtaining the PDF of photovoltaic power generation through kernel density estimation, the obtained PDF is fitted using cubic B-spline. The fitting formula is as follows: Where S(x) is the fitted PDF, P i is the i-th control point, B i,3 (x) is the cubic B-spline basis function corresponding to the i-th control point, which is constructed through a recursive relationship and has the following form:

[0068]

[0069]

[0070] Among them, x i is the i-th node vector, and under a given x value, the coefficient vector M is solved by the interpolation condition;

[0071] S22, calculating the coefficient vector of the basis function corresponding to the control point;

[0072] The relationship between the coefficient vector of the cubic B-spline and the control points is expressed by the following linear equation system: M×{P0,P1,…,P n}={S0,S1,…,S n}, the coefficient vector M is obtained by satisfying the conditions of value continuity, first-order derivative continuity and second-order derivative continuity of the basis function, which represents the relationship between the control point and the interpolation curve.

[0073] Step three: enhance the representation of meteorological characteristic information;

[0074] S31, DWT is used to reduce the noise of meteorological features.

[0075] Meteorological characteristics include direct irradiance, temperature, humidity, wind speed and atmospheric pressure.

[0076] S32, encoding the time information of the meteorological characteristics;

[0077] S33, performing feature splicing on the denoised meteorological features and the encoded time information to form a first spliced feature;

[0078] After the Discrete Wavelet Transform (DWT), meteorological characteristics are divided into two parts: a high-frequency component and a low-frequency component. The high-frequency component is usually noise, while the low-frequency component is a useful feature. This embodiment uses a three-level decomposition and reconstruction to achieve noise reduction. The first decomposition decomposes the original meteorological characteristic signal into a low-frequency component A1 and a high-frequency component D1. The low-frequency component A1 contains coarse signal information, while the high-frequency component D1 contains detailed signal information (usually noise). The low-frequency component A1 obtained from the first decomposition is decomposed again to obtain a new low-frequency component A2 and high-frequency component D2. Similarly, the third decomposition produces a low-frequency component A3 and a high-frequency component D3. After the three-level decomposition, the signal is decomposed into the following four components: the lowest-frequency component A3 contains the most important and smoothest information of the meteorological characteristics. The high-frequency components D1, D2, and D3 contain layer-by-layer details and noise. Noise reduction is achieved by thresholding the high-frequency component. Finally, the processed components are recombined to obtain a denoised signal. The reconstructed signal retains the main features of the original signal while removing most of the noise. Then, the denoised meteorological features are concatenated with the encoded time information so that the final prediction PDF can take into account the changes in meteorological features over time. When processing time series data, the interaction between meteorological features and time is also considered.

[0079] S34: Input the first concatenated features into iTransform to obtain new meteorological features, capture the temporal relationship between different meteorological features, learn the fine-grained relationship between different features, avoid confusion and information loss between channels, and improve the representation power of features;

[0080] In order to identify the contribution of each meteorological feature under the complex relationship between them, this embodiment uses iTransformer to consider each meteorological feature separately and embed each feature independently, so that each meteorological feature can be given equal attention. The formula is as follows:

[0081] Among them, z k It is the result of embedding the kth meteorological feature.

[0082] iTransformer consists of three main components: multi-head attention, layer normalization, and a feedforward network. The encoded meteorological features are fed into the multi-head attention module, where each head focuses on different input meteorological features in a different way. Through multi-head parallel processing, different attention weights and richer contextual information are learned. Layer normalization then independently standardizes each feature, ensuring that the input of each layer has a mean of 0 and a standard deviation of 1, minimizing the impact of differences in measurement units. The calculation formula is as follows:

[0083] Where Z={z1,z2,···,z K} means that it contains k coded meteorological features, z k represents the kth meteorological feature that has been independently encoded.

[0084] Then, the feedforward network is used to perform nonlinear transformation on the independent embedding of each position of the output feature of the multi-head attention, helping the model to better understand complex time series data, as shown in the following formula:

[0085] in, This is the feature after multi-head attention and layer normalization processing. W1 and W2 are weight matrices, b1 and b2 are biases, and f1 and f2 are activation functions. After being processed by the feedforward network and layer normalization again, a multilayer perceptron is used to obtain the final meteorological features containing time series information.

[0086] Step 4: Reconstruct meteorological characteristics;

[0087] S41, uses a multi-head cross-attention mechanism to perform weighted summation on the meteorological features output by iTransformer, so that the meteorological features have the same dimension as the coefficient vector, thereby realizing the reconstruction of the meteorological features;

[0088] Here, the input of the multi-head cross attention mechanism is the coefficient vector obtained in step S22 and the meteorological features obtained in step S34.

[0089] The multi-head cross-attention mechanism can effectively capture the complex relationships between different features and reconstruct the features through weighted summation, thereby enhancing the expressive power of the features.

[0090] The calculation formula is as follows:

[0091] Q i =X c W i Q

[0092] K i =X m W i K

[0093] V i =X m W i V

[0094]

[0095] Among them, X c Represents the input coefficient vector, X m represents the input meteorological characteristics, W i Q , W i K , W i V is the weight matrix of the i-th head (which comes with the multi-head cross attention mechanism). In order to implement multiple heads, each head uses a different weight matrix; softmax is a normalization function used to normalize the output so that it falls within the interval [0,1]; output i represents the output of the i-th head, d n Represents the dimension of the key, which is used for scaling to avoid excessive dot product values. The output of all heads is concatenated and transformed through a linear layer to obtain the output. The formula is as follows:

[0096] F=Concat(output1,output2,···,output i )W o

[0097] Among them, F represents the reconstructed features obtained by multi-head cross attention, Concat(·) represents the splicing function, and W o represents the output weight matrix.

[0098] Step 5: predict PDF evolution;

[0099] S51, the coefficient vector and the reconstructed meteorological features are spliced together to form a more comprehensive and information-rich spliced feature; the spliced feature is weighted with the photovoltaic historical power generation input multi-head cross attention mechanism, the spliced feature is reconstructed, and the predicted coefficient vector is obtained through the fully connected layer (FCNN);

[0100] Here, the coefficient vector of the multi-head cross-attention mechanism input and the photovoltaic historical power generation power are obtained after self-attention in advance.

[0101] The calculation formula is as follows:

[0102] Q i =X p W i Q

[0103] K i =X j W i K

[0104] V i =X j W i V

[0105] Among them, X p Indicates the historical power of the input, X j Represents the concatenated features of the input

[0106]

[0107] F=Concat(output1,output2,···,output i )W o

[0108] The mutual influence between features can improve the feature expression ability, thereby helping the model learn richer information and making the PDF prediction accuracy more accurate.

[0109] S52 , performing an inverse solution of the predicted coefficient vector through cubic B-spline interpolation to obtain the predicted photovoltaic power PDF.

[0110] Embodiment 2, a prediction system for the evolution process of the probability density function of photovoltaic power generation power, is used to implement a prediction method for the evolution process of the probability density function of photovoltaic power generation power, including a weight extraction module, a noise reduction module, a multivariate attention mechanism and a prediction module; the weight extraction module realizes the extraction of basis function coefficient vectors, the noise reduction module realizes meteorological feature noise reduction and time information encoding, the multivariate attention mechanism realizes the reconstruction of meteorological features and the reconstruction after splicing the coefficient vector and the meteorological features, and the prediction module realizes the obtained predicted coefficient vector and the predicted photovoltaic power generation power PDF.

[0111] Photovoltaic power generation is subject to randomness and uncertainty due to the influence of meteorological characteristics such as direct irradiance, temperature, and humidity. This paper constructs a probability density function (PDF) evolution model of photovoltaic power generation to reflect the randomness and uncertainty of power changes over time.

[0112] The present invention uses kernel density estimation to obtain the PDF of photovoltaic power generation, cubic B-spline to calculate the coefficient vector, DWT to denoise historical features, and combines iTransformer and multi-head cross attention mechanism to realize the PDF prediction of photovoltaic power generation.

[0113] The present invention captures the relationship between time and the probability distribution of photovoltaic power generation by establishing a photovoltaic power generation power probability density evolution model that takes into account meteorological information. It can provide a global probability distribution of photovoltaic power generation and reflect the power change trend and its uncertainty at different times.

[0114] By converting the PDF prediction into the prediction of the coefficient vector, the key features of the photovoltaic power generation PDF can be effectively captured as a low-dimensional representation, avoiding redundancy in computation and storage.

[0115] This paper fully considers the interrelationships between photovoltaic power generation, meteorological characteristics, and coefficient vectors, and employs a multi-head cross-attention mechanism for feature reconstruction. By using this multivariate attention mechanism, the model can obtain multiple perspectives on meteorological characteristics from different attention heads, thereby more accurately capturing important patterns in meteorological data across time and space. Through this multi-stage attention mechanism, the model adaptively learns the complex relationships between important features in meteorological information, coefficient vectors, and historical power, ultimately providing precise and expressive feature inputs for prediction tasks, achieving more accurate photovoltaic power PDF predictions.

[0116] The above description is merely a specific embodiment of the present invention, which enables those skilled in the art to understand or implement the present invention. Although detailed descriptions have been made with reference to the aforementioned embodiments, those skilled in the art should understand that they may still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents; and such modifications or replacements do not deviate from the essence of the corresponding technical solutions within the scope of the technical solutions of the embodiments, and they should all be included in the scope of protection of the claims.

Claims

1. A method for predicting the evolution of photovoltaic power generation power probability density function, characterized by: The following steps are involved: Step 1: Obtain the historical probability density function; S11, taking the historical photovoltaic power generation as input, and using kernel density estimation to obtain the probability density function of photovoltaic power generation; Step 2: Obtain the coefficient vector of the basis function; S21, after defining the control points, the probability density function of photovoltaic power generation is fitted using the cubic B-spline interpolation method; S22, calculating the coefficient vector of the basis function corresponding to the control point; Step three: enhance the representation of meteorological characteristic information; S31, denoising the meteorological features; S32, encoding the time information of the meteorological characteristics; S33, performing feature splicing on the denoised meteorological features and the encoded time information to form a first spliced feature; S34, input the first concatenated features into iTransform to obtain new meteorological features, capture the temporal relationship between different meteorological features, and enhance the representation of meteorological information; Step 4: Reconstruct meteorological characteristics; S41, uses a multi-head cross-attention mechanism to perform weighted summation on the meteorological features output by iTransformer, so that the meteorological features have the same dimension as the coefficient vector, thereby realizing the reconstruction of the meteorological features; S42, the multi-head cross attention mechanism input is the coefficient vector obtained in step S22 and the meteorological features obtained in step S34; Step 5: predict the evolution of probability density function; S51, concatenate the coefficient vector and the reconstructed meteorological features to form a more comprehensive and information-rich concatenated feature; weight the concatenated feature and the historical photovoltaic power generation input into the multi-head cross-attention mechanism, reconstruct the concatenated feature, and obtain the predicted coefficient vector through the fully connected layer. The coefficient vector and the historical photovoltaic power generation input into the multi-head cross-attention mechanism are obtained after self-attention in advance; S52 , performing an inverse solution of the predicted coefficient vector through cubic B-spline interpolation to obtain a predicted photovoltaic power generation power probability density function.

2. A method for predicting the evolution of a photovoltaic power generation power probability density function according to claim 1, characterized in that: Meteorological characteristics include direct irradiance, temperature, humidity, wind speed and atmospheric pressure.

3. The method for predicting the evolution of photovoltaic power generation power probability density function according to claim 1, characterized in that: The kernel density estimation formula is: in, represents the probability density estimate at point x, where x is the photovoltaic power generation value to be estimated; n is the total number of data points; κ(·) represents the kernel function; h is the bandwidth parameter used to control the width of the kernel; x i is the actual power value of the i-th photovoltaic.

4. A method for predicting the evolution of a photovoltaic power generation power probability density function according to claim 3, characterized in that: The kernel function uses the Gaussian kernel function, and the formula is in is a standardized variable, indicating the actual x i The relative distance to x.

5. The method for predicting the evolution of photovoltaic power generation power probability density function according to claim 1, characterized in that: Discrete wavelet transform is used to reduce the noise of meteorological characteristics.

6. A method for predicting the evolution of a photovoltaic power generation power probability density function according to any one of claims 1 to 5, characterized in that: The control points are selected in equal proportion from the probability density function curve.

7. A method for predicting the evolution of photovoltaic power generation power probability density function according to claim 6, characterized in that: S21 uses cubic B-spline to fit the obtained PDF. The fitting formula is as follows: Where S(x) is the fitted PDF, P i is the i-th control point, B i,3 (x) is the cubic B-spline basis function corresponding to the i-th control point, which is constructed through a recursive relationship and is in the form of Among them, x i is the i-th node vector, and under a given x value, the coefficient vector M is solved by the interpolation condition; The relationship between the coefficient vector of the S22 cubic B-spline and the control points is expressed by the following linear equations: M·{P0,P1,…,P n }={S0,S1,…,S n }, the coefficient vector M is obtained by satisfying the conditions of value continuity, first-order derivative continuity and second-order derivative continuity of the basis function, which represents the relationship between the control point and the interpolation curve, and the coefficient vector is extracted as the coefficient vector.

8. A prediction system for the evolution of photovoltaic power generation power probability density function, characterized by: A method for predicting the evolution process of a photovoltaic power generation power probability density function as described in any one of claims 1-7 includes a weight extraction module, a noise reduction module, a multivariate attention mechanism and a prediction module; the weight extraction module realizes the extraction of a basis function coefficient vector, the noise reduction module realizes meteorological feature noise reduction and time information encoding, the multivariate attention mechanism realizes the reconstruction of meteorological features and the reconstruction after splicing the coefficient vector and the meteorological features, and the prediction module realizes the acquisition of a predicted coefficient vector and the predicted photovoltaic power generation power PDF.

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