CN116151464A - Photovoltaic power generation power prediction method, system and storable medium - Google Patents
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02S—GENERATION OF ELECTRIC POWER BY CONVERSION OF INFRARED RADIATION, VISIBLE LIGHT OR ULTRAVIOLET LIGHT, e.g. USING PHOTOVOLTAIC [PV] MODULES
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- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
Abstract
The invention discloses a photovoltaic power generation power prediction method, a photovoltaic power generation power prediction system and a storable medium, and relates to the field of photovoltaic power generation. The invention comprises the following steps: collecting relevant parameters affecting photovoltaic power generation power; classifying related parameters affecting the photovoltaic power generation power, clustering classification results by using a K-means clustering algorithm, and removing noise points; establishing matrixes of different types based on the clustering result; and inputting different types of matrix sub-channels into a photovoltaic power generation power prediction model to finish the prediction of the photovoltaic power generation power. According to the invention, the feature matrixes of all input variables are input in a multi-channel mode, different variable feature matrixes are mutually independent, so that feature extraction is respectively carried out on different variable feature matrixes, the network efficiency is effectively improved, and the network can learn more abundant space-time features by fusing the feature information of a plurality of channels, so that the prediction precision is improved.
Description
Technical Field
The invention relates to the field of photovoltaic power generation, in particular to a photovoltaic power generation power prediction method, a photovoltaic power generation power prediction system and a storable medium.
Background
Because of chaos and instability of weather conditions, photovoltaic power generation has strong uncertainty and dynamic property, so that a power grid generates fluctuation, new challenges are brought to management and operation of a photovoltaic power generation system, and real-time control performance is reduced, and accurate prediction of photovoltaic power generation power is one of key solutions for determining a reasonable operation plan and a short-term scheduling plan.
Photovoltaic power generation predictions can be classified into ultra-short-term predictions (0-6 h), short-term predictions (6-24 h), and medium-long-term predictions (1-12 months) according to the predicted time scale. For medium-long term prediction, the prediction error is large because of the long time interval and the strong randomness and uncertainty, and the method is mainly used for long-term planning of the power grid. In order to better determine the real-time scheduling, maintenance planning and operation of the grid market, a higher prediction accuracy is required; the prediction precision is high because the time granularity is smaller and more accurate, so that people pay more attention to the prediction of the photovoltaic power generation power in the extremely short term and the short term, and the prediction is a hot topic in the field.
Disclosure of Invention
In view of the above, the invention provides a photovoltaic power generation power prediction method, a system and a storable medium, which can predict in time that power grid scheduling can be optimized, and can plan the power generation proportion of other modes, and firstly ensure the stability of a power system; secondly, the construction and maintenance work of the power grid and the power station can be guided; thirdly, the energy utilization rate can be improved, the consumption is reduced, and the resource waste is avoided.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
in one aspect, a photovoltaic power generation power prediction method is disclosed, comprising the steps of:
collecting relevant parameters affecting photovoltaic power generation power;
classifying related parameters affecting the photovoltaic power generation power, clustering classification results by using a K-means clustering algorithm, and removing noise points;
establishing matrixes of different types based on the clustering result;
and inputting different types of matrix sub-channels into a photovoltaic power generation power prediction model to finish the prediction of the photovoltaic power generation power.
Optionally, the photovoltaic power generation power prediction model is specifically set as follows: the device comprises a feature extraction module, a maximum pooling layer and a normalization layer; the feature extraction module, the maximum pooling layer and the normalization layer are connected in sequence; the feature extraction module comprises a plurality of convolution layers which are sequentially connected, and the size of the convolution kernel is sequentially decreased.
Optionally, the related parameters include irradiance, temperature, humidity, barometric pressure, wind speed, cloud cover, precipitation, historical power.
Optionally, the classification of the relevant parameters affecting the photovoltaic power generation power is specifically: determining that irradiance and temperature are 2 environmental variables which have the greatest contribution to photovoltaic power generation in related parameters by a correlation analysis algorithm, and dividing annual data into 4 types according to the correlation of environmental factors and photovoltaic power fluctuation characteristics; and secondly, decomposing irradiance, temperature and photovoltaic power generation power by using a fully integrated empirical mode decomposition, singular spectrum decomposition and variation mode decomposition.
Optionally, the method further comprises preprocessing the related parameters by using a linear interpolation method.
Optionally, training the photovoltaic power generation power prediction model is further included, wherein the training set includes: the power sequence is reconstructed by utilizing the bidirectional weight proportion of the autoregressive moving average model based on historical data to serve as a training set, and missing power data reconstructed by delay correlation among the output of the photovoltaic power plant is also used as the training set.
On the other hand also discloses a photovoltaic power generation power prediction system, which comprises: the system comprises a related parameter acquisition module, a related parameter classification module, a matrix establishment module and a photovoltaic power generation power prediction module;
and a related parameter acquisition module: the method comprises the steps of collecting relevant parameters affecting photovoltaic power generation power;
and a related parameter classification module: the method comprises the steps of classifying related parameters affecting photovoltaic power generation power, clustering classification results by using a K-means clustering algorithm, and removing noise points;
and a matrix establishment module: the method comprises the steps of establishing different types of matrixes based on clustering results;
photovoltaic power generation power prediction module: the matrix sub-channel prediction method is used for inputting different types of matrix sub-channels into the photovoltaic power generation power prediction model to complete the prediction of the photovoltaic power generation power.
Optionally, the system further comprises a data preprocessing module, which is used for preprocessing related parameters by using a linear interpolation method.
Finally, a computer storage medium is disclosed, on which a computer program is stored, which when being executed by a processor implements the steps of a photovoltaic power generation power prediction method according to any of the above.
Compared with the prior art, the invention discloses a photovoltaic power generation power prediction method, a photovoltaic power generation power prediction system and a storage medium, and has the following beneficial effects:
1. according to the invention, the feature matrixes of all input variables are input in a multi-channel mode, different variable feature matrixes are mutually independent, so that the feature extraction of different variable feature matrixes is respectively carried out, the network efficiency is effectively improved, and the network can learn more abundant space-time features by fusing the feature information of a plurality of channels, so that the prediction precision is improved;
2. the environment attribute set with the greatest influence on the photovoltaic power generation power is found, the training set is input, the prediction speed and the precision of the model can be improved, and the method has important significance for improving the stability of a power grid and the operation management efficiency of a photovoltaic power station.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required to be used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only embodiments of the present invention, and that other drawings can be obtained according to the provided drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of the present invention;
fig. 2 is a schematic structural view of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The embodiment of the invention discloses a photovoltaic power generation power prediction method, which is shown in fig. 1 and comprises the following steps:
s1: collecting relevant parameters affecting photovoltaic power generation power;
s2: classifying related parameters affecting the photovoltaic power generation power, clustering classification results by using a K-means clustering algorithm, and removing noise points;
s3: establishing matrixes of different types based on the clustering result;
s4: and inputting different types of matrix sub-channels into a photovoltaic power generation power prediction model to finish the prediction of the photovoltaic power generation power.
In S4, the photovoltaic power generation power prediction model is specifically set as: the device comprises a feature extraction module, a maximum pooling layer and a normalization layer; the feature extraction module is connected with the maximum pooling layer and the normalization layer in sequence; the feature extraction module comprises a plurality of convolution layers which are sequentially connected, and the size of the convolution kernel is sequentially decreased. Specifically, in order to enhance the capability of the model to extract important features, 2 layers of convolution layers are arranged for each convolution block; secondly, in order to reduce the noise level in the training process, the learned parameters can keep a larger gradient state, the convergence speed is improved, and batch normalization layers (batch normalization, BN) are arranged behind each convolution layer, so that the BN layer has no effect of extracting features and can be seen as a whole with the convolution layers.
In the embodiment, the expanded convolution and the residual convolution can be added, and the addition of the expanded convolution enables the receptive field of the time convolution network to be larger and more flexible, and the receptive field can even cover the whole sequence; and the addition of the residual block can prolong the effective input of the sequence and increase the information of the time convolution network.
The accommodation amount and the calculation amount can be effectively reduced.
In S1: relevant parameters include irradiance, temperature, humidity, barometric pressure, wind speed, cloud cover, precipitation, historical power.
The related parameters influencing the photovoltaic power generation power are classified specifically as follows: determining that irradiance and temperature are 2 environmental variables which have the greatest contribution to photovoltaic power generation in related parameters by a correlation analysis algorithm, and dividing annual data into 4 types according to the correlation of environmental factors and photovoltaic power fluctuation characteristics; and secondly, decomposing irradiance, temperature and photovoltaic power generation power by using a fully integrated empirical mode decomposition, singular spectrum decomposition and variation mode decomposition.
The method also comprises preprocessing the related parameters by using a linear interpolation method. In addition, in order to improve the continuity of the acquired data, the acquired abnormal data missing needs to be complemented, and the commonly adopted method comprises the following steps: the method comprises a maximum probability method, a sliding average method, a Lagrange interpolation method, a classification fixing processing method, a regression method and a gray prediction method. Abnormal data is divided into three categories: the photovoltaic output power is higher than the upper bound of the probability power curve and does not change along with irradiance; the photovoltaic output power is lower than the lower limit of the probability power curve and does not change along with irradiance; one is that the total irradiance is significantly different from 0, and the photovoltaic power remains at or near 0. The factors influencing the output power are classified, then the optimal copula function is selected for output power fitting, accurate probability distribution is formed, abnormal data points in different influence factor intervals are screened out by utilizing Euclidean distance and K-S value, and finally the maximum power compensation algorithm is adopted for compensating the missing data.
The photovoltaic power generation power prediction model is trained, wherein the training set comprises: the power sequence is reconstructed by utilizing the bidirectional weight proportion of the autoregressive moving average model based on historical data to serve as a training set, and missing power data reconstructed by delay correlation among the output of the photovoltaic power plant is also used as the training set.
Further, the output power of photovoltaic power generation is affected by various external factors, such as solar irradiance, cloud cover, temperature, and the like. However, since the influence of the above-described features on the model output is different, it is necessary to perform feature analysis. In addition, although the small amount of data may cause the model to miss important information in the training process, too many input features inevitably increase the complexity of the model, which may cause problems such as information redundancy. In order for the model to possess better predictive performance, and to adequately compress the input dimensions of the input features, the photovoltaic power generation data obtained herein needs to be analyzed. The present embodiment uses the distance correlation coefficient to comprehensively analyze the relationships of various features.
The distance correlation coefficient (Distance Correlation) can better avoid the problem of the Pearson correlation coefficient. The pearson correlation coefficient has a higher sensitivity to linear relationships, and a value of 0 indicates that the relationship is less intimate. However, in addition to the linear relationship, the nonlinear relationship is also a relationship, so it is difficult to map complex relationships between features using pearson correlation coefficients. In contrast, the distance correlation coefficient has both correlation degrees, and the characterization capability of function input and output is more specific and accurate, so that the application range of the distance correlation coefficient is wider. If the random variables X and Y have a finite square difference, their distance correlation coefficients are defined as follows:
the distance correlation coefficients are all non-negative numbers, namely dCo (X, Y) E [0,1], and the larger the value is, the stronger the correlation is. The distance correlation coefficient obtained here is also derived from the samples, as is the pearson correlation coefficient.
The embodiment also discloses a photovoltaic power generation power prediction system, as shown in fig. 2, including: the system comprises a related parameter acquisition module, a related parameter classification module, a matrix establishment module and a photovoltaic power generation power prediction module;
and a related parameter acquisition module: the method comprises the steps of collecting relevant parameters affecting photovoltaic power generation power;
and a related parameter classification module: the method comprises the steps of classifying related parameters affecting photovoltaic power generation power, clustering classification results by using a K-means clustering algorithm, and removing noise points;
and a matrix establishment module: the method comprises the steps of establishing different types of matrixes based on clustering results;
photovoltaic power generation power prediction module: the matrix sub-channel prediction method is used for inputting different types of matrix sub-channels into the photovoltaic power generation power prediction model to complete the prediction of the photovoltaic power generation power.
Optionally, the system further comprises a data preprocessing module, which is used for preprocessing related parameters by using a linear interpolation method.
Finally, a computer storage medium is disclosed, on which a computer program is stored, which when executed by a processor implements the steps of a photovoltaic power generation power prediction method of any of the above.
In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (9)
1. The photovoltaic power generation power prediction method is characterized by comprising the following steps of:
collecting relevant parameters affecting photovoltaic power generation power;
classifying related parameters affecting the photovoltaic power generation power, clustering classification results by using a K-means clustering algorithm, and removing noise points;
establishing matrixes of different types based on the clustering result;
and inputting different types of matrix sub-channels into a photovoltaic power generation power prediction model to finish the prediction of the photovoltaic power generation power.
2. The photovoltaic power generation power prediction method according to claim 1, wherein the photovoltaic power generation power prediction model is specifically set as follows: the device comprises a feature extraction module, a maximum pooling layer and a normalization layer; the feature extraction module, the maximum pooling layer and the normalization layer are connected in sequence; the feature extraction module comprises a plurality of convolution layers which are sequentially connected, and the size of the convolution kernel is sequentially decreased.
3. The method of claim 1, wherein the parameters include irradiance, temperature, humidity, barometric pressure, wind speed, cloud cover, precipitation, historical power.
4. The method for predicting the photovoltaic power generation power according to claim 1, wherein the classification of the relevant parameters affecting the photovoltaic power generation power is as follows: determining that irradiance and temperature are 2 environmental variables which have the greatest contribution to photovoltaic power generation in related parameters by a correlation analysis algorithm, and dividing annual data into 4 types according to the correlation of environmental factors and photovoltaic power fluctuation characteristics; and secondly, decomposing irradiance, temperature and photovoltaic power generation power by using a fully integrated empirical mode decomposition, singular spectrum decomposition and variation mode decomposition.
5. The method of claim 1, further comprising preprocessing the relevant parameters using linear interpolation.
6. The method of claim, further comprising training the photovoltaic power generation power prediction model, wherein the training set comprises: the power sequence is reconstructed by utilizing the bidirectional weight proportion of the autoregressive moving average model based on historical data to serve as a training set, and missing power data reconstructed by delay correlation among the output of the photovoltaic power plant is also used as the training set.
7. A photovoltaic power generation power prediction system, comprising: the system comprises a related parameter acquisition module, a related parameter classification module, a matrix establishment module and a photovoltaic power generation power prediction module;
and a related parameter acquisition module: the method comprises the steps of collecting relevant parameters affecting photovoltaic power generation power;
and a related parameter classification module: the method comprises the steps of classifying related parameters affecting photovoltaic power generation power, clustering classification results by using a K-means clustering algorithm, and removing noise points;
and a matrix establishment module: the method comprises the steps of establishing different types of matrixes based on clustering results;
photovoltaic power generation power prediction module: the matrix sub-channel prediction method is used for inputting different types of matrix sub-channels into the photovoltaic power generation power prediction model to complete the prediction of the photovoltaic power generation power.
8. The photovoltaic power generation power prediction system according to claim 7, further comprising a data preprocessing module for preprocessing the relevant parameters using linear interpolation.
9. A computer storage medium, wherein a computer program is stored on the computer storage medium, which when executed by a processor, implements the steps of a photovoltaic power generation power prediction method according to any one of claims 1-6.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116579509A (en) * | 2023-07-14 | 2023-08-11 | 天津福天科技有限公司 | Photovoltaic power generation prediction method based on virtual reality |
| CN117709556A (en) * | 2024-02-06 | 2024-03-15 | 山东建筑大学 | Photovoltaic power generation short-term prediction method, system, medium and equipment |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116579509A (en) * | 2023-07-14 | 2023-08-11 | 天津福天科技有限公司 | Photovoltaic power generation prediction method based on virtual reality |
| CN117709556A (en) * | 2024-02-06 | 2024-03-15 | 山东建筑大学 | Photovoltaic power generation short-term prediction method, system, medium and equipment |
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