CN110909919A - Photovoltaic power prediction method of depth neural network model with attention mechanism fused - Google Patents
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Abstract
A photovoltaic power prediction method of a deep neural network model integrating an attention mechanism belongs to the technical field of renewable energy photovoltaic power. The method comprises the steps that firstly, a hybrid neural network based on a long-term and short-term memory neural network and a convolutional neural network is selected as a prediction model according to the characteristics of photovoltaic data, and an optimal connection mode is considered; secondly, in order to reduce the calculation time of the model and more accurately extract high-quality feature information which can be used for photovoltaic prediction, an attention mechanism model is added in the aspect of model feature extraction. The advantages of the proposed hybrid deep learning model are proved through comparison of different prediction models; the selection of high quality features is made possible by the application of attention model. The reasonable mixed model mode can realize the dual pursuit of high prediction precision and low calculation cost.
Description
Technical Field
The invention belongs to the technical field of renewable energy photovoltaic power prediction, and particularly relates to a photovoltaic power prediction method of a deep neural network model fused with an attention mechanism.
Background
Photovoltaic power generation is increasing at a relatively rapid rate every year. Photovoltaic power generation has a natural uncontrollable nature due to the randomness of light and day and night periodicity, which are typical of fluctuating and intermittent power sources. The output of the photovoltaic power generation system is greatly influenced by factors such as weather and climate. These features lead to new challenges for the power system after the high-capacity/high-ratio photovoltaic is connected to the grid, such as increasing the difficulty and complexity of grid dispatching. The prediction of the generated power of the photovoltaic is one of the key basic technologies for improving the running quality of a power system and reducing the reserve capacity.
The photovoltaic power can be classified into direct prediction and indirect prediction according to the prediction process; the prediction time scale classification can be divided into ultra-short term prediction, medium term prediction and long term prediction; the prediction mechanism of the prediction method can be divided into a physical method, a statistical method and a machine learning method. The physical method is that a mathematical model is established according to a photovoltaic power generation principle, data such as solar radiation, temperature, humidity, cloud cover, air pressure, wind speed and the like are obtained by means of numerical weather forecast and the like, and parameters such as a photovoltaic system installation angle, photovoltaic array conversion efficiency, battery conditions and the like are combined to directly calculate to obtain photovoltaic power generation power. The prediction accuracy of the physical method strongly depends on the accuracy degree of numerical weather forecast information, but the bottleneck is met in the aspect of improving the numerical weather forecast accuracy at present. The statistical method is to establish an incidence mapping relation between input data and output data through curve fitting, parameter estimation, incidence relation analysis and other means on processed historical data such as solar radiation degree, photovoltaic power generation output and the like, so as to realize prediction on future photovoltaic power generation output. However, the implementation of the statistical prediction method requires a large amount of processed correct historical data, and the implementation process is difficult in terms of data acquisition and processing calculation amount. Machine learning has the ability to efficiently extract high-dimensional complex nonlinear features and to map directly from input to output. However, for the traditional neural network, problems of overfitting, gradient disappearance, explosion and the like of network training are easily caused by excessive input data, hidden layer number and node number of each hidden layer. The deep neural network has higher feature extraction capability than the common neural network, and can remarkably improve the problem of gradient disappearance of the original neural network. To date, some success has been achieved in predicting wind speed, wind power, load, solar irradiance, etc. based on deep learning predictive models. The attention mechanism is similar to the human selective visual attention mechanism in nature, and the core goal is to select the more critical information for the current task goal from a plurality of information.
Therefore, the photovoltaic electric field power is modeled and predicted by using a deep learning algorithm, and the depth features extracted by the neural network model are subjected to weighted summation by using an attention mechanism, so that feature information with higher weight of a prediction result, namely the selection of high-quality features is obtained. The method has important significance for improving the accuracy and stability of the photovoltaic power prediction model, can reduce the interference of useless information on the model, reduces the calculation time, and provides a new idea for predicting the renewable energy power.
Disclosure of Invention
The invention aims to provide a photovoltaic power prediction method of a depth neural network model fused with an attention mechanism, which comprises the steps of firstly selecting a hybrid neural network based on a long-short term memory neural network and a convolutional neural network as a prediction model according to the characteristics of photovoltaic data, and considering the optimal connection mode; secondly, in order to reduce the calculation time of the model and more accurately extract high-quality feature information which can be used for photovoltaic prediction, an attention mechanism model is added in the aspect of model feature extraction.
The purpose of the invention is realized as follows:
the photovoltaic power prediction method of the depth neural network model with the attention mechanism fused comprises the following steps:
the method comprises the following steps: downloading numerical weather forecast and solar irradiance data including wind speed, wind direction, temperature, humidity, global irradiance, scattering irradiance and other data and corresponding historical power data of a photovoltaic field through a website;
step two: processing the acquired historical data, including removing abnormal data, filling missing data, unifying resolution, converting data formats and the like;
step three: selecting a deep neural network model to form a hybrid model and selecting a connection mode of the hybrid model; setting parameters of the deep hybrid neural network, including parameters such as the number of hidden layers, the number of neurons in each layer, the learning rate, the learning step length, the iteration times, the number of convolution kernels, the number of long and short term neural network units and the like;
step four: selecting a proper attention mechanism model and a model placing position on the attention mechanism model;
step five: dividing the preprocessed historical data into a training set, a verification set and a test set according to a certain proportion;
step six: selecting and training a mixed model according to a trial-and-error method to obtain the optimal model parameters for prediction;
step seven: and comparing the prediction result with different models and analyzing the prediction results obtained by the different models to prove the advantages of the proposed models.
Selecting a long-short term memory neural network (LSTM) to extract the time characteristics of the data in the second step; the internal storage unit and the door mechanism of the long-short term memory neural network overcome the problems of gradient disappearance and gradient explosion in the traditional recurrent neural network training; the door mechanism comprises a forgetting door, an input door, an updating door and an output door; and obtaining the prediction results of the long-term and short-term memory neural network model under different historical data lengths through model training and a large number of experiments.
Selecting a convolutional neural network CNN to extract the spatial characteristics of the data in the second step; the convolutional neural network mainly comprises a convolutional layer, a pooling layer, a full-link layer, an output layer and the like; and obtaining the prediction results of the convolutional neural network model under different historical data lengths through model training and a large number of experiments.
In the fourth step, the selection of high-quality features is extracted by means of an attention mechanism model; the most intuitive method for measuring the importance of the features by the power mechanism model is to rely on the weight, that is, the weight of each feature is calculated firstly during each recognition, then the features are weighted and summed, the larger the weight is, the greater the contribution of the feature to the current recognition is, and the formula is as follows:
wherein L isxValue is the element in Source for the length of Source.
The invention has the beneficial effects that:
the advantages of the proposed hybrid deep learning model are proved through comparison of different prediction models; the selection of high quality features is made possible by the application of attention model. The reasonable mixed model mode can realize the dual pursuit of high prediction precision and low calculation cost.
Drawings
FIG. 1 is a system diagram of photovoltaic power prediction;
FIG. 2 is a historical data breakdown diagram;
FIG. 3 is a diagram of a convolutional neural network architecture;
FIG. 4 is a diagram of a long-short term memory neural network model architecture;
fig. 5 is an essential idea of the attention mechanism.
Detailed Description
The invention is further described below with reference to the accompanying drawings. The drawings are only for the purpose of illustrating the principles of the invention and are not to be construed as limiting the scope of the invention.
The invention mainly aims at the field of renewable energy source prediction, and the deep neural network has a very good effect on processing nonlinear data and extracting the depth characteristics of the data. In order to improve the accuracy of a photovoltaic power prediction model, the invention provides a photovoltaic power prediction model of a hybrid deep neural network fused with an attention mechanism. Firstly, selecting a hybrid neural network based on a long-term and short-term memory neural network and a convolutional neural network as a prediction model according to the characteristics of photovoltaic data, and considering an optimal connection mode; secondly, in order to reduce the calculation time of the model and more accurately extract high-quality feature information which can be used for photovoltaic prediction, an attention mechanism model is added in the aspect of model feature extraction. The system architecture for photovoltaic power prediction is shown in fig. 1. The drawings are only for the purpose of illustrating the principles of the invention and are not to be construed as limiting the scope of the invention.
The invention predicts photovoltaic power based on a deep neural network, considers numerical weather forecast data and irradiance data, including wind speed, wind direction, temperature, humidity, global irradiance, scattered irradiance and other factors, relates to model selection and connection of a hybrid model, and attention mechanism characteristic selection, and specifically comprises the following steps:
step 1) downloading complete numerical weather forecast data, irradiance data and corresponding photovoltaic field historical power data suitable for prediction from a website. Preprocessing the downloaded historical data, including rejecting abnormal data; filling missing data by using technologies such as average value and interpolation; normalizing the data by utilizing a normalization technology, and unifying the data resolution by utilizing a linear interpolation technology; conversion of data format, etc.
And 2) dividing the obtained preprocessed historical data into a training set, a verification set and a test set according to a certain proportion.
And 3) selecting a deep neural network model to form a mixed model, and training and verifying the mixed model. The convolutional neural network CNN is selected in the present invention to extract the spatial features of the data. Local connectivity and weight sharing are two main features of convolutional neural networks. The convolutional neural network mainly comprises a convolutional layer, a pooling layer, a full-link layer, an output layer and the like. Fig. 3 is a main structural diagram of a convolutional neural network. And obtaining the prediction results of the convolutional neural network model under different historical data lengths through model training and a large number of experiments.
And 4) extracting the time characteristics of the data mainly by means of a long-term and short-term memory neural network (LSTM). The long-term and short-term memory neural network is a time recursion neural network and is used for learning long-term dependence information; the internal memory unit and the door mechanism thereof overcome the problems of gradient disappearance and gradient explosion in the traditional recurrent neural network training. The door mechanism comprises a forgetting door, an input door, an updating door and an output door. FIG. 4 is a block diagram of a long-short term memory neural network model. And obtaining the prediction results of the long-term and short-term memory neural network model under different historical data lengths through model training and a large number of experiments.
The core calculation formula of the long-short term memory neural network model is as follows:
ft＝σ(wf×[ht-1,xt]+bf) (1)
it＝σ(wi×[ht-1,xt]+bi) (2)
gt＝tanh(wg×[ht-1,xt]+bg) (3)
ct＝ft×ct-1+it×gt(4)
ot＝σ(wo×[ht-1,xt]+bo) (5)
ht＝ot×tanh(ct) (6)
and 5) selecting high-quality features by means of an attention mechanism model to extract. The most intuitive method for the power mechanism model to measure the importance of the features depends on the weight, namely, the weight of each feature is calculated firstly during each recognition, then the features are weighted and summed, and the larger the weight is, the greater the contribution of the feature to the current recognition is. The attention mechanism model is mainly attached under the framework of an Encoder-Decoder, is a very common calculation framework, and the essential idea of the model is shown in fig. 5, and can be written as the following formula:
wherein L isxIs the length of Source. Essentially, the attention mechanism is to perform weighted summation on Value values of elements in Source, and Query and Key are used forA weight coefficient corresponding to Value is calculated.
And 6) the hybrid neural network carries out photovoltaic power prediction through different combination modes of the hybrid convolutional neural network and the long-term and short-term memory neural network. In the invention, the best combination mode and prediction effect of the mixed model are selected by comparing two mixed models (a mixed model of a convolutional neural network + a long-short term memory neural network (CNN + LSTM) for extracting the spatial characteristics of data and then extracting the time characteristics of the data, and a mixed model of a long-short term memory neural network + a convolutional neural network (LSTM + CNN) for extracting the time characteristics of data and then extracting the spatial characteristics of the data). Where the attention mechanism model is intended to consider extraction of high quality depth features placed before the fully connected layer. The optimal network model parameters are obtained through a trial and error method, and a large number of experiments show that the hybrid model (LSTM + CNN) has the optimal prediction effect.
And 7) comparing the prediction results obtained by different models to prove that the selected mixed model has the best prediction effect.
Claims (4)
1. The photovoltaic power prediction method of the depth neural network model fused with the attention mechanism is characterized by comprising the following steps of:
the method comprises the following steps: downloading numerical weather forecast and solar irradiance data including wind speed, wind direction, temperature, humidity, global irradiance, scattering irradiance and other data and corresponding historical power data of a photovoltaic field through a website;
step two: processing the acquired historical data, including removing abnormal data, filling missing data, unifying resolution, converting data formats and the like;
step three: selecting a deep neural network model to form a hybrid model and selecting a connection mode of the hybrid model; setting parameters of the deep hybrid neural network, such as the number of hidden layers, the number of neurons in each layer, the learning rate, the learning step length, the iteration times, the number of convolution kernels, the number of long and short term neural network units and the like;
step four: selecting a proper attention mechanism model and a model placing position on the attention mechanism model;
step five: dividing the preprocessed historical data into a training set, a verification set and a test set according to a certain proportion;
step six: and selecting and training the mixed model according to a trial and error method to obtain the optimal model parameter for prediction, and obtaining a prediction result.
2. The method for predicting photovoltaic power of the deep neural network model with the integrated attention mechanism according to claim 1, wherein: selecting a long-short term memory neural network (LSTM) to extract the time characteristics of the data in the second step; the internal storage unit and the door mechanism of the long-short term memory neural network overcome the problems of gradient disappearance and gradient explosion in the traditional recurrent neural network training; the door mechanism comprises a forgetting door, an input door, an updating door and an output door; and obtaining the prediction results of the long-term and short-term memory neural network model under different historical data lengths through model training and a large number of experiments.
3. The method for predicting photovoltaic power of the deep neural network model with the integrated attention mechanism according to claim 1, wherein: selecting a convolutional neural network CNN to extract the spatial characteristics of the data in the second step; the convolutional neural network mainly comprises a convolutional layer, a pooling layer, a full-link layer, an output layer and the like; and obtaining the prediction results of the convolutional neural network model under different historical data lengths through model training and a large number of experiments.
4. The method for predicting photovoltaic power of the deep neural network model with the integrated attention mechanism according to claim 1, wherein: in the fourth step, the selection of high-quality features is extracted by means of an attention mechanism model; the most intuitive method for measuring the importance of the features by the attention mechanism model is to rely on the weight, namely, the weight of each feature is calculated firstly during each recognition, then the features are weighted and summed, the larger the weight is, the greater the contribution of the feature to the current recognition is, and the calculation formula is
Wherein L isxIs the length of Source. Value is an element in Source.
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