CN113537582B - Photovoltaic power ultra-short-term prediction method based on short-wave radiation correction - Google Patents
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Abstract
The invention relates to a photovoltaic power station power ultra-short term prediction method based on short wave radiation correction, which comprises the steps of extracting numerical weather forecast (NWP) of a photovoltaic power station, correcting short wave radiation by using a long-term and short-term network (LSTM), predicting an Extreme Learning Machine (ELM), carrying out simulation calculation and evaluating indexes.
Description
Technical Field
The invention relates to the technical field of photovoltaic power stations, in particular to a photovoltaic power station power ultra-short-term prediction method based on short-wave radiation correction.
Background
The photovoltaic power generation has stronger day and night periodicity and seasonal periodicity, and is a typical intermittent power supply; meanwhile, the solar energy collector is very sensitive to the surface solar radiation intensity, has strong randomness of output, and has great influence on frequency modulation, peak regulation, standby and the like of a power grid. The photovoltaic power prediction is a basic key technology for improving the control and scheduling performance of a photovoltaic power station and ensuring the safe and stable operation of the high-ratio photovoltaic power generation access power grid.
The photovoltaic power ultra-short-term prediction refers to prediction and forecast of 4 hours in the future from the prediction moment, and the time resolution is 15 minutes. The significance of the day-ahead prediction is that ultra-short-term photovoltaic power prediction can provide power transient information.
The existing photovoltaic power ultra-short-term prediction generally establishes a mapping relation between historical input data and future power output, and can directly predict a future power value according to historical NWP data, so that higher prediction accuracy is obtained. For the artificial intelligence method, the method has great advantages in processing the nonlinear time series, but cannot reflect the dynamic characteristics of the system. Overall, existing predictions cannot track future power trends.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide the photovoltaic power ultra-short-term prediction method based on the short-wave radiation correction, which has clear physical significance, considers rich time-space correlation among data, and has the advantages of scientific and reasonable method, strong applicability, high prediction precision and accurate prediction result.
The technical scheme adopted for realizing the aim of the invention is as follows: a photovoltaic power ultra-short term prediction method based on short wave radiation correction is characterized by comprising the following steps: it comprises the following steps:
1) partitioning of weather types based on Elkan-K-means clustering
Numerical Weather forecast (NWP) information includes 10 meter wind speed, 30 meter wind speed, 50 meter wind speed, 70 meter wind speed, 10 meter wind direction, 30 meter wind direction, 50 meter wind direction, 70 meter wind direction, temperature, 2 meter relative humidity, 2m humidity, long wave radiation, short wave radiation, cloud cover, air pressure, precipitation;
in order to avoid the influence of redundant information on the prediction precision, a pearson correlation coefficient is utilized to preliminarily screen weather forecast meteorological factors and corresponding photovoltaic power, the pearson correlation coefficient of each meteorological factor sequence and the photovoltaic power sequence is respectively solved, a factor showing a positive correlation relation with the photovoltaic power is selected, and short-wave radiation with the maximum person coefficient is selected as the relevant information input;
2) short wave radiation correction using long-short term memory network (LSTM)
Based on the relevant information extracted in the step 1), judging that when short-wave radiation is predicted, under certain specific environmental conditions, the rule that the predicted value is lower than the actual value is utilized, and a long-short term memory network (LSTM) is adopted to deeply mine complex characteristic information in sample data by utilizing the characteristics of a training sample to correct the data;
the internal calculation process of the long-short term memory network (LSTM) neural unit is as follows:
the method comprises the following steps that a Sigmoid layer of a forgetting gate determines what information is discarded from a cell state, the cell state comprises basic attribute characteristics of photovoltaic power station weather forecast (NWP), therefore, future short wave radiation can be corrected, when new operation data are input, people hope to forget to compare old numerical weather forecast (NWP) distribution information and discard old information from cells, the step of determining is completed through the forgetting gate layer, and a specific function expression of calculation of the forgetting gate is as follows:
ft=sigmoid(θf·[ht-1,xt]+bf) (1)
the next step to decide what information to store in the cell state is to take as input the shortwave radiation provided by the NWP, 2m humidity, temperature, 10 m wind speed, 70 m wind speed, 30m wind direction, the Sigmoid layer called "input gate layer" decides which values to update, expressed by equation (2), the next tanh layer creates a candidate vector Ct, which will be added to the state of the cell, expressed by equation (3), and in the next step, combining equations (2) and (3) to create the updated values, expressed by equation (4),
it=sigmoid(Wi·[ht-1,xt]+bi) (2)
renewal of cell status
This output will be based on the cell state, but will be a filtered version, first running a Sigmoid layer to determine the fraction of the cell state to output, then passing the cell state through tanh, normalizing the value to between-1 and 1, and multiplying it by the output of the Sigmoid gate, two steps to determine the storage of the input in the cell state, expressed by equations (5), 6,
ot=sigmoid(Wo·[ht-1,xt]+bo) (5)
ht=ot·tanh(Ct) (6)
in the formula itIs the output of the input gate or gates,as a candidate for the current layer, C may be added to the cell statetIs the current memory cell state, and the whole process is the process of updating the memory cell state at the previous moment, namely discarding useless information and adding new information, otIs the output of the output gate, htThe final output of the LSTM at the current moment is output, and the corrected short wave radiation at the corresponding time interval is output;
3) training of Extreme learning machine (Extreme learning machine)
If the learning data sample of the ELM is { xj, tj }, and xj and tj respectively represent input data and output data of the model, wherein the input data are the short-wave radiation and the historical photovoltaic power corrected in the step 2), and the output data are that the ultra-short-term photovoltaic power predictions all belong to a real number set R, the mathematical expression of the single hidden layer neural network is as follows:
in the formula: kcon is the number of hidden layer nodes; beta is aiIs the output weight; g is an activation function; omegaiThe connection weight between the input neuron and the ith hidden neuron; omegai·xjThe inner product of the connection weight and the data sample; biBias compensation values for the ith hidden layer neuron; t is tjIs an output vector; m is the total number of samples;
setting T as a matrix form of an output vector, and beta as an output weight vector, and obtaining an expression of the output vector, namely, T is in an H beta form, wherein H is a hidden layer output matrix of the ELM neural network;
h is in a matrix form
The training process of the ELM model comprises the following steps: firstly, determining the number of neurons, and randomly distributing node parameters; then, calculating an output matrix of the Mth row and the Kcon column of the hidden layer, and calculating the photovoltaic output power of the output matrix;
4) simulation calculation
Simulation input quantity: analyzing the actually measured data of the photovoltaic power station; inputting historical data: inputting numerical weather forecast (NWP) data of predicted months for the first two monthly history powers of each quarter; the data sampling interval is 15min, and a final power ultra-short-term prediction result is obtained according to the steps 1) to 3);
5) evaluation index
Let PmiIs the actual average power, P, of the i periodpiPredicted power for i period, CiFor the total starting capacity in the period i, the average absolute error is defined as formula (9) if n is the number of all samples:
the root mean square error is defined by equation (11):
and 4), inputting simulation input quantity, and carrying out error calculation on the predicted power calculated by the model and the actual measured power through the average absolute error (9) and the root-mean-square error (10) in the step 5) to obtain the prediction accuracy.
The invention relates to a photovoltaic power station power ultra-short term prediction method based on short wave radiation correction, which comprises the steps of extracting numerical weather forecast (NWP) of a photovoltaic power station, correcting short wave radiation by utilizing a long-term and short-term network (LSTM), predicting Extreme Learning Machine (ELM), simulating calculation and evaluating indexes.
Drawings
FIG. 1 is a block diagram of a photovoltaic power station power ultra-short term prediction method based on short wave radiation correction according to the present invention;
FIG. 2 is a schematic diagram of a numerical weather forecast (NWP) selection of photovoltaic power at a certain time;
FIG. 3 is a schematic diagram showing comparison between predicted values and actual values in spring for a certain day;
FIG. 4 is a schematic diagram showing comparison between predicted values and actual values of a day in summer;
FIG. 5 is a schematic diagram showing the comparison between the predicted value and the actual value of a day in autumn;
fig. 6 is a schematic diagram showing comparison between predicted values and actual values of a certain day in winter.
Detailed Description
The method for ultra-short-term power prediction of a photovoltaic power station based on short-wave radiation correction is further described below with reference to the accompanying drawings and specific embodiments.
With reference to fig. 1 and fig. 2, the ultrashort-term photovoltaic power station power prediction method based on short-wave radiation correction of the present invention includes the following steps:
1) numerical weather forecast selection
Numerical Weather Prediction (NWP) information includes 10 meter wind speed, 30 meter wind speed, 50 meter wind speed, 70 meter wind speed, 10 meter wind direction, 30 meter wind direction, 50 meter wind direction, 70 meter wind direction, temperature, 2 meter relative humidity, 2m humidity, long wave radiation, short wave radiation, cloud cover, air pressure, precipitation, and the like.
In order to avoid the influence of redundant information on the prediction precision, the pearson correlation coefficient is utilized to preliminarily screen weather forecast meteorological factors and corresponding photovoltaic power, and the pearson correlation coefficient of each meteorological factor sequence and the corresponding photovoltaic power sequence is respectively obtained. And selecting a factor which has positive correlation with the photovoltaic power, and selecting the short-wave radiation with the maximum person coefficient as the relevant information input.
2) Short-wave radiation correction using long-short term memory network (LSTM)
Based on the related information extracted in step 1), it can be determined that, when short-wave radiation is predicted, the prediction deviation has certain regularity under certain specific environmental conditions, such as sunshine time in the daytime, and if most of the predicted values are lower than the actual values, the data can be corrected by using the regularity. Because the correction cannot be expressed by a specific function, a long-short term memory network (LSTM) is a popular deep learning method at present, and has the advantages that the characteristics of the training sample can be fully utilized, the complex characteristic information in the sample data can be deeply mined, and the high-dimensional function approximation can be completed.
The internal calculation process for the LSTM neural unit is as follows:
the Sigmoid layer of the forgetting gate determines what information is discarded from the cell state, the cell state comprises basic attribute characteristics of photovoltaic power station weather forecast (NWP), therefore, the future short wave radiation can be corrected, when new operation data is input, people hope to forget to compare old numerical weather forecast (NWP) distribution information, and discard the old information from the cell, and the step of determining is completed through the forgetting gate layer. The specific function expression of the forgetting gate calculation method is
ft=sigmoid(θf·[ht-1,xt]+bf) (1)
The next step to decide what information is stored in the cell state is to take as input the shortwave radiation provided by the NWP, 2 meters humidity, temperature, 10 meters wind speed, 70 meters wind speed, 30 meters wind direction, the Sigmoid layer called "input gate layer" decides which values to update represented by equation (2), the next tanh layer creates a candidate vector Ct, which is to be added to the state of the cell represented by equation (3), and in the next step, the two vectors are combined to create an updated value represented by equation (4),
it=sigmoid(Wi·[ht-1,xt]+bi) (2)
renewal of cell status
This output will be based on the cell state, but will be a filtered version. First, a Sigmoid layer is run to determine the fraction of cell states to be output, then the cell states are normalized to a value between-1 and 1 by tanh and multiplied by the output of the Sigmoid gate, two steps of determining to store inputs in the cell states are expressed by equations (5) and (6),
ot=sigmoid(Wo·[ht-1,xt]+bo) (5)
ht=ot·tanh(Ct) (6)
in the formula itIs the output of the input gate or gates,as a candidate for the current layer, C may be added to the cell statetIs the current memory cell state, and the whole process is the process of updating the memory cell state at the previous moment, namely discarding useless information and adding new information, otIs the output of the output gate, htThe final output of the LSTM at the current moment is output, and the corrected short wave radiation at the corresponding time interval is output;
(3) training of Extreme learning machine (Extreme learning machine)
If the learning data sample of the ELM is { xj, tj }, and xj and tj respectively represent input data and output data of the model, wherein the input data are the short-wave radiation and the historical photovoltaic power corrected in the step 2), and the output data are that the ultra-short-term photovoltaic power predictions all belong to a real number set R, the mathematical expression of the single hidden layer neural network is
In the formula: kcon is the number of hidden layer nodes; beta is aiIs the output weight; g is an activation function; omegaiThe connection weight between the input neuron and the ith hidden neuron; omegai·xjThe inner product of the connection weight and the data sample; biBias compensation values for the ith hidden layer neuron; t is tjIs an output vector; m is the total number of samples.
Let T be the matrix form of the output vector and β be the output weight vector, an expression of the output vector can be obtained, i.e., T ═ H β form, where H is the hidden layer output matrix of the ELM neural network.
H is in a matrix form
The training process of the ELM model comprises the following steps: firstly, determining the number of neurons, and randomly distributing node parameters; then, the output matrix of the Mth row and the Kcon column of the hidden layer is calculated, and the photovoltaic output power of the output matrix is calculated.
4) Simulation calculation
Simulation input quantity: analyzing the actually measured data of the photovoltaic power station; inputting historical data: inputting numerical weather forecast (NWP) data of predicted months for the first two monthly history powers of each quarter; the data sampling interval is 15min, and a final power ultra-short-term prediction result is obtained according to the steps 1) to 3);
5) evaluation index
Let PmiIs the actual average power of the i period, PpiPredicted power for i period, CiFor the total starting capacity in the period i, the average absolute error is defined as formula (9) if n is the number of all samples:
the root mean square error is defined by equation (11):
and 4), inputting simulation input quantity, and carrying out error calculation on the predicted power calculated by the model and the actual measured power through the average absolute error (9) and the root-mean-square error (10) in the step 5) to obtain the prediction accuracy.
Detailed description of the invention
The method takes measured data and numerical weather forecast (NWP) data of a certain photovoltaic power station as examples for analysis, and the sampling interval is 15 min. The installed capacity of the photovoltaic power station is 30 MW; the results obtained by prediction with different prediction models are shown in fig. 3-6, and fig. 3 is a comparison diagram of the predicted value and the true value in a certain day in spring; FIG. 4 is a schematic diagram showing comparison between predicted values and actual values of a day in summer; FIG. 5 is a schematic diagram showing the comparison between the predicted value and the actual value of a day in autumn; fig. 6 is a schematic diagram showing comparison between a predicted value and a true value in a certain day in winter, and specific evaluation indexes are shown in table 1, so that it can be seen that the prediction effect of the used LSTM-ELM prediction model is superior to that of other prediction methods.
TABLE 1 comparison of evaluation indices of prediction models
Table 1 Comparison of evaluation indexes of prediction model
The description of the present invention is not intended to be exhaustive or to limit the scope of the claims, and those skilled in the art will be able to conceive of other substantially equivalent alternatives, without inventive step, based on the teachings of the embodiments of the present invention, within the scope of the present invention.
Claims (1)
1. A photovoltaic power ultra-short term prediction method based on short wave radiation correction is characterized by comprising the following steps: it comprises the following steps:
1) partitioning of weather types based on Elkan-K-means clustering
Numerical Weather forecast (NWP) information includes 10 meter wind speed, 30 meter wind speed, 50 meter wind speed, 70 meter wind speed, 10 meter wind direction, 30 meter wind direction, 50 meter wind direction, 70 meter wind direction, temperature, 2 meter relative humidity, 2m humidity, long wave radiation, short wave radiation, cloud cover, air pressure, precipitation;
in order to avoid the influence of redundant information on the prediction precision, a pearson correlation coefficient is utilized to preliminarily screen weather forecast meteorological factors and corresponding photovoltaic power, the pearson correlation coefficient of each meteorological factor sequence and the photovoltaic power sequence is respectively solved, a factor showing a positive correlation relation with the photovoltaic power is selected, and short-wave radiation with the maximum person coefficient is selected as the relevant information input;
2) short-wave radiation correction using long-short term memory network (LSTM)
Based on the relevant information extracted in the step 1), judging that when short-wave radiation is predicted, under certain specific environmental conditions, the rule that the predicted value is lower than the actual value is utilized, and a long-short term memory network (LSTM) is adopted to deeply mine complex characteristic information in sample data by utilizing the characteristics of a training sample to correct the data;
the internal calculation process of the long-short term memory network (LSTM) neural unit is as follows:
the method comprises the following steps that a Sigmoid layer of a forgetting gate determines what information is discarded from a cell state, the cell state comprises basic attribute characteristics of photovoltaic power station weather forecast (NWP), therefore, future short wave radiation can be corrected, when new operation data are input, people hope to forget to compare old numerical weather forecast (NWP) distribution information and discard old information from cells, the step of determining is completed through the forgetting gate layer, and a specific function expression of calculation of the forgetting gate is as follows:
ft=sigmoid(θf·[ht-1,xt]+bf) (1)
next, it is decided what information to store in the cell state, first, which values to update are decided by the Sigmoid layer called "input gate layer" using as input the shortwave radiation provided by NWP, 2m humidity, temperature, 10 m wind speed, 70 m wind speed, 30m wind direction, expressed by equation (2), next, a candidate vector Ct is created by a tanh layer, which candidate vector Ct will be added to the state of the cell and expressed by equation (3), and in the next step, the updated values are created by combining equations (2) and (3) and expressed by equation (4),
it=sigmoid(Wi·[ht-1,xt]+bi) (2)
renewal of cell status
Ct=it⊙Ct+ft⊙Ct-1 (4)
This output will be based on the cell state, but will be a filtered version, first running a Sigmoid layer to determine the fraction of the cell state to output, then passing the cell state through tanh, normalizing the value to between-1 and 1, and multiplying it by the output of the Sigmoid gate, two steps to determine the storage of the input in the cell state, expressed by equations (5), 6,
ot=sigmoid(Wo·[ht-1,xt]+bo) (5)
ht=ot·tanh(Ct) (6)
in the formula itIs the output of the input gate or gates,as a candidate for the current layer, C may be added to the cell statetIs the current memory cell state, and the whole process is the process of updating the memory cell state at the previous moment, namely discarding useless information and adding new information, otIs the output of the output gate, htThe final output of the LSTM at the current moment is output, and the corrected short wave radiation at the corresponding time interval is output;
3) training of Extreme learning machine (Extreme learning machine)
If the learning data sample of the ELM is { xj, tj }, and xj and tj respectively represent input data and output data of the model, wherein the input data are the short-wave radiation and the historical photovoltaic power corrected in the step 2), and the output data are that ultra-short-term photovoltaic power predictions all belong to a real number set R, the mathematical expression of the single hidden layer neural network is as follows:
in the formula: kcon is the number of hidden layer nodes; beta is aiIs the output weight; g is an activation function; omegaiThe connection weight between the input neuron and the ith hidden neuron; omegai·xjThe inner product of the connection weight and the data sample; biBias compensation values for the ith hidden layer neuron; t is tjIs an output vector; m is the total number of samples;
setting T as a matrix form of an output vector, and setting beta as an output weight vector, and obtaining an expression of the output vector, namely a form of T ═ H beta, wherein H is a hidden layer output matrix of the ELM neural network;
h is in a matrix form
The training process of the ELM model comprises the following steps: firstly, determining the number of neurons, and randomly distributing node parameters; then, calculating an output matrix of the Mth row and the Kcon column of the hidden layer, and calculating the photovoltaic output power of the output matrix;
4) simulation calculation
Simulation input quantity: analyzing the actually measured data of the photovoltaic power station; inputting historical data: inputting numerical weather forecast (NWP) data of predicted months for the first two monthly history powers of each quarter; the data sampling interval is 15min, and a final power ultra-short-term prediction result is obtained according to the steps 1) to 3);
5) evaluation index
Let PmiIs the actual average power of the i period, PpiPredicted power for i period, CiFor the total starting capacity in the period i, the average absolute error is defined as formula (9) if n is the number of all samples:
the root mean square error is defined by equation (11):
and 4), inputting simulation input quantity, and carrying out error calculation on the predicted power calculated by the model and the actual measured power through the average absolute error (9) and the root-mean-square error (10) in the step 5) to obtain the prediction accuracy.
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