CN112633565B - Photovoltaic power set interval prediction method - Google Patents
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Abstract
The invention discloses a photovoltaic power set interval prediction method, which comprises the following steps: judging the non-stationary period of the preprocessed historical photovoltaic power to obtain the photovoltaic power of the non-stationary period; extracting characteristics of the photovoltaic power in a non-stationary period to obtain an input variable; respectively carrying out point prediction and interval prediction of the photovoltaic power according to the input variables to obtain a point prediction result and an interval prediction result; respectively carrying out multi-objective optimization on the point prediction result and the interval prediction result to obtain a first optimization interval and a second optimization interval; and performing multi-objective optimization by taking the first optimization interval, the second optimization interval and the actual photovoltaic power of the predicted point as inputs together to obtain a photovoltaic power predicted interval. The effect is excellent, the prediction precision is remarkably improved, and the scheduling system can evaluate the fluctuation condition of the photovoltaic output more accurately.
Description
Technical Field
The invention belongs to the technical field of photovoltaic power prediction, and relates to a photovoltaic power set interval prediction method.
Background
The photovoltaic power prediction technology is a technology for predicting photovoltaic output power at a future moment according to conditions such as operating parameters, meteorological features and the like of a photovoltaic power station. In recent years, the most widely used method is an artificial intelligence method, and the relation between input variables implied in historical data and a prediction result is mined through machine learning, so that the prediction of photovoltaic power is realized. However, the photovoltaic power generation is greatly influenced by meteorological factors, when meteorological conditions are unstable in a prediction period, a photovoltaic output curve is not smooth, peak-valley difference is large, and at the moment, the prediction accuracy of a traditional prediction model is poor.
Disclosure of Invention
The invention aims to provide a photovoltaic power set interval prediction method, which solves the problem of poor prediction precision in the prior art.
The technical scheme adopted by the invention is that the photovoltaic power set interval prediction method comprises the following steps:
step 1, preprocessing data of historical photovoltaic power;
step 2, judging the non-stationary period of the historical photovoltaic power processed in the step 1 to obtain the photovoltaic power of the non-stationary period;
step 3, extracting characteristics of the photovoltaic power in a non-stationary period to obtain an input variable;
step 4, respectively carrying out point prediction and interval prediction of the photovoltaic power according to the input variable to obtain a point prediction result and an interval prediction result;
step 5, respectively carrying out multi-objective optimization on the point prediction result and the interval prediction result to obtain a first optimization interval and a second optimization interval;
and 6, performing multi-objective optimization by taking the first optimization interval, the second optimization interval and the actual photovoltaic power of the predicted point as inputs together to obtain a photovoltaic power predicted interval.
The invention is also characterized in that:
the specific process of the step 1 is as follows: and detecting and eliminating abnormal data of the historical photovoltaic power by adopting a central limit theorem, and filling the abnormal data by using a K nearest neighbor full algorithm and a Euclidean distance method.
And 2, judging the non-stationary time period by adopting a spoke power ratio difference judging method.
The step 3 specifically comprises the following steps:
the MIC value of each influencing factor variable of the photovoltaic power in the non-stationary period is calculated respectively, the influencing factor variable with the larger MIC value is selected as an input variable, and the MIC value is calculated in the following manner:
in the above formula, x represents a characteristic factor, y represents a photovoltaic output, a and B are the number of dividing lattices in x and y directions respectively, and B is a variable.
The point prediction method of the photovoltaic power in the step 4 is as follows:
training an LSTM model by taking an input variable as input to obtain a first layer of base learner; then training the first layer base learner by taking a prediction result output by the first layer base learner and an input variable as inputs together to obtain a Stack-LSTM model; and inputting the input variable into a Stack-LSTM model for prediction to obtain a point prediction result.
And 4, inputting the input variable into a BAYES neural network for prediction to obtain a section prediction result.
In step 6, the first optimization interval, the second optimization interval and the actual photovoltaic power of the predicted point are taken as inputs together, and multi-objective optimization is performed by adopting an NSGA-II optimization algorithm to obtain a photovoltaic power predicted interval.
The beneficial effects of the invention are as follows:
according to the photovoltaic power set interval prediction method, the historical data is preprocessed, the MIC theory is adopted for feature selection, irrelevant variables can be eliminated, so that the calculated amount is reduced, and the model training process is quickened; the NSGA-II network model carries out multi-objective optimization on the prediction result, so that the overall prediction precision is improved; the aggregate probability prediction model has excellent effect, remarkably improves prediction precision, and can evaluate fluctuation condition of photovoltaic output more accurately by a scheduling system.
Drawings
FIG. 1 is a flow chart of a photovoltaic power collection interval prediction method of the present invention;
FIG. 2 is a graph of a power station 1-point prediction result by adopting a Stack-LSTM model in a photovoltaic power set interval prediction method;
FIG. 3 is a graph of a 2-point prediction result of a power station adopting a Stack-LSTM model in a photovoltaic power set interval prediction method;
FIG. 4 is a graph of a power station 1-point prediction result using an LSTM model in a photovoltaic power set interval prediction method of the present invention;
FIG. 5 is a graph of a 2-point prediction result of a power station using an LSTM model in a photovoltaic power set interval prediction method of the present invention;
FIG. 6 is a graph of a power station 1-point prediction result using an ANN model in a photovoltaic power set interval prediction method according to the present invention;
FIG. 7 is a graph of a power station 2-point prediction result of an ANN model in a photovoltaic power set interval prediction method;
FIG. 8 is a graph of the prediction result of the section of the power station 1 in the photovoltaic power set section prediction method of the present invention;
FIG. 9 is a graph of the prediction result of the power station 2 interval in the photovoltaic power set interval prediction method of the present invention;
FIG. 10 is a flowchart of an NSGA-II optimization algorithm in a photovoltaic power collection interval prediction method according to the present invention;
FIG. 11 is a comparison chart of the prediction results before and after the power station 1 is optimized in the photovoltaic power set interval prediction method;
fig. 12 is a comparison chart of prediction results before and after the power station 2 is optimized in the photovoltaic power set interval prediction method.
Detailed Description
The invention will be described in detail below with reference to the drawings and the detailed description.
A photovoltaic power set interval prediction method, as shown in figure 1, comprises the following steps:
step 1, preprocessing data of historical photovoltaic power;
and detecting and eliminating abnormal data of the historical photovoltaic power by adopting a central limit theorem, and filling the abnormal data by using a K nearest neighbor full algorithm and a Euclidean distance method.
And 2, the photovoltaic output is greatly influenced by factors such as weather, irradiance and the like, the periodicity is strong, and the common output curve type has a stable output type and a non-stable output type. Judging the non-stationary period of the historical photovoltaic power processed in the step 1 by adopting a radiation power ratio difference judging method to obtain the photovoltaic power in the non-stationary period;
step 2.1, a given parameter is the radiation power ratio difference, and the calculation formula is as follows:
wherein: s is S t-1 Irradiance for the t-1 th sample in the data samples; s is S t Irradiance for the t-th sample in the data samples;
step 2.2, pair X according to the following t Dividing a value range:
step 2.3, pair X t The value of (3) is determined,when X is t Take the value of [0.7,1.3 ]]And when the output is in the upper period, the output is considered to be the stable output period, and the other conditions are all non-stable output periods.
Step 3, carrying out feature extraction on the photovoltaic power in a non-stationary period by adopting an MIC method to obtain an input variable; the MIC method specifically comprises the following steps: dividing the current two-dimensional space into a certain interval number in the x-y direction respectively, and checking the falling condition of the current scattered points in each square, namely calculating the joint probability;
the specific process is as follows: the MIC values of the influencing factor variables of the photovoltaic power in the non-stationary period are calculated respectively, the influencing factor variable with the larger MIC value is selected as an input variable, irradiance, humidity, temperature and wind speed are selected as the input variables in the embodiment, and the MIC values are calculated in the following manner:
in the above formula, x represents a characteristic factor, y represents photovoltaic output, a and B respectively divide the number of lattices in the x and y directions, and B is a variable;
step 4, respectively carrying out point prediction and interval prediction of the photovoltaic power according to the input variable to obtain a point prediction result and an interval prediction result;
step 4.1, training an LSTM model by taking an input variable as input to obtain a first layer of base learner; then training the first layer of base learner by taking a prediction result output by the first layer of base learner and an input variable as inputs to obtain a second layer of base learner, namely a Stack-LSTM model; inputting an input variable into a Stack-LSTM model for prediction to obtain a point prediction result;
specifically, step 4.1.1, data set is collectedDivided into n subsets I 1 ,I 2 ,......I n ；
Step 4.1.2, based on the n subsets, respectively inputting the n subsets into an LSTM algorithm to obtain a first prediction result w 1 ,w 2 ,......w n ；
Step 4.1.3, adding the first prediction result as an additional feature into the original feature to form a new input feature x' 1 ,x′ 2 ,......x′ l ＝(x 1 ,x 2 ,......x l ,w 1 ,w 2 ,......w n ) And inputting the result into the LSTM algorithm again, and carrying out second prediction to obtain a result with higher precision.
And 4.2, inputting the input variable into the BAYES neural network for prediction to obtain a section prediction result. Under complex weather conditions, the short-time output of the photovoltaic power station is unstable, the prediction precision of the deterministic prediction method is obviously reduced, and compared with deterministic prediction, the interval prediction is carried out by using a Bayesian (BAYES) neural network, so that all possible output value interval distribution of the photovoltaic equipment at the prediction moment can be given.
Step 5, respectively carrying out multi-objective optimization on the point prediction result and the interval prediction result by adopting an NSGA-II optimization algorithm to obtain a first optimization interval and a second optimization interval;
and 6, performing multi-objective optimization by taking the first optimization interval, the second optimization interval and the actual photovoltaic power of the predicted point as inputs together to obtain a photovoltaic power predicted interval.
The NSGA-II optimization algorithm performs multi-objective optimization. Firstly, randomly generating an initial population with a scale of N, and obtaining a first generation of offspring population through three basic operations of selection, crossing and mutation of a genetic algorithm after non-dominant sorting; secondly, starting from the second generation, merging the parent population and the child population, carrying out rapid non-dominant sorting, simultaneously carrying out crowding degree calculation on individuals in each non-dominant layer, and selecting proper individuals to form a new parent population according to non-dominant relations and the crowding degree of the individuals; finally, generating a new offspring population through basic operations of the genetic algorithm; and so on until the condition for ending the program is satisfied, the specific flowchart is shown in fig. 10.
Step 6.1, taking the actual photovoltaic power of the first optimization interval, the second optimization interval and the predicted point as input;
step 6.2, constructing a basic NSGA-II network model through input, and performing multi-objective optimization, wherein the optimization objectives are minimum interval width (PINAW) and maximum interval coverage (PICP);
and 6.3, verifying the NSGA-II network model.
Examples
The invention selects the data provided by the new second photovoltaic forecast large-competition official of the national energy day to conduct forecast research, selects the output data of the power station 1 and the power station 2 in 2017 year and year, wherein the original data are the data in 2017 year and year, the data time step is 15 minutes, the data are sampled in 24 hours a day, the total data of 32848 groups of power station 1 and the total data of 33060 groups of power station 2. Due to the periodicity and intermittence of the photovoltaic output, the invention only selects the data at the time of generating electricity in the daytime, eliminates the data at the time of generating electricity at night, processes and repairs the abnormal data, the missing data and the error data in the data, and finally the power station 1 totally reserves 16018 groups of effective data, and the power station 2 totally reserves 16427 groups of effective data. The first 90% is the training set and the last 10% is the test set.
Through the optimization processing of the invention, the prediction precision is greatly improved, and in order to quantify the degree of improvement of the uncertainty, specific data pair is shown in a table 1:
table 1 multi-objective optimization front-to-back comparison table
As can be seen from table 1, the interval width is reduced by 10% -20% compared to the non-optimized prediction model under the same interval coverage, which means that the prediction accuracy is improved by at least 10%. Compared with the boundary estimation theory method, under the same interval coverage rate, the prediction model method disclosed by the invention has the advantages that after multi-objective optimization is carried out on a deterministic prediction result and an interval prediction result, as shown in figures 11-12, the prediction precision is improved by more than 20%, and the prediction precision of photovoltaic power prediction is obviously improved.
Two groups of data from different sources are selected for verification by a Stack-LSTM model and a BAYES neural network respectively, and as apparent from figures 2-7, the Stack-LSTM model has the highest curve fitting degree and the highest prediction precision compared with the LSTM model and the ANN model. 8-9, compared with deterministic point prediction, in the non-stationary output period, the interval prediction can predict the possible output condition of the point as much as possible, which can enable the scheduling system to adjust the scheduling strategy in time and ensure the safe and stable operation of the power grid to the greatest extent.
Through the mode, the photovoltaic power set interval prediction method is used for preprocessing historical data, and adopting the MIC theory to perform feature selection, so that irrelevant variables can be eliminated, the calculated amount is reduced, and the model training process is quickened; the NSGA-II network model carries out multi-objective optimization on the prediction result, so that the overall prediction precision is improved; the aggregate probability prediction model has excellent effect, remarkably improves prediction precision, and can evaluate fluctuation condition of photovoltaic output more accurately by a scheduling system.
Claims (5)
1. The photovoltaic power set interval prediction method is characterized by comprising the following steps of:
step 1, preprocessing data of historical photovoltaic power;
step 2, judging the non-stationary period of the historical photovoltaic power processed in the step 1 to obtain the photovoltaic power of the non-stationary period;
step 3, extracting characteristics of the photovoltaic power in the non-stationary period to obtain an input variable;
step 4, respectively carrying out point prediction and interval prediction of the photovoltaic power according to the input variables to obtain a point prediction result and an interval prediction result;
step 5, respectively carrying out multi-objective optimization on the point prediction result and the interval prediction result to obtain a first optimization interval and a second optimization interval;
step 6, performing multi-objective optimization by taking the first optimization interval, the second optimization interval and the actual photovoltaic power of the predicted point as inputs together to obtain a photovoltaic power predicted interval;
in the step 2, a spoke power ratio difference discrimination method is adopted to discriminate a non-stationary period;
the step 3 specifically comprises the following steps:
the MIC value of each influencing factor variable of the photovoltaic power in the non-stationary period is calculated respectively, the influencing factor variable with the larger MIC value is selected as an input variable, and the MIC value is calculated in the following manner:
（3）；
in the above formula, x represents a characteristic factor, y represents a photovoltaic output, a and B are the number of dividing lattices in x and y directions respectively, and B is a variable.
2. The method for predicting a photovoltaic power set interval according to claim 1, wherein the specific process in step 1 is as follows: and detecting and eliminating abnormal data of the historical photovoltaic power by adopting a central limit theorem, and filling the abnormal data by using a K nearest neighbor full algorithm and a Euclidean distance method.
3. The method for predicting a photovoltaic power set interval according to claim 1, wherein the method for predicting a point of photovoltaic power in step 4 is as follows:
training an LSTM model by taking the input variable as input to obtain a first layer of base learner; then training the first layer base learner by taking a prediction result output by the first layer base learner and an input variable as inputs together to obtain a Stack-LSTM model; and inputting the input variable into a Stack-LSTM model for prediction to obtain a point prediction result.
4. The method for predicting the interval of the photovoltaic power set according to claim 1, wherein in step 4, the input variable is input into a bayer neural network for prediction, so as to obtain an interval prediction result.
5. The method for predicting a photovoltaic power set interval according to claim 1, wherein in step 6, the actual photovoltaic power of the first optimization interval, the second optimization interval and the predicted point is taken as input together, and a NSGA-II optimization algorithm is adopted to perform multi-objective optimization to obtain a photovoltaic power prediction interval.
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