CN112927097A - Photovoltaic power generation short-term prediction method based on GRA-ABC-Elman model - Google Patents
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Abstract
The invention discloses a short-term photovoltaic power generation power prediction method based on a GRA-ABC-Elman model, which mainly comprises the following steps: collecting historical power generation power data of a photovoltaic power station and meteorological information of a meteorological station in a corresponding time period as meteorological and power sample data; removing abnormal and repeated data in meteorological data, performing grey correlation degree analysis on all features, and extracting important features as a training input set of a neural network; establishing an Elman neural network, determining a network topology structure, and initializing various parameters of the network topology structure; continuously optimizing the weight and the threshold of the Elman neural network by using an artificial bee colony algorithm; establishing a photovoltaic power generation short-term prediction model by using the optimized network and inputting meteorological-power sample data for training; and inputting the meteorological information of the day to be predicted into the trained prediction model, and predicting the generated power of the day to be predicted. The method can accurately predict the short-term power generation power of the photovoltaic power station, and is convenient for the dispatching and operation of the power grid.
Description
Technical Field
The invention relates to the field of photovoltaic power generation, in particular to a short-term photovoltaic power generation power prediction method based on a GRA-ABC-Elman model.
Background
In recent years, the problem of environmental pollution is becoming more serious, the requirements of countries in the world on energy conservation and emission reduction are continuously improved, and the global demand for electric energy is increasing year by year, so that the development and utilization of renewable energy are important ways for solving the energy crisis. The photovoltaic power generation rapidly becomes a hot point of world attention and research with unique charm of cleanness, no pollution and inexhaustibility, however, the output of the photovoltaic power generation has strong fluctuation and intermittency due to meteorological environment factors, the stability of the photovoltaic power generation after grid connection is influenced, and the photovoltaic power generation is difficult to be applied in a large scale. Therefore, the generated power of the photovoltaic power station is predicted, so that the adverse effect of photovoltaic grid connection on a power system can be reduced; and the photovoltaic power generation is combined with power grid dispatching, load distribution and the like, the whole power system can be planned, the stability and the utilization rate of the system are greatly improved, and the method has important significance for photovoltaic power stations and power systems.
The photovoltaic power generation output power can adopt various prediction methods. According to the source of the prediction information, a physical method, a statistical method and an artificial intelligence method can be divided. The physical prediction method is to predict by means of a sunlight irradiation transfer equation, a photovoltaic module operation equation and the like according to geographic information and meteorological information of the position of the photovoltaic power station. The method does not depend on historical data, can predict data acquired on site according to geographic information and parameters of the photovoltaic module, but is difficult to model, and the parameters of the photovoltaic module can change slowly, so that the method is limited in anti-interference capability and not strong in robustness. A representative physical prediction method includes: ASHRAE model on sunny days, HOTTEL model, REST model, Nielsen model with cloud weather, etc. Different from a physical method, the statistical prediction method depends on historical data, and searches a statistical rule according to input historical meteorological data, power data and operation conditions to predict the output power. A representative statistical prediction method includes: time series methods, regression analysis methods, grey theory methods, fuzzy theory methods, space-time correlation methods, and the like.
In recent years, an artificial intelligence prediction method mainly based on an artificial neural network has attracted much attention, is an operational model formed by interconnection of a large number of nodes, has high fitting and generalization capabilities, and has the characteristic of short training time. However, the conventional neural network only has feedforward and no feedback, has poor sensitivity to historical data and weak capability of processing dynamic information, and does not have the capability of adapting to time-varying characteristics. Therefore, the Elman neural network is adopted, and the network is characterized in that a carrying layer is added to an implicit layer on the basis of the traditional BP network and serves as a one-step delay operator to achieve the purpose of memorizing, so that various defects and problems of the traditional neural network can be effectively solved.
The photovoltaic power station short-term power prediction method is various, a hybrid improved Kmeans-GRA-Elman algorithm is a model with a novel prediction method and an accurate prediction result, but compared with the method for performing parameter optimization on the weight and the threshold of the Elman neural network by using the artificial bee colony algorithm, the method effectively solves the problems that the network generalization capability is not strong and the Elman neural network is easy to fall into a minimum value and the like due to random initialization of the artificial neural network weight and the threshold, the development capability of the bee colony algorithm is enhanced, the optimal solution of the Elman neural network weight and the threshold can be quickly found, and the predicted photovoltaic power station short-term power is more accurate and effective. At present, no study for applying the GRA-ABC-Elman model to photovoltaic power station short-term power prediction is found in published documents and patents.
Disclosure of Invention
In view of the above, the invention aims to provide a short-term photovoltaic power generation power prediction method based on a GRA-ABC-Elman model, which can predict the short-term photovoltaic power generation power more quickly and accurately.
In order to solve the problems in the prior art, the technical scheme adopted by the invention is as follows:
the photovoltaic power generation short-term prediction method based on the GRA-ABC-Elman model comprises the following steps:
collecting historical power generation power data of a photovoltaic power station and meteorological information of a meteorological station in a corresponding time period as meteorological and power sample data;
eliminating abnormal and repeated data in the meteorological data, performing grey correlation degree analysis on all features in the meteorological data, and extracting important features in the meteorological data to be used as a training input set of an Elman neural network;
establishing an Elman neural network, determining the network topology structure and the number of nodes of an input layer, an output layer and a hidden layer, and initializing all parameters of the Elman neural network;
fourthly, continuously optimizing the weight and the threshold of the Elman neural network by utilizing an artificial bee colony algorithm;
establishing a photovoltaic power generation short-term prediction model by using the optimized neural network, and inputting meteorological and power sample data to train the prediction model;
sixthly, inputting the weather information of the day to be predicted, which is obtained from the numerical weather forecast NWP, into the trained prediction model, and predicting the power generation power of the day to be predicted.
Further, the meteorological information provided by the meteorological station in the step (i) mainly comprises illumination radiance, temperature, humidity, wind speed and atmospheric pressure.
Further, the grey correlation analysis is performed on all the characteristics in the meteorological data in the step two, and the method mainly comprises the following steps:
a. data normalization:
in the formula (1), xA(i) Representing a value obtained after the meteorological sample data is normalized; x is the number ofmax、xminRespectively representing the maximum value and the minimum value in the meteorological sample data, wherein x represents the meteorological sample data value at a certain moment;
b. and (3) calculating a correlation coefficient:
in the formula (2), ζA(i) Representing a correlation coefficient; rho represents an adjustable coefficient for controlling the discrimination of the correlation coefficient;
c. calculating the relevance:
d. and extracting the important features with the relevance close to 1 as an Elman neural network training input set.
Further, the nonlinear state space expression of the Elman neural network established in the third step is as follows:
y(k)＝g(w2h(k)) (4)
h(k)＝f(w1u(k-1)+w3xc(k)) (5)
xc(k)＝h(k-1) (6)
in the formulas (4), (5) and (6), f (—) is the excitation function of hidden layer neurons; g (—) is the excitation function of the neurons of the output layer; y is an m-dimensional output node vector; h is an n-dimensional hidden layer node unit vector; u is an r-dimensional input node vector; x is the number ofcFeeding back a state vector for the n-dimensional carrying layer; w is a1，w2，w3Respectively representing the weight from the input layer to the hidden layer, the weight from the hidden layer to the output layer, and the weight from the receiving layer to the hidden layer.
Further, the step IV of continuously optimizing the weight and the threshold of the Elman neural network by using the artificial bee colony algorithm comprises the following steps:
a. initializing a honey source vector:
xmi＝li+rand(0，1)*(ui-li) (7)
in the formula (7), xmiRepresenting all honey source vectors, wherein m is 1,2.. N represents a total of N solution vectors, and i is 1,2.. N represents that each solution vector contains N variables; u. ofiAnd liRespectively represent xmiMaximum and minimum boundary values of;
calculating the value fit of each initial honey source fitnessm(xm) The calculation formula is as follows:
b. bee hiring stage:
in the formula (9), vmiRepresenting a new honey source vector found by the employed bees; x is the number ofkiRepresenting a randomly selected honey source;
a random number representing a degree of control disturbance;
c. following the bee stage:
in the formula (10), PmA probability indicating whether a following bee chooses to follow a employed bee;
d. and (3) a bee scouting stage:
formula (9) is calculated together with formula (9) for hiring bees to find new honey sources.
The invention has the advantages and beneficial effects that:
according to the photovoltaic power generation short-term prediction method based on the GRA-ABC-Elman model, parameter optimization is carried out on the weight and the threshold of the Elman neural network by using the artificial bee colony algorithm, the problems that the network generalization capability is not strong and the Elman neural network is easy to fall into a minimum value and the like due to random initialization of the weight and the threshold of the artificial neural network are effectively solved, the development capability of the bee colony algorithm is enhanced, the optimal solution of the weight and the threshold of the Elman neural network can be quickly found, the photovoltaic power generation short-term power generation power can be quickly and accurately predicted, and the photovoltaic power generation short-term prediction method has the advantages of time-varying characteristic adapting capability and high sensitivity. The method is combined with power grid dispatching, load distribution and the like, and the whole power system can be planned and operated, so that the stability and the utilization rate of the system are greatly improved, and the dispatching operation of the power grid is facilitated.
Drawings
The invention is described in detail below with reference to the following figures and examples:
FIG. 1 is a flow chart of an implementation process of the photovoltaic power generation short-term prediction method based on the GRA-ABC-Elman model;
FIG. 2 is a flow chart of data preprocessing of the photovoltaic power generation power short-term prediction method based on the GRA-ABC-Elman model;
FIG. 3 is a schematic structural diagram of an Elman neural network;
FIG. 4 is a flow chart of an artificial bee colony optimization algorithm;
Detailed Description
As shown in FIG. 1, the photovoltaic power generation power short-term prediction method based on the GRA-ABC-Elman model, wherein GRA refers to grey correlation analysis, ABC refers to artificial bee colony algorithm, comprises the following steps:
collecting historical power generation power data of a photovoltaic power station and meteorological information of a meteorological station in a corresponding time period as meteorological and power sample data. The meteorological information provided by the meteorological station mainly comprises illumination radiance, temperature, humidity, wind speed and atmospheric pressure.
And secondly, eliminating abnormal and repeated data in the meteorological data, and performing grey correlation degree analysis on all the features to extract important features as a training input set of the neural network.
And thirdly, establishing an Elman neural network, determining a network topology structure and initializing various parameters of the network topology structure.
And fourthly, continuously optimizing the weight and the threshold of the Elman neural network by utilizing an artificial bee colony algorithm.
And fifthly, establishing a photovoltaic power generation short-term prediction model by using the optimized network and inputting meteorological-power sample data for training.
Sixthly, inputting the weather information of the day to be predicted, which is obtained from the numerical weather forecast NWP, into the trained prediction model, and predicting the power generation power of the day to be predicted.
As shown in fig. 2, the grey correlation analysis of all the features in the weather sample data in the second step mainly includes the following steps:
a. and (4) normalizing the data, reducing the difference of absolute numerical values of the data, and scaling various data to a range of [0, 1 ]. The specific formula is as follows:
in the formula (1), xA(i) And representing the value obtained by normalizing the meteorological sample data at the ith moment of the characteristic A. x is the number ofmaxRepresents the maximum value, x, in the meteorological sample data at the ith timeminAnd x represents the value of the meteorological sample data at the ith moment.
b. And (3) calculating a correlation coefficient:
in the formula (2), ζA(i) Representing the correlation coefficient of the ith moment in the characteristic A sample data; rho represents an adjustable coefficient for controlling the discrimination of the correlation coefficient, and the value is 0.5.
c. Carrying out average calculation on each correlation coefficient to obtain the correlation degree, wherein the calculation formula is as follows;
d. and extracting the important features with the relevance close to 1 as an Elman neural network training input set.
As shown in fig. 3, the nonlinear state space expression of the Elman neural network established in step three is:
y(k)＝g(w2h(k)) (4)
h(k)＝f(w1u(k-1)+w3xc(k)) (5)
xc(k)＝h(k-1) (6)
in the formulas (4), (5) and (6), f (—) is the excitation function of hidden layer neurons; g (—) is the excitation function of the neurons of the output layer; y ═ y1，y2，...，ym]Outputting a node vector for m dimensions; h ═ h1，h2，...，hn]A hidden layer node unit vector is expressed in n dimensions; u ═ u1，u2，...，ur]Inputting a node vector in an r dimension; x is the number ofc＝[xc1，xc2，...，xcn]Feeding back a state vector for the n-dimensional carrying layer; w is a1，w2，w3Respectively representing the weight from the input layer to the hidden layer, the weight from the hidden layer to the output layer, and the weight from the receiving layer to the hidden layer.
As shown in fig. 4, the step of optimizing the weight and the threshold of the Elman neural network by the artificial bee colony algorithm in the step (iv) is as follows:
a. initializing all honey source vectors, wherein the initialization parameters comprise: the number and the position of honey sources, the maximum iteration number iter max and a threshold limit;
xmi＝li+rand(0，1)*(ui-li) (7)
in the formula (7), xmiRepresenting all honey source vectors, wherein m is 1,2.. N represents a total of N solution vectors, and i is 1,2.. N represents that each solution vector contains N variables; u. ofiAnd liRespectively represent xmiMaximum and minimum boundary values.
Calculating the value fit of each initial honey source fitnessm(xm) The calculation formula is as follows, and the value of the honey source fitness represents the quality of the generated solution;
b. employing bees to search for a new honey source, namely generating a new solution;
in the formula (9), vmiRepresenting a new source of honey found by the employed bees; x is the number ofkiRepresenting a randomly selected honey source;
a random number representing a degree of control disturbance, taken as [ -1, 1 [ ]]。
Calculating the value fit of the fitness of the new solutionm(xm) Comparing the solution with the old solution, and if the fitness value of the new solution is better than that of the old solution, hiring bees to remember that the new solution forgets the old solution; otherwise, the old solution will be retained.
c. A bee following stage;
the follower bees in the honeycomb analyze the information brought back by the hired bee, and a roulette strategy is adopted to select the honey source for tracking exploitation, and the calculation formula is as follows:
in the formula (10), PmA probability indicating whether a following bee chooses to follow a employed bee; fitm(xm) Representing the value of the fitness of the honey source.
If the calculated selection probability is larger than the randomly generated numerical value, the following bees select to follow the employed bees, otherwise, the following bees are converted into scout bees, and a new honey source is searched.
d. And (3) a bee scouting stage:
the honey source has a parameter trim, the initial value is 0, and the value of the mined primary trim is added with 1. If a honey source is mined for a plurality of times, namely the value of the real is too high and exceeds the preset threshold limit, the honey source needs to be abandoned, and the scout bees, which are also employed bees, need to find a new food source v againmi. Formula (9) is calculated together with formula (9) for hiring bees to find new honey sources.
Claims (5)
1. The photovoltaic power generation short-term prediction method based on the GRA-ABC-Elman model is characterized by comprising the following steps of:
collecting historical power generation power data of a photovoltaic power station and meteorological information of a meteorological station in a corresponding time period as meteorological and power sample data;
eliminating abnormal and repeated data in the meteorological data, performing grey correlation degree analysis on all features in the meteorological data, and extracting important features in the meteorological data to be used as a training input set of an Elman neural network;
establishing an Elman neural network, determining the network topology structure and the number of nodes of an input layer, an output layer and a hidden layer, and initializing all parameters of the Elman neural network;
fourthly, continuously optimizing the weight and the threshold of the Elman neural network by utilizing an artificial bee colony algorithm;
establishing a photovoltaic power generation short-term prediction model by using the optimized neural network, and inputting meteorological and power sample data to train the prediction model;
sixthly, inputting the weather information of the day to be predicted, which is obtained from the numerical weather forecast NWP, into the trained prediction model, and predicting the power generation power of the day to be predicted.
2. The GRA-ABC-Elman model-based short-term prediction method of photovoltaic power generation power as claimed in claim 1, wherein: the meteorological information provided by the meteorological station in the step I mainly comprises illumination radiance, temperature, humidity, wind speed and atmospheric pressure.
3. The GRA-ABC-Elman model-based short-term prediction method of photovoltaic power generation power as claimed in claim 1, wherein: performing grey correlation analysis on all the characteristics in the meteorological data in the second step, mainly comprising the following steps:
a. data normalization:
in the formula (1), xA(i) Representing a value obtained after the meteorological sample data is normalized; x is the number ofmax、xminRespectively representing the maximum value and the minimum value in the meteorological sample data, wherein x represents the meteorological sample data value at a certain moment;
b. and (3) calculating a correlation coefficient:
in the formula (2), ζA(i) Representing a correlation coefficient; rho represents an adjustable coefficient for controlling the discrimination of the correlation coefficient;
c. calculating the relevance:
d. and extracting the important features with the relevance close to 1 as an Elman neural network training input set.
4. The GRA-ABC-Elman model-based short-term prediction method of photovoltaic power generation power as claimed in claim 1, wherein: the nonlinear state space expression of the Elman neural network established in the step three is as follows:
y(k)＝g(w2h(k)) (4)
h(k)＝f(w1u(k-1)+w3xc(k)) (5)
xc(k)＝h(k-1) (6)
in the formulas (4), (5) and (6), f (—) is the excitation function of hidden layer neurons; g (—) is the excitation function of the neurons of the output layer; y is an m-dimensional output node vector; h is an n-dimensional hidden layer node unit vector; u is an r-dimensional input node vector; x is the number ofcFeeding back a state vector for the n-dimensional carrying layer; w is a1，w2，w3Respectively representing the weight from the input layer to the hidden layer, the weight from the hidden layer to the output layer, and the weight from the receiving layer to the hidden layer.
5. The GRA-ABC-Elman model-based short-term prediction method of photovoltaic power generation power as claimed in claim 1, wherein: the step IV of continuously optimizing the weight and the threshold of the Elman neural network by utilizing the artificial bee colony algorithm comprises the following steps:
a. initializing a honey source vector:
xmi＝li+rand(0,1)*(ui-li) (7)
in the formula (7), xmiRepresenting all honey source vectors, wherein m is 1,2.. N represents a total of N solution vectors, and i is 1,2.. N represents that each solution vector contains N variables; u. ofiAnd liRespectively represent xmiMaximum and minimum boundary values of;
calculating the value fit of each initial honey source fitnessm(xm) The calculation formula is as follows:
b. bee hiring stage:
in the formula (9), vmiRepresenting a new honey source vector found by the employed bees; x is the number ofkiRepresenting a randomly selected honey source;
a random number representing a degree of control disturbance;
c. following the bee stage:
in the formula (10), PmA probability indicating whether a following bee chooses to follow a employed bee;
d. and (3) a bee scouting stage:
in the formula (11), vmiRepresenting a new honey source vector found by the employed bees; x is the number ofkiRepresenting a randomly selected honey source;
representing a random number that controls the degree of disturbance.
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| CN113507128A (en) * | 2021-07-14 | 2021-10-15 | 国网青海省电力公司 | Near-field reactive power optimization configuration method for extra-high voltage direct current converter station |
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