CN106650784A - Feature clustering comparison-based power prediction method and device for photovoltaic power station - Google Patents
Feature clustering comparison-based power prediction method and device for photovoltaic power station Download PDFInfo
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Abstract
The present invention relates to a feature clustering comparison-based power prediction method and a device for a photovoltaic power station and belongs to the technical field of photovoltaic power generation. According to the technical scheme of the method, firstly, three most significant feature amounts that influence the prediction precision of the photovoltaic power are acquired and accumulated as historical meteorological data. Secondly, the obtained historical meteorological data are adopted as data samples to be clustered, so that the samples are divided into k types of high similarity. Meanwhile, the cluster center of each type of samples is obtained. Thirdly, a prediction model of a corresponding type is established based on the historical data of each type. Fourthly, a corresponding prediction model nearest to the cluster center of a current object is selected for prediction. In this way, different meteorological data are divided into different types of samples. Meanwhile, photovoltaic output prediction models in different meteorological conditions are established, so that the prediction models are trained in the more targeted manner. Moreover, the power prediction is conducted based on the power prediction models established in different meteorological conditions. Therefore, the prediction accuracy of the photovoltaic power is improved.
Description
Technical field
The present invention relates to the predicting power of photovoltaic plant method and device that a kind of feature based cluster compares, belongs to photovoltaic and sends out
Electro-technical field.
Background technology
Photovoltaic generation worldwide all receives extensive attention, Chinese large-sized ground as one kind of clean energy resource
Face photovoltaic plant, distributed roof photovoltaic power station is more and more, but because photovoltaic generation has fluctuation and intermittence,
Very big difficulty is brought to dispatching of power netwoks, the development of photovoltaic power prediction effectively alleviates this problem, but at present on the market
Photovoltaic power predicated error it is larger, it is difficult to dispatching of power netwoks effectively referring to.
At present, China has carried out portion of techniques research, existing forecast model for the power prediction of photovoltaic plant
Including neural network model, RBF model and Multilayer Perception model etc..Wherein neural network model is most widely used,
The effect of neural network model various neurons in input layer, hidden layer and output layer generates output quantity, then is with error
Object function is constantly corrected to network weight until error reaches requirement, it is trained after network can be carried out prediction
Photovoltaic module is power output.But, current neural network model can only control predicated error 20% or so, if running into
Thunderstorm gale weather, precision of prediction can be worse, wants further to improve precision of prediction, and rational pre- place need to be done to input data
Reason, ensures in addition the accuracy of the historical data of training sample.
It is entitled《Based on weather pattern cluster and the photovoltaic power generation output forecasting of LS-SVM》Paper in provide a kind of prediction side
Method, the method is clustered history meteorological data by season type, obtains four kinds of different types cluster sample in each season, is formed
Corresponding forecast model；During actual prediction, first the season according to belonging to the date to be predicted determines, found by day Meteorological Characteristics to be predicted
Corresponding prediction submodel, is predicted, although a kind of sample range that reduces is given in article seeks optimum prediction submodel
The Forecasting Methodology being predicted, but because the date to be predicted is to divide season according to the time, seek to predict submodel, it is such
Division methods have certain subjectivity and mandatory, and then also limit the process for seeking optimum prediction model, part sample
Data sample data especially within the season alternation time period occurs unavoidably because seeking to cause less than optimum prediction model in advance
Survey the situation that precision is reduced.
The content of the invention
It is an object of the invention to provide a kind of predicting power of photovoltaic plant method that feature based cluster compares, and it is huge
Current predicting power of photovoltaic plant precision is low, the problem that accuracy is not high.Additionally provide a kind of feature based cluster simultaneously to compare
Predicting power of photovoltaic plant device.
The present invention provides the predicting power of photovoltaic plant that a kind of feature based cluster compares to solve above-mentioned technical problem
The step of method, Forecasting Methodology, is as follows：
1) collection affects the characteristic quantity of photovoltaic power precision of prediction, global radiation, wind speed and temperature, and going through using characteristic quantity
History data constitute sample set；
2) sample set for obtaining is carried out into feature clustering, sample set is divided into into the higher k classes of similitude, and obtain all kinds of
The cluster centre C of sample datai, i=1,2 ... ..., k；
3) Different categories of samples data correspondence establishment k class forecast models are respectively adopted；
4) the distance between cluster centre of current predictive object and Different categories of samples data is calculated, is chosen and current predictive pair
As the closest corresponding forecast model of cluster centre place class is predicted.
Further, the step 2) feature clustering is carried out using K-means algorithms, all kinds of initial cluster centers are adopted
The thought of Huffman construction trees is obtained.
Further, the step 3) forecast model adopt BP neural network forecast model, the forecast model is using bag
The three-decker of input layer, hidden layer and output layer is included, input layer adopts global radiation, temperature, three characteristic quantities of wind speed, output layer
For photovoltaic plant power output.
Further, the step 4) in the distance between current predictive object and all kinds of cluster centres adopt weighting method
Euclidean distance be calculated.
Further, the step 2) sample set is divided into into three classes, overcast and rainy, cloudy and fine day is represented respectively.
Present invention also offers the predicting power of photovoltaic plant device that a kind of feature based cluster compares, the prediction meanss bag
Include acquisition module, feature clustering module, forecast model and set up module and prediction module,
Described acquisition module be used for collection affect photovoltaic power precision of prediction characteristic quantity, global radiation, wind speed and temperature,
And the historical data using characteristic quantity constitutes sample set；
Described feature clustering module is used to for the sample set for obtaining to carry out feature clustering, and sample set is divided into into similitude
Higher k classes, and obtain the cluster centre C of Different categories of samples datai, i=1,2 ... ..., k；
Described prediction module sets up module and is utilized respectively Different categories of samples data foundation correspondence k class forecast models；
Described prediction module is used to calculate the distance between cluster centre of current predictive object and Different categories of samples data,
Choose the corresponding forecast model of nearest with current predictive object distance cluster centre place class to be predicted.
Further, described feature clustering module carries out feature clustering, all kinds of initial clusterings using K-means algorithms
Center is obtained using the thought that Huffman constructs tree.
Further, described prediction module sets up module using BP neural network forecast model, and the forecast model is adopted
Including the three-decker of input layer, hidden layer and output layer, input layer adopts global radiation, temperature, three characteristic quantities of wind speed, output
Layer is photovoltaic plant power output.
Further, distance of the described prediction module between calculating current predictive object and all kinds of cluster centres is adopted
It is calculated with the Euclidean distance of weighting method.
Further, sample set is divided into three classes by described feature clustering module, is represented respectively overcast and rainy, cloudy and fine
My god.
The invention has the beneficial effects as follows:The present invention obtains first three main features for affecting photovoltaic power precision of prediction
Amount, and it is history meteorological data to accumulate；Then the history meteorological data for obtaining is clustered as data sample, by sample point
Into the higher k classes of similitude, and obtain all kinds of cluster centers；Set up the prediction mould of respective class using all kinds of historical datas respectively
Type；Selection is predicted with the forecast model corresponding to the closest cluster center of existing object.The present invention is by different meteorological numbers
According to different sample types, and the photovoltaic power generation output forecasting model set up under DIFFERENT METEOROLOGICAL CONDITIONS is divided into, the training of forecast model is made
More targetedly, the power prediction model set up using DIFFERENT METEOROLOGICAL CONDITIONS is predicted, and improves luminous power precision of prediction.
Description of the drawings
Fig. 1 is the flow chart of the predicting power of photovoltaic plant method that feature based cluster of the present invention compares；
Fig. 2 is the construction process schematic diagram of Huffman trees；
Fig. 3 is that the initial cluster center based on Huffman trees chooses flow chart；
Fig. 4 is based on the forecast model figure of BP neural network.
Specific embodiment
The specific embodiment of the present invention is described further below in conjunction with the accompanying drawings.
The embodiment of the predicting power of photovoltaic plant method that feature based cluster of the present invention compares
The predicting power of photovoltaic plant method of the present invention obtains first impact photovoltaic power precision of prediction characteristic quantity, using spy
The historical data of the amount of levying constitutes sample set；Then sample is gathered for k classes by feature clustering algorithm, using all kinds of historical datas
Set up the forecast model of respective class；The distance between existing object and all kinds of cluster centers are finally calculated, and is chosen and existing object
The closest corresponding forecast model of cluster center place class is predicted existing object, so as to realize the pre- of photovoltaic plant power
Survey.The implementing procedure of the method is as shown in figure 1, comprise the following steps that.
1. three main characteristic quantities for affecting photovoltaic power precision of prediction are obtained, and using the historical data structure of characteristic quantity
Into sample set.
Three main characteristic quantities of the impact photovoltaic power precision of prediction selected by the present invention are respectively global radiation, wind speed
And temperature, these three characteristic quantities can obtain from NWP numerical weather forecasts.
2. the sample set for obtaining is carried out into feature clustering, sample is divided into into the higher k classes of similitude, and obtain all kinds of gathering
Class center Ci, i=1,2 ... ..., k.
Realizing the algorithm of sample clustering has various, based on the clustering algorithm for dividing, based on the clustering algorithm of level, based on net
Clustering algorithm, density-based algorithms of lattice etc., the cluster process being capable of achieving in the present invention, below with poly- based on dividing
In class algorithm as a example by most typical K-means algorithms, the cluster process of the present invention is described in detail, is embodied as follows：
(1) eigenmatrix is set up.
Using photovoltaic plant history meteorological data as sample, characteristic quantity is respectively global radiation, temperature, wind speed, then sample structure
Into as follows：
Each sample data covers each characteristic quantity
Wherein, ui1, ui2, ui3Global radiation, wind speed, 3 characteristic quantities of temperature are represented respectively.
(2) initial cluster center is chosen.
The present embodiment is adopted and chooses initial cluster center based on Huffman trees tectonic ideology.Vectorial number be n, sample
Collection dimension is 3, and it is 3 classes to intend cluster species, and cluster species can be adjusted according to actual conditions, and 3 classes are only given in the present embodiment
Situation, initial center point choose comprise the following steps that.
The first step：Calculate the Euclidean distance two-by-two between vector.
Wherein i=1,2 ... n；J=1,2 ... n；And i ≠ j.
Obtain dissimilarity matrix, matrix such as following formula：
Second step：Find minimum of a value d in matrixij, calculate ui,ujTwo vectorial mean values, obtain vectorial V1。
3rd step：U is deleted from former eigenmatrixi,ujTwo vectors, and add incoming vector V1, obtain new eigenmatrix.
4th step：Repeat step one, step 2, step 3, only remain next vector, iterative process in eigenmatrix
The process of middle construction tree is as shown in Figure 2.
5th step：Data are gathered for 5 classes, from summit JmRise and successively cut node from tree, it is 4 to cut number, structure
Set into 5, as shown in Figure 3.
6th step, asks for respectively the mean value of the knot vector of 3 tree bottoms, obtains vectorAs just
Beginning cluster centre.
(3) mean error criterion function under initial cluster center is calculated.
Wherein k=3, represents the number of institute's cluster dividing；ui (0)Represent the center of 3 clusters.
(4) new cluster centre is calculated.
N1Expression belongs toElement number
N2Expression belongs toElement number
N3Expression belongs toElement number
Wherein N1+N2+N3=n n represent sample data total amount
(5) repeat the above steps (2), (3), (4), until meeting following relation.
And there is E(s+1)≤E(s), illustrate after s time clusters iteration, to put down
Error rule function is presented convergence state, and cluster center no longer changes, and stops iteration, and so far cluster result is completed.
3. photovoltaic power forecast model is set up.
Used respectively after cluster as the forecast model of luminous power using BP neural network forecast model in the present embodiment
All kinds of historical datas are trained to BP neural network model, to obtain and all kinds of corresponding forecast models.As shown in figure 4, BP
Neural network prediction model adopts three-decker, input layer, hidden layer, output layer, input layer to adopt global radiation, temperature, wind speed
Three characteristic quantities, output layer is photovoltaic plant power output, hidden layer neuron number is obtained by engineering actual verification.According to
Cluster result is obtained in step 2, the sample data of three classes is brought into respectively in BP neural network forecast model and is trained, i.e.,
The different forecast model of available three classes.Implement process as follows：
The first step, netinit：The random number in an interval (- 1,1) is assigned respectively to each connection weight, sets error
Function e, gives computational accuracy value Δ and maximum study number of times.
Second step, choose k input sample (historical datas of nearest 30 days) and correspondingly desired output (desired output is
Corresponding 30 days actual generation powers).
3rd step, calculates the input and output of each neuron of hidden layer.
4th step, using output result after network desired output (actual generation power) and hands-on, calculation error letter
Several partial derivatives to exporting each neuron.
5th step, using the error partial derivative of each neuron of output layer and the output of each neuron of hidden layer connection is corrected
Weights.
6th step, using the error partial derivative and the output amendment connection weight of each neuron of input layer of each neuron of hidden layer
Value.
7th step, calculates global error.
8th step, judges whether network error meets requirement, when error reaches default precision or study number of times more than setting
Maximum times, then terminate algorithm, otherwise, choose next learning sample and corresponding desired output, return to the 3rd step, enter
Enter next round study.
4. the distance between current predictive object and all kinds of cluster centres are calculated, is chosen with current predictive object distance most
The near corresponding forecast model of cluster centre place class is predicted.
The first step, obtains the meteorological data of current predictive object, according to clustering algorithm output result, selectes forecast model.
Calculate existing object meteorological data and all kinds of cluster centres between weighted euclidean distance, if with the i-th class cluster in
Heart CiDistance is minimum, then be predicted using the i-th class forecast model.
Second step, meteorological data normalized.
3rd step, using current weather data as input, loads corresponding forecast model, and output predicts the outcome.
The power prediction model that can be set up according to different meteorological conditions by the said process present invention, can be effectively improved
The low problem of photovoltaic power output precision of prediction under the conditions of rainy weather.
The embodiment of the predicting power of photovoltaic plant device that feature based cluster of the present invention compares
Prediction meanss in the present embodiment include that acquisition module, feature clustering module, forecast model set up module and prediction
Module；Acquisition module is used for collection affects the characteristic quantity of photovoltaic power precision of prediction, global radiation, wind speed and temperature, and utilizes special
The historical data of the amount of levying constitutes sample set；Feature clustering module is used to for the sample set for obtaining to carry out feature clustering, by sample set
Change is divided into the higher k classes of similitude, and obtains the cluster centre C of Different categories of samples datai, i=1,2 ... ..., k；Prediction module is built
Formwork erection block is utilized respectively Different categories of samples data and sets up correspondence k class forecast models；Prediction module be used for calculate current predictive object with
The distance between cluster centre of Different categories of samples data, chooses and the nearest cluster centre place class pair of current predictive object distance
The forecast model answered is predicted.The means that implement of each module are illustrated in the example of method, are no longer gone to live in the household of one's in-laws on getting married here
State.
Claims (10)
1. the predicting power of photovoltaic plant method that a kind of feature based cluster compares, it is characterised in that the step of the Forecasting Methodology
It is as follows：
1) characteristic quantity of collection impact photovoltaic power precision of prediction, global radiation, wind speed and temperature, and using the history number of characteristic quantity
According to composition sample set；
2) sample set for obtaining is carried out into feature clustering, sample set is divided into into the higher k classes of similitude, and obtain Different categories of samples
The cluster centre C of datai, i=1,2 ... ..., k；
3) Different categories of samples data correspondence establishment k class forecast models are respectively adopted；
4) the distance between cluster centre of current predictive object and Different categories of samples data is calculated, is chosen with current predictive to image distance
It is predicted from the nearest corresponding forecast model of cluster centre place class.
2. the predicting power of photovoltaic plant method that feature based cluster according to claim 1 compares, it is characterised in that institute
State step 2) feature clustering is carried out using K-means algorithms, all kinds of initial cluster centers constructs the thought of tree using Huffman
Obtain.
3. the predicting power of photovoltaic plant method that feature based cluster according to claim 1 and 2 compares, its feature exists
In the step 3) forecast model adopt BP neural network forecast model, the forecast model is using including input layer, hidden layer
With the three-decker of output layer, using global radiation, temperature, three characteristic quantities of wind speed, output layer is photovoltaic plant output to input layer
Power.
4. the predicting power of photovoltaic plant method that feature based cluster according to claim 1 compares, it is characterised in that institute
State step 4) in the distance between current predictive object and all kinds of cluster centres be calculated using the Euclidean distance of weighting method.
5. the predicting power of photovoltaic plant method that feature based cluster according to claim 2 compares, it is characterised in that institute
State step 2) sample set is divided into into three classes, overcast and rainy, cloudy and fine day is represented respectively.
6. the predicting power of photovoltaic plant device that a kind of feature based cluster compares, it is characterised in that the prediction meanss include adopting
Collection module, feature clustering module, forecast model set up module and prediction module,
Described acquisition module is used for collection affects the characteristic quantity of photovoltaic power precision of prediction, global radiation, wind speed and temperature, and profit
Sample set is constituted with the historical data of characteristic quantity；
Described feature clustering module is used to for the sample set for obtaining to carry out feature clustering, sample set is divided into into similitude higher
K classes, and obtain the cluster centre C of Different categories of samples datai, i=1,2 ... ..., k；
Described prediction module sets up module and is utilized respectively Different categories of samples data foundation correspondence k class forecast models；
Described prediction module is used to calculate the distance between cluster centre of current predictive object and Different categories of samples data, chooses
The corresponding forecast model of the cluster centre place class nearest with current predictive object distance is predicted.
7. the predicting power of photovoltaic plant device that feature based cluster according to claim 6 compares, it is characterised in that institute
The feature clustering module stated carries out feature clustering using K-means algorithms, and all kinds of initial cluster centers are constructed using Huffman
The thought of tree is obtained.
8. the predicting power of photovoltaic plant device that the feature based cluster according to claim 6 or 7 compares, its feature exists
In described prediction module sets up module using BP neural network forecast model, and the forecast model is adopted to be included input layer, imply
The three-decker of layer and output layer, input layer adopts global radiation, temperature, three characteristic quantities of wind speed, and output layer is that photovoltaic plant is defeated
Go out power.
9. the predicting power of photovoltaic plant device that feature based cluster according to claim 6 compares, it is characterised in that institute
The prediction module stated is calculating Euclidean distance of the distance between current predictive object and all kinds of cluster centres using weighting method
It is calculated.
10. the predicting power of photovoltaic plant device that feature based cluster according to claim 7 compares, it is characterised in that
Sample set is divided into three classes by described feature clustering module, and overcast and rainy, cloudy and fine day is represented respectively.
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