CN103942621A - Wind power short-term prediction method using composite data source based on Sigmoid kernel function support vector machine - Google Patents

Abstract

The invention discloses a wind power short-term prediction method using a composite data source based on a Sigmoid kernel function support vector machine. The method mainly comprises the steps that the composite data source based on the Sigmoid kernel function support vector machine is adopted, and model training is performed on wind power to be detected; the wind power to be detected is predicted on the basis of the a model training result of the wind power to be detected. The wind power short-term prediction method using the composite data source based on the Sigmoid kernel function support vector machine has the advantages of being capable of overcoming the defect that prediction accuracy is low in the prior art, and achieving high-accuracy short-term prediction of the wind power.

Description

Adopt the short-term wind power prediction method of complex data source based on Sigmoid kernel function support vector machine

Technical field

The present invention relates to wind power electric powder prediction in generation of electricity by new energy process, particularly, relate to a kind of short-term wind power prediction method of complex data source based on Sigmoid kernel function support vector machine that adopt.

Background technology

The large-scale new forms of energy base majority that China's wind-powered electricity generation produces after entering the large-scale development stage is positioned at " three northern areas of China " (northwest, northeast, North China); large-scale new forms of energy base is generally away from load center, and its electric power need to be transported to load center and dissolve through long-distance, high voltage.Due to intermittence, randomness and the undulatory property of wind, light resources, cause wind-powered electricity generation, the photovoltaic generation in extensive new forms of energy base to be exerted oneself fluctuation in a big way can occur thereupon, further cause the fluctuation of power transmission network charge power, bring series of problems to safe operation of electric network.

By in January, 2014, the installed capacity of Gansu Power Grid grid connected wind power has reached 7,020,000 kilowatts, accounts for 22% of Gansu Power Grid total installation of generating capacity, becomes the second largest main force power supply that is only second to thermoelectricity; Photovoltaic generation installed capacity has reached 4,350,000 kilowatts, accounts for 13% of Gansu Power Grid total installation of generating capacity, and simultaneously Gansu becomes China's photovoltaic generation largest province of installing.At present, Gansu Power Grid wind-powered electricity generation, photovoltaic generation installation exceed 1/3 of Gansu Power Grid total installation of generating capacity.Along with improving constantly of new-energy grid-connected scale, wind-powered electricity generation, photovoltaic generation uncertainty and uncontrollability are brought problems to the safety and stability economical operation of electrical network.Accurately estimating available power generating wind resource is the basis to large-scale wind power Optimized Operation.Wind power in wind-power electricity generation process is predicted, be can be that generation of electricity by new energy Real-Time Scheduling, generation of electricity by new energy are planned, generation of electricity by new energy monthly plan, generation of electricity by new energy capability evaluation and abandon wind-powered electricity generation amount and estimate to provide crucial reference data a few days ago.

Realizing in process of the present invention, inventor finds at least to exist in prior art the defects such as precision of prediction is low.

Summary of the invention

The object of the invention is to, for the problems referred to above, propose to adopt the short-term wind power prediction method of complex data source based on Sigmoid kernel function support vector machine, possess the advantage of high-precision short-term wind power prediction.

For achieving the above object, the technical solution used in the present invention is: adopt the short-term wind power prediction method of complex data source based on Sigmoid kernel function support vector machine, mainly comprise:

A, the complex data source of employing based on Sigmoid kernel function support vector machine, carry out model training to wind power to be measured;

B, model training result based on wind power to be measured, predict wind power to be measured.

Further, described step a, specifically comprises:

Step a1, the input of model training basic data;

Step a2: data pre-service;

The training of step a3:SVM sorter;

Step a4, obtain SVM model.

Further, described step a1, specifically comprises:

Wind power forecast system model training required input data, comprise wind energy turbine set Back ground Information, historical wind speed data, historical power data, and the Geographic Information System GIS data that comprise wind energy turbine set/blower fan coordinate, anemometer tower coordinate, booster stations coordinate; Wherein, when GIS data are mainly used in power prediction, carry out the optimization of short-term forecasting result according to the upstream and downstream relation of each wind energy turbine set, basic data is input to and in forecast model, carries out model training.

Further, described step a2, specifically comprises:

First air speed data and power data are comprised to alignment of data and normalized pre-service, and GIS data are determined power station upstream and downstream relation by pre-service.

Further, described step a3, specifically comprises:

Svm classifier device is a multilayer perceptron that comprises a hidden layer, automatically determines the number of hidden nodes by algorithm through training process;

Non-linear short-term wind power prediction model representation based on svm classifier device is:

Wherein, x is and the closely-related influence factor of wind power, as numerical weather forecast NWP data, historical power, wind energy turbine set upstream and downstream relation; D is the dimension of input variable; F (x) is performance number to be predicted; the Nonlinear Mapping from the input space to higher dimensional space, i.e. kernel function; W is model parameter, and b is prediction residual item;

Definition penalty is that optimization aim is:

$\min \frac{1}{2}{\left|\right|w\left|\right|}^2+\frac{1}{2}r\underset{i=1}{\overset{N}{\mathrm{\Σ}}}{e}_i^2;$

Wherein, e _ibe error term, r is regularization parameter, and N is sample number;

Introduce after Lagrange multiplier λ, the Nonlinear Prediction Models expression formula based on svm classifier device be converted into:

$f\left(x\right)=\underset{i=1}{\overset{N}{\mathrm{\Σ}}}{\mathrm{\λ}}_{i}K(x,x_i)+b;$

Wherein, λ _i(i=1,2 ..., N) and b be model coefficient, K () represent be that non-linear space is the Nonlinear Mapping of linear space to high-order feature space from the input space;

Kernel function K () adopts Sigmoid functional form, for:

K(x,x _i)=tanh(v(x·x _i)+c)；

Wherein, x _i(i=1,2 ..., N) be the training sample of input, v (xx _i) expression x and x _iinner product, c is parameter.

Further, described step a4, specifically comprises:

By the training of input sample data, determine function parameter, obtain SVM forecast model.

Further, described step b, specifically comprises:

Step b1: power prediction basic data input;

Step b2: noise filtering and data pre-service;

Step b3: the short term power prediction based on SVM;

Step b4: output and displaying predict the outcome.

Further, in step b1, wind power prediction required input data comprise source monitor system data and operation monitoring system data two parts, and wherein, source monitor system packet is containing wind-resources Monitoring Data, wind energy predicted data and numerical weather forecast NWP data; Operation monitoring system data comprise fan monitor data, booster stations Monitoring Data and data acquisition and supervisor control SCADA;

And/or,

In step b2,

What employing noise filtering module collected real-time monitoring system is with the noisy filtering processing of carrying out, and removes bad data and singular value; The operation that adopts data preprocessing module to comprise alignment, normalized and category filter to data, can use for model the data of input.

Further, described step b3, specifically comprises:

Power prediction process is by wind-resources data and wind-powered electricity generation operational monitoring data input SVM model, the output that obtains predicting the outcome;

And/or,

Described step b4, specifically comprises:

First export predicting the outcome, and show predicting the outcome by the output form that comprises figure and form.

The short-term wind power prediction method of the employing complex data source of various embodiments of the present invention based on Sigmoid kernel function support vector machine, owing to mainly comprising: adopt the complex data source based on Sigmoid kernel function support vector machine, wind power to be measured is carried out to model training; Model training result based on wind power to be measured, predicts wind power to be measured; Thereby can overcome the low defect of precision of prediction in prior art, to realize the advantage of high-precision short-term wind-electricity power prediction.

Other features and advantages of the present invention will be set forth in the following description, and, partly from instructions, become apparent, or understand by implementing the present invention.

Below by drawings and Examples, technical scheme of the present invention is described in further detail.

Brief description of the drawings

Accompanying drawing is used to provide a further understanding of the present invention, and forms a part for instructions, for explaining the present invention, is not construed as limiting the invention together with embodiments of the present invention.In the accompanying drawings:

Fig. 1 is the schematic flow sheet that the present invention adopts the short-term wind power prediction method of complex data source based on Sigmoid kernel function support vector machine.

Embodiment

Below in conjunction with accompanying drawing, the preferred embodiments of the present invention are described, should be appreciated that preferred embodiment described herein, only for description and interpretation the present invention, is not intended to limit the present invention.

Wind power prediction relies on huge, data set accurately containing the Operation of Electric Systems of large-scale wind power, if can effectively improve precision of prediction by these data effective integration utilizations.Different from conventional electric power system SCADA monitoring, outside the data such as all kinds of electric, machinery and heating power, wind-powered electricity generation Monitoring Data also comprises a large amount of monitoring resources, operational monitoring and geography information etc.

According to the embodiment of the present invention, as shown in Figure 1, provide and adopted the short-term wind power prediction method of complex data source based on Sigmoid kernel function support vector machine.

The short-term wind power prediction method of the employing complex data source of the present embodiment based on Sigmoid kernel function support vector machine, can be divided into two stages: model training stage and power prediction stage, specific as follows:

Stage 1: model training

Step 1.1: model training basic data input

Wind power forecast system model training required input data comprise, wind energy turbine set Back ground Information, historical wind speed data, historical power data, Geographic Information System (GIS) data (wind energy turbine set/blower fan coordinate, anemometer tower coordinate, booster stations coordinate etc.), carry out the optimization of short-term forecasting result according to the upstream and downstream relation of each wind energy turbine set when wherein GIS data are mainly used in power prediction.Basic data is input to and in forecast model, carries out model training.

Step 1.2: data pre-service

First air speed data and power data are carried out to the pre-service such as alignment of data and normalization, and GIS data are determined power station upstream and downstream relation by pre-service.

The training of step 1.3:SVM sorter

Svm classifier device is a multilayer perceptron that comprises a hidden layer, and the number of hidden nodes is automatically definite through training process by algorithm, and the advantage that SVM compares neural network is that SVM can not be absorbed in local minimum point.

Non-linear short-term wind power prediction model based on svm classifier device can be expressed as:

Wherein, x is and the closely-related influence factor of wind power, as numerical weather forecast (NWP) data, historical power, wind energy turbine set upstream and downstream relation etc.D is the dimension of input variable; F (x) is performance number to be predicted; the Nonlinear Mapping from the input space to higher dimensional space, i.e. kernel function; W is model parameter, and b is prediction residual item.

Definition penalty is that optimization aim is:

$\min \frac{1}{2}{\left|\right|w\left|\right|}^2+\frac{1}{2}r\underset{i=1}{\overset{N}{\mathrm{\Σ}}}{e}_i^2;$

Wherein, e _ibe error term, r is regularization parameter, and N is sample number.

Introduce after Lagrange multiplier λ, the Nonlinear Prediction Models expression formula based on svm classifier device can be converted into:

$f\left(x\right)=\underset{i=1}{\overset{N}{\mathrm{\Σ}}}{\mathrm{\λ}}_{i}K(x,x_i)+b;$

Wherein, λ _i(i=1,2 ..., N) and b be model coefficient, K () represents the Nonlinear Mapping from the input space (non-linear space) to high-order feature space (linear space).

Kernel function K () can adopt Sigmoid functional form, for:

K(x,x _i)=tanh(v(x·x _i)+c)；

Wherein, x _i(i=1,2 ..., N) be the training sample of input, v (xx _i) expression x and x _iinner product, c is parameter.

Step 1.4: obtain SVM model

By the training of input sample data, determine function parameter, obtain SVM forecast model.

Stage 2: power prediction

Step 2.1: power prediction basic data input

Wind power prediction required input data comprise source monitor system data and operation monitoring system data two parts, and wherein, source monitor system packet is containing wind-resources Monitoring Data, wind energy predicted data and numerical weather forecast (NWP) data; Operation monitoring system data comprise fan monitor data, booster stations Monitoring Data and data acquisition and supervisor control (SCADA) etc.

Step 2.2: noise filtering and data pre-service

What noise filtering module collected real-time monitoring system is with the noisy filtering processing of carrying out, and removes bad data and singular value; Data preprocessing module to data align, the operation such as normalized and category filter, can be model use to make the data of input.

Step 2.3: the short term power prediction based on SVM

Power prediction process is by wind-resources data and wind-powered electricity generation operational monitoring data input SVM model, thus the output that obtains predicting the outcome.

Step 2.4: output and displaying predict the outcome

First this step is exported predicting the outcome, and shows predicting the outcome by the form such as figure and form.

Finally it should be noted that: the foregoing is only the preferred embodiments of the present invention, be not limited to the present invention, although the present invention is had been described in detail with reference to previous embodiment, for a person skilled in the art, its technical scheme that still can record aforementioned each embodiment is modified, or part technical characterictic is wherein equal to replacement.Within the spirit and principles in the present invention all, any amendment of doing, be equal to replacement, improvement etc., within all should being included in protection scope of the present invention.

Claims (9)

1. adopt the short-term wind power prediction method of complex data source based on Sigmoid kernel function support vector machine, it is characterized in that, mainly comprise:

A, the complex data source of employing based on Sigmoid kernel function support vector machine, carry out model training to wind power to be measured;

B, model training result based on wind power to be measured, predict wind power to be measured.

2. the short-term wind power prediction method of employing complex data according to claim 1 source based on Sigmoid kernel function support vector machine, is characterized in that, described step a, specifically comprises:

Step a1, the input of model training basic data;

Step a2: data pre-service;

The training of step a3:SVM sorter;

Step a4, obtain SVM model.

3. the short-term wind power prediction method of employing complex data according to claim 2 source based on Sigmoid kernel function support vector machine, is characterized in that, described step a1, specifically comprises:

Wind power forecast system model training required input data, comprise wind energy turbine set Back ground Information, historical wind speed data, historical power data, and the Geographic Information System GIS data that comprise wind energy turbine set/blower fan coordinate, anemometer tower coordinate, booster stations coordinate; Wherein, when GIS data are mainly used in power prediction, carry out the optimization of short-term forecasting result according to the upstream and downstream relation of each wind energy turbine set, basic data is input to and in forecast model, carries out model training.

4. the short-term wind power prediction method of employing complex data according to claim 2 source based on Sigmoid kernel function support vector machine, is characterized in that, described step a2, specifically comprises:

First air speed data and power data are comprised to alignment of data and normalized pre-service, and GIS data are determined power station upstream and downstream relation by pre-service.

5. the short-term wind power prediction method of employing complex data according to claim 2 source based on Sigmoid kernel function support vector machine, is characterized in that, described step a3, specifically comprises:

Svm classifier device is a multilayer perceptron that comprises a hidden layer, automatically determines the number of hidden nodes by algorithm through training process;

Non-linear short-term wind power prediction model representation based on svm classifier device is:

Wherein, x is and the closely-related influence factor of wind power, as numerical weather forecast NWP data, historical power, wind energy turbine set upstream and downstream relation; D is the dimension of input variable; F (x) is performance number to be predicted; the Nonlinear Mapping from the input space to higher dimensional space, i.e. kernel function; W is model parameter, and b is prediction residual item;

Definition penalty is that optimization aim is:

$\min \frac{1}{2}{\left|\right|w\left|\right|}^2+\frac{1}{2}r\underset{i=1}{\overset{N}{\mathrm{\Σ}}}{e}_i^2;$

Wherein, e _ibe error term, r is regularization parameter, and N is sample number;

Introduce after Lagrange multiplier λ, the Nonlinear Prediction Models expression formula based on svm classifier device be converted into:

$f\left(x\right)=\underset{i=1}{\overset{N}{\mathrm{\Σ}}}{\mathrm{\λ}}_{i}K(x,x_i)+b;$

Wherein, λ _i(i=1,2 ..., N) and b be model coefficient, K () represent be that non-linear space is the Nonlinear Mapping of linear space to high-order feature space from the input space;

Kernel function K () adopts Sigmoid functional form, for:

K(x,x _i)=tanh(v(x·x _i)+c)；

Wherein, x _i(i=1,2 ..., N) be the training sample of input, v (xx _i) expression x and x _iinner product, c is parameter.

6. the short-term wind power prediction method of employing complex data according to claim 2 source based on Sigmoid kernel function support vector machine, is characterized in that, described step a4, specifically comprises:

By the training of input sample data, determine function parameter, obtain SVM forecast model.

7. the short-term wind power prediction method based on Sigmoid kernel function support vector machine according to the employing complex data source described in any one in claim 2-6, is characterized in that, described step b, specifically comprises:

Step b1: power prediction basic data input;

Step b2: noise filtering and data pre-service;

Step b3: the short term power prediction based on SVM;

Step b4: output and displaying predict the outcome.

8. the short-term wind power prediction method of employing complex data according to claim 7 source based on Sigmoid kernel function support vector machine, it is characterized in that, in step b1, wind power prediction required input data comprise source monitor system data and operation monitoring system data two parts, wherein, source monitor system packet is containing wind-resources Monitoring Data, wind energy predicted data and numerical weather forecast NWP data; Operation monitoring system data comprise fan monitor data, booster stations Monitoring Data and data acquisition and supervisor control SCADA;

And/or,

In step b2,

What employing noise filtering module collected real-time monitoring system is with the noisy filtering processing of carrying out, and removes bad data and singular value; The operation that adopts data preprocessing module to comprise alignment, normalized and category filter to data, can use for model the data of input.

9. the short-term wind power prediction method of employing complex data according to claim 7 source based on Sigmoid kernel function support vector machine, is characterized in that, described step b3, specifically comprises:

Power prediction process is by wind-resources data and wind-powered electricity generation operational monitoring data input SVM model, the output that obtains predicting the outcome;

And/or,

Described step b4, specifically comprises:

First export predicting the outcome, and show predicting the outcome by the output form that comprises figure and form.