CN103955757A - Photovoltaic power generation power short-term prediction method by adopting composite data source based on polynomial kernel function support vector machine - Google Patents

Abstract

The invention discloses a photovoltaic power generation power short-term prediction method by adopting a composite data source based on a polynomial kernel function support vector machine. The method mainly comprises the steps of adopting the composite data source based on the polynomial kernel function support vector machine, carrying out model training on photovoltaic power generation power to be detected, and carrying out short-term prediction on the photovoltaic power generation power to be detected on the basis of the result of the model training of the photovoltaic power generation power to be detected. The photovoltaic power generation power short-term prediction method by adopting the composite data source based on the polynomial kernel function support vector machine can overcome the defect that the prediction precision of the photovoltaic power generation power is low in the prior art, and has the advantages of achieving high-precision photovoltaic power generation power short-term prediction.

Description

Adopt the photovoltaic generation power short-term forecasting method of complex data source based on polynomial expression kernel function support vector machine

Technical field

The present invention relates to photovoltaic generation power prediction technical field in generation of electricity by new energy process, particularly, relate to the photovoltaic generation power short-term forecasting method of complex data source based on polynomial expression kernel function support vector machine that adopt.

Background technology

China's wind-powered electricity generation enters the large-scale new forms of energy base majority that large-scale development produces after the stage and 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.Intermittence, randomness and undulatory property due to 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, to safe operation of electric network, bring series of problems.

By in Dec, 2013, 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 surpass 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 and photovoltaic generation process and photovoltaic generation power are 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/abandon optical quantum and estimate to provide key message a few days ago.

In realizing process of the present invention, inventor finds at least to exist in prior art the low defect of photovoltaic generation power short-term forecasting precision.

Summary of the invention

The object of the invention is to, for the problems referred to above, propose to adopt the photovoltaic generation power short-term forecasting method of complex data source based on polynomial expression kernel function support vector machine, to realize the advantage of high-precision photovoltaic generation power short-term forecasting.

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

A, the complex data source of employing based on polynomial expression kernel function support vector machine, treat photometry volt generated output and carry out model training;

B, the model training result based on photovoltaic generation power to be measured, treat photometry volt generated output and carry out short-term forecasting.

Further, described step a, specifically comprises:

Step a1, the input of model training basic data;

Step a2, data pre-service;

Step a3, the training of svm classifier device;

Step a4, obtain SVM model.

Further, described step a1, specifically comprises:

Photovoltaic generation rate forecast system model training required input data, comprise photovoltaic plant Back ground Information, historical irradiation data, historical power data, and the Geographic Information System GIS data that comprise photovoltaic plant coordinate, anemometer tower coordinate, booster stations coordinate; Wherein, when GIS data are mainly used in power prediction, according to the upstream and downstream relation of each photovoltaic plant, carry out the optimization of short-term forecasting result, basic data is input to and in forecast model, carries out model training.

Further, described step a2, specifically comprises:

First irradiation 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, by algorithm, through training process, automatically determines the number of hidden nodes;

Non-linear photovoltaic generated output Short-term Forecasting Model based on svm classifier device is expressed as:

Wherein, x is and the closely-related influence factor of photovoltaic generation power to comprise 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 ..., be N) model coefficient with b, it is that non-linear space is the Nonlinear Mapping of linear space to high-order feature space from the input space that K () represents;

Kernel function K () adopts polynomial form, for:

K(x,x _i)=[(x·x _i)+1] ^q；

Wherein, x _i(i=1,2 ..., be N) training sample of input, q is the rank of polynomial kernel function.

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, the input of power prediction basic data;

Step b2, noise filtering and data pre-service;

Step b3, the prediction of the short term power based on SVM;

Step b4, output and show predicts the outcome.

Further, in step b1, photovoltaic generation power prediction required input data comprise source monitor system data and operation monitoring system data two parts, wherein, source monitor system packet detects data, sun power predicted data and NWP data containing light resources Monitoring Data, total sky imager TSI system; Operation monitoring system data comprise photovoltaic module Monitoring Data, booster stations Monitoring Data and data acquisition and supervisor control SCADA;

And/or,

In step b2, adopt the noisy filtering processing of carrying out of being with that noise filtering module collects real-time monitoring system, remove 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 light resources Monitoring Data and photovoltaic generation operational monitoring data input SVM model, thus the output that obtains predicting the outcome;

And/or,

Described step b4, specifically comprises:

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

The photovoltaic generation power short-term forecasting method of the employing complex data source of various embodiments of the present invention based on polynomial expression kernel function support vector machine, owing to mainly comprising: adopt the complex data source based on polynomial expression kernel function support vector machine, treat photometry volt generated output and carry out model training; Model training result based on photovoltaic generation power to be measured, treats photometry volt generated output and carries out short-term forecasting; Thereby can overcome the low defect of photovoltaic generation power short-term forecasting precision in prior art, to realize the advantage of high-precision photovoltaic generation power short-term forecasting.

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.

Accompanying drawing explanation

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 photovoltaic generation power short-term forecasting method of complex data source based on polynomial expression 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.

Photovoltaic generation power prediction containing the Operation of Electric Systems of large-scale photovoltaic generating, relies on huge, data set accurately, 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, photovoltaic 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 the photovoltaic generation power short-term forecasting method of complex data source based on polynomial expression kernel function support vector machine that adopt.

The photovoltaic generation power short-term forecasting method of the employing complex data source of the present embodiment based on polynomial expression 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

Photovoltaic generation rate forecast system model training required input data comprise, photovoltaic plant Back ground Information, historical irradiation data, historical power data, Geographic Information System (GIS) data (photovoltaic plant 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 photovoltaic plant 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 irradiation 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 photovoltaic generated output Short-term Forecasting Model based on svm classifier device can be expressed as:

Wherein, x is and the closely-related influence factor of photovoltaic generation 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 ..., be N) model coefficient with b, K () represents the Nonlinear Mapping from the input space (non-linear space) to high-order feature space (linear space).

Kernel function K () can adopt polynomial form, for:

K(x,x _i)=[(x·x _i)+1] ^q；

Wherein, x _i(i=1,2 ..., be N) training sample of input, q is the rank of polynomial kernel function.

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

Photovoltaic generation power prediction required input data comprise source monitor system data and operation monitoring system data two parts, wherein, source monitor system packet detects data, sun power predicted data and NWP data etc. containing light resources Monitoring Data, total sky imager (TSI) system; Operation monitoring system data comprise photovoltaic module Monitoring Data, booster stations Monitoring Data and data acquisition and supervisor control (SCADA) etc.

Step 2.2: noise filtering and data pre-service

Noise filtering module collects 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, to the data of input can be used for model.

Step 2.3: the short term power prediction based on SVM

Power prediction process is by light resources Monitoring Data and photovoltaic 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 to predicting the outcome, export, and show predicting the outcome by forms such as figure and forms.

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 modification 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 photovoltaic generation power short-term forecasting method of complex data source based on polynomial expression kernel function support vector machine, it is characterized in that, mainly comprise:

A, the complex data source of employing based on polynomial expression kernel function support vector machine, treat photometry volt generated output and carry out model training;

B, the model training result based on photovoltaic generation power to be measured, treat photometry volt generated output and carry out short-term forecasting.

2. the photovoltaic generation power short-term forecasting method of employing complex data according to claim 1 source based on polynomial expression 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;

Step a3, the training of svm classifier device;

Step a4, obtain SVM model.

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

Photovoltaic generation rate forecast system model training required input data, comprise photovoltaic plant Back ground Information, historical irradiation data, historical power data, and the Geographic Information System GIS data that comprise photovoltaic plant coordinate, anemometer tower coordinate, booster stations coordinate; Wherein, when GIS data are mainly used in power prediction, according to the upstream and downstream relation of each photovoltaic plant, carry out the optimization of short-term forecasting result, basic data is input to and in forecast model, carries out model training.

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

First irradiation 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 photovoltaic generation power short-term forecasting method of employing complex data according to claim 2 source based on polynomial expression 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, by algorithm, through training process, automatically determines the number of hidden nodes;

Non-linear photovoltaic generated output Short-term Forecasting Model based on svm classifier device is expressed as:

Wherein, x is and the closely-related influence factor of photovoltaic generation power to comprise 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 ..., be N) model coefficient with b, it is that non-linear space is the Nonlinear Mapping of linear space to high-order feature space from the input space that K () represents;

Kernel function K () adopts polynomial form, for:

K(x,x _i)=[(x·x _i)+1] ^q；

Wherein, x _i(i=1,2 ..., be N) training sample of input, q is the rank of polynomial kernel function.

6. the photovoltaic generation power short-term forecasting method of employing complex data according to claim 2 source based on polynomial expression 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 photovoltaic generation power short-term forecasting method based on polynomial expression kernel function support vector machine according to the employing complex data source described in any one in claim 1-6, is characterized in that, described step b, specifically comprises:

Step b1, the input of power prediction basic data;

Step b2, noise filtering and data pre-service;

Step b3, the prediction of the short term power based on SVM;

Step b4, output and show predicts the outcome.

8. the photovoltaic generation power short-term forecasting method of employing complex data according to claim 7 source based on polynomial expression kernel function support vector machine, it is characterized in that, in step b1, photovoltaic generation power prediction required input data comprise source monitor system data and operation monitoring system data two parts, wherein, source monitor system packet detects data, sun power predicted data and NWP data containing light resources Monitoring Data, total sky imager TSI system; Operation monitoring system data comprise photovoltaic module Monitoring Data, booster stations Monitoring Data and data acquisition and supervisor control SCADA;

And/or,

In step b2, adopt the noisy filtering processing of carrying out of being with that noise filtering module collects real-time monitoring system, remove 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 photovoltaic generation power short-term forecasting method of employing complex data according to claim 7 source based on polynomial expression kernel function support vector machine, is characterized in that,

Described step b3, specifically comprises:

Power prediction process is by light resources Monitoring Data and photovoltaic generation operational monitoring data input SVM model, thus the output that obtains predicting the outcome;

And/or,

Described step b4, specifically comprises:

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