CN103927600A - Ultra-short-term photovoltaic generation power prediction method based on composite data source autoregression model - Google Patents

Ultra-short-term photovoltaic generation power prediction method based on composite data source autoregression model Download PDF

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CN103927600A
CN103927600A CN201410163590.XA CN201410163590A CN103927600A CN 103927600 A CN103927600 A CN 103927600A CN 201410163590 A CN201410163590 A CN 201410163590A CN 103927600 A CN103927600 A CN 103927600A
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data
model
photovoltaic generation
input
generation power
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汪宁渤
路亮
何世恩
马彦宏
赵龙
周强
马明
张健美
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State Grid Corp of China SGCC
State Grid Gansu Electric Power Co Ltd
Wind Power Technology Center of Gansu Electric Power Co Ltd
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State Grid Corp of China SGCC
State Grid Gansu Electric Power Co Ltd
Wind Power Technology Center of Gansu Electric Power Co Ltd
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    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

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Abstract

The invention discloses an ultra-short-term photovoltaic generation power prediction method based on a composite data source autoregression model. The ultra-short-term photovoltaic generation power prediction method based on the composite data source autoregression model comprises the steps that data are input to enable parameters of the autoregression model to be obtained; input data required by photovoltaic generation power prediction are input into the autoregression model which is determined according to the parameters of the autoregression model, so that a prediction result is obtained; model training basic data are input, order determination is conducted on the autoregression model AR(p) according to a residual variogram method, and the parameters of the model AR(p) with the determined order are estimated according to a moment estimation method. Key information is provided for new energy power generation real-time scheduling, a new energy power generation day-ahead plan, a new energy power generation monthly plan, new energy power generation capability evaluation and wind curtailment power estimation by predicting the photovoltaic generation power generated during photovoltaic generation. The ultra-short-term photovoltaic generation power prediction accuracy is effectively improved due to the fact a composite data source is introduced, and thus the on-grid energy of new energy resources is effectively increased on the premise that safe, stable and economical operation of a power grid is guaranteed.

Description

Be derived from regression model photovoltaic generation power ultra-short term Forecasting Methodology based on complex data
Technical field
The present invention relates to photovoltaic generation power prediction technical field in generation of electricity by new energy process, particularly, relate to a kind of photovoltaic generation power ultra-short term Forecasting Methodology that is derived from regression model based on complex data.
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 April, 2014, 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, photovoltaic generation uncertainty and uncontrollability are brought problems to the safety and stability economical operation of electrical network.Accurately estimating available generating light resources is the basis to large-scale photovoltaic generating Optimized Operation.Photovoltaic generation power in photovoltaic 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 optical quantum and estimate to provide key message a few days ago.
Summary of the invention
The object of the invention is to, for the problems referred to above, propose one and be derived from regression model photovoltaic generation power ultra-short term Forecasting Methodology based on complex data, to realize the advantage of high precision photovoltaic generation power ultra-short term prediction.
For achieving the above object, the technical solution used in the present invention is:
One is derived from regression model photovoltaic generation power ultra-short term Forecasting Methodology based on complex data, comprises that input data obtain Parameters of Autoregressive Models;
And input photovoltaic generation power prediction required input data are to according to being predicted the outcome in the definite autoregressive model of the parameter of above-mentioned autoregressive model;
Described input data obtain Parameters of Autoregressive Models and specifically comprise step 101, input model training basic data,
Step 102, employing residual error variogram method are determined rank to autoregressive model AR (p),
Step 103, employing square method of estimation are estimated AR (p) model parameter of determining rank.
According to a preferred embodiment of the invention, described step 101 input model training basic data, input data comprise, photovoltaic plant Back ground Information, historical irradiance data, historical power data and Geographic Information System (GIS) data.
According to a preferred embodiment of the invention, described step 102 adopts residual error variogram method to determine rank to autoregressive model AR (p):
Be specially and establish x tfor the item that needs are estimated, x t-1, x t-2..., x t-nfor known historical power sequence, autoregressive model AR (p), it is exactly the value of determining Model Parameter p that model is determined rank;
The models fitting original series increasing progressively gradually with serial exponent number all calculates residual sum of squares (RSS) at every turn then draw exponent number and figure, when exponent number is during by little increase, can significantly decline, reach after true exponent number value can tend towards stability gradually, even on the contrary increase,
The observed value item number of actual observed value number actual use while referring to model of fit, for the sequence with N observed value, matching AR (p) model, the actual observed value using mostly is N-p most, model parameter number refers to the actual number of parameters comprising in set up model, and for the model that contains average, model parameter number is that model order adds 1, for the sequence of N observed reading, the residual error estimator of AR model is:
According to a preferred embodiment of the invention, described step 103 adopts square method of estimation to estimate that to AR (p) model parameter of determining rank concrete steps are:
Historical photovoltaic plant power data is utilized to data sequence x 1, x 2..., x trepresent, its sample autocovariance is defined as
γ ^ k = 1 n Σ t = k + 1 n x t x t - k ,
Wherein, k=0,1,2 ..., n-1, x tand x t-kbe data sequence x 1, x 2..., x tin numerical value;
? γ ^ 0 = 1 n Σ t = 1 n x t 2
Historical power data sample autocorrelation function is:
ρ ^ k = γ ^ k γ ^ 0 = 1 n Σ t = k + 1 n x t x t - k 1 n Σ t = 1 n x t 2 = Σ t = k + 1 n x t x t - k Σ t = 1 n x t 2 ,
Wherein, k=0,1,2 ..., n-1;
The square of AR part is estimated as,
Order
Covariance function is
With estimation replace γ k,
Can obtain parameter
According to a preferred embodiment of the invention, described input photovoltaic generation power prediction required input data are to comprising according to the step that obtains predicting the outcome in the definite autoregressive model of the parameter of above-mentioned autoregressive model,
Step 201, power input fundamentals of forecasting data;
Step 202, to input basic data carry out noise filtering and data pre-service;
Step 203, set up autoregressive model according to definite parameter, thereby and data input after treatment is predicted the outcome;
Step 204, the output that will predict the outcome, and show and predict the outcome by chart and curve.
According to a preferred embodiment of the invention, described power input fundamentals of forecasting data comprise source monitor system data and operation monitoring system data, and described source monitor system packet is containing light resources Monitoring Data; Described operation monitoring system data comprise photovoltaic module Monitoring Data, booster stations Monitoring Data and data acquisition and supervisor control data.
According to a preferred embodiment of the invention, described noise filtering and data pre-service are specially: what noise filtering module obtained monitoring system Real-time Collection is with noisy data to carry out filtering processing, remove bad data and singular value; Data preprocessing module to data align, normalized and category filter processing.
According to a preferred embodiment of the invention, described autoregressive model is:
Wherein, coefficient, α tit is white noise sequence.
Technical scheme of the present invention has following beneficial effect:
Technical scheme of the present invention predicts by the photovoltaic generation power in photovoltaic generation process, for generation of electricity by new energy Real-Time Scheduling, generation of electricity by new energy are planned a few days ago, generation of electricity by new energy monthly plan, generation of electricity by new energy capability evaluation and abandon optical quantum and estimate to provide key message.Effectively improve photovoltaic generation power ultra-short term precision of prediction by introducing complex data source, under the prerequisite that ensures electricity net safety stable economical operation, effectively improve new forms of energy electricity volume object thereby realize.
Below by drawings and Examples, technical scheme of the present invention is described in further detail.
Brief description of the drawings
Fig. 1 is derived from the theory diagram of regression model photovoltaic generation power ultra-short term Forecasting Methodology based on complex data described in the embodiment of the present invention.
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.
One is derived from regression model photovoltaic generation power ultra-short term Forecasting Methodology based on complex data, comprises that input data obtain Parameters of Autoregressive Models,
And input photovoltaic generation power prediction required input data are to according to being predicted the outcome in the definite autoregressive model of the parameter of above-mentioned autoregressive model; Wherein input data and obtain Parameters of Autoregressive Models and specifically comprise step 101, input model training basic data,
Step 102, employing residual error variogram method are determined rank to autoregressive model AR (p),
Step 103, employing square method of estimation are estimated AR (p) model parameter of determining rank.
Photovoltaic generation power prediction relies on huge, data set accurately containing the Operation of Electric Systems of large-scale photovoltaic generating, 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.
As shown in Figure 1, the photovoltaic generation power ultra-short term prediction that technical solution of the present invention proposes can be divided into two stages: model training stage and power prediction stage.
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 irradiance data, historical power data, Geographic Information System (GIS) data (photovoltaic plant coordinate, photometry station coordinates, booster stations coordinate etc.).Basic data is input to and in forecast model, carries out model training
Step 1.2: model is determined rank
Need to set up estimation function with the item of how many known time sequences owing to cannot determining in advance, so need to carry out determining rank judgement to model.
If x tfor the item that needs are estimated, x t-1, x t-2..., x t-nfor known historical power sequence, for autoregressive model AR (p), it is exactly the value of determining Model Parameter p that model is determined rank.
Adopt residual error variogram method to carry out model and determine rank.Hypothetical model is limited rank autoregressive models, if the exponent number arranging is less than true exponent number, be a kind of not enough matching, thereby matching residual sum of squares (RSS) must be bigger than normal, now can significantly reduce residual sum of squares (RSS) by improving exponent number.Otherwise, if exponent number has reached actual value, increase so again exponent number, be exactly overfitting, now increase exponent number and can not make residual sum of squares (RSS) significantly reduce, even can slightly increase.
Carry out matching original series with the model that a series of exponent numbers increase progressively gradually like this, all calculate residual sum of squares (RSS) at every turn then draw exponent number and figure.When exponent number is during by little increase, can significantly decline, reach after true exponent number value can tend towards stability gradually, sometimes even on the contrary increase.The estimator of residual error variance is:
The observed value item number of actual use when " actual observed value number " refers to model of fit, for the sequence with N observed value, matching AR (p) model, the actual observed value using mostly is N-p most.
" model parameter number " refers to the actual number of parameters comprising in set up model, and for the model that contains average, model parameter number is that model order adds 1.For the sequence of N observed reading, the residual error estimator of corresponding AR model is:
Wherein, in formula, the sum of squares function that Q is error of fitting, be model coefficient, N is observation sequence length, the constant term in model parameter, general knowledge value according to different the constant term changing is different contrast different value.
Step 1.3: model parameter estimation
Adopt square method of estimation to estimate the model parameter of ARMA (p).First, historical photovoltaic plant power data is utilized to data sequence x 1, x 2..., x trepresent, its sample autocovariance is defined as
γ ^ k = 1 n Σ t = k + 1 n x t x t - k (formula 2)
Wherein, k=0,1,2 ..., n-1, x tand x t-kbe data sequence x 1, x 2..., x tin numerical value.
Especially,
γ ^ 0 = 1 n Σ t = 1 n x t 2 (formula 3)
Historical power data sample autocorrelation function is:
ρ ^ k = γ ^ k γ ^ 0 = 1 n Σ t = k + 1 n x t x t - k 1 n Σ t = 1 n x t 2 = Σ t = k + 1 n x t x t - k Σ t = 1 n x t 2 (formula 4)
Wherein, k=0,1,2 ..., n-1.
The square of AR part is estimated as
Order
Covariance function is
With estimation replace γ k, have
Can obtain parameter
Find by above-mentioned solution procedure, solve the exponent number of time series models, will obtain seasonal effect in time series predicted value; Obtain seasonal effect in time series predicted value, must first set up concrete anticipation function; Set up concrete anticipation function, must know the exponent number of model.
Draw according to practice, time series models exponent number is generally no more than 5 rank.So in the time of this algorithm specific implementation, first hypothesized model is 1 rank, utilize method for parameter estimation in step 1.3 to obtain the parameter of first order modeling, and then set up estimation function and just can estimate to obtain each predicted value in the hope of first order modeling time series models, thereby try to achieve the residual error variance of first order modeling; Afterwards, hypothesized model is second order, tries to achieve the residual error of second-order model with said method; By that analogy, can obtain the residual error of 1 to 5 rank model, select the exponent number of model of residual error minimum as the exponent number of final mask.Determine after model order, just can calculate parameter value.
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, and wherein, source monitor system packet is containing light resources Monitoring Data; Operation monitoring system data comprise photovoltaic module Monitoring Data, booster stations Monitoring Data and data acquisition and supervisor control data (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: ultra-short term power prediction
By model parameter estimation out after, in conjunction with the model order of having estimated, just can obtain the time series equation for photovoltaic generation power ultra-short term prediction.The p value drawing according to above-mentioned steps 2 and step 3, and value set up autoregressive model;
Autoregressive moving-average model is as follows:
Wherein, coefficient, α tit is white noise sequence.
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. be derived from a regression model photovoltaic generation power ultra-short term Forecasting Methodology based on complex data, it is characterized in that, comprise that input data obtain Parameters of Autoregressive Models;
And input photovoltaic generation power prediction required input data are to according to being predicted the outcome in the definite autoregressive model of the parameter of above-mentioned autoregressive model;
Described input data obtain Parameters of Autoregressive Models and specifically comprise step 101, input model training basic data,
Step 102, employing residual error variogram method are determined rank to autoregressive model AR (p),
Step 103, employing square method of estimation are estimated AR (p) model parameter of determining rank.
2. be according to claim 1ly derived from regression model photovoltaic generation power ultra-short term Forecasting Methodology based on complex data, it is characterized in that, described step 101 input model training basic data, input data comprise, photovoltaic plant Back ground Information, historical irradiance data, historical power data and Geographic Information System (GIS) data.
3. be according to claim 2ly derived from regression model photovoltaic generation power ultra-short term Forecasting Methodology based on complex data, it is characterized in that, described step 102 adopts residual error variogram method to determine rank to autoregressive model AR (p):
Be specially and establish x tfor the item that needs are estimated, x t-1, x t-2..., x t-nfor known historical power sequence, autoregressive model AR (p), it is exactly the value of determining Model Parameter p that model is determined rank;
The models fitting original series increasing progressively gradually with serial exponent number all calculates residual sum of squares (RSS) at every turn then draw exponent number and figure, when exponent number is during by little increase, can significantly decline, reach after true exponent number value can tend towards stability gradually, even on the contrary increase,
The observed value item number of actual observed value number actual use while referring to model of fit, for the sequence with N observed value, matching AR (p) model, the actual observed value using mostly is N-p most, model parameter number refers to the actual number of parameters comprising in set up model, and for the model that contains average, model parameter number is that model order adds 1, for the sequence of N observed reading, the residual error estimator of AR model is:
Wherein, the sum of squares function that Q is error of fitting, be model coefficient, N is observation sequence length, it is the constant term in model parameter.
4. be according to claim 3ly derived from regression model photovoltaic generation power ultra-short term Forecasting Methodology based on complex data, it is characterized in that, described step 103 adopts square method of estimation to estimate that to AR (p) model parameter of determining rank concrete steps are:
Historical photovoltaic plant power data is utilized to data sequence x 1, x 2..., x trepresent, its sample autocovariance is defined as
γ ^ k = 1 n Σ t = k + 1 n x t x t - k ,
Wherein, k=0,1,2 ..., n-1, x tand x t-kbe data sequence x 1, x 2..., x tin numerical value;
? γ ^ 0 = 1 n Σ t = 1 n x t 2
Historical power data sample autocorrelation function is:
ρ ^ k = γ ^ k γ ^ 0 = 1 n Σ t = k + 1 n x t x t - k 1 n Σ t = 1 n x t 2 = Σ t = k + 1 n x t x t - k Σ t = 1 n x t 2 ,
Wherein, k=0,1,2 ..., n-1;
The square of AR part is estimated as,
Order
Covariance function is
With estimation replace γ k,
Can obtain parameter
5. be according to claim 4ly derived from regression model photovoltaic generation power ultra-short term Forecasting Methodology based on complex data, it is characterized in that, described input photovoltaic generation power prediction required input data are to comprising according to the step that obtains predicting the outcome in the definite autoregressive model of the parameter of above-mentioned autoregressive model
Step 201, power input fundamentals of forecasting data;
Step 202, to input basic data carry out noise filtering and data pre-service;
Step 203, set up autoregressive model according to definite parameter, thereby and data input after treatment is predicted the outcome.
6. be according to claim 5ly derived from regression model photovoltaic generation power ultra-short term Forecasting Methodology based on complex data, it is characterized in that, also comprise,
Step 204, the output that will predict the outcome, and show and predict the outcome by chart and curve.
7. be according to claim 6ly derived from regression model photovoltaic generation power ultra-short term Forecasting Methodology based on complex data, it is characterized in that, described power input fundamentals of forecasting data comprise source monitor system data and operation monitoring system data, and described source monitor system packet is containing light resources Monitoring Data; Described operation monitoring system data comprise photovoltaic module Monitoring Data, booster stations Monitoring Data and data acquisition and supervisor control data.
8. be according to claim 6ly derived from regression model photovoltaic generation power ultra-short term Forecasting Methodology based on complex data, it is characterized in that, described noise filtering and data pre-service are specially: what noise filtering module obtained monitoring system Real-time Collection is with noisy data to carry out filtering processing, remove bad data and singular value; Data preprocessing module to data align, normalized and category filter processing.
9. be according to claim 6ly derived from regression model photovoltaic generation power ultra-short term Forecasting Methodology based on complex data, it is characterized in that, described autoregressive model is:
Wherein, coefficient, α tit is white noise sequence.
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