CN104504465A - Power generation fuel supply prediction method - Google Patents

Power generation fuel supply prediction method Download PDF

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Publication number
CN104504465A
CN104504465A CN201410778580.7A CN201410778580A CN104504465A CN 104504465 A CN104504465 A CN 104504465A CN 201410778580 A CN201410778580 A CN 201410778580A CN 104504465 A CN104504465 A CN 104504465A
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China
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model
amp
autocorrelogram
seasonal
power generation
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CN201410778580.7A
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Chinese (zh)
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冯杰
范丹丹
赵玉柱
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国电南京自动化股份有限公司
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Publication of CN104504465A publication Critical patent/CN104504465A/en

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    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00Administration; Management
    • G06Q10/04Forecasting or optimisation, e.g. linear programming, "travelling salesman problem" or "cutting stock problem"
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/06Electricity, gas or water supply

Abstract

The invention discloses a power generation fuel supply prediction method, comprising the following steps of implementing stationary processing; implementing model recognition and order determination, i.e. establishing a prediction model according to an autocorrelogram and a partial autocorrelogram to determine model parameters; evaluating the model parameters, i.e. determining the related parameters of the model according to the autocorrelogram, the partial autocorrelogram and stationarity; testing model adaptability, i.e. implementing a residue independence test or a heteroskedasticity test, and modifying the prediction model till a residue sequence is a white noise sequence, and all useful information is extracted. According to the power generation fuel supply prediction method based on a multiplicative seasonal model provided by the invention, the prediction model is a combination of a random seasonal model and an ARIMA (Autoregressive Integrated Moving Average) model; under the premise of considering historical data and influencing factors, the seasonal factors of power generation fuel supply are reflected better, so that the power generation fuel prediction precision is improved.

Description

A kind of fuel used to generate electricity supply Forecasting Methodology

Technical field

The present invention relates to a kind of fuel used to generate electricity supply Forecasting Methodology, belong to thermal power generating technology field.

Background technology

The supply of fuel used to generate electricity is subject to the impact of the Various Complex factors such as energy policy, Supply and demand trend, resource distribution, supply price, communications and transportation, Market Games, lacks rationally effective supply Forecasting Methodology and technological means for a long time.But, along with the enforcement that factory's net is separated, the grasp wretched insufficiency that grid company and power dispatching station are supplied fuel used to generate electricity, can not meet the requirement of electric power supply work, in the period of especially hemiplegia in water, power supply shortage, the prediction of fuel used to generate electricity supply is carried out and is sent out a management of power use to alleviation imbalance between power supply and demand, in order and play a part very important.

By the different in kind of Forecasting Methodology, prediction can be divided into qualitative forecasting and quantitative forecast.Conventional Qualitative Forecast Methods has subjective probability method, the poll projected method, Delphi method, analogy method, Study on Relative Factors method etc.Quantivative approach can be divided into causal method and time series analysis method etc. again, and causal method is also structural relation analytic approach.It is the reason by analyzing change, finds out the contact method between cause and effect, sets up forecast model, and predicts following development tendency and possibility level accordingly.Time series analysis method is also history extension method.It is based on the time series data of history, uses certain mathematical method to find data movement rule and stretches out, the development tendency that prediction is following.

The supply situation of fuel used to generate electricity is subject to the impact of Various Complex factor, and particularly along with the variation in season, fuel used to generate electricity supply there will be larger change.This sequential containing seasonal move, mathematically its development law of matching carry out predicting to be quite complicated.If but we can manage to isolate secular trend from sequential, and find out the rule of seasonal move, the two is combined prediction, problem just can be made to be simplified, also can reach the requirement of precision of prediction.

Summary of the invention

The object of the invention is to overcome deficiency of the prior art, provide a kind of fuel used to generate electricity to supply Forecasting Methodology, consider seasonal effect factor, can Accurate Prediction fuel used to generate electricity.

For achieving the above object, the technical solution adopted in the present invention is: a kind of fuel used to generate electricity supply Forecasting Methodology, comprises the steps:

Step one: tranquilization process: the non-stationary of raw data is converted to stationary time series;

Step 2: Model Identification and determine rank: according to autocorrelogram and partial autocorrelation figure, set up forecast model, Confirming model parameter:

The forecast model set up is:

Its exponent number (p, d, q) × (P, D, Q) srepresent, wherein:

θ(B)=1-θ 1B-θ 2B 2-…θ qB q

U(B S)=1-u 1B S-u 2B 2S-…u PB PS

V(B S)=1-v 1B S-v 2B 2S-…v QB QS

S represents the observation number in a Seasonal Cycle, represent the correlationship of different cycles point in same period, represent the correlationship on the same period point of different cycles;

Step 3: model parameter estimation: by autocorrelogram determination parameter q and Q, determines p and P by partial autocorrelation figure, in conjunction with AIC and BIC criterion, finally determines p, q, P, Q; According to the stationarity determination parameter d of data; Periodicity according to data determines parameter D;

Step 4: Model suitability is checked: carry out residual error independence test or test for heteroscedasticity, amendment forecast model, until residual sequence is white noise sequence, all useful informations are extracted.

The concrete operation step of described tranquilization process is: carry out log-transformation, first order difference and seasonal difference computing successively to the non-stationary of raw data, be converted to stationary time series.

The formula of described residual error independence test is as follows:

χ m 2 = n ( n + 2 ) Σ k = 1 m r k 2 n - k , Wherein r k 2 = Σ t = 1 n - k ϵ t ϵ t + k Σ t = 1 n ϵ t 2 ;

Wherein: x represents random number, n represents degree of freedom, and m represents exponent number, r krepresent word related coefficient, ε trepresent residual sequence.

Compared with prior art, the beneficial effect that the present invention reaches is: the fuel used to generate electricity supply Forecasting Methodology based on Multiplicative Seasonality Model of proposition, forecast model adopts the convolution of random seaconal model and ARIMA model, under the prerequisite considering historical data and influence factor, better reflect the Seasonal of fuel used to generate electricity supply, improve fuel used to generate electricity precision of prediction.

Accompanying drawing explanation

Fig. 1 is operational flowchart of the present invention.

Fig. 2 is the time series chart of south electric network fuel used to generate electricity raw data.

Fig. 3 is the autocorrelogram of south electric network fuel used to generate electricity raw data.

Fig. 4 is the partial autocorrelation figure of south electric network fuel used to generate electricity raw data

Fig. 5 is the time series chart after the tranquilization process corresponding with Fig. 2.

Fig. 6 is the autocorrelogram after the tranquilization process corresponding with Fig. 3.

Fig. 7 is the partial autocorrelation figure after the tranquilization process corresponding with Fig. 4.

Fig. 8 is the residual error autocorrelogram of south electric network forecast model when carrying out residual test.

Fig. 9 is the residual error partial autocorrelation figure of south electric network forecast model when carrying out residual test.

Figure 10 is south electric network the whole network fuel used to generate electricity supply predicted value and actual value comparison diagram.

Embodiment

The basic ideas of seasonal move Forecasting Methodology are: first find and describe the whole mathematical model of sequential overall development trend and the tendency equation of separation trend; Next finds out the impact of seasonal move on forecasting object, is namely separated seasonal effect; Finally tendency equation and seasonal effect factor are merged, obtain the forecast model that can describe time series overall development rule, and for prediction.

Introduce random seaconal model, ARIMA model below respectively:

1, random seaconal model: be to the same period point of different cycles in seasonal random sequence between a kind of matching of correlationship.

AR (1): can be reduced to:

MA(1): W t = e t - θ 1 e t - S ⇔ W t = ( 1 - θ 1 B S ) e t , Can be reduced to: ▿ S X t = ( 1 - θ 1 B S ) e t .

The ARMA expression-form of seasonal form model is:

U(B S)W t=V(B S)e t

Here,

2, ARIMA model:

If { X tfor zero-mean stationary time series (t is time parameter t=1,2 ...), if and meet following condition:

(1) with θ (B) without common factor, wherein b is delay operator, BX t=X t-1, B ε tt-1, B jx t=X t-j;

(3) { ε tit is white noise sequence;

(4)E(X tε s)=0,t<s。

Then deserving to be called the model stated is autoregressive moving-average model, is designated as ARMA (p, q).Wherein p is called Autoregressive, and q is called running mean exponent number, real coefficient be called autoregressive coefficient, θ 1, θ 2..., θ qbe called running mean coefficient.

Arma modeling race is the most important model race of one of Stationary Time Series, but for there being seasonal grade for Non-stationary time-series, this model is just no longer practical, and the thought of ARIMA modeling is exactly by difference by sequential tranquilization, then adopts arma modeling that differentiated sequential is discussed.ARIMA (p, d, the q) model form of general Out of season is:

Multiplicative Seasonality Model is the convolution of random seaconal model and ARIMA model, its exponent number (p, d, q) × (P, D, Q) s, form is:

Wherein:

θ(B)=1-θ 1B-θ 2B 2-…θ qB q

U(B S)=1-u 1B S-u 2B 2S-…u PB PS

V(B S)=1-v 1B S-v 2B 2S-…v QB QS

The value of s is the number observed in a Seasonal Cycle, here represent the correlationship of different cycles point in same period, then describe the correlationship on the same period point of different cycles, the two combines the effect just simultaneously featuring two factors.

Below in conjunction with accompanying drawing, the invention will be further described.

As shown in Figure 1, fuel used to generate electricity supply Forecasting Methodology, comprises the steps:

Step one: tranquilization process: the non-stationary of raw data is converted to stationary time series, be specially: log-transformation, first order difference and seasonal difference computing are carried out successively to the non-stationary of raw data, is converted to stationary time series.

Step 2: Model Identification and determine rank: according to autocorrelogram and partial autocorrelation figure, set up forecast model, Confirming model parameter:

The forecast model set up is:

Its exponent number (p, d, q) × (P, D, Q) srepresent, wherein:

θ(B)=1-θ 1B-θ 2B 2-…θ qB q

U(B S)=1-u 1B S-u 2B 2S-…u PB PS

V(B S)=1-v 1B S-v 2B 2S-…v QB QS

S represents the observation number in a Seasonal Cycle, represent the correlationship of different cycles point in same period, represent the correlationship on the same period point of different cycles.

Step 3: model parameter estimation: by autocorrelogram determination parameter q and Q, determines p and P by partial autocorrelation figure, in conjunction with AIC and BIC criterion, finally determines p, q, P, Q; According to the stationarity determination parameter d of data; Periodicity according to data determines parameter D;

Step 4: Model suitability is checked: carry out residual error independence test or test for heteroscedasticity, amendment forecast model, until residual sequence is white noise sequence, all useful informations are extracted.

The concrete operation step of Model suitability inspection is as follows:

Step 401: residual error independence test: inspection formula is as follows:

χ m 2 = n ( n + 2 ) Σ k = 1 m r k 2 n - k , Wherein r k 2 = Σ t = 1 n - k ϵ t ϵ t + k Σ t = 1 n ϵ t 2 ;

Wherein: x represents random number, n represents degree of freedom, and m represents exponent number, r krepresent word related coefficient, ε trepresent residual sequence.

White noise verification method is: whether the adjoint p value of observing q statistic increases, if reduce to show residual sequence ε tfor white noise sequence; Otherwise, represent residual sequence ε tnot white noise sequence, also there is useful information in residual sequence and be not extracted, need to revise forecast model further.

Be described in further detail the present invention below in conjunction with specific embodiment, following examples only for technical scheme of the present invention is clearly described, and can not limit the scope of the invention with this.

Now be predicted as embodiment with southern the whole network grid generation fuel duty, concrete operation step is as follows:

Step one: tranquilization process:

As shown in Figure 2, Figure 3, Figure 4, the time series chart of south electric network fuel used to generate electricity raw data, autocorrelogram and partial autocorrelation figure is respectively.If mean value and the variance of a sequence are always constant, then it is claimed to be stable.Need jiggly time series to be converted into stationary sequence before series model in estimated time.Assess a seasonal effect in time series stationarity by data plot and autocorrelation function and its figure.If data plot presents linear or secondary trend, then time series is jiggly.If autocorrelation function drops to 0 after the several value of first few, then sequence is stable.If after front several value, autocorrelation function does not drop to 0, but successively reduces, then sequence is not steady.As can be seen from Fig. 2 to Fig. 4 all, former sequence chart is obviously not steady, there is fluctuation tendency.As shown in Figures 5 to 7, be the figure corresponding after tranquilization process of Fig. 2 to Fig. 3 respectively, as can be seen from Fig. 5 to Fig. 7, the trend after data log-transformation after first order difference and seasonal difference in sequence obtains elimination.

Step 2: Model Identification and determine rank:

Composition graphs 5 to Fig. 7, attempts setting up ARIMA (p, d, q) (P, D, Q) model, has carried out first order difference and single order seasonal difference, therefore d=D=1 above to data after taking the logarithm.

Step 3: model parameter estimation:

By autocorrelogram and partial autocorrelation figure and correlation criterion, after tentative calculation relatively, Selection parameter is chosen as ARIMA (0,1,1) (0,1,0) 12.

Step 4: Model suitability is checked:

The adaptive test of model, i.e. the residual sequence ε of model tindependence test.If residual sequence is not white noise sequence, illustrates that the information also had in residual sequence is not extracted, need improve further master mould.Here we use the Chi-square Test of residual sequence, and formula is:

χ m 2 = n ( n + 2 ) Σ k = 1 m r k 2 n - k , Wherein r k 2 = Σ t = 1 n - k ϵ t ϵ t + k Σ t = 1 n ϵ t 2

From Fig. 8 residual error autocorrelogram and Fig. 9 partial autocorrelation figure: residual error is white noise, and all information is extracted.Model suitability is upchecked.

By the identification of raw data tranquilization process, model and determine the steps such as rank, model parameter estimation, Model suitability inspection, south electric network the whole network fuel used to generate electricity predicts that final institute established model is:

(1-B 12)(1-B)lnx t=ε t-0.415ε t-1

South electric network the whole network fuel used to generate electricity supply predicted value and actual value comparison diagram are as shown in Figure 10, the fuel used to generate electricity supply Forecasting Methodology based on Multiplicative Seasonality Model that the present invention proposes, model adopts the convolution of random seaconal model and ARIMA model, under the prerequisite considering historical data and influence factor, better reflect the Seasonal of fuel used to generate electricity supply.By predicting south electric network the whole network fuel used to generate electricity supply, by the contrast with actual value, proving that this Forecasting Methodology predicts the outcome comparatively accurate, there is certain feasibility.

The above is only the preferred embodiment of the present invention; it should be pointed out that for those skilled in the art, under the prerequisite not departing from the technology of the present invention principle; can also make some improvement and distortion, these improve and distortion also should be considered as protection scope of the present invention.

Claims (3)

1. a fuel used to generate electricity supply Forecasting Methodology, is characterized in that, comprise the steps:
Step one: tranquilization process: the non-stationary of raw data is converted to stationary time series;
Step 2: Model Identification and determine rank: according to autocorrelogram and partial autocorrelation figure, set up forecast model, Confirming model parameter:
The forecast model set up is:
Its exponent number (p, d, q) × (P, D, Q) srepresent, wherein:
θ(B)=1-θ 1B-θ 2B 2-…θ qB q
U(B S)=1-u 1B S-u 2B 2S-…u PB PS
V(B S)=1-v 1B S-v 2B 2S-…v QB QS
S represents the observation number in a Seasonal Cycle, represent the correlationship of different cycles point in same period, represent the correlationship on the same period point of different cycles;
Step 3: model parameter estimation: by autocorrelogram determination parameter q and Q, determines p and P by partial autocorrelation figure, in conjunction with AIC and BIC criterion, finally determines p, q, P, Q; According to the stationarity determination parameter d of data; Periodicity according to data determines parameter D;
Step 4: Model suitability is checked: carry out residual error independence test or test for heteroscedasticity, amendment forecast model, until residual sequence is white noise sequence, all useful informations are extracted.
2. fuel used to generate electricity supply Forecasting Methodology according to claim 1, it is characterized in that, the concrete operation step of described tranquilization process is: carry out log-transformation, first order difference and seasonal difference computing successively to the non-stationary of raw data, be converted to stationary time series.
3. fuel used to generate electricity supply Forecasting Methodology according to claim 1, it is characterized in that, the formula of described residual error independence test is as follows:
χ m 2 = n ( n + 2 ) Σ k = 1 m r k 2 n - k , Wherein r k 2 = Σ t = 1 n - k ϵ t ϵ t + k Σ t = 1 n ϵ t 2 ;
Wherein: x represents random number, n represents degree of freedom, and m represents exponent number, r krepresent word related coefficient, ε trepresent residual sequence.
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