A method of predicating ultra-short-term wind power based on self-learning composite data source includes following steps. Model parameters of an autoregression moving average model are obtained by inputting data. A predication result is obtained by inputting data required by wind power predication into the autoregression moving average model. A post-evaluation is performed to the predication result by analyzing error between the predication result and measured values, and performing model order determination and model parameters estimation again while the error is greater than an allowable maximum error.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method of predicating ultra-short-term wind power based on self-learning composite data source, the method comprising: obtaining model parameters of an autoregression moving average model by inputting data; obtaining a predication result by inputting data required by wind power predication into the autoregression moving average model; and performing post-evaluation to the predication result by analyzing error between the predication result and measured values, and performing model order determination and model parameters estimation again while the error is greater than an allowable maximum error. 2. The method of claim 1, wherein the model parameters of the autoregression moving average model is obtained by: inputting basic data of model training; determining a model order; and estimating the model parameters via moment estimation method. 3. The method of claim 2, wherein the basic data of model training comprises wind farm's basic information, historical wind speed data, historical power data, and geographic information system data. 4. The method of claim 2, wherein the model order is determined by: determining the model order by using a residual variance map, wherein xt is assumed as an item to be estimated, and xt-1, xt-2, . . . , xt-n is the known historical power sequence; for a ARMA (p, q) model, the determining model order is to determine values of the model parameters p and q; fitting an original sequence with a series of progressively increasing order model, calculating residual sum of squares {circumflex over (σ)}a2, and drawing the order and graphics of {circumflex over (σ)}a2, wherein while the order increase, {circumflex over (σ)}a2 decreases dramatically; while the order reaches actual order, {circumflex over (σ)}a2 is gradually leveled off, or even increase, {circumflex over (σ)}a2=Squares of fitting error/((number of actually observed values)−(number of model parameters)); wherein a number of actually observed values are observed values which applied in the fitting model; in a sequence with N observed values, the maximum number of observed values is N−p in fitting AR(p) model; the number of model parameters is the number of parameters applied in constructing model; while the model comprises mean values, the number of model parameters equals to the number of order plus one; In the sequence with N observed values, the ARMA model residuals estimator is: σ ^ a 2  ( p , q ) = Q  ( μ ^ ,  ϕ ^ 1 , …  , ϕ ^ p , θ ^ 1 , …  , θ ^ q ) ( N - p ) - ( p + q + 1 ) ; wherein Q is a sum of squares of fitting error; φ1(1≦i≦p) and θj(1≦j≦q) are model coefficients; N is a length of observed sequence; {circumflex over (μ)} is a constant of model parameters, and determined by φ1(1≦i≦p) and θj(1≦j≦q). 5. The method of claim 4, wherein the estimating the parameters of ARMA (p,q) model via moment estimation method comprises: defining the historical power data of wind farm as a data sequence x1, x2, . . . , xt, and autocovariance of x1, x2, . . ., xt is defined as: γ ^ k = 1 n  ∑ t = k + 1 n   x t  x t - k , wherein k=0, 1, 2, . . . , n−1, xt and xt-k are values in the sequence x1, x2, . . . , xt; then γ ^ 0 = 1 n  ∑ t = 1 n   x t 2 . 6. The method of claim 5, wherein an autocorrelation function of historical power data is: ρ ^ k = γ ^ k γ ^ 0 = 1 n  ∑ t = k + 1 n   x t  x t - k 1 n  ∑ t = 1 n   x t 2 = ∑ t = k + 1 n   x t  x t - k ∑ t = 1 n   x t 2 , wherein k=0, 1, 2, . . . , n−1. 7. The method of claim 6, wherein the moments estimation of the AR is: [ ϕ 1 ϕ 2 … ϕ p ] = [ ρ ^ q ρ ^ q - 1 … ρ ^ q - p + 1 ρ ^ q + 1 ρ ^ q … ρ ^ q - p + 2 … … … … ρ ^ q + p - 1 ρ ^ q + p - 2 ρ ^ q ] - 1  [ ρ ^ q + 1 ρ ^ q + 2 … ρ ^ q + p ] ; assuming: yt=x1−φ1xt-1− . . . −φpxt-p; thus a covariance function is: γ k  ( y t ) =  E  ( y t  y t + k ) =  E  [ ( x t - ϕ 1  x t - 1 - … - ϕ p  x t - p )  ( x t + k - ϕ 1  x t - 1 - … - ϕ p  x t + k - p ) ] =  ∑ i , j = 0 n   ϕ i  ϕ j  γ k + j - i ; substituting γk with estimate of {circumflex over (γ)}k: γ k  ( y t ) = ∑ i , j = 0 n   ϕ i  ϕ j  γ ^ k + j - i , thus parameters φ1, φ2, . . . , φp is obtained; applying moments estimation to the model parameters θ1, θ2, . . . , θq of model MA(q): γ0(yt)=(1+θ12+θ22+ . . . +θq2)σa2, until γk(yt)=(−θk+θ1θk+1+ . . . +θq-kθq)σa2, wherein k=1, 2, . . . , m; the model parameters of the autoregression moving average model of the m+1 nonlinear equations listed above is resolved via iteration. 8. The method of claim 7, wherein the obtaining a predication result by inputting data required by wind power predication into the autoregression moving average model comprises: inputting basic data of power prediction; dealing with the basic data via filtering and preprocessing; and establishing autoregressive moving average model based on the certain parameters, and inputting the basic data after being dealt with into the autoregressive moving average model to obtain prediction result. 9. The method of claim 8, further comprises outputting the predication result to a database, showing the prediction results by charts and curves, and showing the results of comparing prediction results and measured results. 10. The method of claim 9, wherein the basic data comprises resource monitoring system data and operational monitoring system data, and the resource monitoring system data comprises wind resource monitoring system resource monitoring data; the operation monitoring system data comprises fans data, booster station data, and supervisory control and data acquisition system. 11. The method of claim 9, wherein the dealing with the basic data via filtering and preprocessing comprises: the noise filter module filter the data obtained from the real-time acquisition monitoring system to remove bad data and singular value; data preprocessing module deal with the data via alignment, normalization, and classification filtering process. 12. The method of claim 1, wherein utoregressive moving average model is expressed as: X t = ∑ i = 1 p   ϕ i  X t - i + ∑ t = 1 q   θ i  α t - i + α t , φ1(1≦i≦p) and θj(1≦j≦q) are coefficients, αt are white noise sequence.