Patent ID: 11888316
Assignee: WUHAN UNIVERSITY
Field: Computer technology (Electrical engineering)
Classification: CPC H  G  Y | IPC G  H

Claim 10:
11. A system of predicting an electric system load based on wavelet noise reduction and empirical mode decomposition-autoregressive integrated moving average (EMD-ARIMA), comprising:
a memory, configured to store a program instruction; and
a processor, coupled to the memory and configured to execute the program instruction to:
obtain electric load data of an electric system corresponding to different moments, wherein interpolation is performed on the electric load data to obtain the electric load data provided at equal intervals in response to the electric load data is provided at unequal intervals, wherein the electric load data provided at the equal intervals of the electric system is: data={a1, a2, . . . , ai} i∈[1, K], wherein K is K pieces of the electric load data corresponding to K moments, and ai is a value of an ith point in the electric load data;
perform a wavelet noise reduction process on the electric load data through wavelet analysis, wherein data obtained after the wavelet noise reduction are performed is: x(t)={x1, x2, . . . xt} t∈[1, K], wherein K is K pieces of the electric load data corresponding to K moments, and xt is a value of a tth point in the electric load data;
identify all maximum points and all minimum points in an original series x(t), fitting and forming an upper envelope xup(t) and a lower envelope xlow(t) by adopting a cubic spline interpolation method, calculating an envelope mean m(t):, m
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      x
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    +
    
     
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   2
  
 

 of the upper envelope and the lower envelope;
calculate and mark a difference value between the original series x(t) and the envelop mean m(t) as: h(t): h(t)=x(t)−m(t);
determine whether h(t) satisfies intrinsic mode function (IMF) constraint conditions, treat h(t) as a new input series if no is determined;
treating h(t) as a first IMF component if yes is determined, marking h(t) as c1(t)=h(t), separating c1(t) from the original series x(t), obtaining a residual component r1(t): r1(t)=x(t)−c1(t) in response to the IMF constraint conditions are satisfied;
treat the residual component r1(t) as a new original series to obtain other IMF components and one residual component, wherein a final result of EMD is represented as r(t)=x(t)−ci(t), wherein ci(t) is an ith IMF component, r(t) is a final residual component representing a trend term of the original series;
build ARIMA models for the different IMF components and the residual component of the load data obtained through EMD;
optimize each of the ARIMA models of the different IMF components and the residual component;
perform ARIMA prediction on the different IMF components and the residual component obtained through optimization; and
synthesize results predicted to obtain a final load prediction result.