Method and system of predicting electric system load based on wavelet noise reduction and EMD-ARIMA

A method of predicting an electric system load based on wavelet noise reduction and empirical mode decomposition-autoregressive integrated moving average (EMD-ARIMA), adapted to a computer comprising a memory and a processor, wherein the memory storing a program instruction and the processor executing the program instruction to implement the method, wherein the method comprising:

• (1) obtaining 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;
• (2) performing 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;
• (3) further processing the noise-reduced electric load data through an EMD method to obtain different load components, wherein step (3) further comprises:
• (3.1) identifying 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
• ⁡
• (
• t
• )
• =
• x
• up
• (
• t
• )
• +
• x
• low
• (
• t
• )
• 2
• of the upper envelope and the lower envelope;
• (3.2) calculating and marking a difference value between the original series x(t) and the envelop mean m(t) as:
• h(t):
• h(t)=x(t)−m(t);
• (3.3) determining whether h(t) satisfies intrinsic mode function (IMF) constraint conditions, treating h(t) as a new input series if no is determined, repeatedly performing step (3.1) to step (3.3) until the IMF constraint conditions are satisfied, 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);
• and
• (3.4) treating the residual component r1(t) as a new original series, executing step (3.1) again until other IMF components and one residual component are obtained, 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;
• (4) building ARIMA models corresponding to the different load components;
• (5) optimizing each of the ARIMA models through an Akaike information criterion (AIC) and a Bayesian information criterion (BIC), wherein the each of the ARIMA models is corresponding to each of the different load components;
• and
• (6) reconstructing the different load components obtained by predicting the optimized ARIMA models to obtain a final prediction result.