1. A prediction method for monitoring performance of power plant instruments, comprising: displaying measurement data in a matrix; normalizing the measurement data into a normalized measurement data set; trisecting the normalized measurement data set into three data sets, wherein the three trisected data sets comprising a normalized training data set, a normalized optimization data set, and a normalized test data set; extracting principal components of each of the three trisected data sets; calculating an optimal constant of a Support Vector Regression (SVR) model, based on the normalized measurement data set and the extracted principal components thereof, to optimize prediction value errors of data for optimization with a response surface method; generating the SVR training model with the optimal constant; obtaining a Kernel function matrix with the normalized test data set as an input and predicting an output value of the SVR training model; and de-normalizing the output value into an original range to obtain a predicted value of a variable, wherein the displaying of the measurement data in the matrix is represented by the following equation: X = [ X 1 , 1 X 1 , 2 … X 1 , m X 2 , 1 X 2 , 2 … X 2 , m ⋮ ⋮ ⋱ M X 3 ⁢ n , 1 X 3 ⁢ n , 2 … X 3 ⁢ n , m ] = [ X 1 X 2 … X m ] ⁢ ⁢ X ts = [ X 3 ⁢ i + 1 , 1 ⁢ ⁢ X 3 ⁢ i + 1 , 2 ⁢ ⁢ … ⁢ , X 3 ⁢ i + 1 , m ] = [ X ts ⁢ ⁢ 1 ⁢ ⁢ X ts ⁢ ⁢ 2 ⁢ ⁢ … ⁢ ⁢ X tsm ] ⁢ ⁢ X tr = [ X 3 ⁢ i + 2 , 1 ⁢ ⁢ X 3 ⁢ i + 2 , 2 ⁢ ⁢ … ⁢ , X 3 ⁢ i + 2 , m ] = [ X tr ⁢ ⁢ 1 ⁢ ⁢ ⁢ X tr ⁢ ⁢ 2 ⁢ ⁢ … ⁢ ⁢ X trm ] ⁢ ⁢ X op = [ X 3 ⁢ i + 1 , 1 ⁢ ⁢ X 3 ⁢ i + 1 , 2 ⁢ ⁢ … ⁢ , X 3 ⁢ i + 3 , m ] = [ X op ⁢ ⁢ 1 ⁢ ⁢ X op ⁢ ⁢ 2 ⁢ ⁢ … ⁢ ⁢ X opm ] where: X is a matrix representing the measurement data, X tr , X op , and X ts are matrices respectively representing a data set for training, a data set for optimization, and a data set for test, n is a positive integer, m is the number of the power plant instruments, and i=0, 1, 2,. .. , n−1.