LCC prediction model of a transformer substation in a high-latitude severe cold area of an LSSVM
A prediction model and substation technology, applied in the field of electric power, can solve the problems of reduced prediction accuracy, easy to fall into local optimum, unable to obtain the optimum value, etc., to achieve the effect of improving accuracy and realizing economic and technical evaluation.
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[0052] The present invention is an SSA-LSSVM-based LCC prediction model for substations in high-latitude severe cold regions. Next, a support vector machine regression model with high regression fitting ability for solving small samples, nonlinear and high-dimensional data is used to establish LCC prediction for substations in high-cold regions. Model.
[0053] Such as literature: UkilA.Support Vector Machine[J]. Computer Science, 2002, 1(4): 1-28.
[0054] UkilA. Support Vector Machine [J] Computer Science, 2002, 1(4):1-28
[0055] Suykens JAK, Vandewalle J. Least Squares Support Vector Machine Classifiers [J]. Neural Processing Letters, 1999, 9(3): 293-300.
[0056] Suykens JAK, Vandewalle J. Least squares support vector machine classification [J]. Neural Processing Articles 1999, 9(3): 293-300.
[0057] Huo Juan, Sun Xiaowei, Zhang Mingjie, Comparison of Power Load Forecasting Algorithms - Random Forest and Support Vector Machines [J / OL]. Journal of Electric Power Syst...
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