Forecasting model based on BIC and SVRM for dissolved gas in transformer oil

A forecasting model of dissolved gases in transformer oil is established based on v-SVRM (v-Support Vector Regression Machine) algorithm and the Bayesian framework is introduced to optimally select its parameters. An evaluation mechanism combining forecasting accuracy and model simplicity is set and the improved BIC (Bayesian Information Criterion) is taken as the final evaluation function to quantify the evaluation mechanism. The case study shows that, compared with GM (Gray Model), v-SVRM forecasting model has higher forecasting accuracy with the same small-scale samples and better performance in the proposed model evaluation function.