基于支持向量机的变压器碳排放预测模型

目的 解决变压器中主要设计参数影响下的碳排放量预测问题。方法 本文利用随机森林(Random Forest，RF)算法和支持向量机(Support Vector Machine，SVM)算法进行对比，构建一个变压器碳排放预测模型。结果 通过对变压器的全生命周期进行评价，确定铁芯的长宽比为影响碳排放量的主要因素，对给定参数下的碳排放量进行预测，并与实际值进行对比分析得出，3类预测模型中，SVM高斯核模型的平均绝对误差值约为5.37，与碳排放实际值最为接近，故采用高斯核函数的非线性支持向量机预测模型最优。结论 证明支持向量机高斯核函数预测模型更具有预测准确性和有效性，以期能为生产企业进行低碳设计提供参考依据，为电力行业生产设备的可持续设计研究提供一定的借鉴意义。

Transformer Carbon Emission Prediction Model Based on Support Vector Machine

The work aims to solve the problem of predicting carbon emissions under the effect of main design parameters in transformers. Random forest (RF) algorithm and Support Vector Machine (SVM) algorithm were compared to build a prediction model of transformer carbon emissions. Through the assessment of the life cycle of the transformer, length-width ratio of iron core was identified as the main factor affecting the carbon emissions and the carbon emissions under the given parameters were predicted and compared with the actual values. According to the analysis, among the three prediction models, the average absolute error of SVM Gaussian kernel model was about 5.37 and the prediction value was the closest to the actual value of carbon emissions, so the nonlinear support vector machine prediction model with Gaussian kernel function was the best. It is proved that the support vector machine prediction model with Gaussian kernel function has more predictive accuracy and effectiveness, aiming at providing reference basis for low-carbon design of production enterprises and certain reference significance for sustainable design research of production equipment in the power industry.