针对现阶段商业楼宇碳排放监测精准度较低的问题,提出基于最小二乘支持向量机的分类和回归算法(Least Squares Support Vector Machine,LSSVM)的商业楼宇碳排放量动态监测方法.获取碳排放量数据并计算碳排放指标,设计基于LSSVM算法的商业楼宇碳排放量动态监测流程,实现对商业楼宇碳排放量的动态监测.通过设计对比实验,表明该研究方法监测精准度更高,具有更好的监测效果.
Dynamic Monitoring Methods for Carbon Emissions in Commercial Buildings Based on the LSSVM Algorithm
By mining the carbon emission data of existing monitoring methods for carbon emissions in commercial buildings,it is found that the monitoring accuracy of carbon emissions is low,so a dynamic monitoring method for carbon emissions in commercial buildings based on the classification and regression algorithm of the least squares support vector machine(LSSVM)is proposed.By this method,carbon emission data is obtained,carbon emission indicators are calculated based on the obtained results,and the dynamic monitoring process for carbon emissions in commercial buildings based on the LSSVM algorithm is designed in combination with indicators,in order to achieve the dynamic monitoring of carbon emissions in commercial buildings.By designing comparative experiments,it is shown that this research method has higher monitoring accuracy and better monitoring effect.
Regression algorithmDynamic monitoring of carbon emissionsIndicator calculationMonitoring method
万方数据
维普
