基于梯度提升决策模型的空间占用检测研究

Occupancy detection based on extreme gradient boosting decision model

随着绿色建筑和绿色生态城区经济激励机制基本形成,面对大量多维空间占用数据,大数据绿色建筑节能体系应运而生。然而大量多维的建筑数据却没有被充分利用,且传统空间占用检测模型分类精度还不够准确,模型时间复杂度较高。利用UCI占用检测数据集,在原始数据集上加入时间戳,使模型分类精度均获得提高,同时利用MCMR(最大相关最小冗余)方法进行特征选择,通过随机森林作为分类器验证分类效果,获取最优特征子集。利用选取的特征子集构建占用检测模型,其中XGBoost模型与随机森林模型(RF)进行比对,分类精度较高,且时间复杂度更低。

With the green buildings and green-economic environmental cities are gradually formed,big data green building energy conservation systems come into being.However,a large number of multi-dimensional building data are not fully utilized and occupancy detection with accuracy of traditional algorithms is not accurate with the higher time complexity.This article acquired the data of occupancy detection from UCI.Adding a timestamp to the original dataset,it increased the accuracy.Using the MCMR method to select features with maximum correlation and minimum redundancy,it used random forest as classifier to verify classification effect.The XGBoost model constructed by the optimal subset was compared with the random forest model(RF),and the classification accuracy is higher and the time complexity is lower.