基于二氧化碳测量的室内人数估计算法

Indoor Occupancy Estimation Method Based on Carbon Dioxide Measurement

根据室内人数与二氧化碳浓度动态变化之间的关系,设计基于参数模型训练和卡尔曼滤波的室内人数估计算法。该算法包括模型训练阶段和人数估计阶段,模型训练阶段利用历史测量数据识别描述室内二氧化碳浓度变化的参数模型,估计阶段利用已识别的参数模型设计卡尔曼滤波器实时估计室内人数。实验结果表明:该算法可利用历史测量数据准确识别室内二氧化碳浓度变化模型,在门窗闭合时实时估计室内人数。

According to relation o f indoor occupancy and carbon dioxide concentration dynamic variation, design theindoor occupancy estimation algorithm based on parameter model training and Kalman filtering. The algorithm includes themodel training phase and occupancy estimation phase. The model training phase uses historical measurement data toidentify and describe the parameter model indoor carbon dioxide concentration variation, and the estimation phase uses theidentified parameter model to design the Kalman filter to estimate indoor occupancy in real time. The test results show thatthe algorithm can uses historical measure data to indentify the indoor dioxide concentration variation model correctly, andestimate real time indoor occupancy when door and window are closed.