粒子滤波与扩展卡尔曼滤波在建筑围护结构传热系数预测中的比较

Xiaoqin Wang, Xiaolong Wang
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引用次数: 4

摘要

建筑围护结构换热系数是衡量建筑能效的重要指标。换热系数的检测总是受到周围环境和噪声的影响。同时,积累足够丰富的数据作为输入来计算换热系数是不现实的。本文采用粒子滤波(PF)和扩展卡尔曼滤波(EKF)在温控箱热流模型的基础上进行了换热系数的预测。将两个预测值与实际测量值进行比较,结果表明该粒子滤波算法比扩展卡尔曼滤波算法效率高,精度高。仿真结果表明,该算法具有较高的精度。PF的预算结果更接近实际值。然后利用粒子滤波估计的计算结果计算墙体换热系数。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The comparison of particle filter and extended Kalman filter in predicting building envelope heat transfer coefficient
The building envelope heat transfer coefficient is an important measurement of building energy efficiency. The detection of the heat transfer coefficient is always impacted by surrounding environment and noises. Meanwhile it is impractical to accumulate rich enough data as input to calculate the heat transfer coefficient. The Particle Filter (PF) and Extended Kalman Filter(EKF) are employed in this paper in predicting the heat transfer coefficient based on the temperature control box-heat flow model. With the comparison of the two predicted values with the real measured one, the Particle Filter shows high efficiency and better accurate than Extended Kalman Filter. The simulation results show that the accuracy of PF is high. The budget result of PF is more close to the real values. Then the estimated calculation according to Particle Filter is used to calculate wall body heat transfer coefficient.
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